WorldWideScience

Sample records for biological models

  1. Laboratory of Biological Modeling

    Data.gov (United States)

    Federal Laboratory Consortium — The Laboratory of Biological Modeling is defined by both its methodologies and its areas of application. We use mathematical modeling in many forms and apply it to...

  2. Lagrangians for biological models

    CERN Document Server

    Nucci, M C

    2011-01-01

    We show that a method presented in [S.L. Trubatch and A. Franco, Canonical Procedures for Population Dynamics, J. Theor. Biol. 48 (1974), 299-324] and later in [G.H. Paine, The development of Lagrangians for biological models, Bull. Math. Biol. 44 (1982) 749-760] for finding Lagrangians of classic models in biology, is actually based on finding the Jacobi Last Multiplier of such models. Using known properties of Jacobi Last Multiplier we show how to obtain linear Lagrangians of those first-order systems and nonlinear Lagrangian of the corresponding single second-order equations that can be derived from them, even in the case where those authors failed such as the host-parasite model.

  3. ECO-BIOLOGICAL SYSTEM MODELING

    Directory of Open Access Journals (Sweden)

    T. I. Burak

    2015-01-01

    Full Text Available The methodology for computer modeling of complex eco-biological models is presented in this paper. It is based on system approach of J. Forrester. Developed methodology is universal for complex ecological and biological systems. Modeling algorithm considers specialties of eco-biological systems and shows adequate and accurate results in practice. 

  4. Mathematical modeling of biological processes

    CERN Document Server

    Friedman, Avner

    2014-01-01

    This book on mathematical modeling of biological processes includes a wide selection of biological topics that demonstrate the power of mathematics and computational codes in setting up biological processes with a rigorous and predictive framework. Topics include: enzyme dynamics, spread of disease, harvesting bacteria, competition among live species, neuronal oscillations, transport of neurofilaments in axon, cancer and cancer therapy, and granulomas. Complete with a description of the biological background and biological question that requires the use of mathematics, this book is developed for graduate students and advanced undergraduate students with only basic knowledge of ordinary differential equations and partial differential equations; background in biology is not required. Students will gain knowledge on how to program with MATLAB without previous programming experience and how to use codes in order to test biological hypothesis.

  5. Modelling coordination in biological systems

    OpenAIRE

    Clarke, David; Oliveira Costa, de, David; Arbab, Farhad

    2004-01-01

    We present an application of the Reo coordination paradigm to provide a compositional formal model for describing and reasoning about the behaviour of biological systems, such as regulatory gene networks. Reo governs the interaction and flow of data between components by allowing the construction of connector circuits which have a precise formal semantics. When applied to systems biology, the result is a graphical model, which is comprehensible, mathematically precise, and flexible

  6. [Biological mutualism, concepts and models].

    Science.gov (United States)

    Perru, Olivier

    2011-01-01

    Mutualism is a biological association for a mutual benefit between two different species. In this paper, firstly, we examine the history and signification of mutualism in relation to symbiosis. Then, we consider the link between concepts and models of mutualism. Models of mutualism depend on different concepts we use: If mutualism is situated at populations' level, it will be expressed by Lotka-Volterra models, concerning exclusively populations' size. If mutualism is considered as a resources' exchange or a biological market increasing the fitness of these organisms, it will be described at an individual level by a cost-benefit model. Our analysis will be limited to the history and epistemology of Lotka-Volterra models and we hypothesize that these models are adapted at first to translate dynamic evolutions of mutualism. They render stability or variations of size and assume that there are clear distinctions and a state of equilibrium between populations of different species. Italian mathematician Vito Volterra demonstrated that biological associations consist in a constant relation between some species. In 1931 and 1935, Volterra described the general form of antagonistic or mutualistic biological associations by the same differential equations. We recognize that these equations have been more used to model competition or prey-predator interactions, but a simple sign change allows describing mutualism. The epistemological problem is the following: Volterra's equations help us to conceptualize a global phenomenon. However, mutualistic interactions may have stronger effects away from equilibrium and these effects may be better understood at individual level. We conclude that, between 1985 and 2000, some researchers carried on working and converting Lotka-Volterra models but this description appeared as insufficient. So, other researchers adopted an economical viewpoint, considering mutualism as a biological market. PMID:22288336

  7. [Biological mutualism, concepts and models].

    Science.gov (United States)

    Perru, Olivier

    2011-01-01

    Mutualism is a biological association for a mutual benefit between two different species. In this paper, firstly, we examine the history and signification of mutualism in relation to symbiosis. Then, we consider the link between concepts and models of mutualism. Models of mutualism depend on different concepts we use: If mutualism is situated at populations' level, it will be expressed by Lotka-Volterra models, concerning exclusively populations' size. If mutualism is considered as a resources' exchange or a biological market increasing the fitness of these organisms, it will be described at an individual level by a cost-benefit model. Our analysis will be limited to the history and epistemology of Lotka-Volterra models and we hypothesize that these models are adapted at first to translate dynamic evolutions of mutualism. They render stability or variations of size and assume that there are clear distinctions and a state of equilibrium between populations of different species. Italian mathematician Vito Volterra demonstrated that biological associations consist in a constant relation between some species. In 1931 and 1935, Volterra described the general form of antagonistic or mutualistic biological associations by the same differential equations. We recognize that these equations have been more used to model competition or prey-predator interactions, but a simple sign change allows describing mutualism. The epistemological problem is the following: Volterra's equations help us to conceptualize a global phenomenon. However, mutualistic interactions may have stronger effects away from equilibrium and these effects may be better understood at individual level. We conclude that, between 1985 and 2000, some researchers carried on working and converting Lotka-Volterra models but this description appeared as insufficient. So, other researchers adopted an economical viewpoint, considering mutualism as a biological market.

  8. Kinetic Modeling of Biological Systems

    Energy Technology Data Exchange (ETDEWEB)

    Resat, Haluk; Petzold, Linda; Pettigrew, Michel F.

    2009-04-21

    The dynamics of how its constituent components interact define the spatio-temporal response of a natural system to stimuli. Modeling the kinetics of the processes that represent a biophysical system has long been pursued with the aim of improving our understanding of the studied system. Due to the unique properties of biological systems, in addition to the usual difficulties faced in modeling the dynamics of physical or chemical systems, biological simulations encounter difficulties that result from intrinsic multiscale and stochastic nature of the biological processes. This chapter discusses the implications for simulation of models involving interacting species with very low copy numbers, which often occur in biological systems and give rise to significant relative fluctuations. The conditions necessitating the use of stochastic kinetic simulation methods and the mathematical foundations of the stochastic simulation algorithms are presented. How the well-organized structural hierarchies often seen in biological systems can lead to multiscale problems, and possible ways to address the encountered computational difficulties are discussed. We present the details of the existing kinetic simulation methods, and discuss their strengths and shortcomings. A list of the publicly available kinetic simulation tools and our reflections for future prospects are also provided.

  9. Modelling coordination in biological systems

    NARCIS (Netherlands)

    Clarke, D.G.; Oliveira Costa, D.F. de; Arbab, F.

    2004-01-01

    We present an application of the Reo coordination paradigm to provide a compositional formal model for describing and reasoning about the behaviour of biological systems, such as regulatory gene networks. Reo governs the interaction and flow of data between components by allowing the construction of

  10. Mathematical models in biological discovery

    CERN Document Server

    Walter, Charles

    1977-01-01

    When I was asked to help organize an American Association for the Advancement of Science symposium about how mathematical models have con­ tributed to biology, I agreed immediately. The subject is of immense importance and wide-spread interest. However, too often it is discussed in biologically sterile environments by "mutual admiration society" groups of "theoreticians", many of whom have never seen, and most of whom have never done, an original scientific experiment with the biolog­ ical materials they attempt to describe in abstract (and often prejudiced) terms. The opportunity to address the topic during an annual meeting of the AAAS was irresistable. In order to try to maintain the integrity ;,f the original intent of the symposium, it was entitled, "Contributions of Mathematical Models to Biological Discovery". This symposium was organized by Daniel Solomon and myself, held during the 141st annual meeting of the AAAS in New York during January, 1975, sponsored by sections G and N (Biological and Medic...

  11. Time lags in biological models

    CERN Document Server

    MacDonald, Norman

    1978-01-01

    In many biological models it is necessary to allow the rates of change of the variables to depend on the past history, rather than only the current values, of the variables. The models may require discrete lags, with the use of delay-differential equations, or distributed lags, with the use of integro-differential equations. In these lecture notes I discuss the reasons for including lags, especially distributed lags, in biological models. These reasons may be inherent in the system studied, or may be the result of simplifying assumptions made in the model used. I examine some of the techniques available for studying the solution of the equations. A large proportion of the material presented relates to a special method that can be applied to a particular class of distributed lags. This method uses an extended set of ordinary differential equations. I examine the local stability of equilibrium points, and the existence and frequency of periodic solutions. I discuss the qualitative effects of lags, and how these...

  12. Integrating systems biology models and biomedical ontologies

    Directory of Open Access Journals (Sweden)

    de Bono Bernard

    2011-08-01

    Full Text Available Abstract Background Systems biology is an approach to biology that emphasizes the structure and dynamic behavior of biological systems and the interactions that occur within them. To succeed, systems biology crucially depends on the accessibility and integration of data across domains and levels of granularity. Biomedical ontologies were developed to facilitate such an integration of data and are often used to annotate biosimulation models in systems biology. Results We provide a framework to integrate representations of in silico systems biology with those of in vivo biology as described by biomedical ontologies and demonstrate this framework using the Systems Biology Markup Language. We developed the SBML Harvester software that automatically converts annotated SBML models into OWL and we apply our software to those biosimulation models that are contained in the BioModels Database. We utilize the resulting knowledge base for complex biological queries that can bridge levels of granularity, verify models based on the biological phenomenon they represent and provide a means to establish a basic qualitative layer on which to express the semantics of biosimulation models. Conclusions We establish an information flow between biomedical ontologies and biosimulation models and we demonstrate that the integration of annotated biosimulation models and biomedical ontologies enables the verification of models as well as expressive queries. Establishing a bi-directional information flow between systems biology and biomedical ontologies has the potential to enable large-scale analyses of biological systems that span levels of granularity from molecules to organisms.

  13. Track structure in biological models.

    Science.gov (United States)

    Curtis, S B

    1986-01-01

    High-energy heavy ions in the galactic cosmic radiation (HZE particles) may pose a special risk during long term manned space flights outside the sheltering confines of the earth's geomagnetic field. These particles are highly ionizing, and they and their nuclear secondaries can penetrate many centimeters of body tissue. The three dimensional patterns of ionizations they create as they lose energy are referred to as their track structure. Several models of biological action on mammalian cells attempt to treat track structure or related quantities in their formulation. The methods by which they do this are reviewed. The proximity function is introduced in connection with the theory of Dual Radiation Action (DRA). The ion-gamma kill (IGK) model introduces the radial energy-density distribution, which is a smooth function characterizing both the magnitude and extension of a charged particle track. The lethal, potentially lethal (LPL) model introduces lambda, the mean distance between relevant ion clusters or biochemical species along the track. Since very localized energy depositions (within approximately 10 nm) are emphasized, the proximity function as defined in the DRA model is not of utility in characterizing track structure in the LPL formulation.

  14. Track structure in biological models.

    Science.gov (United States)

    Curtis, S B

    1986-01-01

    High-energy heavy ions in the galactic cosmic radiation (HZE particles) may pose a special risk during long term manned space flights outside the sheltering confines of the earth's geomagnetic field. These particles are highly ionizing, and they and their nuclear secondaries can penetrate many centimeters of body tissue. The three dimensional patterns of ionizations they create as they lose energy are referred to as their track structure. Several models of biological action on mammalian cells attempt to treat track structure or related quantities in their formulation. The methods by which they do this are reviewed. The proximity function is introduced in connection with the theory of Dual Radiation Action (DRA). The ion-gamma kill (IGK) model introduces the radial energy-density distribution, which is a smooth function characterizing both the magnitude and extension of a charged particle track. The lethal, potentially lethal (LPL) model introduces lambda, the mean distance between relevant ion clusters or biochemical species along the track. Since very localized energy depositions (within approximately 10 nm) are emphasized, the proximity function as defined in the DRA model is not of utility in characterizing track structure in the LPL formulation. PMID:11537218

  15. Modeling biological systems with Answer Set Programming

    OpenAIRE

    Thiele, Sven

    2012-01-01

    Biology has made great progress in identifying and measuring the building blocks of life. The availability of high-throughput methods in molecular biology has dramatically accelerated the growth of biological knowledge for various organisms. The advancements in genomic, proteomic and metabolomic technologies allow for constructing complex models of biological systems. An increasing number of biological repositories is available on the web, incorporating thousands of biochemical reactions and ...

  16. Asmparts: assembly of biological model parts.

    Science.gov (United States)

    Rodrigo, Guillermo; Carrera, Javier; Jaramillo, Alfonso

    2007-12-01

    We propose a new computational tool to produce models of biological systems by assembling models from biological parts. Our software not only takes advantage of modularity, but it also enforces standardisation in part characterisation by considering a model of each part. We have used model parts in SBML to design transcriptional networks. Our software is open source, it works in linux and windows platforms, and it could be used to automatically produce models in a server. Our tool not only facilitates model design, but it will also help to promote the establishment of a registry of model parts.

  17. Integer Programming Models for Computational Biology Problems

    Institute of Scientific and Technical Information of China (English)

    Giuseppe Lancia

    2004-01-01

    The recent years have seen an impressive increase in the use of Integer Programming models for the solution of optimization problems originating in Molecular Biology. In this survey, some of the most successful Integer Programming approaches are described, while a broad overview of application areas being is given in modern Computational Molecular Biology.

  18. Toward synthesizing executable models in biology.

    Science.gov (United States)

    Fisher, Jasmin; Piterman, Nir; Bodik, Rastislav

    2014-01-01

    Over the last decade, executable models of biological behaviors have repeatedly provided new scientific discoveries, uncovered novel insights, and directed new experimental avenues. These models are computer programs whose execution mechanistically simulates aspects of the cell's behaviors. If the observed behavior of the program agrees with the observed biological behavior, then the program explains the phenomena. This approach has proven beneficial for gaining new biological insights and directing new experimental avenues. One advantage of this approach is that techniques for analysis of computer programs can be applied to the analysis of executable models. For example, one can confirm that a model agrees with experiments for all possible executions of the model (corresponding to all environmental conditions), even if there are a huge number of executions. Various formal methods have been adapted for this context, for example, model checking or symbolic analysis of state spaces. To avoid manual construction of executable models, one can apply synthesis, a method to produce programs automatically from high-level specifications. In the context of biological modeling, synthesis would correspond to extracting executable models from experimental data. We survey recent results about the usage of the techniques underlying synthesis of computer programs for the inference of biological models from experimental data. We describe synthesis of biological models from curated mutation experiment data, inferring network connectivity models from phosphoproteomic data, and synthesis of Boolean networks from gene expression data. While much work has been done on automated analysis of similar datasets using machine learning and artificial intelligence, using synthesis techniques provides new opportunities such as efficient computation of disambiguating experiments, as well as the ability to produce different kinds of models automatically from biological data. PMID:25566538

  19. Towards Synthesizing Executable Models in Biology

    Directory of Open Access Journals (Sweden)

    Jasmin eFisher

    2014-12-01

    Full Text Available Over the last decade, executable models of biological behaviors have repeatedly provided new scientific discoveries, uncovered novel insights, and directed new experimental avenues. These models are computer programs whose execution mechanistically simulates aspects of the cell’s behaviors. If the observed behavior of the program agrees with the observed biological behavior, then the program explains the phenomena. This approach has proven beneficial for gaining new biological insights and directing new experimental avenues. One advantage of this approach is that techniques for analysis of computer programs can be applied to the analysis of executable models. For example, one can confirm that a model agrees with experiments for all possible executions of the model (corresponding to all environmental conditions, even if there are a huge number of executions. Various formal methods have been adapted for this context, for example, model checking or symbolic analysis of state spaces. To avoid manual construction of executable models, one can apply synthesis, a method to produce programs automatically from high-level specifications. In the context of biological modelling, synthesis would correspond to extracting executable models from experimental data. We survey recent results about the usage of the techniques underlying synthesis of computer programs for the inference of biological models from experimental data. We describe synthesis of biological models from curated mutation experiment data, inferring network connectivity models from phosphoproteomic data, and synthesis of Boolean networks from gene expression data. While much work has been done on automated analysis of similar datasets using machine learning and artificial intelligence, using synthesis techniques provides new opportunities such as efficient computation of disambiguating experiments, as well as the ability to produce different kinds of models automatically from biological data.

  20. Integrating interactive computational modeling in biology curricula.

    Directory of Open Access Journals (Sweden)

    Tomáš Helikar

    2015-03-01

    Full Text Available While the use of computer tools to simulate complex processes such as computer circuits is normal practice in fields like engineering, the majority of life sciences/biological sciences courses continue to rely on the traditional textbook and memorization approach. To address this issue, we explored the use of the Cell Collective platform as a novel, interactive, and evolving pedagogical tool to foster student engagement, creativity, and higher-level thinking. Cell Collective is a Web-based platform used to create and simulate dynamical models of various biological processes. Students can create models of cells, diseases, or pathways themselves or explore existing models. This technology was implemented in both undergraduate and graduate courses as a pilot study to determine the feasibility of such software at the university level. First, a new (In Silico Biology class was developed to enable students to learn biology by "building and breaking it" via computer models and their simulations. This class and technology also provide a non-intimidating way to incorporate mathematical and computational concepts into a class with students who have a limited mathematical background. Second, we used the technology to mediate the use of simulations and modeling modules as a learning tool for traditional biological concepts, such as T cell differentiation or cell cycle regulation, in existing biology courses. Results of this pilot application suggest that there is promise in the use of computational modeling and software tools such as Cell Collective to provide new teaching methods in biology and contribute to the implementation of the "Vision and Change" call to action in undergraduate biology education by providing a hands-on approach to biology.

  1. Quantum Biological Channel Modeling and Capacity Calculation

    Directory of Open Access Journals (Sweden)

    Ivan B. Djordjevic

    2012-12-01

    Full Text Available Quantum mechanics has an important role in photosynthesis, magnetoreception, and evolution. There were many attempts in an effort to explain the structure of genetic code and transfer of information from DNA to protein by using the concepts of quantum mechanics. The existing biological quantum channel models are not sufficiently general to incorporate all relevant contributions responsible for imperfect protein synthesis. Moreover, the problem of determination of quantum biological channel capacity is still an open problem. To solve these problems, we construct the operator-sum representation of biological channel based on codon basekets (basis vectors, and determine the quantum channel model suitable for study of the quantum biological channel capacity and beyond. The transcription process, DNA point mutations, insertions, deletions, and translation are interpreted as the quantum noise processes. The various types of quantum errors are classified into several broad categories: (i storage errors that occur in DNA itself as it represents an imperfect storage of genetic information, (ii replication errors introduced during DNA replication process, (iii transcription errors introduced during DNA to mRNA transcription, and (iv translation errors introduced during the translation process. By using this model, we determine the biological quantum channel capacity and compare it against corresponding classical biological channel capacity. We demonstrate that the quantum biological channel capacity is higher than the classical one, for a coherent quantum channel model, suggesting that quantum effects have an important role in biological systems. The proposed model is of crucial importance towards future study of quantum DNA error correction, developing quantum mechanical model of aging, developing the quantum mechanical models for tumors/cancer, and study of intracellular dynamics in general.

  2. From biological membranes to biomimetic model membranes

    Directory of Open Access Journals (Sweden)

    Eeman, M.

    2010-01-01

    Full Text Available Biological membranes play an essential role in the cellular protection as well as in the control and the transport of nutrients. Many mechanisms such as molecular recognition, enzymatic catalysis, cellular adhesion and membrane fusion take place into the biological membranes. In 1972, Singer et al. provided a membrane model, called fluid mosaic model, in which each leaflet of the bilayer is formed by a homogeneous environment of lipids in a fluid state including globular assembling of proteins and glycoproteins. Since its conception in 1972, many developments were brought to this model in terms of composition and molecular organization. The main development of the fluid mosaic model was made by Simons et al. (1997 and Brown et al. (1997 who suggested that membrane lipids are organized into lateral microdomains (or lipid rafts with a specific composition and a molecular dynamic that are different to the composition and the dynamic of the surrounding liquid crystalline phase. The discovery of a phase separation in the plane of the membrane has induced an explosion in the research efforts related to the biology of cell membranes but also in the development of new technologies for the study of these biological systems. Due to the high complexity of biological membranes and in order to investigate the biological processes that occur on the membrane surface or within the membrane lipid bilayer, a large number of studies are performed using biomimicking model membranes. This paper aims at revisiting the fundamental properties of biological membranes in terms of membrane composition, membrane dynamic and molecular organization, as well as at describing the most common biomimicking models that are frequently used for investigating biological processes such as membrane fusion, membrane trafficking, pore formation as well as membrane interactions at a molecular level.

  3. Structured population models in biology and epidemiology

    CERN Document Server

    Ruan, Shigui

    2008-01-01

    This book consists of six chapters written by leading researchers in mathematical biology. These chapters present recent and important developments in the study of structured population models in biology and epidemiology. Topics include population models structured by age, size, and spatial position; size-structured models for metapopulations, macroparasitc diseases, and prion proliferation; models for transmission of microparasites between host populations living on non-coincident spatial domains; spatiotemporal patterns of disease spread; method of aggregation of variables in population dynamics; and biofilm models. It is suitable as a textbook for a mathematical biology course or a summer school at the advanced undergraduate and graduate level. It can also serve as a reference book for researchers looking for either interesting and specific problems to work on or useful techniques and discussions of some particular problems.

  4. Bridging Physics and Biology Teaching through Modeling

    CERN Document Server

    Hoskinson, Anne-Marie; Zwickl, Benjamin M; Hinko, Kathleen; Caballero, Marcos D

    2013-01-01

    As the frontiers of biology become increasingly interdisciplinary, the physics education community has engaged in ongoing efforts to make physics classes more relevant to life sciences majors. These efforts are complicated by the many apparent differences between these fields, including the types of systems that each studies, the behavior of those systems, the kinds of measurements that each makes, and the role of mathematics in each field. Nonetheless, physics and biology are both fundamental sciences that rely on observations and measurements to construct models of the natural world. In the present theoretical article, we propose that efforts to bridge the teaching of these two disciplines must emphasize shared scientific practices, particularly scientific modeling. We define modeling using language common to both disciplines and highlight how an understanding of the modeling process can help reconcile apparent differences between physics and biology. We elaborate how models can be used for explanatory, pre...

  5. The Strategies of Modeling in Biology Education

    Science.gov (United States)

    Svoboda, Julia; Passmore, Cynthia

    2013-01-01

    Modeling, like inquiry more generally, is not a single method, but rather a complex suite of strategies. Philosophers of biology, citing the diverse aims, interests, and disciplinary cultures of biologists, argue that modeling is best understood in the context of its epistemic aims and cognitive payoffs. In the science education literature, modeling has been discussed in a variety of ways, but often without explicit reference to the diversity of roles models play in scientific practice. We aim to expand and bring clarity to the myriad uses of models in science by presenting a framework from philosopher of biology Jay Odenbaugh that describes five pragmatic strategies of model use in the biological sciences. We then present illustrative examples of each of these roles from an empirical study of an undergraduate biological modeling curriculum, which highlight how students used models to help them frame their research question, explore ideas, and refine their conceptual understanding in an educational setting. Our aim is to begin to explicate the definition of modeling in science in a way that will allow educators and curriculum developers to make informed choices about how and for what purpose modeling enters science classrooms.

  6. Bridging physics and biology teaching through modeling

    Science.gov (United States)

    Hoskinson, Anne-Marie; Couch, Brian A.; Zwickl, Benjamin M.; Hinko, Kathleen A.; Caballero, Marcos D.

    2014-05-01

    As the frontiers of biology become increasingly interdisciplinary, the physics education community has engaged in ongoing efforts to make physics classes more relevant to life science majors. These efforts are complicated by the many apparent differences between these fields, including the types of systems that each studies, the behavior of those systems, the kinds of measurements that each makes, and the role of mathematics in each field. Nonetheless, physics and biology are both sciences that rely on observations and measurements to construct models of the natural world. In this article, we propose that efforts to bridge the teaching of these two disciplines must emphasize shared scientific practices, particularly scientific modeling. We define modeling using language common to both disciplines and highlight how an understanding of the modeling process can help reconcile apparent differences between the teaching of physics and biology. We elaborate on how models can be used for explanatory, predictive, and functional purposes and present common models from each discipline demonstrating key modeling principles. By framing interdisciplinary teaching in the context of modeling, we aim to bridge physics and biology teaching and to equip students with modeling competencies applicable in any scientific discipline.

  7. Unified data model for biological data

    International Nuclear Information System (INIS)

    A data model empowers us to store, retrieve and manipulate data in a unified way. We consider the biological data consists of DNA (De-Oxyribonucleic Acid), RNA (Ribonucleic Acid) and protein structures. In our Bioinformatics Lab (Bioinformatics Lab, Alkhawarizmi Institute of Computer Science, University of Engineering and Technology, Lahore, Pakistan), we have already proposed two data models for DNA and protein structures individually. In this paper, we propose a unified data model by using the data models of TOS (Temporal Object Oriented System) after making some necessary modifications to this data model and our already proposed the two data models. This proposed unified data model can be used for the modeling and maintaining the biological data (i.e. DNA, RNA and protein structures), in a single unified way. (author)

  8. Laser interaction with biological material mathematical modeling

    CERN Document Server

    Kulikov, Kirill

    2014-01-01

    This book covers the principles of laser interaction with biological cells and tissues of varying degrees of organization. The problems of biomedical diagnostics are considered. Scattering of laser irradiation of blood cells is modeled for biological structures (dermis, epidermis, vascular plexus). An analytic theory is provided which is based on solving the wave equation for the electromagnetic field. It allows the accurate analysis of interference effects arising from the partial superposition of scattered waves. Treated topics of mathematical modeling are: optical characterization of biological tissue with large-scale and small-scale inhomogeneities in the layers, heating blood vessel under laser irradiation incident on the outer surface of the skin and thermo-chemical denaturation of biological structures at the example of human skin.

  9. Building phenomenological models of complex biological processes

    Science.gov (United States)

    Daniels, Bryan; Nemenman, Ilya

    2009-11-01

    A central goal of any modeling effort is to make predictions regarding experimental conditions that have not yet been observed. Overly simple models will not be able to fit the original data well, but overly complex models are likely to overfit the data and thus produce bad predictions. Modern quantitative biology modeling efforts often err on the complexity side of this balance, using myriads of microscopic biochemical reaction processes with a priori unknown kinetic parameters to model relatively simple biological phenomena. In this work, we show how Bayesian model selection (which is mathematically similar to low temperature expansion in statistical physics) can be used to build coarse-grained, phenomenological models of complex dynamical biological processes, which have better predictive powers than microscopically correct, but poorely constrained mechanistic molecular models. We illustrate this on the example of a multiply-modifiable protein molecule, which is a simplified description of multiple biological systems, such as an immune receptors and an RNA polymerase complex. Our approach is similar in spirit to the phenomenological Landau expansion for the free energy in the theory of critical phenomena.

  10. Introduction to stochastic models in biology

    DEFF Research Database (Denmark)

    Ditlevsen, Susanne; Samson, Adeline

    2013-01-01

    This chapter is concerned with continuous time processes, which are often modeled as a system of ordinary differential equations (ODEs). These models assume that the observed dynamics are driven exclusively by internal, deterministic mechanisms. However, real biological systems will always be exp...

  11. Mesoscopic models of biological membranes

    DEFF Research Database (Denmark)

    Venturoli, M.; Sperotto, Maria Maddalena; Kranenburg, M.;

    2006-01-01

    , as model systems to understand the fundamental properties of biomembranes. The properties of lipid bilayers can be studied at different time and length scales. For some properties it is sufficient to envision a membrane as an elastic sheet, while for others it is important to take into account the details...... of the individual atoms. In this review, we focus on an intermediate level, where groups of atoms are lumped into pseudo-particles to arrive at a coarse-grained, or mesoscopic, description of a bilayer, which is subsequently studied using molecular simulation. The aim of this review is to compare various strategies...

  12. On validation and invalidation of biological models

    Directory of Open Access Journals (Sweden)

    Anderson James

    2009-05-01

    Full Text Available Abstract Background Very frequently the same biological system is described by several, sometimes competing mathematical models. This usually creates confusion around their validity, ie, which one is correct. However, this is unnecessary since validity of a model cannot be established; model validation is actually a misnomer. In principle the only statement that one can make about a system model is that it is incorrect, ie, invalid, a fact which can be established given appropriate experimental data. Nonlinear models of high dimension and with many parameters are impossible to invalidate through simulation and as such the invalidation process is often overlooked or ignored. Results We develop different approaches for showing how competing ordinary differential equation (ODE based models of the same biological phenomenon containing nonlinearities and parametric uncertainty can be invalidated using experimental data. We first emphasize the strong interplay between system identification and model invalidation and we describe a method for obtaining a lower bound on the error between candidate model predictions and data. We then turn to model invalidation and formulate a methodology for discrete-time and continuous-time model invalidation. The methodology is algorithmic and uses Semidefinite Programming as the computational tool. It is emphasized that trying to invalidate complex nonlinear models through exhaustive simulation is not only computationally intractable but also inconclusive. Conclusion Biological models derived from experimental data can never be validated. In fact, in order to understand biological function one should try to invalidate models that are incompatible with available data. This work describes a framework for invalidating both continuous and discrete-time ODE models based on convex optimization techniques. The methodology does not require any simulation of the candidate models; the algorithms presented in this paper have a

  13. Notions of similarity for computational biology models

    KAUST Repository

    Waltemath, Dagmar

    2016-03-21

    Computational models used in biology are rapidly increasing in complexity, size, and numbers. To build such large models, researchers need to rely on software tools for model retrieval, model combination, and version control. These tools need to be able to quantify the differences and similarities between computational models. However, depending on the specific application, the notion of similarity may greatly vary. A general notion of model similarity, applicable to various types of models, is still missing. Here, we introduce a general notion of quantitative model similarities, survey the use of existing model comparison methods in model building and management, and discuss potential applications of model comparison. To frame model comparison as a general problem, we describe a theoretical approach to defining and computing similarities based on different model aspects. Potentially relevant aspects of a model comprise its references to biological entities, network structure, mathematical equations and parameters, and dynamic behaviour. Future similarity measures could combine these model aspects in flexible, problem-specific ways in order to mimic users\\' intuition about model similarity, and to support complex model searches in databases.

  14. Simplified models of biological networks.

    Science.gov (United States)

    Sneppen, Kim; Krishna, Sandeep; Semsey, Szabolcs

    2010-01-01

    The function of living cells is controlled by complex regulatory networks that are built of a wide diversity of interacting molecular components. The sheer size and intricacy of molecular networks of even the simplest organisms are obstacles toward understanding network functionality. This review discusses the achievements and promise of a bottom-up approach that uses well-characterized subnetworks as model systems for understanding larger networks. It highlights the interplay between the structure, logic, and function of various types of small regulatory circuits. The bottom-up approach advocates understanding regulatory networks as a collection of entangled motifs. We therefore emphasize the potential of negative and positive feedback, as well as their combinations, to generate robust homeostasis, epigenetics, and oscillations. PMID:20192769

  15. Networks in Cell Biology = Modelling cell biology with networks

    OpenAIRE

    Buchanan, Mark; Caldarelli, Guido; De Los Rios, Paolo; Rao, Francesco; Vendruscolo, M.

    2010-01-01

    The science of complex biological networks is transforming research in areas ranging from evolutionary biology to medicine. This is the first book on the subject, providing a comprehensive introduction to complex network science and its biological applications. With contributions from key leaders in both network theory and modern cell biology, this book discusses the network science that is increasingly foundational for systems biology and the quantitative understanding of living systems. It ...

  16. Inferring Biologically Relevant Models: Nested Canalyzing Functions

    CERN Document Server

    Hinkelmann, Franziska

    2010-01-01

    Inferring dynamic biochemical networks is one of the main challenges in systems biology. Given experimental data, the objective is to identify the rules of interaction among the different entities of the network. However, the number of possible models fitting the available data is huge and identifying a biologically relevant model is of great interest. Nested canalyzing functions, where variables in a given order dominate the function, have recently been proposed as a framework for modeling gene regulatory networks. Previously we described this class of functions as an algebraic toric variety. In this paper, we present an algorithm that identifies all nested canalyzing models that fit the given data. We demonstrate our methods using a well-known Boolean model of the cell cycle in budding yeast.

  17. Post-16 Biology--Some Model Approaches?

    Science.gov (United States)

    Lock, Roger

    1997-01-01

    Outlines alternative approaches to the teaching of difficult concepts in A-level biology which may help student learning by making abstract ideas more concrete and accessible. Examples include models, posters, and poems for illustrating meiosis, mitosis, genetic mutations, and protein synthesis. (DDR)

  18. Ultrafast spectroscopy of model biological membranes

    NARCIS (Netherlands)

    Ghosh, Avishek

    2009-01-01

    In this PhD thesis, I have described the novel time-resolved sum-frequency generation (TR-SFG) spectroscopic technique that I developed during the course of my PhD research and used it study the ultrafast vibrational, structural and orientational dynamics of water molecules at model biological membr

  19. From Biology to Mathematical Models and Back: Teaching Modeling to Biology Students, and Biology to Math and Engineering Students

    Science.gov (United States)

    Chiel, Hillel J.; McManus, Jeffrey M.; Shaw, Kendrick M.

    2010-01-01

    We describe the development of a course to teach modeling and mathematical analysis skills to students of biology and to teach biology to students with strong backgrounds in mathematics, physics, or engineering. The two groups of students have different ways of learning material and often have strong negative feelings toward the area of knowledge…

  20. Biological Event Modeling for Response Planning

    Science.gov (United States)

    McGowan, Clement; Cecere, Fred; Darneille, Robert; Laverdure, Nate

    People worldwide continue to fear a naturally occurring or terrorist-initiated biological event. Responsible decision makers have begun to prepare for such a biological event, but critical policy and system questions remain: What are the best courses of action to prepare for and react to such an outbreak? Where resources should be stockpiled? How many hospital resources—doctors, nurses, intensive-care beds—will be required? Will quarantine be necessary? Decision analysis tools, particularly modeling and simulation, offer ways to address and help answer these questions.

  1. Statistical Model Checking for Biological Systems

    DEFF Research Database (Denmark)

    David, Alexandre; Larsen, Kim Guldstrand; Legay, Axel;

    2014-01-01

    Statistical Model Checking (SMC) is a highly scalable simulation-based verification approach for testing and estimating the probability that a stochastic system satisfies a given linear temporal property. The technique has been applied to (discrete and continuous time) Markov chains, stochastic...... timed automata and most recently hybrid systems using the tool Uppaal SMC. In this paper we enable the application of SMC to complex biological systems, by combining Uppaal SMC with ANIMO, a plugin of the tool Cytoscape used by biologists, as well as with SimBiology®, a plugin of Matlab to simulate...

  2. Institute for Multiscale Modeling of Biological Interactions

    Energy Technology Data Exchange (ETDEWEB)

    Paulaitis, Michael E; Garcia-Moreno, Bertrand; Lenhoff, Abraham

    2009-12-26

    The Institute for Multiscale Modeling of Biological Interactions (IMMBI) has two primary goals: Foster interdisciplinary collaborations among faculty and their research laboratories that will lead to novel applications of multiscale simulation and modeling methods in the biological sciences and engineering; and Building on the unique biophysical/biology-based engineering foundations of the participating faculty, train scientists and engineers to apply computational methods that collectively span multiple time and length scales of biological organization. The success of IMMBI will be defined by the following: Size and quality of the applicant pool for pre-doctoral and post-doctoral fellows; Academic performance; Quality of the pre-doctoral and post-doctoral research; Impact of the research broadly and to the DOE (ASCR program) mission; Distinction of the next career step for pre-doctoral and post-doctoral fellows; and Faculty collaborations that result from IMMBI activities. Specific details about accomplishments during the three years of DOE support for IMMBI have been documented in Annual Progress Reports (April 2005, June 2006, and March 2007) and a Report for a National Academy of Sciences Review (October 2005) that were submitted to DOE on the dates indicated. An overview of these accomplishments is provided.

  3. Graphical Modelling in Genetics and Systems Biology

    OpenAIRE

    Scutari, Marco

    2012-01-01

    Graphical modelling has a long history in statistics as a tool for the analysis of multivariate data, starting from Wright's path analysis and Gibbs' applications to statistical physics at the beginning of the last century. In its modern form, it was pioneered by Lauritzen and Wermuth and Pearl in the 1980s, and has since found applications in fields as diverse as bioinformatics, customer satisfaction surveys and weather forecasts. Genetics and systems biology are unique among these fields in...

  4. Computational Biology: Modeling Chronic Renal Allograft Injury.

    Science.gov (United States)

    Stegall, Mark D; Borrows, Richard

    2015-01-01

    New approaches are needed to develop more effective interventions to prevent long-term rejection of organ allografts. Computational biology provides a powerful tool to assess the large amount of complex data that is generated in longitudinal studies in this area. This manuscript outlines how our two groups are using mathematical modeling to analyze predictors of graft loss using both clinical and experimental data and how we plan to expand this approach to investigate specific mechanisms of chronic renal allograft injury.

  5. From biology to mathematical models and back: teaching modeling to biology students, and biology to math and engineering students.

    Science.gov (United States)

    Chiel, Hillel J; McManus, Jeffrey M; Shaw, Kendrick M

    2010-01-01

    We describe the development of a course to teach modeling and mathematical analysis skills to students of biology and to teach biology to students with strong backgrounds in mathematics, physics, or engineering. The two groups of students have different ways of learning material and often have strong negative feelings toward the area of knowledge that they find difficult. To give students a sense of mastery in each area, several complementary approaches are used in the course: 1) a "live" textbook that allows students to explore models and mathematical processes interactively; 2) benchmark problems providing key skills on which students make continuous progress; 3) assignment of students to teams of two throughout the semester; 4) regular one-on-one interactions with instructors throughout the semester; and 5) a term project in which students reconstruct, analyze, extend, and then write in detail about a recently published biological model. Based on student evaluations and comments, an attitude survey, and the quality of the students' term papers, the course has significantly increased the ability and willingness of biology students to use mathematical concepts and modeling tools to understand biological systems, and it has significantly enhanced engineering students' appreciation of biology.

  6. Stochasticity in cell biology: Modeling across levels

    Science.gov (United States)

    Pedraza, Juan Manuel

    2009-03-01

    Effective modeling of biological processes requires focusing on a particular level of description, and this requires summarizing de details of lower levels into effective variables and properly accounting for the constrains that other levels impose. In the context of stochasticity in gene expression, I will show how the details of the stochastic process can be characterized by a few effective parameters, which facilitates modeling but complicates interpretation of current experiments. I will show how the resulting noise can provide advantageous or deleterious phenotypic fluctuation and how noise control in the copy number control system of plasmids can change the selective pressures. This system illustrates the direct connection between molecular dynamics and evolutionary dynamics.

  7. Falsifying Oscillation Properties of Parametric Biological Models

    Directory of Open Access Journals (Sweden)

    Thao Dang

    2013-08-01

    Full Text Available We propose an approach to falsification of oscillation properties of parametric biological models, based on the recently developed techniques for testing continuous and hybrid systems. In this approach, an oscillation property can be specified using a hybrid automaton, which is then used to guide the exploration in the state and input spaces to search for the behaviors that do not satisfy the property. We illustrate the approach with the Laub-Loomis model for spontaneous oscillations during the aggregation stage of Dictyostelium.

  8. From Biology to Mathematical Models and Back: Teaching Modeling to Biology Students, and Biology to Math and Engineering Students

    OpenAIRE

    Chiel, Hillel J.; McManus, Jeffrey M.; Shaw, Kendrick M

    2010-01-01

    We describe the development of a course to teach modeling and mathematical analysis skills to students of biology and to teach biology to students with strong backgrounds in mathematics, physics, or engineering. The two groups of students have different ways of learning material and often have strong negative feelings toward the area of knowledge that they find difficult. To give students a sense of mastery in each area, several complementary approaches are used in the course: 1) a “live” tex...

  9. Mathematical modeling in biology: A critical assessment

    International Nuclear Information System (INIS)

    The molecular revolution and the development of biology-derived industry have led in the last fifty years to an unprecedented 'lead forward' of life sciences in terms of experimental data. Less success has been achieved in the organisation of such data and in the consequent development of adequate explanatory and predictive theories and models. After a brief historical excursus inborn difficulties of mathematisation of biological objects and processes derived from the complex dynamics of life are discussed along with the logical tools (simplifications, choice of observation points etc.) used to overcome them. 'Autistic', monodisciplinary attitudes towards biological modeling of mathematicians, physicists, biologists aimed in each case at the use of the tools of other disciplines to solve 'selfish' problems are also taken into account and a warning against derived dangers (reification of mono disciplinary metaphors, lack of falsification etc.) is given. Finally 'top.down' (deductive) and 'bottom up' (inductive) heuristic interactive approaches to mathematisation are critically discussed with the help of serie of examples

  10. Unit testing, model validation, and biological simulation.

    Science.gov (United States)

    Sarma, Gopal P; Jacobs, Travis W; Watts, Mark D; Ghayoomie, S Vahid; Larson, Stephen D; Gerkin, Richard C

    2016-01-01

    The growth of the software industry has gone hand in hand with the development of tools and cultural practices for ensuring the reliability of complex pieces of software. These tools and practices are now acknowledged to be essential to the management of modern software. As computational models and methods have become increasingly common in the biological sciences, it is important to examine how these practices can accelerate biological software development and improve research quality. In this article, we give a focused case study of our experience with the practices of unit testing and test-driven development in OpenWorm, an open-science project aimed at modeling Caenorhabditis elegans. We identify and discuss the challenges of incorporating test-driven development into a heterogeneous, data-driven project, as well as the role of model validation tests, a category of tests unique to software which expresses scientific models. PMID:27635225

  11. Unit testing, model validation, and biological simulation

    Science.gov (United States)

    Watts, Mark D.; Ghayoomie, S. Vahid; Larson, Stephen D.; Gerkin, Richard C.

    2016-01-01

    The growth of the software industry has gone hand in hand with the development of tools and cultural practices for ensuring the reliability of complex pieces of software. These tools and practices are now acknowledged to be essential to the management of modern software. As computational models and methods have become increasingly common in the biological sciences, it is important to examine how these practices can accelerate biological software development and improve research quality. In this article, we give a focused case study of our experience with the practices of unit testing and test-driven development in OpenWorm, an open-science project aimed at modeling Caenorhabditis elegans. We identify and discuss the challenges of incorporating test-driven development into a heterogeneous, data-driven project, as well as the role of model validation tests, a category of tests unique to software which expresses scientific models.

  12. Unit testing, model validation, and biological simulation

    Science.gov (United States)

    Watts, Mark D.; Ghayoomie, S. Vahid; Larson, Stephen D.; Gerkin, Richard C.

    2016-01-01

    The growth of the software industry has gone hand in hand with the development of tools and cultural practices for ensuring the reliability of complex pieces of software. These tools and practices are now acknowledged to be essential to the management of modern software. As computational models and methods have become increasingly common in the biological sciences, it is important to examine how these practices can accelerate biological software development and improve research quality. In this article, we give a focused case study of our experience with the practices of unit testing and test-driven development in OpenWorm, an open-science project aimed at modeling Caenorhabditis elegans. We identify and discuss the challenges of incorporating test-driven development into a heterogeneous, data-driven project, as well as the role of model validation tests, a category of tests unique to software which expresses scientific models. PMID:27635225

  13. Evaluation of biological models using Spacelab

    Science.gov (United States)

    Tollinger, D.; Williams, B. A.

    1980-01-01

    Biological models of hypogravity effects are described, including the cardiovascular-fluid shift, musculoskeletal, embryological and space sickness models. These models predict such effects as loss of extracellular fluid and electrolytes, decrease in red blood cell mass, and the loss of muscle and bone mass in weight-bearing portions of the body. Experimentation in Spacelab by the use of implanted electromagnetic flow probes, by fertilizing frog eggs in hypogravity and fixing the eggs at various stages of early development and by assessing the role of the vestibulocular reflex arc in space sickness is suggested. It is concluded that the use of small animals eliminates the uncertainties caused by corrective or preventive measures employed with human subjects.

  14. An electrostatic model for biological cell division

    CERN Document Server

    Faraggi, Eshel

    2010-01-01

    Probably the most fundamental processes for biological systems is their ability to create themselves through the use of cell division and cell differentiation. In this work a simple physical model is proposed for biological cell division. The model consists of a positive ionic gradient across the cell membrane, and concentration of charge at the nodes of the spindle and on the chromosomes. A simple calculation, based on Coulomb's Law, shows that under such circumstances a chromosome will tend to break up to its constituent chromatids and that the chromatids will be separated by a distance that is an order of thirty percent of the distance between the spindle nodes. Further repulsion between the nodes will tend to stretch the cell and eventually break the cell membrane between the separated chromatids, leading to cell division. The importance of this work is in continuing the understanding of the electromagnetic basis of cell division and providing it with an analytical model. A central implication of this and...

  15. Multiscale mechanical modeling of soft biological tissues

    Science.gov (United States)

    Stylianopoulos, Triantafyllos

    2008-10-01

    Soft biological tissues include both native and artificial tissues. In the human body, tissues like the articular cartilage, arterial wall, and heart valve leaflets are examples of structures composed of an underlying network of collagen fibers, cells, proteins and molecules. Artificial tissues are less complex than native tissues and mainly consist of a fiber polymer network with the intent of replacing lost or damaged tissue. Understanding of the mechanical function of these materials is essential for many clinical treatments (e.g. arterial clamping, angioplasty), diseases (e.g. arteriosclerosis) and tissue engineering applications (e.g. engineered blood vessels or heart valves). This thesis presents the derivation and application of a multiscale methodology to describe the macroscopic mechanical function of soft biological tissues incorporating directly their structural architecture. The model, which is based on volume averaging theory, accounts for structural parameters such as the network volume fraction and orientation, the realignment of the fibers in response to strain, the interactions among the fibers and the interactions between the fibers and the interstitial fluid in order to predict the overall tissue behavior. Therefore, instead of using a constitutive equation to relate strain to stress, the tissue microstructure is modeled within a representative volume element (RVE) and the macroscopic response at any point in the tissue is determined by solving a micromechanics problem in the RVE. The model was applied successfully to acellular collagen gels, native blood vessels, and electrospun polyurethane scaffolds and provided accurate predictions for permeability calculations in isotropic and oriented fiber networks. The agreement of model predictions with experimentally determined mechanical properties provided insights into the mechanics of tissues and tissue constructs, while discrepancies revealed limitations of the model framework.

  16. Experimental models to study cholangiocyte biology

    Institute of Scientific and Technical Information of China (English)

    Pamela S. Tietz; Xian-Ming Chen; Ai-Yu Gong; Robert C. Huebert; Anatoliy Masyuk; Tatyana Masyuk; Patrick L. Splinter; Nicholas F. LaRusso

    2002-01-01

    Cholangiocytes-the epithelial cells which line the bileducts-are increasingly recognized as importanttransporting epithelia actively involved in the absorptionand secretion of water, ions, and solutes. Thisrecognition is due in part to the recent development ofnew experimental models. New biologic concepts haveemerged including the identification and topography ofreceptors and flux proteins on the apical and/orbasolateral membrane which are involved in the molecularmechanisms of ductal bile secretion. Individually isolatedand/or perfused bile duct units from livers of rats andmice serve as new, physiologically relevant in vitromodels to study cholangiocyte transport. Biliary treedimensions and novel insights into anatomic remodeling ofproliferating bile ducts have emerged from three-dimensional reconstruction using CT scanning andsophisticated software. Moreover, new pathologicconcepts have arisen regarding the interaction ofcholangiocytes with pathogens such as Cryptosporidiumparvum. These concepts and associated methodologiesmay provide the framework to develop new therapies for the cholangiopathies, a group of important hepatobiliarydiseases in which cholangiocytes are the target cell.Tietz PS, Chen XM, Gong AY, Huebert RC, Masyuk A, MasyukT, Splinter PL, LaRusso NF. Experimental models to studycoholangiocyte biology.

  17. Model Checking the Biological Model of Membrane Computing with Probabilistic Symbolic Model Checker by Using Two Biological Systems

    Directory of Open Access Journals (Sweden)

    Ravie c. Muniyandi

    2010-01-01

    Full Text Available Problem statement: Membrane computing formalism has provided better modeling capabilities for biological systems in comparison to conventional mathematical models. Model checking could be used to reason about the biological system in detail and with precision by verifying formally whether membrane computing model meets the properties of the system. Approach: This study was carried to investigate the preservation of properties of two biological systems that had been modeled and simulated in membrane computing by a method of model checking using PRISM. The two biological systems were prey-predator population and signal processing in the legend-receptor networks of protein TGF-ß. Results: The model checking of membrane computing model of the biological systems with five different properties showed that the properties of the biological systems could be preserved in the membrane computing model. Conclusion: Membrane computing model not only provides a better approach in representing and simulating a biological system but also able to sustain the basic properties of the system.

  18. ACTIVE AND PARTICIPATORY METHODS IN BIOLOGY: MODELING

    Directory of Open Access Journals (Sweden)

    Brînduşa-Antonela SBÎRCEA

    2011-01-01

    Full Text Available By using active and participatory methods it is hoped that pupils will not only come to a deeper understanding of the issues involved, but also that their motivation will be heightened. Pupil involvement in their learning is essential. Moreover, by using a variety of teaching techniques, we can help students make sense of the world in different ways, increasing the likelihood that they will develop a conceptual understanding. The teacher must be a good facilitator, monitoring and supporting group dynamics. Modeling is an instructional strategy in which the teacher demonstrates a new concept or approach to learning and pupils learn by observing. In the teaching of biology the didactic materials are fundamental tools in the teaching-learning process. Reading about scientific concepts or having a teacher explain them is not enough. Research has shown that modeling can be used across disciplines and in all grade and ability level classrooms. Using this type of instruction, teachers encourage learning.

  19. Computational Modeling of Biological Systems From Molecules to Pathways

    CERN Document Server

    2012-01-01

    Computational modeling is emerging as a powerful new approach for studying and manipulating biological systems. Many diverse methods have been developed to model, visualize, and rationally alter these systems at various length scales, from atomic resolution to the level of cellular pathways. Processes taking place at larger time and length scales, such as molecular evolution, have also greatly benefited from new breeds of computational approaches. Computational Modeling of Biological Systems: From Molecules to Pathways provides an overview of established computational methods for the modeling of biologically and medically relevant systems. It is suitable for researchers and professionals working in the fields of biophysics, computational biology, systems biology, and molecular medicine.

  20. Evaluation models for contaminated sites – biological system at risk

    OpenAIRE

    Golomeova, Mirjana; Krstev, Boris; Golomeov, Blagoj; Zendelska, Afrodita; Krstev, Aleksandar

    2009-01-01

    The paper presents the different methods that can be used correspond to three types of approaches, testing, monitoring, and modeling: experimental models, in situ indicators and mathematical models, and choice of model for contaminated sites – biological system at risk.

  1. Summing up dynamics: modelling biological processes in variable temperature scenarios

    NARCIS (Netherlands)

    Tijskens, L.M.M.; Verdenius, F.

    2000-01-01

    The interest of modelling biological processes with dynamically changing external conditions (temperature, relative humidity, gas conditions) increases. Several modelling approaches are currently available. Among them are approaches like modelling under standard conditions, temperature sum models an

  2. A unified biological modeling and simulation system for analyzing biological reaction networks

    Science.gov (United States)

    Yu, Seok Jong; Tung, Thai Quang; Park, Junho; Lim, Jongtae; Yoo, Jaesoo

    2013-12-01

    In order to understand the biological response in a cell, a researcher has to create a biological network and design an experiment to prove it. Although biological knowledge has been accumulated, we still don't have enough biological models to explain complex biological phenomena. If a new biological network is to be created, integrated modeling software supporting various biological models is required. In this research, we design and implement a unified biological modeling and simulation system, called ezBioNet, for analyzing biological reaction networks. ezBioNet designs kinetic and Boolean network models and simulates the biological networks using a server-side simulation system with Object Oriented Parallel Accelerator Library framework. The main advantage of ezBioNet is that a user can create a biological network by using unified modeling canvas of kinetic and Boolean models and perform massive simulations, including Ordinary Differential Equation analyses, sensitivity analyses, parameter estimates and Boolean network analysis. ezBioNet integrates useful biological databases, including the BioModels database, by connecting European Bioinformatics Institute servers through Web services Application Programming Interfaces. In addition, we employ Eclipse Rich Client Platform, which is a powerful modularity framework to allow various functional expansions. ezBioNet is intended to be an easy-to-use modeling tool and a simulation system for understanding the control mechanism by monitoring the change of each component in a biological network. The simulation result can be managed and visualized on ezBioNet, which is available free of charge at http://ezbionet.sourceforge.net or http://ezbionet.cbnu.ac.kr.

  3. Model checking biological systems described using ambient calculus

    DEFF Research Database (Denmark)

    Mardare, Radu Iulian; Priami, Corrado; Qualia, Paola;

    2005-01-01

    Model checking biological systems described using ambient calculus. In Proc. of the second International Workshop on Computational Methods in Systems Biology (CMSB04), Lecture Notes in Bioinformatics 3082:85-103, Springer, 2005....

  4. Biologically based multistage modeling of radiation effects

    Energy Technology Data Exchange (ETDEWEB)

    William Hazelton; Suresh Moolgavkar; E. Georg Luebeck

    2005-08-30

    This past year we have made substantial progress in modeling the contribution of homeostatic regulation to low-dose radiation effects and carcinogenesis. We have worked to refine and apply our multistage carcinogenesis models to explicitly incorporate cell cycle states, simple and complex damage, checkpoint delay, slow and fast repair, differentiation, and apoptosis to study the effects of low-dose ionizing radiation in mouse intestinal crypts, as well as in other tissues. We have one paper accepted for publication in ''Advances in Space Research'', and another manuscript in preparation describing this work. I also wrote a chapter describing our combined cell-cycle and multistage carcinogenesis model that will be published in a book on stochastic carcinogenesis models edited by Wei-Yuan Tan. In addition, we organized and held a workshop on ''Biologically Based Modeling of Human Health Effects of Low dose Ionizing Radiation'', July 28-29, 2005 at Fred Hutchinson Cancer Research Center in Seattle, Washington. We had over 20 participants, including Mary Helen Barcellos-Hoff as keynote speaker, talks by most of the low-dose modelers in the DOE low-dose program, experimentalists including Les Redpath (and Mary Helen), Noelle Metting from DOE, and Tony Brooks. It appears that homeostatic regulation may be central to understanding low-dose radiation phenomena. The primary effects of ionizing radiation (IR) are cell killing, delayed cell cycling, and induction of mutations. However, homeostatic regulation causes cells that are killed or damaged by IR to eventually be replaced. Cells with an initiating mutation may have a replacement advantage, leading to clonal expansion of these initiated cells. Thus we have focused particularly on modeling effects that disturb homeostatic regulation as early steps in the carcinogenic process. There are two primary considerations that support our focus on homeostatic regulation. First, a number of

  5. Toward university modeling instruction--biology: adapting curricular frameworks from physics to biology.

    Science.gov (United States)

    Manthey, Seth; Brewe, Eric

    2013-06-01

    University Modeling Instruction (UMI) is an approach to curriculum and pedagogy that focuses instruction on engaging students in building, validating, and deploying scientific models. Modeling Instruction has been successfully implemented in both high school and university physics courses. Studies within the physics education research (PER) community have identified UMI's positive impacts on learning gains, equity, attitudinal shifts, and self-efficacy. While the success of this pedagogical approach has been recognized within the physics community, the use of models and modeling practices is still being developed for biology. Drawing from the existing research on UMI in physics, we describe the theoretical foundations of UMI and how UMI can be adapted to include an emphasis on models and modeling for undergraduate introductory biology courses. In particular, we discuss our ongoing work to develop a framework for the first semester of a two-semester introductory biology course sequence by identifying the essential basic models for an introductory biology course sequence.

  6. Oscillation and stability of delay models in biology

    CERN Document Server

    Agarwal, Ravi P; Saker, Samir H

    2014-01-01

    Environmental variation plays an important role in many biological and ecological dynamical systems. This monograph focuses on the study of oscillation and the stability of delay models occurring in biology. The book presents recent research results on the qualitative behavior of mathematical models under different physical and environmental conditions, covering dynamics including the distribution and consumption of food. Researchers in the fields of mathematical modeling, mathematical biology, and population dynamics will be particularly interested in this material.

  7. INTERVAL OBSERVER FOR A BIOLOGICAL REACTOR MODEL

    Directory of Open Access Journals (Sweden)

    T. A. Kharkovskaia

    2014-05-01

    Full Text Available The method of an interval observer design for nonlinear systems with parametric uncertainties is considered. The interval observer synthesis problem for systems with varying parameters consists in the following. If there is the uncertainty restraint for the state values of the system, limiting the initial conditions of the system and the set of admissible values for the vector of unknown parameters and inputs, the interval existence condition for the estimations of the system state variables, containing the actual state at a given time, needs to be held valid over the whole considered time segment as well. Conditions of the interval observers design for the considered class of systems are shown. They are: limitation of the input and state, the existence of a majorizing function defining the uncertainty vector for the system, Lipschitz continuity or finiteness of this function, the existence of an observer gain with the suitable Lyapunov matrix. The main condition for design of such a device is cooperativity of the interval estimation error dynamics. An individual observer gain matrix selection problem is considered. In order to ensure the property of cooperativity for interval estimation error dynamics, a static transformation of coordinates is proposed. The proposed algorithm is demonstrated by computer modeling of the biological reactor. Possible applications of these interval estimation systems are the spheres of robust control, where the presence of various types of uncertainties in the system dynamics is assumed, biotechnology and environmental systems and processes, mechatronics and robotics, etc.

  8. Morphogenesis and pattern formation in biological systems experiments and models

    CERN Document Server

    Noji, Sumihare; Ueno, Naoto; Maini, Philip

    2003-01-01

    A central goal of current biology is to decode the mechanisms that underlie the processes of morphogenesis and pattern formation. Concerned with the analysis of those phenomena, this book covers a broad range of research fields, including developmental biology, molecular biology, plant morphogenesis, ecology, epidemiology, medicine, paleontology, evolutionary biology, mathematical biology, and computational biology. In Morphogenesis and Pattern Formation in Biological Systems: Experiments and Models, experimental and theoretical aspects of biology are integrated for the construction and investigation of models of complex processes. This collection of articles on the latest advances by leading researchers not only brings together work from a wide spectrum of disciplines, but also provides a stepping-stone to the creation of new areas of discovery.

  9. Modelling biological complexity: a physical scientist's perspective

    OpenAIRE

    Coveney, Peter V.; Fowler, Philip W.

    2005-01-01

    We discuss the modern approaches of complexity and self-organization to understanding dynamical systems and how these concepts can inform current interest in systems biology. From the perspective of a physical scientist, it is especially interesting to examine how the differing weights given to philosophies of science in the physical and biological sciences impact the application of the study of complexity. We briefly describe how the dynamics of the heart and circadian rhythms, canonical exa...

  10. Bridging Physics and Biology Teaching through Modeling

    OpenAIRE

    Hoskinson, Anne-Marie; Couch, Brian A.; Zwickl, Benjamin M.; Hinko, Kathleen; Caballero, Marcos D.

    2013-01-01

    As the frontiers of biology become increasingly interdisciplinary, the physics education community has engaged in ongoing efforts to make physics classes more relevant to life sciences majors. These efforts are complicated by the many apparent differences between these fields, including the types of systems that each studies, the behavior of those systems, the kinds of measurements that each makes, and the role of mathematics in each field. Nonetheless, physics and biology are both sciences t...

  11. OFFl Models: Novel Schema for Dynamical Modeling of Biological Systems.

    Science.gov (United States)

    Ogbunugafor, C Brandon; Robinson, Sean P

    2016-01-01

    Flow diagrams are a common tool used to help build and interpret models of dynamical systems, often in biological contexts such as consumer-resource models and similar compartmental models. Typically, their usage is intuitive and informal. Here, we present a formalized version of flow diagrams as a kind of weighted directed graph which follow a strict grammar, which translate into a system of ordinary differential equations (ODEs) by a single unambiguous rule, and which have an equivalent representation as a relational database. (We abbreviate this schema of "ODEs and formalized flow diagrams" as OFFL.) Drawing a diagram within this strict grammar encourages a mental discipline on the part of the modeler in which all dynamical processes of a system are thought of as interactions between dynamical species that draw parcels from one or more source species and deposit them into target species according to a set of transformation rules. From these rules, the net rate of change for each species can be derived. The modeling schema can therefore be understood as both an epistemic and practical heuristic for modeling, serving both as an organizational framework for the model building process and as a mechanism for deriving ODEs. All steps of the schema beyond the initial scientific (intuitive, creative) abstraction of natural observations into model variables are algorithmic and easily carried out by a computer, thus enabling the future development of a dedicated software implementation. Such tools would empower the modeler to consider significantly more complex models than practical limitations might have otherwise proscribed, since the modeling framework itself manages that complexity on the modeler's behalf. In this report, we describe the chief motivations for OFFL, carefully outline its implementation, and utilize a range of classic examples from ecology and epidemiology to showcase its features. PMID:27270918

  12. OFFl Models: Novel Schema for Dynamical Modeling of Biological Systems.

    Directory of Open Access Journals (Sweden)

    C Brandon Ogbunugafor

    Full Text Available Flow diagrams are a common tool used to help build and interpret models of dynamical systems, often in biological contexts such as consumer-resource models and similar compartmental models. Typically, their usage is intuitive and informal. Here, we present a formalized version of flow diagrams as a kind of weighted directed graph which follow a strict grammar, which translate into a system of ordinary differential equations (ODEs by a single unambiguous rule, and which have an equivalent representation as a relational database. (We abbreviate this schema of "ODEs and formalized flow diagrams" as OFFL. Drawing a diagram within this strict grammar encourages a mental discipline on the part of the modeler in which all dynamical processes of a system are thought of as interactions between dynamical species that draw parcels from one or more source species and deposit them into target species according to a set of transformation rules. From these rules, the net rate of change for each species can be derived. The modeling schema can therefore be understood as both an epistemic and practical heuristic for modeling, serving both as an organizational framework for the model building process and as a mechanism for deriving ODEs. All steps of the schema beyond the initial scientific (intuitive, creative abstraction of natural observations into model variables are algorithmic and easily carried out by a computer, thus enabling the future development of a dedicated software implementation. Such tools would empower the modeler to consider significantly more complex models than practical limitations might have otherwise proscribed, since the modeling framework itself manages that complexity on the modeler's behalf. In this report, we describe the chief motivations for OFFL, carefully outline its implementation, and utilize a range of classic examples from ecology and epidemiology to showcase its features.

  13. OFFl Models: Novel Schema for Dynamical Modeling of Biological Systems.

    Science.gov (United States)

    Ogbunugafor, C Brandon; Robinson, Sean P

    2016-01-01

    Flow diagrams are a common tool used to help build and interpret models of dynamical systems, often in biological contexts such as consumer-resource models and similar compartmental models. Typically, their usage is intuitive and informal. Here, we present a formalized version of flow diagrams as a kind of weighted directed graph which follow a strict grammar, which translate into a system of ordinary differential equations (ODEs) by a single unambiguous rule, and which have an equivalent representation as a relational database. (We abbreviate this schema of "ODEs and formalized flow diagrams" as OFFL.) Drawing a diagram within this strict grammar encourages a mental discipline on the part of the modeler in which all dynamical processes of a system are thought of as interactions between dynamical species that draw parcels from one or more source species and deposit them into target species according to a set of transformation rules. From these rules, the net rate of change for each species can be derived. The modeling schema can therefore be understood as both an epistemic and practical heuristic for modeling, serving both as an organizational framework for the model building process and as a mechanism for deriving ODEs. All steps of the schema beyond the initial scientific (intuitive, creative) abstraction of natural observations into model variables are algorithmic and easily carried out by a computer, thus enabling the future development of a dedicated software implementation. Such tools would empower the modeler to consider significantly more complex models than practical limitations might have otherwise proscribed, since the modeling framework itself manages that complexity on the modeler's behalf. In this report, we describe the chief motivations for OFFL, carefully outline its implementation, and utilize a range of classic examples from ecology and epidemiology to showcase its features.

  14. Dynamics and kinetics of model biological systems

    Science.gov (United States)

    Mirigian, Stephen

    In this work we study three systems of biological interest: the translocation of a heterogeneously charged polymer through an infinitely thin pore, the wrapped of a rigid particle by a soft vesicle and the modification of the dynamical properties of a gel due to the presence of rigid inclusions. We study the kinetics of translocation for a heterogeneously charged polyelectrolyte through an infinitely narrow pore using the Fokker-Planck formalism to compute mean first passage times, the probability of successful translocation, and the mean successful translocation time for a diblock copolymer. We find, in contrast to the homopolymer result, that details of the boundary conditions lead to qualitatively different behavior. Under experimentally relevant conditions for a diblock copolymer we find that there is a threshold length of the charged block, beyond which the probability of successful translocation is independent of charge fraction. Additionally, we find that mean successful translocation time exhibits non-monotonic behavior with increasing length of the charged fraction; there is an optimum length of the charged block where the mean successful translocation time is slowest and there can be a substantial range of charge fraction where it is slower than a minimally charged chain. For a fixed total charge on the chain, we find that finer distributions of the charge along the chain leads to a significant reduction in mean translocation time compared to the diblock distribution. Endocytosis is modeled using a simple geometrical model from the literature. We map the process of wrapping a rigid spherical bead onto a one-dimensional stochastic process described by the Fokker-Planck equation to compute uptake rates as a function of membrane properties and system geometry. We find that simple geometrical considerations pick an optimal particle size for uptake and a corresponding maximal uptake rate, which can be controlled by altering the material properties of the

  15. Mathematical Manipulative Models: In Defense of "Beanbag Biology"

    Science.gov (United States)

    Jungck, John R.; Gaff, Holly; Weisstein, Anton E.

    2010-01-01

    Mathematical manipulative models have had a long history of influence in biological research and in secondary school education, but they are frequently neglected in undergraduate biology education. By linking mathematical manipulative models in a four-step process--1) use of physical manipulatives, 2) interactive exploration of computer…

  16. Modeling Co-evolution of Speech and Biology.

    Science.gov (United States)

    de Boer, Bart

    2016-04-01

    Two computer simulations are investigated that model interaction of cultural evolution of language and biological evolution of adaptations to language. Both are agent-based models in which a population of agents imitates each other using realistic vowels. The agents evolve under selective pressure for good imitation. In one model, the evolution of the vocal tract is modeled; in the other, a cognitive mechanism for perceiving speech accurately is modeled. In both cases, biological adaptations to using and learning speech evolve, even though the system of speech sounds itself changes at a more rapid time scale than biological evolution. However, the fact that the available acoustic space is used maximally (a self-organized result of cultural evolution) is constant, and therefore biological evolution does have a stable target. This work shows that when cultural and biological traits are continuous, their co-evolution may lead to cognitive adaptations that are strong enough to detect empirically.

  17. Learning (from) the errors of a systems biology model

    OpenAIRE

    Benjamin Engelhardt; Holger Frőhlich; Maik Kschischo

    2016-01-01

    Mathematical modelling is a labour intensive process involving several iterations of testing on real data and manual model modifications. In biology, the domain knowledge guiding model development is in many cases itself incomplete and uncertain. A major problem in this context is that biological systems are open. Missed or unknown external influences as well as erroneous interactions in the model could thus lead to severely misleading results. Here we introduce the dynamic elastic-net, a dat...

  18. Modelling the structure and dynamics of biological pathways

    OpenAIRE

    O'Hara, Laura; Livigni, Alessandra; Theocharidis, Thanos; Boyer, Benjamin; Angus, Tim; Wright, Derek; Chen, Sz-Hau; Raza, Sobia; Barnett, Mark; Digard, Paul; Smith, Lee; Freeman, Thomas

    2016-01-01

    There is a need for formalised diagrams that both summarise current biological pathway knowledge and support modelling approaches that explain and predict their behaviour. Here we present a new, freely-available modelling framework that includes: a biologist-friendly pathway modelling language (mEPN); a simple but sophisticated method to support model parameterisation using accessible biological information, a stochastic flow algorithm that simulates the dynamics of pathway activity, and a 3D...

  19. MEMBRANE COMPUTING AS THE PARADIGM FOR MODELING SYSTEMS BIOLOGY

    Directory of Open Access Journals (Sweden)

    Ravie Chandren Muniyandi

    2013-01-01

    Full Text Available Membrane computing is a field in computer science that is inspired from the structure and the processes of living cells and is being considered as an alternative in solving the limitations in conventional mathematical approaches by taking into consideration its essential features that are of interest for research in systems biology. Advancements in computability make it feasible to handle huge volumes of data in biology and propose a new and better approach using a discreet computer science model, such as membrane computing. In this respect, membrane-computing abilities, to enhance the understanding of the system level of biological systems, have been explored. This study discusses experiences in applying membrane computing in modeling biological systems as well as possibilities of incorporating membrane computing into other computer science paradigms to enhance the use of membrane computing in systems biology. Experiences in modeling aspects of systems biology with membrane computing demonstrate additional advantages and possibilities compared with conventional methods. However, they are not yet used widely to model or simulate biological processes or systems. A general framework of modeling and verifying biological systems using membrane computing is essential as a guideline for biologists in their research in systems biology.

  20. Computer Models and Automata Theory in Biology and Medicine

    CERN Document Server

    Baianu, I C

    2004-01-01

    The applications of computers to biological and biomedical problem solving goes back to the very beginnings of computer science, automata theory [1], and mathematical biology [2]. With the advent of more versatile and powerful computers, biological and biomedical applications of computers have proliferated so rapidly that it would be virtually impossible to compile a comprehensive review of all developments in this field. Limitations of computer simulations in biology have also come under close scrutiny, and claims have been made that biological systems have limited information processing power [3]. Such general conjectures do not, however, deter biologists and biomedical researchers from developing new computer applications in biology and medicine. Microprocessors are being widely employed in biological laboratories both for automatic data acquisition/processing and modeling; one particular area, which is of great biomedical interest, involves fast digital image processing and is already established for rout...

  1. A guide to numerical modelling in systems biology

    CERN Document Server

    Deuflhard, Peter

    2015-01-01

    This book is intended for students of computational systems biology with only a limited background in mathematics. Typical books on systems biology merely mention algorithmic approaches, but without offering a deeper understanding. On the other hand, mathematical books are typically unreadable for computational biologists. The authors of the present book have worked hard to fill this gap. The result is not a book on systems biology, but on computational methods in systems biology. This book originated from courses taught by the authors at Freie Universität Berlin. The guiding idea of the courses was to convey those mathematical insights that are indispensable for systems biology, teaching the necessary mathematical prerequisites by means of many illustrative examples and without any theorems. The three chapters cover the mathematical modelling of biochemical and physiological processes, numerical simulation of the dynamics of biological networks, and identification of model parameters by means of comparisons...

  2. Mechanistic modeling confronts the complexity of molecular cell biology

    OpenAIRE

    Phair, Robert D.

    2014-01-01

    Mechanistic modeling has the potential to transform how cell biologists contend with the inescapable complexity of modern biology. I am a physiologist–electrical engineer–systems biologist who has been working at the level of cell biology for the past 24 years. This perspective aims 1) to convey why we build models, 2) to enumerate the major approaches to modeling and their philosophical differences, 3) to address some recurrent concerns raised by experimentalists, and then 4) to imagine a fu...

  3. Learning (from) the errors of a systems biology model

    Science.gov (United States)

    Engelhardt, Benjamin; Frőhlich, Holger; Kschischo, Maik

    2016-02-01

    Mathematical modelling is a labour intensive process involving several iterations of testing on real data and manual model modifications. In biology, the domain knowledge guiding model development is in many cases itself incomplete and uncertain. A major problem in this context is that biological systems are open. Missed or unknown external influences as well as erroneous interactions in the model could thus lead to severely misleading results. Here we introduce the dynamic elastic-net, a data driven mathematical method which automatically detects such model errors in ordinary differential equation (ODE) models. We demonstrate for real and simulated data, how the dynamic elastic-net approach can be used to automatically (i) reconstruct the error signal, (ii) identify the target variables of model error, and (iii) reconstruct the true system state even for incomplete or preliminary models. Our work provides a systematic computational method facilitating modelling of open biological systems under uncertain knowledge.

  4. A biologically inspired model for pattern recognition*

    OpenAIRE

    Gonzalez, Eduardo; Liljenström, Hans; Ruiz, Yusely; Li, Guang

    2010-01-01

    In this paper, a novel bionic model and its performance in pattern recognition are presented and discussed. The model is constructed from a bulb model and a three-layered cortical model, mimicking the main features of the olfactory system. The olfactory bulb and cortex models are connected by feedforward and feedback fibers with distributed delays. The Breast Cancer Wisconsin dataset consisting of data from 683 patients divided into benign and malignant classes is used to demonstrate the capa...

  5. Mechanistic modeling confronts the complexity of molecular cell biology.

    Science.gov (United States)

    Phair, Robert D

    2014-11-01

    Mechanistic modeling has the potential to transform how cell biologists contend with the inescapable complexity of modern biology. I am a physiologist-electrical engineer-systems biologist who has been working at the level of cell biology for the past 24 years. This perspective aims 1) to convey why we build models, 2) to enumerate the major approaches to modeling and their philosophical differences, 3) to address some recurrent concerns raised by experimentalists, and then 4) to imagine a future in which teams of experimentalists and modelers build-and subject to exhaustive experimental tests-models covering the entire spectrum from molecular cell biology to human pathophysiology. There is, in my view, no technical obstacle to this future, but it will require some plasticity in the biological research mind-set.

  6. Mathematics in the Biology Classroom: A Model of Interdisciplinary Education

    Science.gov (United States)

    Hodgson, Ted; Keck, Robert; Patterson, Richard; Maki, Dan

    2005-01-01

    This article describes an interdisciplinary course that develops essential mathematical modeling skills within an introductory biology setting. The course embodies recent recommendations regarding the need for interdisciplinary, inquiry-based mathematical preparation of undergraduates in the biological sciences. Evaluation indicates that the…

  7. Uncertainty in biology a computational modeling approach

    CERN Document Server

    Gomez-Cabrero, David

    2016-01-01

    Computational modeling of biomedical processes is gaining more and more weight in the current research into the etiology of biomedical problems and potential treatment strategies.  Computational modeling allows to reduce, refine and replace animal experimentation as well as to translate findings obtained in these experiments to the human background. However these biomedical problems are inherently complex with a myriad of influencing factors, which strongly complicates the model building and validation process.  This book wants to address four main issues related to the building and validation of computational models of biomedical processes: Modeling establishment under uncertainty Model selection and parameter fitting Sensitivity analysis and model adaptation Model predictions under uncertainty In each of the abovementioned areas, the book discusses a number of key-techniques by means of a general theoretical description followed by one or more practical examples.  This book is intended for graduate stude...

  8. Completing and adapting models of biological processes

    OpenAIRE

    Margaria, Tiziana; Hinchey, Michael G.; Raffelt, Harald; Rash, James L.; Rouff, Christopher A.; Steffen, Bernhard

    2006-01-01

    We present a learning-based method for model completion and adaptation, which is based on the combination of two approaches: 1) R2D2C, a technique for mechanically transforming system requirements via provably equivalent models to running code, and 2) automata learning-based model extrapolation. The intended impact of this new combination is to make model completion and adaptation accessible to experts of the field, like biologists or engineers. The principle is briefly illustrated by gene...

  9. Theoretical Biology and Medical Modelling: ensuring continued growth and future leadership

    OpenAIRE

    Nishiura, Hiroshi; Rietman, Edward A.; Wu, Rongling

    2013-01-01

    Theoretical biology encompasses a broad range of biological disciplines ranging from mathematical biology and biomathematics to philosophy of biology. Adopting a broad definition of "biology", Theoretical Biology and Medical Modelling, an open access journal, considers original research studies that focus on theoretical ideas and models associated with developments in biology and medicine.

  10. Recent Applications of Hidden Markov Models in Computational Biology

    Institute of Scientific and Technical Information of China (English)

    Khar Heng Choo; Joo Chuan Tong; Louxin Zhang

    2004-01-01

    This paper examines recent developments and applications of Hidden Markov Models (HMMs) to various problems in computational biology, including multiple sequence alignment, homology detection, protein sequences classification, and genomic annotation.

  11. Computational Modeling, Formal Analysis, and Tools for Systems Biology.

    Directory of Open Access Journals (Sweden)

    Ezio Bartocci

    2016-01-01

    Full Text Available As the amount of biological data in the public domain grows, so does the range of modeling and analysis techniques employed in systems biology. In recent years, a number of theoretical computer science developments have enabled modeling methodology to keep pace. The growing interest in systems biology in executable models and their analysis has necessitated the borrowing of terms and methods from computer science, such as formal analysis, model checking, static analysis, and runtime verification. Here, we discuss the most important and exciting computational methods and tools currently available to systems biologists. We believe that a deeper understanding of the concepts and theory highlighted in this review will produce better software practice, improved investigation of complex biological processes, and even new ideas and better feedback into computer science.

  12. Modeling human liver biology using stem cell-derived hepatocytes

    OpenAIRE

    Sun, Pingnan; Zhou, XiaoLing; Farnworth, Sarah; Arvind H Patel; Hay, David C.

    2013-01-01

    Stem cell-derived hepatocytes represent promising models to study human liver biology and disease. This concise review discusses the recent progresses in the field, with a focus on human liver disease, drug metabolism and virus infection.

  13. Modeling Human Liver Biology Using Stem Cell-Derived Hepatocytes

    OpenAIRE

    Arvind H Patel; Hay, David C.; Farnworth, Sarah L.; Pingnan Sun; Xiaoling Zhou

    2013-01-01

    Stem cell-derived hepatocytes represent promising models to study human liver biology and disease. This concise review discusses the recent progresses in the field, with a focus on human liver disease, drug metabolism and virus infection.

  14. Computational Modeling, Formal Analysis, and Tools for Systems Biology.

    Science.gov (United States)

    Bartocci, Ezio; Lió, Pietro

    2016-01-01

    As the amount of biological data in the public domain grows, so does the range of modeling and analysis techniques employed in systems biology. In recent years, a number of theoretical computer science developments have enabled modeling methodology to keep pace. The growing interest in systems biology in executable models and their analysis has necessitated the borrowing of terms and methods from computer science, such as formal analysis, model checking, static analysis, and runtime verification. Here, we discuss the most important and exciting computational methods and tools currently available to systems biologists. We believe that a deeper understanding of the concepts and theory highlighted in this review will produce better software practice, improved investigation of complex biological processes, and even new ideas and better feedback into computer science.

  15. Mathematical modeling of the evolution of a simple biological system

    Digital Repository Service at National Institute of Oceanography (India)

    Gonsalves, M.J.B.D.; Neetu, S.; Krishnan, K.P.; Attri, K.; LokaBharathi, P.A.

    physical system, where a model usually can be developed from well established fundamental principles, in case of biological systems such principles are not always available offering an opportunity for innovation. The success of the innovation, of course...

  16. Mathematical models in biology bringing mathematics to life

    CERN Document Server

    Ferraro, Maria; Guarracino, Mario

    2015-01-01

    This book presents an exciting collection of contributions based on the workshop “Bringing Maths to Life” held October 27-29, 2014 in Naples, Italy.  The state-of-the art research in biology and the statistical and analytical challenges facing huge masses of data collection are treated in this Work. Specific topics explored in depth surround the sessions and special invited sessions of the workshop and include genetic variability via differential expression, molecular dynamics and modeling, complex biological systems viewed from quantitative models, and microscopy images processing, to name several. In depth discussions of the mathematical analysis required to extract insights from complex bodies of biological datasets, to aid development in the field novel algorithms, methods and software tools for genetic variability, molecular dynamics, and complex biological systems are presented in this book. Researchers and graduate students in biology, life science, and mathematics/statistics will find the content...

  17. Prediction of postharvest firmness of apple using biological switch model.

    Science.gov (United States)

    van der Sman, R G M; Sanders, M

    2012-10-01

    In this paper we present a model that predicts the softening of apple during ripening in the postharvest phase. Apple ripening starts with an autocatalytic production of ethylene, which triggers a multitude of biochemical processes like the degradation of cell wall material. This triggering of the ripening process has been modelled as a biological switch-using the activator-depleted substrate model, which is proposed earlier by Meinhardt in the field of developmental biology. The model has been calibrated using storage experiments using various apple cultivars. Furthermore, the model is proven to be valid using independent experimental data of Elstar apple under dynamic storage conditions.

  18. Uncertainty in biology: a computational modeling approach

    OpenAIRE

    2015-01-01

    Computational modeling of biomedical processes is gaining more and more weight in the current research into the etiology of biomedical problems and potential treatment strategies. Computational modeling allows to reduce, refine and replace animal experimentation as well as to translate findings obtained in these experiments to the human background. However these biomedical problems are inherently complex with a myriad of influencing factors, which strongly complicates the model building...

  19. Modeling Small Oscillating Biological Networks in Analog VLSI

    OpenAIRE

    Ryckebusch, Sylvie; Bower, James M.; Mead, Carver

    1989-01-01

    We have used analog VLSI technology to model a class of small oscillating biological neural circuits known as central pattern generators (CPG). These circuits generate rhythmic patterns of activity which drive locomotor behaviour in the animal. We have designed, fabricated, and tested a model neuron circuit which relies on many of the same mechanisms as a biological central pattern generator neuron, such as delays and internal feedback. We show that this neuron can be use...

  20. Biological models for active vision: Towards a unified architecture

    OpenAIRE

    Terzic K.; Lobato D.; Saleiro M.; Martins J; Farrajota M.; Rodrigues J.M.F.; Du Buf J.M.H.

    2013-01-01

    Building a general-purpose, real-time active vision system completely based on biological models is a great challenge. We apply a number of biologically plausible algorithms which address different aspects of vision, such as edge and keypoint detection, feature extraction,optical flow and disparity, shape detection, object recognition and scene modelling into a complete system. We present some of the experiments from our ongoing work, where our system leverages a combination of algorithms to ...

  1. Modeling the Biological Diversity of Pig Carcasses

    DEFF Research Database (Denmark)

    Erbou, Søren Gylling Hemmingsen

    extracting and modeling meaningful information from the vast amount of information available from non-invasive imaging data. The lean meat percentage (LMP) is a common standard for measuring the quality of pig carcasses. Measuring the LMP using CT and using this as a reference for calibration of online...... equipment is investigated, without the need for a calibration against a less accurate manual dissection. The rest of the contributions regard the construction and use of point distribution models (PDM). PDM’s are able to capture the shape variation of a population of shapes, in this case a 3D surface of a...... specific bone structure in the ham. These models can assist developers of robotic tools by enabling population based testing before actual construction of the tools. Sparse models are compared to the standard PCA based model and a method for fitting PDM’s to sparse data is proposed. The former provides...

  2. Bayesian parameter estimation for nonlinear modelling of biological pathways

    Directory of Open Access Journals (Sweden)

    Ghasemi Omid

    2011-12-01

    Full Text Available Abstract Background The availability of temporal measurements on biological experiments has significantly promoted research areas in systems biology. To gain insight into the interaction and regulation of biological systems, mathematical frameworks such as ordinary differential equations have been widely applied to model biological pathways and interpret the temporal data. Hill equations are the preferred formats to represent the reaction rate in differential equation frameworks, due to their simple structures and their capabilities for easy fitting to saturated experimental measurements. However, Hill equations are highly nonlinearly parameterized functions, and parameters in these functions cannot be measured easily. Additionally, because of its high nonlinearity, adaptive parameter estimation algorithms developed for linear parameterized differential equations cannot be applied. Therefore, parameter estimation in nonlinearly parameterized differential equation models for biological pathways is both challenging and rewarding. In this study, we propose a Bayesian parameter estimation algorithm to estimate parameters in nonlinear mathematical models for biological pathways using time series data. Results We used the Runge-Kutta method to transform differential equations to difference equations assuming a known structure of the differential equations. This transformation allowed us to generate predictions dependent on previous states and to apply a Bayesian approach, namely, the Markov chain Monte Carlo (MCMC method. We applied this approach to the biological pathways involved in the left ventricle (LV response to myocardial infarction (MI and verified our algorithm by estimating two parameters in a Hill equation embedded in the nonlinear model. We further evaluated our estimation performance with different parameter settings and signal to noise ratios. Our results demonstrated the effectiveness of the algorithm for both linearly and nonlinearly

  3. In Vivo Models to Study Chemokine Biology.

    Science.gov (United States)

    Amaral, F A; Boff, D; Teixeira, M M

    2016-01-01

    Chemokines are essential mediators of leukocyte movement in vivo. In vitro assays of leukocyte migration cannot mimic the complex interactions with other cell types and matrix needed for cells to extravasate and migrate into tissues. Therefore, in vivo strategies to study the effects and potential relevance of chemokines for the migration of particular leukocyte subsets are necessary. Here, we describe methods to study the effects and endogenous role of chemokine in mice. Advantages and pitfalls of particular models are discussed and we focus on description in model's joint and pleural cavity inflammation and the effects and relevance of CXCR2 and CCR2 ligands on cell migration.

  4. Nematodes: Model Organisms in High School Biology

    Science.gov (United States)

    Bliss, TJ; Anderson, Margery; Dillman, Adler; Yourick, Debra; Jett, Marti; Adams, Byron J.; Russell, RevaBeth

    2007-01-01

    In a collaborative effort between university researchers and high school science teachers, an inquiry-based laboratory module was designed using two species of insecticidal nematodes to help students apply scientific inquiry and elements of thoughtful experimental design. The learning experience and model are described in this article. (Contains 4…

  5. MODEL ORGANISMS USED IN MOLECULAR BIOLOGY OR MEDICAL RESEARCH

    OpenAIRE

    Pandey Govind

    2011-01-01

    A model organism is a non-human species that is studied to understand specific biological phenomena with the expectation that investigations made in the organism model will provide insight into the workings of other organisms. The model organisms are widely used to explore potential causes and treatments for human as well as animal diseases when experiments on animals or humans would be unfeasible or considered less ethical. Studying model organisms may be informative, but care must be taken ...

  6. Predictive modeling of nanomaterial exposure effects in biological systems

    OpenAIRE

    Liu X; Tang K.; Harper S.; Harper B; Steevens JA; Xu R

    2013-01-01

    Xiong Liu,1 Kaizhi Tang,1 Stacey Harper,2 Bryan Harper,2 Jeffery A Steevens,3 Roger Xu1 1Intelligent Automation, Inc., Rockville, MD, USA; 2Department of Environmental and Molecular Toxicology, School of Chemical, Biological, and Environmental Engineering, Oregon State University, Corvallis, OR, USA; 3ERDC Environmental Laboratory, Vicksburg, MS, USA Background: Predictive modeling of the biological effects of nanomaterials is critical for industry and policymakers to assess the potential ha...

  7. Flicker Noise in a Model of Coevolving Biological Populations

    OpenAIRE

    Rikvold, Per Arne; Zia, R. K. P.

    2003-01-01

    We present long Monte Carlo simulations of a simple model of biological macroevolution in which births, deaths, and mutational changes in the genome take place at the level of individual organisms. The model displays punctuated equilibria and flicker noise with a 1/f-like power spectrum, consistent with some current theories of evolutionary dynamics.

  8. 3D Modelling of Biological Systems for Biomimetics

    Institute of Scientific and Technical Information of China (English)

    Shujun Zhang; Kevin Hapeshi; Ashok K. Bhattacharya

    2004-01-01

    With the advanced development of computer-based enabling technologies, many engineering, medical, biology,chemistry, physics and food science etc have developed to the unprecedented levels, which lead to many research and development interests in various multi-discipline areas. Among them, biomimetics is one of the most promising and attractive branches of study. Biomimetics is a branch of study that uses biological systems as a model to develop synthetic systems.To learn from nature, one of the fundamental issues is to understand the natural systems such animals, insects, plants and human beings etc. The geometrical characterization and representation of natural systems is an important fundamental work for biomimetics research. 3D modeling plays a key role in the geometrical characterization and representation, especially in computer graphical visualization. This paper firstly presents the typical procedure of 3D modelling methods and then reviews the previous work of 3D geometrical modelling techniques and systems developed for industrial, medical and animation applications. Especially the paper discusses the problems associated with the existing techniques and systems when they are applied to 3D modelling of biological systems. Based upon the discussions, the paper proposes some areas of research interests in 3D modelling of biological systems and for Biomimetics.

  9. Self-organized Critical Model Of Biological Evolution

    OpenAIRE

    Chau, H. F.; Mak, L; Kwok, P. K.

    1994-01-01

    A punctuated equilibrium model of biological evolution with relative fitness between different species being the fundamental driving force of evolution is introduced. Mutation is modeled as a fitness updating cellular automaton process where the change in fitness after mutation follows a Gaussian distribution with mean $x>0$ and standard deviation $\\sigma$. Scaling behaviors are observed in our numerical simulation, indicating that the model is self-organized critical. Besides, the numerical ...

  10. Nutritional systems biology modeling: from molecular mechanisms to physiology.

    OpenAIRE

    de Graaf, Albert A.; Freidig, Andreas P.; Baukje De Roos; Neema Jamshidi; Matthias Heinemann; Rullmann, Johan A.C.; Hall, Kevin D.; Martin Adiels; Ben van Ommen

    2009-01-01

    The use of computational modeling and simulation has increased in many biological fields, but despite their potential these techniques are only marginally applied in nutritional sciences. Nevertheless, recent applications of modeling have been instrumental in answering important nutritional questions from the cellular up to the physiological levels. Capturing the complexity of today's important nutritional research questions poses a challenge for modeling to become truly integrative in the co...

  11. Nutritional Systems Biology Modeling: From Molecular Mechanisms to Physiology

    OpenAIRE

    de Graaf, A A; Freidig, A.P.; Roos, B.; Jamshidi, N.; M. Heinemann; Rullmann, J.A.C.; Hall, K. D.; Adiels, M.; Ommen, B. van

    2009-01-01

    The use of computational modeling and simulation has increased in many biological fields, but despite their potential these techniques are only marginally applied in nutritional sciences. Nevertheless, recent applications of modeling have been instrumental in answering important nutritional questions from the cellular up to the physiological levels. Capturing the complexity of today's important nutritional research questions poses a challenge for modeling to become truly integrative in the co...

  12. Nutritional Systems Biology Modeling: From Molecular Mechanisms to Physiology

    OpenAIRE

    de Graaf, Albert A.; Freidig, Andreas P.; de Roos, Baukje; Jamshidi, Neema; Heinemann, Matthias; Rullmann, Johan A.C.; Hall, Kevin D.; Adiels, Martin; van Ommen, Ben; Bourne, Philip E.

    2009-01-01

    The use of computational modeling and simulation has increased in many biological fields, but despite their potential these techniques are only marginally applied in nutritional sciences. Nevertheless, recent applications of modeling have been instrumental in answering important nutritional questions from the cellular up to the physiological levels. Capturing the complexity of today’s important nutritional research questions poses a challenge for modeling to become truly integrative in the co...

  13. Human pluripotent stem cells: an emerging model in developmental biology

    OpenAIRE

    Zhu, Zengrong; Huangfu, Danwei

    2013-01-01

    Developmental biology has long benefited from studies of classic model organisms. Recently, human pluripotent stem cells (hPSCs), including human embryonic stem cells and human induced pluripotent stem cells, have emerged as a new model system that offers unique advantages for developmental studies. Here, we discuss how studies of hPSCs can complement classic approaches using model organisms, and how hPSCs can be used to recapitulate aspects of human embryonic development ‘in a dish’. We also...

  14. Modeling Biology Spanning Different Scales: An Open Challenge

    Directory of Open Access Journals (Sweden)

    Filippo Castiglione

    2014-01-01

    Full Text Available It is coming nowadays more clear that in order to obtain a unified description of the different mechanisms governing the behavior and causality relations among the various parts of a living system, the development of comprehensive computational and mathematical models at different space and time scales is required. This is one of the most formidable challenges of modern biology characterized by the availability of huge amount of high throughput measurements. In this paper we draw attention to the importance of multiscale modeling in the framework of studies of biological systems in general and of the immune system in particular.

  15. Biological implications of the Weibull and Gompertz models of aging.

    Science.gov (United States)

    Ricklefs, Robert E; Scheuerlein, Alex

    2002-02-01

    Gompertz and Weibull functions imply contrasting biological causes of demographic aging. The terms describing increasing mortality with age are multiplicative and additive, respectively, which could result from an increase in the vulnerability of individuals to extrinsic causes in the Gompertz model and the predominance of intrinsic causes at older ages in the Weibull model. Experiments that manipulate extrinsic mortality can distinguish these biological models. To facilitate analyses of experimental data, we defined a single index for the rate of aging (omega) for the Weibull and Gompertz functions. Each function described the increase in aging-related mortality in simulated ages at death reasonably well. However, in contrast to the Weibull omega(W), the Gompertz omega(G) was sensitive to variation in the initial mortality rate independently of aging-related mortality. Comparisons between wild and captive populations appear to support the intrinsic-causes model for birds, but give mixed support for both models in mammals.

  16. Modeling of Biological Intelligence for SCM System Optimization

    Directory of Open Access Journals (Sweden)

    Shengyong Chen

    2012-01-01

    Full Text Available This article summarizes some methods from biological intelligence for modeling and optimization of supply chain management (SCM systems, including genetic algorithms, evolutionary programming, differential evolution, swarm intelligence, artificial immune, and other biological intelligence related methods. An SCM system is adaptive, dynamic, open self-organizing, which is maintained by flows of information, materials, goods, funds, and energy. Traditional methods for modeling and optimizing complex SCM systems require huge amounts of computing resources, and biological intelligence-based solutions can often provide valuable alternatives for efficiently solving problems. The paper summarizes the recent related methods for the design and optimization of SCM systems, which covers the most widely used genetic algorithms and other evolutionary algorithms.

  17. Boolean Models of Biological Processes Explain Cascade-Like Behavior.

    Science.gov (United States)

    Chen, Hao; Wang, Guanyu; Simha, Rahul; Du, Chenghang; Zeng, Chen

    2016-01-01

    Biological networks play a key role in determining biological function and therefore, an understanding of their structure and dynamics is of central interest in systems biology. In Boolean models of such networks, the status of each molecule is either "on" or "off" and along with the molecules interact with each other, their individual status changes from "on" to "off" or vice-versa and the system of molecules in the network collectively go through a sequence of changes in state. This sequence of changes is termed a biological process. In this paper, we examine the common perception that events in biomolecular networks occur sequentially, in a cascade-like manner, and ask whether this is likely to be an inherent property. In further investigations of the budding and fission yeast cell-cycle, we identify two generic dynamical rules. A Boolean system that complies with these rules will automatically have a certain robustness. By considering the biological requirements in robustness and designability, we show that those Boolean dynamical systems, compared to an arbitrary dynamical system, statistically present the characteristics of cascadeness and sequentiality, as observed in the budding and fission yeast cell- cycle. These results suggest that cascade-like behavior might be an intrinsic property of biological processes.

  18. Boolean Models of Biological Processes Explain Cascade-Like Behavior.

    Science.gov (United States)

    Chen, Hao; Wang, Guanyu; Simha, Rahul; Du, Chenghang; Zeng, Chen

    2016-01-01

    Biological networks play a key role in determining biological function and therefore, an understanding of their structure and dynamics is of central interest in systems biology. In Boolean models of such networks, the status of each molecule is either "on" or "off" and along with the molecules interact with each other, their individual status changes from "on" to "off" or vice-versa and the system of molecules in the network collectively go through a sequence of changes in state. This sequence of changes is termed a biological process. In this paper, we examine the common perception that events in biomolecular networks occur sequentially, in a cascade-like manner, and ask whether this is likely to be an inherent property. In further investigations of the budding and fission yeast cell-cycle, we identify two generic dynamical rules. A Boolean system that complies with these rules will automatically have a certain robustness. By considering the biological requirements in robustness and designability, we show that those Boolean dynamical systems, compared to an arbitrary dynamical system, statistically present the characteristics of cascadeness and sequentiality, as observed in the budding and fission yeast cell- cycle. These results suggest that cascade-like behavior might be an intrinsic property of biological processes. PMID:26821940

  19. Learning through Creating Robotic Models of Biological Systems

    Science.gov (United States)

    Cuperman, Dan; Verner, Igor M.

    2013-01-01

    This paper considers an approach to studying issues in technology and science, which integrates design and inquiry activities towards creating and exploring technological models of scientific phenomena. We implemented this approach in a context where the learner inquires into a biological phenomenon and develops its representation in the form of a…

  20. Modeling Radial Holoblastic Cleavage: A Laboratory Activity for Developmental Biology.

    Science.gov (United States)

    Ellis, Linda K.

    2000-01-01

    Introduces a laboratory activity designed for an undergraduate developmental biology course. Uses Play-Doh (plastic modeling clay) to build a multicellular embryo in order to provide a 3-D demonstration of cleavage. Includes notes for the instructor and student directions. (YDS)

  1. A Calculus for Modelling, Simulating and Analysing Compartmentalized Biological Systems

    DEFF Research Database (Denmark)

    Mardare, Radu Iulian; Ihekwaba, Adoha

    2007-01-01

    A. Ihekwaba, R. Mardare. A Calculus for Modelling, Simulating and Analysing Compartmentalized Biological Systems. Case study: NFkB system. In Proc. of International Conference of Computational Methods in Sciences and Engineering (ICCMSE), American Institute of Physics, AIP Proceedings, N 2...

  2. Simple model of self-organized biological evolution

    Energy Technology Data Exchange (ETDEWEB)

    de Boer, J.; Derrida, B.; Flyvbjerg, H.; Jackson, A.D.; Wettig, T. (Department of Physics, State University of New York at Stony Brook, Stony Brook, New York 11794-3800 (United States) The Isaac Newton Institute for Mathematical Sciences, 20 Clarkson Road, Cambridge, CB4 0EH (United Kingdom) Laboratoire de Physique Statistique, Ecole Normale Superieure, 24 rue Lhomond, F-75005 Paris (France) Service de Physique Theorique, Centre de Etudes Nucleaires de Saclay, F-91191, Gif-Sur-Yvette (France) CONNECT, The Niels Bohr Institute, Blegdamsvej 17, DK-2100 Copenhagen (Denmark))

    1994-08-08

    We give an exact solution of a recently proposed self-organized critical model of biological evolution. We show that the model has a power law distribution of durations of coevolutionary avalanches'' with a mean field exponent 3/2. We also calculate analytically the finite size effects which cut off this power law at times of the order of the system size.

  3. Kinetic hierarchy and propagation of chaos in biological swarm models

    OpenAIRE

    Carlen, Eric; Chatelin, Robin; Degond, Pierre; Wennberg, Bernt

    2011-01-01

    We consider two models of biological swarm behavior. In these models, pairs of particles interact to adjust their velocities one to each other. In the first process, called 'BDG', they join their average velocity up to some noise. In the second process, called 'CL', one of the two particles tries to join the other one's velocity. This paper establishes the master equations and BBGKY hierarchies of these two processes. It investigates the infinite particle limit of the hierarchies at large tim...

  4. Evaluation of radiobiological effects in 3 distinct biological models

    International Nuclear Information System (INIS)

    Full text of publication follows. The present work aims at sharing the process of development of advanced biological models to study radiobiological effects. Recognizing several known limitations and difficulties of the current monolayer cellular models, as well as the increasing difficulties to use advanced biological models, our group has been developing advanced biological alternative models, namely three-dimensional cell cultures and a less explored animal model (the Zebra fish - Danio rerio - which allows the access to inter-generational data, while characterized by a great genetic homology towards the humans). These 3 models (monolayer cellular model, three-dimensional cell cultures and zebra fish) were externally irradiated with 100 mGy, 500 mGy or 1 Gy. The consequences of that irradiation were studied using cellular and molecular tests. Our previous experimental studies with 100 mGy external gamma irradiation of HepG2 monolayer cells showed a slight increase in the proliferation rate 24 h, 48 h and 72 h post irradiation. These results also pointed into the presence of certain bystander effects 72 h post irradiation, constituting the starting point for the need of a more accurate analysis realized with this work. At this stage, we continue focused on the acute biological effects. Obtained results, namely MTT and clonogenic assays for evaluating cellular metabolic activity and proliferation in the in vitro models, as well as proteomics for the evaluation of in vivo effects will be presented, discussed and explained. Several hypotheses will be presented and defended based on the facts previously demonstrated. This work aims at sharing the actual state and the results already available from this medium-term project, building the proof of the added value on applying these advanced models, while demonstrating the strongest and weakest points from all of them (so allowing the comparison between them and to base the subsequent choice for research groups starting

  5. Modeling drug- and chemical- induced hepatotoxicity with systems biology approaches

    Directory of Open Access Journals (Sweden)

    Sudin eBhattacharya

    2012-12-01

    Full Text Available We provide an overview of computational systems biology approaches as applied to the study of chemical- and drug-induced toxicity. The concept of ‘toxicity pathways’ is described in the context of the 2007 US National Academies of Science report, Toxicity testing in the 21st Century: A Vision and A Strategy. Pathway mapping and modeling based on network biology concepts are a key component of the vision laid out in this report for a more biologically-based analysis of dose-response behavior and the safety of chemicals and drugs. We focus on toxicity of the liver (hepatotoxicity – a complex phenotypic response with contributions from a number of different cell types and biological processes. We describe three case studies of complementary multi-scale computational modeling approaches to understand perturbation of toxicity pathways in the human liver as a result of exposure to environmental contaminants and specific drugs. One approach involves development of a spatial, multicellular virtual tissue model of the liver lobule that combines molecular circuits in individual hepatocytes with cell-cell interactions and blood-mediated transport of toxicants through hepatic sinusoids, to enable quantitative, mechanistic prediction of hepatic dose-response for activation of the AhR toxicity pathway. Simultaneously, methods are being developing to extract quantitative maps of intracellular signaling and transcriptional regulatory networks perturbed by environmental contaminants, using a combination of gene expression and genome-wide protein-DNA interaction data. A predictive physiological model (DILIsymTM to understand drug-induced liver injury (DILI, the most common adverse event leading to termination of clinical development programs and regulatory actions on drugs, is also described. The model initially focuses on reactive metabolite-induced DILI in response to administration of acetaminophen, and spans multiple biological scales.

  6. Methods and models in mathematical biology deterministic and stochastic approaches

    CERN Document Server

    Müller, Johannes

    2015-01-01

    This book developed from classes in mathematical biology taught by the authors over several years at the Technische Universität München. The main themes are modeling principles, mathematical principles for the analysis of these models, and model-based analysis of data. The key topics of modern biomathematics are covered: ecology, epidemiology, biochemistry, regulatory networks, neuronal networks, and population genetics. A variety of mathematical methods are introduced, ranging from ordinary and partial differential equations to stochastic graph theory and  branching processes. A special emphasis is placed on the interplay between stochastic and deterministic models.

  7. An Intuitive Automated Modelling Interface for Systems Biology

    Directory of Open Access Journals (Sweden)

    Ozan Kahramanoğulları

    2009-11-01

    Full Text Available We introduce a natural language interface for building stochastic pi calculus models of biological systems. In this language, complex constructs describing biochemical events are built from basic primitives of association, dissociation and transformation. This language thus allows us to model biochemical systems modularly by describing their dynamics in a narrative-style language, while making amendments, refinements and extensions on the models easy. We demonstrate the language on a model of Fc-gamma receptor phosphorylation during phagocytosis. We provide a tool implementation of the translation into a stochastic pi calculus language, Microsoft Research's SPiM.

  8. Modeling and Simulation Tools: From Systems Biology to Systems Medicine.

    Science.gov (United States)

    Olivier, Brett G; Swat, Maciej J; Moné, Martijn J

    2016-01-01

    Modeling is an integral component of modern biology. In this chapter we look into the role of the model, as it pertains to Systems Medicine, and the software that is required to instantiate and run it. We do this by comparing the development, implementation, and characteristics of tools that have been developed to work with two divergent methodologies: Systems Biology and Pharmacometrics. From the Systems Biology perspective we consider the concept of "Software as a Medical Device" and what this may imply for the migration of research-oriented, simulation software into the domain of human health.In our second perspective, we see how in practice hundreds of computational tools already accompany drug discovery and development at every stage of the process. Standardized exchange formats are required to streamline the model exchange between tools, which would minimize translation errors and reduce the required time. With the emergence, almost 15 years ago, of the SBML standard, a large part of the domain of interest is already covered and models can be shared and passed from software to software without recoding them. Until recently the last stage of the process, the pharmacometric analysis used in clinical studies carried out on subject populations, lacked such an exchange medium. We describe a new emerging exchange format in Pharmacometrics which covers the non-linear mixed effects models, the standard statistical model type used in this area. By interfacing these two formats the entire domain can be covered by complementary standards and subsequently the according tools.

  9. Childhood trauma and personality disorder: toward a biological model.

    Science.gov (United States)

    Lee, Royce

    2006-02-01

    Cross-sectional and prospective associations of personality disorder with childhood trauma provide an important clue regarding the biological mechanism of personality disorder. In this review, empirical literature from several domains is summarized. These include relevant findings from behavioral genetics, preclinical models of early life parental care, and clinical translational studies of personality disorder. Identification of the biological mechanism by which childhood trauma exerts an effect on personality disorder may require modification of the conceptualization of personality disorder, either as a set of categories or dimensions.

  10. Predictive modeling of nanomaterial exposure effects in biological systems

    Directory of Open Access Journals (Sweden)

    Liu X

    2013-09-01

    Full Text Available Xiong Liu,1 Kaizhi Tang,1 Stacey Harper,2 Bryan Harper,2 Jeffery A Steevens,3 Roger Xu1 1Intelligent Automation, Inc., Rockville, MD, USA; 2Department of Environmental and Molecular Toxicology, School of Chemical, Biological, and Environmental Engineering, Oregon State University, Corvallis, OR, USA; 3ERDC Environmental Laboratory, Vicksburg, MS, USA Background: Predictive modeling of the biological effects of nanomaterials is critical for industry and policymakers to assess the potential hazards resulting from the application of engineered nanomaterials. Methods: We generated an experimental dataset on the toxic effects experienced by embryonic zebrafish due to exposure to nanomaterials. Several nanomaterials were studied, such as metal nanoparticles, dendrimer, metal oxide, and polymeric materials. The embryonic zebrafish metric (EZ Metric was used as a screening-level measurement representative of adverse effects. Using the dataset, we developed a data mining approach to model the toxic endpoints and the overall biological impact of nanomaterials. Data mining techniques, such as numerical prediction, can assist analysts in developing risk assessment models for nanomaterials. Results: We found several important attributes that contribute to the 24 hours post-fertilization (hpf mortality, such as dosage concentration, shell composition, and surface charge. These findings concur with previous studies on nanomaterial toxicity using embryonic zebrafish. We conducted case studies on modeling the overall effect/impact of nanomaterials and the specific toxic endpoints such as mortality, delayed development, and morphological malformations. The results show that we can achieve high prediction accuracy for certain biological effects, such as 24 hpf mortality, 120 hpf mortality, and 120 hpf heart malformation. The results also show that the weighting scheme for individual biological effects has a significant influence on modeling the overall impact of

  11. Programming biological models in Python using PySB.

    Science.gov (United States)

    Lopez, Carlos F; Muhlich, Jeremy L; Bachman, John A; Sorger, Peter K

    2013-01-01

    Mathematical equations are fundamental to modeling biological networks, but as networks get large and revisions frequent, it becomes difficult to manage equations directly or to combine previously developed models. Multiple simultaneous efforts to create graphical standards, rule-based languages, and integrated software workbenches aim to simplify biological modeling but none fully meets the need for transparent, extensible, and reusable models. In this paper we describe PySB, an approach in which models are not only created using programs, they are programs. PySB draws on programmatic modeling concepts from little b and ProMot, the rule-based languages BioNetGen and Kappa and the growing library of Python numerical tools. Central to PySB is a library of macros encoding familiar biochemical actions such as binding, catalysis, and polymerization, making it possible to use a high-level, action-oriented vocabulary to construct detailed models. As Python programs, PySB models leverage tools and practices from the open-source software community, substantially advancing our ability to distribute and manage the work of testing biochemical hypotheses. We illustrate these ideas using new and previously published models of apoptosis.

  12. Multiway modeling and analysis in stem cell systems biology

    Directory of Open Access Journals (Sweden)

    Vandenberg Scott L

    2008-07-01

    Full Text Available Abstract Background Systems biology refers to multidisciplinary approaches designed to uncover emergent properties of biological systems. Stem cells are an attractive target for this analysis, due to their broad therapeutic potential. A central theme of systems biology is the use of computational modeling to reconstruct complex systems from a wealth of reductionist, molecular data (e.g., gene/protein expression, signal transduction activity, metabolic activity, etc.. A number of deterministic, probabilistic, and statistical learning models are used to understand sophisticated cellular behaviors such as protein expression during cellular differentiation and the activity of signaling networks. However, many of these models are bimodal i.e., they only consider row-column relationships. In contrast, multiway modeling techniques (also known as tensor models can analyze multimodal data, which capture much more information about complex behaviors such as cell differentiation. In particular, tensors can be very powerful tools for modeling the dynamic activity of biological networks over time. Here, we review the application of systems biology to stem cells and illustrate application of tensor analysis to model collagen-induced osteogenic differentiation of human mesenchymal stem cells. Results We applied Tucker1, Tucker3, and Parallel Factor Analysis (PARAFAC models to identify protein/gene expression patterns during extracellular matrix-induced osteogenic differentiation of human mesenchymal stem cells. In one case, we organized our data into a tensor of type protein/gene locus link × gene ontology category × osteogenic stimulant, and found that our cells expressed two distinct, stimulus-dependent sets of functionally related genes as they underwent osteogenic differentiation. In a second case, we organized DNA microarray data in a three-way tensor of gene IDs × osteogenic stimulus × replicates, and found that application of tensile strain to a

  13. Biological exposure models for oil spill impact analysis

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    The oil spill impact analysis (OSIA) software system has been developed to supply a tool for comprehensive, quantitative environmental impact assessments resulting from oil spills. In the system, a biological component evaluates potential effects on exposed organisms based on results from a physico-chemieal fates component, including the extent and characteristics of the surface slick, and dissolved and total concentrations of hydrocarbons in the water column. The component includes a particle-based exposure model for migratory adult fish populations, a particle-based exposure model for spawning planktonic organisms (eggs and larvae), and an exposure model for wildlife species (sea birds or marine mammals). The exposure model for migratory adult fish populations simulates the migration behaviors of fish populations migrating to or staying in their feeding areas, over-wintering areas or spawning areas, and determines the acute effects (mortality) and chronic accumulation (body burdens) from the dissolved contaminant. The exposure model for spawning planktonic organisms simulates the release of eggs and larvae, also as particles, from specific spawning areas during the spawning period, and determines their potential exposure to contaminants in the water or sediment. The exposure model for wild species calculates the exposure to surrace oil of wildlife (bird and marine mammal ) categories inhabiting the contaminated area. Compared with the earlier models in which all kinds of organisms are assumed evenly and randomly distributed, the updated biological exposure models can more realistically estimate potential effects on marine ecological system from oil spill pollution events.

  14. Nutritional Systems Biology Modeling: From Molecular Mechanisms to Physiology

    Science.gov (United States)

    de Graaf, Albert A.; Freidig, Andreas P.; De Roos, Baukje; Jamshidi, Neema; Heinemann, Matthias; Rullmann, Johan A.C.; Hall, Kevin D.; Adiels, Martin; van Ommen, Ben

    2009-01-01

    The use of computational modeling and simulation has increased in many biological fields, but despite their potential these techniques are only marginally applied in nutritional sciences. Nevertheless, recent applications of modeling have been instrumental in answering important nutritional questions from the cellular up to the physiological levels. Capturing the complexity of today's important nutritional research questions poses a challenge for modeling to become truly integrative in the consideration and interpretation of experimental data at widely differing scales of space and time. In this review, we discuss a selection of available modeling approaches and applications relevant for nutrition. We then put these models into perspective by categorizing them according to their space and time domain. Through this categorization process, we identified a dearth of models that consider processes occurring between the microscopic and macroscopic scale. We propose a “middle-out” strategy to develop the required full-scale, multilevel computational models. Exhaustive and accurate phenotyping, the use of the virtual patient concept, and the development of biomarkers from “-omics” signatures are identified as key elements of a successful systems biology modeling approach in nutrition research—one that integrates physiological mechanisms and data at multiple space and time scales. PMID:19956660

  15. Nutritional systems biology modeling: from molecular mechanisms to physiology.

    Directory of Open Access Journals (Sweden)

    Albert A de Graaf

    2009-11-01

    Full Text Available The use of computational modeling and simulation has increased in many biological fields, but despite their potential these techniques are only marginally applied in nutritional sciences. Nevertheless, recent applications of modeling have been instrumental in answering important nutritional questions from the cellular up to the physiological levels. Capturing the complexity of today's important nutritional research questions poses a challenge for modeling to become truly integrative in the consideration and interpretation of experimental data at widely differing scales of space and time. In this review, we discuss a selection of available modeling approaches and applications relevant for nutrition. We then put these models into perspective by categorizing them according to their space and time domain. Through this categorization process, we identified a dearth of models that consider processes occurring between the microscopic and macroscopic scale. We propose a "middle-out" strategy to develop the required full-scale, multilevel computational models. Exhaustive and accurate phenotyping, the use of the virtual patient concept, and the development of biomarkers from "-omics" signatures are identified as key elements of a successful systems biology modeling approach in nutrition research--one that integrates physiological mechanisms and data at multiple space and time scales.

  16. Integrative biological systems modeling:challenges and opportunities

    Institute of Scientific and Technical Information of China (English)

    Jialiang WU; Eberhard VOIT

    2009-01-01

    Most biological systems are by nature hybrids consist of interacting discrete and continuous components,which may even operate on different time scales. Therefore," it is desirable to establish modeling frameworks that are capable of combining deterministic and stochastic, discrete and continuous, as well as multi-timescale features. In the context of molecular systems biology, an example for the need of such a combination is the investigation of integrated biological pathways that contain gene regulatory, metabolic and signaling components, which may operate on different time scales and involve on-off switches as well as stochastic effects. The implementation of integrated hybrid systems is not trivial because most software is limited to one or the other of the dichotomies above. In this study, we first review the motivation for hybrid modeling. Secondly, by using the example of a toggle switch model, we illustrate a recently developed modeling framework that is based on the combination of biochemical systems theory (BST) and hybrid functional Petri nets (HFPN). Finally, we discuss remaining challenges and future opportunities.

  17. Towards Modelling and Simulation of Crowded Environments in Cell Biology

    Science.gov (United States)

    Bittig, Arne T.; Jeschke, Matthias; Uhrmacher, Adelinde M.

    2010-09-01

    In modelling and simulation of cell biological processes, spatial homogeneity in the distribution of components is a common but not always valid assumption. Spatial simulation methods differ in computational effort and accuracy, and usually rely on tool-specific input formats for model specification. A clear separation between modelling and simulation allows a declarative model specification thereby facilitating reuse of models and exploiting different simulators. We outline a modelling formalism covering both stochastic spatial simulation at the population level and simulation of individual entities moving in continuous space as well as the combination thereof. A multi-level spatial simulator is presented that combines populations of small particles simulated according to the Next Subvolume Method with individually represented large particles following Brownian motion. This approach entails several challenges that need to be overcome, but nicely balances between calculation effort and required levels of detail.

  18. An Abstraction Theory for Qualitative Models of Biological Systems

    CERN Document Server

    Banks, Richard; 10.4204/EPTCS.40.3

    2010-01-01

    Multi-valued network models are an important qualitative modelling approach used widely by the biological community. In this paper we consider developing an abstraction theory for multi-valued network models that allows the state space of a model to be reduced while preserving key properties of the model. This is important as it aids the analysis and comparison of multi-valued networks and in particular, helps address the well-known problem of state space explosion associated with such analysis. We also consider developing techniques for efficiently identifying abstractions and so provide a basis for the automation of this task. We illustrate the theory and techniques developed by investigating the identification of abstractions for two published MVN models of the lysis-lysogeny switch in the bacteriophage lambda.

  19. Enterococcus infection biology: lessons from invertebrate host models.

    Science.gov (United States)

    Yuen, Grace J; Ausubel, Frederick M

    2014-03-01

    The enterococci are commensals of the gastrointestinal tract of many metazoans, from insects to humans. While they normally do not cause disease in the intestine, they can become pathogenic when they infect sites outside of the gut. Recently, the enterococci have become important nosocomial pathogens, with the majority of human enterococcal infections caused by two species, Enterococcus faecalis and Enterococcus faecium. Studies using invertebrate infection models have revealed insights into the biology of enterococcal infections, as well as general principles underlying host innate immune defense. This review highlights recent findings on Enterococcus infection biology from two invertebrate infection models, the greater wax moth Galleria mellonella and the free-living bacteriovorous nematode Caenorhabditis elegans. PMID:24585051

  20. Modeling Cancer Metastasis using Global, Quantitative and Integrative Network Biology

    DEFF Research Database (Denmark)

    Schoof, Erwin; Erler, Janine

    cancer networks using Network Biology. Technologies key to this, such as Mass Spectrometry (MS), Next-Generation Sequencing (NGS) and High-Content Screening (HCS) are briefly described. In Chapter II, we cover how signaling networks and mutational data can be modeled in order to gain a better...... number of biological aspects that would need to be understood to enable comprehensive treatment regimens specific to each patient (i.e. personalized medicine). However, in the approaches outlined in this thesis, we chose metastasis as a key process for interrogating the clinical potential of targeting...... can be generated using MS, and how this can be modeled using a computational framework for deciphering kinase-substrate dynamics. This framework is described in depth in Article 3, and covers the design of KinomeXplorer, which allows the prediction of kinases responsible for modulating observed...

  1. Cellular systems biology profiling applied to cellular models of disease.

    Science.gov (United States)

    Giuliano, Kenneth A; Premkumar, Daniel R; Strock, Christopher J; Johnston, Patricia; Taylor, Lansing

    2009-11-01

    Building cellular models of disease based on the approach of Cellular Systems Biology (CSB) has the potential to improve the process of creating drugs as part of the continuum from early drug discovery through drug development and clinical trials and diagnostics. This paper focuses on the application of CSB to early drug discovery. We discuss the integration of protein-protein interaction biosensors with other multiplexed, functional biomarkers as an example in using CSB to optimize the identification of quality lead series compounds.

  2. Homotopy Analysis Method for Solving Biological Population Model

    Institute of Scientific and Technical Information of China (English)

    A.M. Arafa; S,Z. Rida; H. Mohamed

    2011-01-01

    In this paper, the homotopy analysis method (HAM) is applied to solve generalized biological population models. The fractional derivatives are described by Caputo's sense. The method introduces a significant improvement in this field over existing techniques. Results obtained using the scheme presented here agree well with the analytical solutions and the numerical results presented in Ref [6]. However, the fundamental solutions of these equations still exhibit useful scaling properties that make them attractive for applications.

  3. Thermal model of local ultrasound heating of biological tissue

    Science.gov (United States)

    Nedogovor, V. A.; Sigal, V. L.; Popsuev, E. I.

    1996-09-01

    Possibilities of creation of controlled temperature fields in deep-seated biological tissue with the use of an endocavity ultrasound applicator with surface cooling are considered. Mathematical models are proposed and calculated that make it possible to construct acoustic and thermal fields in biotissues depending on the thermophysical and ultrasound characteristics of the medium being irradiated and to reveal situations and effects that are important for solving problems of practical medicine in the field of local ultrasound hyperthermia and thermotherapy of tissue.

  4. A nude mouse model of endometriosis and its biological behaviors

    Institute of Scientific and Technical Information of China (English)

    WANG Dan-bo; ZHANG Shu-lan; NIU Hui-yan; LU Jing-ming

    2005-01-01

    @@ Endometriosis (EM) as a common and intractable gynecological disease is characterized by unknown etiology and complex pathologic changes. Many factors of the disease are uncertain at the molecular level and it is difficult to study clinically. In this study, we attempted to establish a nude mice model of EM for dynamical observation of the genesis and development of the disease, morphological changes in tissue, and biological behaviors.

  5. Mathematical and Statistical Modeling in Cancer Systems Biology

    Directory of Open Access Journals (Sweden)

    Rachael eHageman Blair

    2012-06-01

    Full Text Available Cancer is a major health problem with high mortality rates. In the post-genome era, investigators have access to massive amounts of rapidly accumulating high-throughput data in publicly available databases, some of which are exclusively devoted to housing Cancer data. However, data interpretation efforts have not kept pace with data collection, and gained knowledge is not necessarily translating into better diagnoses and treatments. A fundamental problem is to integrate and interpret data to further our understanding in Cancer Systems Biology. Viewing cancer as a network provides insights into the complex mechanisms underlying the disease. Mathematical and statistical models provide an avenue for cancer network modeling. In this article, we review two widely used modeling paradigms: deterministic metabolic models and statistical graphical models. The strength of these approaches lies in their flexibility and predictive power. Once a model has been validated, it can be used to make predictions and generate hypotheses. We describe a number of diverse applications to Cancer Biology, including, the system-wide effects of drug-treatments, disease prognosis, tumor classification, forecasting treatment outcomes, and survival predictions.

  6. Biological Jumping Mechanism Analysis and Modeling for Frog Robot

    Institute of Scientific and Technical Information of China (English)

    Meng Wang; Xi-zhe Zang; Ji-zhuang Fan; Jie Zhao

    2008-01-01

    This paper presents a mechanical model of jumping robot based on the biological mechanism analysis of frog. By biological observation and kinematic analysis the frog jump is divided into take-off phase, aerial phase and landing phase. We find the similar trajectories of hindlimb joints during jump, the important effect of foot during take-off and the role of forelimb in supporting the body. Based on the observation, the frog jump is simplified and a mechanical model is put forward. The robot leg is represented by a 4-bar spring/linkage mechanism model, which has three Degrees of Freedom (DOF) at hip joint and one DOF (passive) at tarsometatarsal joint on the foot. The shoulder and elbow joints each has one DOF for the balancing function of arm.The ground reaction force of the model is analyzed and compared with that of frog during take-off. The results show that the model has the same advantages of low likelihood of premature lift-off and high efficiency as the frog. Analysis results and the model can be employed to develop and control a robot capable of mimicking the jumping behavior of flog.

  7. Automated parameter estimation for biological models using Bayesian statistical model checking

    OpenAIRE

    Hussain, Faraz; Langmead, Christopher J.; Mi, Qi; Dutta-Moscato, Joyeeta; Vodovotz, Yoram; Jha, Sumit K.

    2015-01-01

    Background Probabilistic models have gained widespread acceptance in the systems biology community as a useful way to represent complex biological systems. Such models are developed using existing knowledge of the structure and dynamics of the system, experimental observations, and inferences drawn from statistical analysis of empirical data. A key bottleneck in building such models is that some system variables cannot be measured experimentally. These variables are incorporated into the mode...

  8. Continuous Modeling of Calcium Transport Through Biological Membranes

    Science.gov (United States)

    Jasielec, J. J.; Filipek, R.; Szyszkiewicz, K.; Sokalski, T.; Lewenstam, A.

    2016-08-01

    In this work an approach to the modeling of the biological membranes where a membrane is treated as a continuous medium is presented. The Nernst-Planck-Poisson model including Poisson equation for electric potential is used to describe transport of ions in the mitochondrial membrane—the interface which joins mitochondrial matrix with cellular cytosis. The transport of calcium ions is considered. Concentration of calcium inside the mitochondrion is not known accurately because different analytical methods give dramatically different results. We explain mathematically these differences assuming the complexing reaction inside mitochondrion and the existence of the calcium set-point (concentration of calcium in cytosis below which calcium stops entering the mitochondrion).

  9. Mass Extinction in a Simple Mathematical Biological Model

    CERN Document Server

    Tokita, K; Tokita, Kei; Yasutomi, Ayumu

    1997-01-01

    Introducing the effect of extinction into the so-called replicator equations in mathematical biology, we construct a general model of ecosystems. The present model shows mass extinction by its own extinction dynamics when the system initially has a large number of species ( diversity). The extinction dynamics shows several significant features such as a power law in basin size distribution, induction time, etc. The present theory can be a mathematical foundation of the species-area effect in the paleontologic theory for mass extinction.

  10. Caenorhabditis elegans, a Biological Model for Research in Toxicology.

    Science.gov (United States)

    Tejeda-Benitez, Lesly; Olivero-Verbel, Jesus

    2016-01-01

    Caenorhabditis elegans is a nematode of microscopic size which, due to its biological characteristics, has been used since the 1970s as a model for research in molecular biology, medicine, pharmacology, and toxicology. It was the first animal whose genome was completely sequenced and has played a key role in the understanding of apoptosis and RNA interference. The transparency of its body, short lifespan, ability to self-fertilize and ease of culture are advantages that make it ideal as a model in toxicology. Due to the fact that some of its biochemical pathways are similar to those of humans, it has been employed in research in several fields. C. elegans' use as a biological model in environmental toxicological assessments allows the determination of multiple endpoints. Some of these utilize the effects on the biological functions of the nematode and others use molecular markers. Endpoints such as lethality, growth, reproduction, and locomotion are the most studied, and usually employ the wild type Bristol N2 strain. Other endpoints use reporter genes, such as green fluorescence protein, driven by regulatory sequences from other genes related to different mechanisms of toxicity, such as heat shock, oxidative stress, CYP system, and metallothioneins among others, allowing the study of gene expression in a manner both rapid and easy. These transgenic strains of C. elegans represent a powerful tool to assess toxicity pathways for mixtures and environmental samples, and their numbers are growing in diversity and selectivity. However, other molecular biology techniques, including DNA microarrays and MicroRNAs have been explored to assess the effects of different toxicants and samples. C. elegans has allowed the assessment of neurotoxic effects for heavy metals and pesticides, among those more frequently studied, as the nematode has a very well defined nervous system. More recently, nanoparticles are emergent pollutants whose toxicity can be explored using this nematode

  11. Exogenous control of biological and ecological systems through evolutionary modelling

    Directory of Open Access Journals (Sweden)

    Alessandro Ferrarini

    2013-09-01

    Full Text Available The controllability of network-like systems is a topical issue in ecology and biology. It relies on the ability to lead a system's behaviour towards the desired state through the appropriate handling of input variables. Up to now, controllability of networks is based on the permanent control of a set of driver nodes that can guide the system's dynamics. This assumption seems motivated by real-world networks observation, where a decentralized control is often applied only to part of the nodes. While in a previous paper I showed that ecological and biological networks can be efficaciously controlled from the inside, here I further introduce a new framework for network controllability based on the employment of exogenous controllers and evolutionary modelling, and provide an exemplification of its application.

  12. Coupled model of physical and biological processes affecting maize pollination

    Science.gov (United States)

    Arritt, R.; Westgate, M.; Riese, J.; Falk, M.; Takle, E.

    2003-04-01

    Controversy over the use of genetically modified (GM) crops has led to increased interest in evaluating and controlling the potential for inadvertent outcrossing in open-pollinated crops such as maize. In response to this problem we have developed a Lagrangian model of pollen dispersion as a component of a coupled end-to-end (anther to ear) physical-biological model of maize pollination. The Lagrangian method is adopted because of its generality and flexibility: first, the method readily accommodates flow fields of arbitrary complexity; second, each element of the material being transported can be identified by its source, time of release, or other properties of interest. The latter allows pollen viability to be estimated as a function of such factors as travel time, temperature, and relative humidity, so that the physical effects of airflow and turbulence on pollen dispersion can be considered together with the biological aspects of pollen release and viability. Predicted dispersion of pollen compares well both to observations and to results from a simpler Gaussian plume model. Ability of the Lagrangian model to handle complex air flows is demonstrated by application to pollen dispersion in the vicinity of an agricultural shelter belt. We also show results indicating that pollen viability can be quantified by an "aging function" that accounts for temperature, humidity, and time of exposure.

  13. MODEL ORGANISMS USED IN MOLECULAR BIOLOGY OR MEDICAL RESEARCH

    Directory of Open Access Journals (Sweden)

    Pandey Govind

    2011-11-01

    Full Text Available A model organism is a non-human species that is studied to understand specific biological phenomena with the expectation that investigations made in the organism model will provide insight into the workings of other organisms. The model organisms are widely used to explore potential causes and treatments for human as well as animal diseases when experiments on animals or humans would be unfeasible or considered less ethical. Studying model organisms may be informative, but care must be taken when generalizing from one organism to another. Often, model organisms are chosen on the basis that they are amenable to experimental manipulation. When researchers look for an organism to use in their studies, they look for several traits. Among these are size, generation time, accessibility, manipulation, genetics, conservation of mechanisms and potential economic benefit. As comparative molecular biology has become more common, some researchers have sought model organisms from a wider assortment of lineages on the tree of life. There are many model organisms, such as viruses (e.g., Phage lambda virus, Tobacco mosaic virus, etc., bacteria (e.g., Bacillus subtilis, Escherichia coli, Pseudomonas fluorescens, Vibrio fischeri, etc., algae (e.g., Chlamydomonas reinhardtii, Emiliania huxleyi, etc., molds (e.g., Aspergillus nidulans, Neurospora crassa, etc., yeasts (e.g., Saccharomyces cerevisiae, Ustilago maydis, etc., higher plants (e.g., Arabidopsis thaliana, Lemna gibba, Lotus japonicus, Nicotiana tabaccum, Oryza sativa, Physcomitrella patens, Zea mays, etc. and animals (e.g., Caenorhabditis elegans, guinea pig, hamster, mouse, rat, cat, chicken, dog, frog, Hydra, Drosophila melanogaster fruit fly, fish, etc..

  14. Lessons learned from quantitative dynamical modeling in systems biology.

    Directory of Open Access Journals (Sweden)

    Andreas Raue

    Full Text Available Due to the high complexity of biological data it is difficult to disentangle cellular processes relying only on intuitive interpretation of measurements. A Systems Biology approach that combines quantitative experimental data with dynamic mathematical modeling promises to yield deeper insights into these processes. Nevertheless, with growing complexity and increasing amount of quantitative experimental data, building realistic and reliable mathematical models can become a challenging task: the quality of experimental data has to be assessed objectively, unknown model parameters need to be estimated from the experimental data, and numerical calculations need to be precise and efficient. Here, we discuss, compare and characterize the performance of computational methods throughout the process of quantitative dynamic modeling using two previously established examples, for which quantitative, dose- and time-resolved experimental data are available. In particular, we present an approach that allows to determine the quality of experimental data in an efficient, objective and automated manner. Using this approach data generated by different measurement techniques and even in single replicates can be reliably used for mathematical modeling. For the estimation of unknown model parameters, the performance of different optimization algorithms was compared systematically. Our results show that deterministic derivative-based optimization employing the sensitivity equations in combination with a multi-start strategy based on latin hypercube sampling outperforms the other methods by orders of magnitude in accuracy and speed. Finally, we investigated transformations that yield a more efficient parameterization of the model and therefore lead to a further enhancement in optimization performance. We provide a freely available open source software package that implements the algorithms and examples compared here.

  15. Revision history aware repositories of computational models of biological systems

    Directory of Open Access Journals (Sweden)

    Nickerson David P

    2011-01-01

    Full Text Available Abstract Background Building repositories of computational models of biological systems ensures that published models are available for both education and further research, and can provide a source of smaller, previously verified models to integrate into a larger model. One problem with earlier repositories has been the limitations in facilities to record the revision history of models. Often, these facilities are limited to a linear series of versions which were deposited in the repository. This is problematic for several reasons. Firstly, there are many instances in the history of biological systems modelling where an 'ancestral' model is modified by different groups to create many different models. With a linear series of versions, if the changes made to one model are merged into another model, the merge appears as a single item in the history. This hides useful revision history information, and also makes further merges much more difficult, as there is no record of which changes have or have not already been merged. In addition, a long series of individual changes made outside of the repository are also all merged into a single revision when they are put back into the repository, making it difficult to separate out individual changes. Furthermore, many earlier repositories only retain the revision history of individual files, rather than of a group of files. This is an important limitation to overcome, because some types of models, such as CellML 1.1 models, can be developed as a collection of modules, each in a separate file. The need for revision history is widely recognised for computer software, and a lot of work has gone into developing version control systems and distributed version control systems (DVCSs for tracking the revision history. However, to date, there has been no published research on how DVCSs can be applied to repositories of computational models of biological systems. Results We have extended the Physiome Model

  16. Biologic

    CERN Document Server

    Kauffman, L H

    2002-01-01

    In this paper we explore the boundary between biology and the study of formal systems (logic). In the end, we arrive at a summary formalism, a chapter in "boundary mathematics" where there are not only containers but also extainers ><, entities open to interaction and distinguishing the space that they are not. The boundary algebra of containers and extainers is to biologic what boolean algebra is to classical logic. We show how this formalism encompasses significant parts of the logic of DNA replication, the Dirac formalism for quantum mechanics, formalisms for protein folding and the basic structure of the Temperley Lieb algebra at the foundations of topological invariants of knots and links.

  17. Experimental, statistical, and biological models of radon carcinogenesis

    International Nuclear Information System (INIS)

    Risk models developed for underground miners have not been consistently validated in studies of populations exposed to indoor radon. Imprecision in risk estimates results principally from differences between exposures in mines as compared to domestic environments and from uncertainties about the interaction between cigarette-smoking and exposure to radon decay products. Uncertainties in extrapolating miner data to domestic exposures can be reduced by means of a broad-based health effects research program that addresses the interrelated issues of exposure, respiratory tract dose, carcinogenesis (molecular/cellular and animal studies, plus developing biological and statistical models), and the relationship of radon to smoking and other copollutant exposures. This article reviews experimental animal data on radon carcinogenesis observed primarily in rats at Pacific Northwest Laboratory. Recent experimental and mechanistic carcinogenesis models of exposures to radon, uranium ore dust, and cigarette smoke are presented with statistical analyses of animal data. 20 refs., 1 fig

  18. Experimental, statistical and biological models of radon carcinogenesis

    International Nuclear Information System (INIS)

    Risk models developed for underground miners have not been consistently validated in studies of populations exposed to indoor radon. Imprecision in risk estimates results principally from differences between exposures in mines as compared with domestic environments and from uncertainties about the interaction between cigarette smoking and exposure to radon decay products. Uncertainties in extrapolating miner data to domestic exposures can be reduced by means of a broad-based health effects research programme that addresses the interrelated issues of exposure, respiratory tract dose, carcinogenesis (molecular/cellular and animal studies, plus developing biological and statistical models) and the relationship of radon to smoking and other co-pollutant exposures. This article reviews experimental animal data on radon carcinogenesis observed primarily in rats at Pacific Northwest Laboratory. Recent experimental and mechanistic carcinogenesis models of exposures to radon, uranium ore dust, and cigarette smoke are presented with statistical analyses of animal data. (author)

  19. Testing Models: A Key Aspect to Promote Teaching Activities Related to Models and Modelling in Biology Lessons?

    Science.gov (United States)

    Krell, Moritz; Krüger, Dirk

    2016-01-01

    This study investigated biology teachers' (N = 148) understanding of models and modelling (MoMo), their model-related teaching activities and relations between the two. A framework which distinguishes five aspects of MoMo in science ("nature of models," "multiple models," "purpose of models," "testing…

  20. A biological model for controlling interface growth and morphology.

    Energy Technology Data Exchange (ETDEWEB)

    Hoyt, Jeffrey John; Holm, Elizabeth Ann

    2004-01-01

    Biological systems create proteins that perform tasks more efficiently and precisely than conventional chemicals. For example, many plants and animals produce proteins to control the freezing of water. Biological antifreeze proteins (AFPs) inhibit the solidification process, even below the freezing point. These molecules bond to specific sites at the ice/water interface and are theorized to suppress solidification chemically or geometrically. In this project, we investigated the theoretical and experimental data on AFPs and performed analyses to understand the unique physics of AFPs. The experimental literature was analyzed to determine chemical mechanisms and effects of protein bonding at ice surfaces, specifically thermodynamic freezing point depression, suppression of ice nucleation, decrease in dendrite growth kinetics, solute drag on the moving solid/liquid interface, and stearic pinning of the ice interface. Stearic pinning was found to be the most likely candidate to explain experimental results, including freezing point depression, growth morphologies, and thermal hysteresis. A new stearic pinning model was developed and applied to AFPs, with excellent quantitative results. Understanding biological antifreeze mechanisms could enable important medical and engineering applications, but considerable future work will be necessary.

  1. Development of Plant Model to Study Biological Effects of Nanodilutions

    Directory of Open Access Journals (Sweden)

    Delinick (Delinikou A.N.

    2007-12-01

    Full Text Available Pea (Pisum sativum as a model plant has been used extensively in fundamental research in different biological sciences. In vivo and in vitro pea models were used, as well, to study stress factors. Applying environment friendly technologies for overcoming biotic/abiotic stress increases its importance for sustainable agriculture. In this respect studies in the field of nanotechnology can contribute to solve some problems and to understanding of phenomena or practices that still lack methodology or specific instrumentation for scientific explanations. The interest to such studies was provoked by attempting an explanation on the potentization process and its therapeutic effect, and also by the possibility to apply similar approach in sustainable agriculture. The objectives of the experiments were to examine if potentized nanodilutions (PNDs have effects on different stages of seed development of pea aiming at the development of a plant model. Copper was chosen as stress factor as its excess is toxic and affects seed development. The experiments show for the first time that potentized nanodilutions (PNDs of metallic copprer have biological effects on pea seed development which are similar to the effect of copper (water solutions of CuSO4. The results, also, show that PNDs can stimulate response for overcoming the stress applied to seeds.

  2. Modeling biological systems with delays in Bio-PEPA

    CERN Document Server

    Caravagna, Giulio; 10.4204/EPTCS.40.7

    2010-01-01

    Delays in biological systems may be used to model events for which the underlying dynamics cannot be precisely observed, or to provide abstraction of some behavior of the system resulting more compact models. In this paper we enrich the stochastic process algebra Bio-PEPA, with the possibility of assigning delays to actions, yielding a new non-Markovian process algebra: Bio-PEPAd. This is a conservative extension meaning that the original syntax of Bio-PEPA is retained and the delay specification which can now be associated with actions may be added to existing Bio-PEPA models. The semantics of the firing of the actions with delays is the delay-as-duration approach, earlier presented in papers on the stochastic simulation of biological systems with delays. These semantics of the algebra are given in the Starting-Terminating style, meaning that the state and the completion of an action are observed as two separate events, as required by delays. Furthermore we outline how to perform stochastic simulation of Bio...

  3. Modeling of biological clogging in unsaturated porous media

    Science.gov (United States)

    Soleimani, Sahar; Van Geel, Paul J.; Isgor, O. Burkan; Mostafa, Mohamed B.

    2009-04-01

    A two-dimensional unsaturated flow and transport model, which includes microbial growth and decay, has been developed to simulate biological clogging in unsaturated soils, specifically biofilters. The bacterial growth and rate of solute reduction due to biodegradation is estimated using the Monod equation. The effect of microbial growth is considered in the proposed conceptual model that relates the relative permeability term for unsaturated flow to the microbial growth. Two applications of the model are presented in this study. Using the model, the clogging mechanism in different soils has been simulated. The results of the model indicate that the time to reach a clogged state is influenced by the hydraulic properties of the soil. Clogging is delayed in soils with higher saturated hydraulic conductivities, and higher porosities. For the relative permeability model proposed, higher van Genuchten n values lead to a delay in clogging. The model was also used to simulate the progressive clogging of a septic bed as the biomat initially forms at the up-gradient end of the distribution pipe, displacing wastewater infiltration and biomat formation further down-gradient over time.

  4. Bridging Mechanistic and Phenomenological Models of Complex Biological Systems

    Science.gov (United States)

    Transtrum, Mark K.; Qiu, Peng

    2016-01-01

    The inherent complexity of biological systems gives rise to complicated mechanistic models with a large number of parameters. On the other hand, the collective behavior of these systems can often be characterized by a relatively small number of phenomenological parameters. We use the Manifold Boundary Approximation Method (MBAM) as a tool for deriving simple phenomenological models from complicated mechanistic models. The resulting models are not black boxes, but remain expressed in terms of the microscopic parameters. In this way, we explicitly connect the macroscopic and microscopic descriptions, characterize the equivalence class of distinct systems exhibiting the same range of collective behavior, and identify the combinations of components that function as tunable control knobs for the behavior. We demonstrate the procedure for adaptation behavior exhibited by the EGFR pathway. From a 48 parameter mechanistic model, the system can be effectively described by a single adaptation parameter τ characterizing the ratio of time scales for the initial response and recovery time of the system which can in turn be expressed as a combination of microscopic reaction rates, Michaelis-Menten constants, and biochemical concentrations. The situation is not unlike modeling in physics in which microscopically complex processes can often be renormalized into simple phenomenological models with only a few effective parameters. The proposed method additionally provides a mechanistic explanation for non-universal features of the behavior. PMID:27187545

  5. Bridging Mechanistic and Phenomenological Models of Complex Biological Systems.

    Directory of Open Access Journals (Sweden)

    Mark K Transtrum

    2016-05-01

    Full Text Available The inherent complexity of biological systems gives rise to complicated mechanistic models with a large number of parameters. On the other hand, the collective behavior of these systems can often be characterized by a relatively small number of phenomenological parameters. We use the Manifold Boundary Approximation Method (MBAM as a tool for deriving simple phenomenological models from complicated mechanistic models. The resulting models are not black boxes, but remain expressed in terms of the microscopic parameters. In this way, we explicitly connect the macroscopic and microscopic descriptions, characterize the equivalence class of distinct systems exhibiting the same range of collective behavior, and identify the combinations of components that function as tunable control knobs for the behavior. We demonstrate the procedure for adaptation behavior exhibited by the EGFR pathway. From a 48 parameter mechanistic model, the system can be effectively described by a single adaptation parameter τ characterizing the ratio of time scales for the initial response and recovery time of the system which can in turn be expressed as a combination of microscopic reaction rates, Michaelis-Menten constants, and biochemical concentrations. The situation is not unlike modeling in physics in which microscopically complex processes can often be renormalized into simple phenomenological models with only a few effective parameters. The proposed method additionally provides a mechanistic explanation for non-universal features of the behavior.

  6. A heat transfer model for biological wastewater treatment system

    Science.gov (United States)

    Lin, S. H.

    A heat transfer model for predicting the water temperature of aeration tank in a biological wastewater treatment plant is presented. The heat transfer mechanisms involved in the development of the heat transfer model include heat gains from solar radiation and biochemical reaction and heat losses from evaporation, aeration, wind blowing and conduction through tank walls. Several empirical correlations were adopted and appropriate assumptions made to facilitate the model development. Experiments were conducted in the biological wastewater treatment plant of a chemical fiber company over a year's period. The operational, weather and temperature data were registered. The daily water temperature data were averaged over a month period and compared with the theoretical prediction. Excellent agreement has been obtained between the predicted and measured temperatures, verifying the proposed heat transfer model. Zusammenfassung Es wird ein Wärmeübergangsmodell zur Berechnung der Wassertemperatur im Belüftungstank einer Anlage zur biologischen Abwasserbehandlung vorgestellt. Die in das Modell eingehenden Wärmeübergangsmechanismen umfassen: solare Wärmeeinstrahlung, biochemische Reaktion, Wärmeverluste durch Verdampfung, Belüftung, Windeinfluß und Leitung durch die Behälterwände. Mehrere empirische Beziehungen sowie vertretbare Annahmen tragen zur Modellvereinfachung bei. An der biologischen Abwasser-Kläranlage einer Chemiefaserfirma wurden ein Jahr lang Experimente durchgeführt und dabei Betriebs-, Wetter- und Temperaturdaten aufgezeichnet. Die täglichen Wassertemperaturen, gemittelt über einen Monat, zeigten ausgezeichnete Übereinstimmung mit den theoretischen Vorausberechnungen und bestätigten so die Brauchbarkeit des vorgeschlagenen Wärmeübergangsmodells.

  7. Neural network models for biological waste-gas treatment systems.

    Science.gov (United States)

    Rene, Eldon R; Estefanía López, M; Veiga, María C; Kennes, Christian

    2011-12-15

    This paper outlines the procedure for developing artificial neural network (ANN) based models for three bioreactor configurations used for waste-gas treatment. The three bioreactor configurations chosen for this modelling work were: biofilter (BF), continuous stirred tank bioreactor (CSTB) and monolith bioreactor (MB). Using styrene as the model pollutant, this paper also serves as a general database of information pertaining to the bioreactor operation and important factors affecting gas-phase styrene removal in these biological systems. Biological waste-gas treatment systems are considered to be both advantageous and economically effective in treating a stream of polluted air containing low to moderate concentrations of the target contaminant, over a rather wide range of gas-flow rates. The bioreactors were inoculated with the fungus Sporothrix variecibatus, and their performances were evaluated at different empty bed residence times (EBRT), and at different inlet styrene concentrations (C(i)). The experimental data from these bioreactors were modelled to predict the bioreactors performance in terms of their removal efficiency (RE, %), by adequate training and testing of a three-layered back propagation neural network (input layer-hidden layer-output layer). Two models (BIOF1 and BIOF2) were developed for the BF with different combinations of easily measurable BF parameters as the inputs, that is concentration (gm(-3)), unit flow (h(-1)) and pressure drop (cm of H(2)O). The model developed for the CSTB used two inputs (concentration and unit flow), while the model for the MB had three inputs (concentration, G/L (gas/liquid) ratio, and pressure drop). Sensitivity analysis in the form of absolute average sensitivity (AAS) was performed for all the developed ANN models to ascertain the importance of the different input parameters, and to assess their direct effect on the bioreactors performance. The performance of the models was estimated by the regression

  8. Modeling human risk: Cell ampersand molecular biology in context

    International Nuclear Information System (INIS)

    It is anticipated that early in the next century manned missions into outer space will occur, with a mission to Mars scheduled between 2015 and 2020. However, before such missions can be undertaken, a realistic estimation of the potential risks to the flight crews is required. One of the uncertainties remaining in this risk estimation is that posed by the effects of exposure to the radiation environment of outer space. Although the composition of this environment is fairly well understood, the biological effects arising from exposure to it are not. The reasons for this are three-fold: (1) A small but highly significant component of the radiation spectrum in outer space consists of highly charged, high energy (HZE) particles which are not routinely experienced on earth, and for which there are insufficient data on biological effects; (2) Most studies on the biological effects of radiation to date have been high-dose, high dose-rate, whereas in space, with the exception of solar particle events, radiation exposures will be low-dose, low dose-rate; (3) Although it has been established that the virtual absence of gravity in space has a profound effect on human physiology, it is not clear whether these effects will act synergistically with those of radiation exposure. A select panel will evaluate the utilizing experiments and models to accurately predict the risks associated with exposure to HZE particles. Topics of research include cellular and tissue response, health effects associated with radiation damage, model animal systems, and critical markers of Radiation response

  9. Fluctuating Nonlinear Spring Model of Mechanical Deformation of Biological Particles.

    Directory of Open Access Journals (Sweden)

    Olga Kononova

    2016-01-01

    Full Text Available The mechanical properties of virus capsids correlate with local conformational dynamics in the capsid structure. They also reflect the required stability needed to withstand high internal pressures generated upon genome loading and contribute to the success of important events in viral infectivity, such as capsid maturation, genome uncoating and receptor binding. The mechanical properties of biological nanoparticles are often determined from monitoring their dynamic deformations in Atomic Force Microscopy nanoindentation experiments; but a comprehensive theory describing the full range of observed deformation behaviors has not previously been described. We present a new theory for modeling dynamic deformations of biological nanoparticles, which considers the non-linear Hertzian deformation, resulting from an indenter-particle physical contact, and the bending of curved elements (beams modeling the particle structure. The beams' deformation beyond the critical point triggers a dynamic transition of the particle to the collapsed state. This extreme event is accompanied by a catastrophic force drop as observed in the experimental or simulated force (F-deformation (X spectra. The theory interprets fine features of the spectra, including the nonlinear components of the FX-curves, in terms of the Young's moduli for Hertzian and bending deformations, and the structural damage dependent beams' survival probability, in terms of the maximum strength and the cooperativity parameter. The theory is exemplified by successfully describing the deformation dynamics of natural nanoparticles through comparing theoretical curves with experimental force-deformation spectra for several virus particles. This approach provides a comprehensive description of the dynamic structural transitions in biological and artificial nanoparticles, which is essential for their optimal use in nanotechnology and nanomedicine applications.

  10. Fluctuating Nonlinear Spring Model of Mechanical Deformation of Biological Particles.

    Science.gov (United States)

    Kononova, Olga; Snijder, Joost; Kholodov, Yaroslav; Marx, Kenneth A; Wuite, Gijs J L; Roos, Wouter H; Barsegov, Valeri

    2016-01-01

    The mechanical properties of virus capsids correlate with local conformational dynamics in the capsid structure. They also reflect the required stability needed to withstand high internal pressures generated upon genome loading and contribute to the success of important events in viral infectivity, such as capsid maturation, genome uncoating and receptor binding. The mechanical properties of biological nanoparticles are often determined from monitoring their dynamic deformations in Atomic Force Microscopy nanoindentation experiments; but a comprehensive theory describing the full range of observed deformation behaviors has not previously been described. We present a new theory for modeling dynamic deformations of biological nanoparticles, which considers the non-linear Hertzian deformation, resulting from an indenter-particle physical contact, and the bending of curved elements (beams) modeling the particle structure. The beams' deformation beyond the critical point triggers a dynamic transition of the particle to the collapsed state. This extreme event is accompanied by a catastrophic force drop as observed in the experimental or simulated force (F)-deformation (X) spectra. The theory interprets fine features of the spectra, including the nonlinear components of the FX-curves, in terms of the Young's moduli for Hertzian and bending deformations, and the structural damage dependent beams' survival probability, in terms of the maximum strength and the cooperativity parameter. The theory is exemplified by successfully describing the deformation dynamics of natural nanoparticles through comparing theoretical curves with experimental force-deformation spectra for several virus particles. This approach provides a comprehensive description of the dynamic structural transitions in biological and artificial nanoparticles, which is essential for their optimal use in nanotechnology and nanomedicine applications. PMID:26821264

  11. Modeling human risk: Cell & molecular biology in context

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    1997-06-01

    It is anticipated that early in the next century manned missions into outer space will occur, with a mission to Mars scheduled between 2015 and 2020. However, before such missions can be undertaken, a realistic estimation of the potential risks to the flight crews is required. One of the uncertainties remaining in this risk estimation is that posed by the effects of exposure to the radiation environment of outer space. Although the composition of this environment is fairly well understood, the biological effects arising from exposure to it are not. The reasons for this are three-fold: (1) A small but highly significant component of the radiation spectrum in outer space consists of highly charged, high energy (HZE) particles which are not routinely experienced on earth, and for which there are insufficient data on biological effects; (2) Most studies on the biological effects of radiation to date have been high-dose, high dose-rate, whereas in space, with the exception of solar particle events, radiation exposures will be low-dose, low dose-rate; (3) Although it has been established that the virtual absence of gravity in space has a profound effect on human physiology, it is not clear whether these effects will act synergistically with those of radiation exposure. A select panel will evaluate the utilizing experiments and models to accurately predict the risks associated with exposure to HZE particles. Topics of research include cellular and tissue response, health effects associated with radiation damage, model animal systems, and critical markers of Radiation response.

  12. Separable Watermarking Technique Using the Biological Color Model

    Directory of Open Access Journals (Sweden)

    David Nino

    2009-01-01

    Full Text Available Problem statement: The issue of having robust and fragile watermarking is still main focus for various researchers worldwide. Performance of a watermarking technique depends on how complex as well as how feasible to implement. These issues are tested using various kinds of attacks including geometry and transformation. Watermarking techniques in color images are more challenging than gray images in terms of complexity and information handling. In this study, we focused on implementation of watermarking technique in color images using the biological model. Approach: We proposed a novel method for watermarking using spatial and the Discrete Cosine Transform (DCT domains. The proposed method deled with colored images in the biological color model, the Hue, Saturation and Intensity (HSI. Technique was implemented and used against various colored images including the standard ones such as pepper image. The experiments were done using various attacks such as cropping, transformation and geometry. Results: The method robustness showed high accuracy in retrieval data and technique is fragile against geometric attacks. Conclusion: Watermark security was increased by using the Hadamard transform matrix. The watermarks used were meaningful and of varying sizes and details.

  13. Coarse Dynamics for Coarse Modeling: An Example From Population Biology

    Directory of Open Access Journals (Sweden)

    Justin Bush

    2014-06-01

    Full Text Available Networks have become a popular way to concisely represent complex nonlinear systems where the interactions and parameters are imprecisely known. One challenge is how best to describe the associated dynamics, which can exhibit complicated behavior sensitive to small changes in parameters. A recently developed computational approach that we refer to as a database for dynamics provides a robust and mathematically rigorous description of global dynamics over large ranges of parameter space. To demonstrate the potential of this approach we consider two classical age-structured population models that share the same network diagram and have a similar nonlinear overcompensatory term, but nevertheless yield different patterns of qualitative behavior as a function of parameters. Using a generalization of these models we relate the different structure of the dynamics that are observed in the context of biologically relevant questions such as stable oscillations in populations, bistability, and permanence.

  14. Modeling of biological doses and mechanical effects on bone transduction

    CERN Document Server

    Rieger, Romain; Jennane, Rachid; 10.1016/j.jtbi.2011.01.003

    2012-01-01

    Shear stress, hormones like parathyroid and mineral elements like calcium mediate the amplitude of stimulus signal which affects the rate of bone remodeling. The current study investigates the theoretical effects of different metabolic doses in stimulus signal level on bone. The model was built considering the osteocyte as the sensing center mediated by coupled mechanical shear stress and some biological factors. The proposed enhanced model was developed based on previously published works dealing with different aspects of bone transduction. It describes the effects of physiological doses variations of Calcium, Parathyroid Hormone, Nitric Oxide and Prostaglandin E2 on the stimulus level sensed by osteocytes in response to applied shear stress generated by interstitial fluid flow. We retained the metabolic factors (Parathyroid Hormone, Nitric Oxide, and Prostaglandin E2) as parameters of bone cell mechanosensitivity because stimulation/inhibition of induced pathways stimulates osteogenic response in vivo. We t...

  15. Fluctuating Nonlinear Spring Model of Mechanical Deformation of Biological Particles

    CERN Document Server

    Kononova, Olga; Marx, Kenneth A; Wuite, Gijs J L; Roos, Wouter H; Barsegov, Valeri

    2015-01-01

    We present a new theory for modeling forced indentation spectral lineshapes of biological particles, which considers non-linear Hertzian deformation due to an indenter-particle physical contact and bending deformations of curved beams modeling the particle structure. The bending of beams beyond the critical point triggers the particle dynamic transition to the collapsed state, an extreme event leading to the catastrophic force drop as observed in the force (F)-deformation (X) spectra. The theory interprets fine features of the spectra: the slope of the FX curves and the position of force-peak signal, in terms of mechanical characteristics --- the Young's moduli for Hertzian and bending deformations E_H and E_b, and the probability distribution of the maximum strength with the strength of the strongest beam F_b^* and the beams' failure rate m. The theory is applied to successfully characterize the $FX$ curves for spherical virus particles --- CCMV, TrV, and AdV.

  16. The autonomy of biological individuals and artificial models.

    Science.gov (United States)

    Moreno, Alvaro; Etxeberria, Arantza; Umerez, Jon

    2008-02-01

    This paper aims to offer an overview of the meaning of autonomy for biological individuals and artificial models rooted in a specific perspective that pays attention to the historical and structural aspects of its origins and evolution. Taking autopoiesis and the recursivity characteristic of its circular logic as a starting point, we depart from some of its consequences to claim that the theory of autonomy should also take into account historical and structural features. Autonomy should not be considered only in internal or constitutive terms, the largely neglected interactive aspects stemming from it should be equally addressed. Artificial models contribute to get a better understanding of the role of autonomy for life and the varieties of its organization and phenomenological diversity. PMID:17719170

  17. Multiscale modeling of emergent materials: biological and soft matter

    DEFF Research Database (Denmark)

    Murtola, Teemu; Bunker, Alex; Vattulainen, Ilpo;

    2009-01-01

    In this review, we focus on four current related issues in multiscale modeling of soft and biological matter. First, we discuss how to use structural information from detailed models (or experiments) to construct coarse-grained ones in a hierarchical and systematic way. This is discussed...... in the context of the so-called Henderson theorem and the inverse Monte Carlo method of Lyubartsev and Laaksonen. In the second part, we take a different look at coarse graining by analyzing conformations of molecules. This is done by the application of self-organizing maps, i.e., a neural network type approach....... Such an approach can be used to guide the selection of the relevant degrees of freedom. Then, we discuss technical issues related to the popular dissipative particle dynamics (DPD) method. Importantly, the potentials derived using the inverse Monte Carlo method can be used together with the DPD thermostat...

  18. Kinetic hierarchy and propagation of chaos in biological swarm models

    CERN Document Server

    Carlen, Eric; Degond, Pierre; Wennberg, Bernt

    2011-01-01

    We consider two models of biological swarm behavior. In these models, pairs of particles interact to adjust their velocities one to each other. In the first process, called 'BDG', they join their average velocity up to some noise. In the second process, called 'CL', one of the two particles tries to join the other one's velocity. This paper establishes the master equations and BBGKY hierarchies of these two processes. It investigates the infinite particle limit of the hierarchies at large time-scale. It shows that the resulting kinetic hierarchy for the CL process does not satisfy propagation of chaos. Numerical simulations indicate that the BDG process has similar behavior to the CL process.

  19. Introduction to mathematical biology modeling, analysis, and simulations

    CERN Document Server

    Chou, Ching Shan

    2016-01-01

    This book is based on a one semester course that the authors have been teaching for several years, and includes two sets of case studies. The first includes chemostat models, predator-prey interaction, competition among species, the spread of infectious diseases, and oscillations arising from bifurcations. In developing these topics, readers will also be introduced to the basic theory of ordinary differential equations, and how to work with MATLAB without having any prior programming experience. The second set of case studies were adapted from recent and current research papers to the level of the students. Topics have been selected based on public health interest. This includes the risk of atherosclerosis associated with high cholesterol levels, cancer and immune interactions, cancer therapy, and tuberculosis. Readers will experience how mathematical models and their numerical simulations can provide explanations that guide biological and biomedical research. Considered to be the undergraduate companion to t...

  20. Modelling biological behaviours with the unified modelling language: an immunological case study and critique.

    Science.gov (United States)

    Read, Mark; Andrews, Paul S; Timmis, Jon; Kumar, Vipin

    2014-10-01

    We present a framework to assist the diagrammatic modelling of complex biological systems using the unified modelling language (UML). The framework comprises three levels of modelling, ranging in scope from the dynamics of individual model entities to system-level emergent properties. By way of an immunological case study of the mouse disease experimental autoimmune encephalomyelitis, we show how the framework can be used to produce models that capture and communicate the biological system, detailing how biological entities, interactions and behaviours lead to higher-level emergent properties observed in the real world. We demonstrate how the UML can be successfully applied within our framework, and provide a critique of UML's ability to capture concepts fundamental to immunology and biology more generally. We show how specialized, well-explained diagrams with less formal semantics can be used where no suitable UML formalism exists. We highlight UML's lack of expressive ability concerning cyclic feedbacks in cellular networks, and the compounding concurrency arising from huge numbers of stochastic, interacting agents. To compensate for this, we propose several additional relationships for expressing these concepts in UML's activity diagram. We also demonstrate the ambiguous nature of class diagrams when applied to complex biology, and question their utility in modelling such dynamic systems. Models created through our framework are non-executable, and expressly free of simulation implementation concerns. They are a valuable complement and precursor to simulation specifications and implementations, focusing purely on thoroughly exploring the biology, recording hypotheses and assumptions, and serve as a communication medium detailing exactly how a simulation relates to the real biology. PMID:25142524

  1. Modelling biological behaviours with the unified modelling language: an immunological case study and critique.

    Science.gov (United States)

    Read, Mark; Andrews, Paul S; Timmis, Jon; Kumar, Vipin

    2014-10-01

    We present a framework to assist the diagrammatic modelling of complex biological systems using the unified modelling language (UML). The framework comprises three levels of modelling, ranging in scope from the dynamics of individual model entities to system-level emergent properties. By way of an immunological case study of the mouse disease experimental autoimmune encephalomyelitis, we show how the framework can be used to produce models that capture and communicate the biological system, detailing how biological entities, interactions and behaviours lead to higher-level emergent properties observed in the real world. We demonstrate how the UML can be successfully applied within our framework, and provide a critique of UML's ability to capture concepts fundamental to immunology and biology more generally. We show how specialized, well-explained diagrams with less formal semantics can be used where no suitable UML formalism exists. We highlight UML's lack of expressive ability concerning cyclic feedbacks in cellular networks, and the compounding concurrency arising from huge numbers of stochastic, interacting agents. To compensate for this, we propose several additional relationships for expressing these concepts in UML's activity diagram. We also demonstrate the ambiguous nature of class diagrams when applied to complex biology, and question their utility in modelling such dynamic systems. Models created through our framework are non-executable, and expressly free of simulation implementation concerns. They are a valuable complement and precursor to simulation specifications and implementations, focusing purely on thoroughly exploring the biology, recording hypotheses and assumptions, and serve as a communication medium detailing exactly how a simulation relates to the real biology.

  2. Models for integrated pest control and their biological implications.

    Science.gov (United States)

    Tang, Sanyi; Cheke, Robert A

    2008-09-01

    Successful integrated pest management (IPM) control programmes depend on many factors which include host-parasitoid ratios, starting densities, timings of parasitoid releases, dosages and timings of insecticide applications and levels of host-feeding and parasitism. Mathematical models can help us to clarify and predict the effects of such factors on the stability of host-parasitoid systems, which we illustrate here by extending the classical continuous and discrete host-parasitoid models to include an IPM control programme. The results indicate that one of three control methods can maintain the host level below the economic threshold (ET) in relation to different ET levels, initial densities of host and parasitoid populations and host-parasitoid ratios. The effects of host intrinsic growth rate and parasitoid searching efficiency on host mean outbreak period can be calculated numerically from the models presented. The instantaneous pest killing rate of an insecticide application is also estimated from the models. The results imply that the modelling methods described can help in the design of appropriate control strategies and assist management decision-making. The results also indicate that a high initial density of parasitoids (such as in inundative releases) and high parasitoid inter-generational survival rates will lead to more frequent host outbreaks and, therefore, greater economic damage. The biological implications of this counter intuitive result are discussed.

  3. A Color-Opponency Based Biological Model for Color Constancy

    Directory of Open Access Journals (Sweden)

    Yongjie Li

    2011-05-01

    Full Text Available Color constancy is the ability of the human visual system to adaptively correct color-biased scenes under different illuminants. Most of the existing color constancy models are nonphysiologically plausible. Among the limited biological models, the great majority is Retinex and its variations, and only two or three models directly simulate the feature of color-opponency, but only of the very earliest stages of visual pathway, i.e., the single-opponent mechanisms involved at the levels of retinal ganglion cells and lateral geniculate nucleus (LGN neurons. Considering the extensive physiological evidences supporting that both the single-opponent cells in retina and LGN and the double-opponent neurons in primary visual cortex (V1 are the building blocks for color constancy, in this study we construct a color-opponency based color constancy model by simulating the opponent fashions of both the single-opponent and double-opponent cells in a forward manner. As for the spatial structure of the receptive fields (RF, both the classical RF (CRF center and the nonclassical RF (nCRF surround are taken into account for all the cells. The proposed model was tested on several typical image databases commonly used for performance evaluation of color constancy methods, and exciting results were achieved.

  4. Micrasterias as a model system in plant cell biology

    Directory of Open Access Journals (Sweden)

    Ursula Luetz-Meindl

    2016-07-01

    Full Text Available The unicellular freshwater alga Micrasterias denticulata is an exceptional organism due to its extraordinary star-shaped, highly symmetric morphology and has thus attracted the interest of researchers for many decades. As a member of the Streptophyta, Micrasterias is not only genetically closely related to higher land plants but shares common features with them in many physiological and cell biological aspects. These facts, together with its considerable cell size of about 200 µm, its modest cultivation conditions and the uncomplicated accessibility particularly to any microscopic techniques, make Micrasterias a very well suited cell biological plant model system. The review focuses particularly on cell wall formation and composition, dictyosomal structure and function, cytoskeleton control of growth and morphogenesis as well as on ionic regulation and signal transduction. It has been also shown in the recent years that Micrasterias is a highly sensitive indicator for environmental stress impact such as heavy metals, high salinity, oxidative stress or starvation. Stress induced organelle degradation, autophagy, adaption and detoxification mechanisms have moved in the center of interest and have been investigated with modern microscopic techniques such as 3-D- and analytical electron microscopy as well as with biochemical, physiological and molecular approaches. This review is intended to summarize and discuss the most important results obtained in Micrasterias in the last 20 years and to compare the results to similar processes in higher plant cells.

  5. Models to Study NK Cell Biology and Possible Clinical Application.

    Science.gov (United States)

    Zamora, Anthony E; Grossenbacher, Steven K; Aguilar, Ethan G; Murphy, William J

    2015-08-03

    Natural killer (NK) cells are large granular lymphocytes of the innate immune system, responsible for direct targeting and killing of both virally infected and transformed cells. NK cells rapidly recognize and respond to abnormal cells in the absence of prior sensitization due to their wide array of germline-encoded inhibitory and activating receptors, which differs from the receptor diversity found in B and T lymphocytes that is due to the use of recombination-activation gene (RAG) enzymes. Although NK cells have traditionally been described as natural killers that provide a first line of defense prior to the induction of adaptive immunity, a more complex view of NK cells is beginning to emerge, indicating they may also function in various immunoregulatory roles and have the capacity to shape adaptive immune responses. With the growing appreciation for the diverse functions of NK cells, and recent technological advancements that allow for a more in-depth understanding of NK cell biology, we can now begin to explore new ways to manipulate NK cells to increase their clinical utility. In this overview unit, we introduce the reader to various aspects of NK cell biology by reviewing topics ranging from NK cell diversity and function, mouse models, and the roles of NK cells in health and disease, to potential clinical applications. © 2015 by John Wiley & Sons, Inc.

  6. Micrasterias as a Model System in Plant Cell Biology

    Science.gov (United States)

    Lütz-Meindl, Ursula

    2016-01-01

    The unicellular freshwater alga Micrasterias denticulata is an exceptional organism due to its complex star-shaped, highly symmetric morphology and has thus attracted the interest of researchers for many decades. As a member of the Streptophyta, Micrasterias is not only genetically closely related to higher land plants but shares common features with them in many physiological and cell biological aspects. These facts, together with its considerable cell size of about 200 μm, its modest cultivation conditions and the uncomplicated accessibility particularly to any microscopic techniques, make Micrasterias a very well suited cell biological plant model system. The review focuses particularly on cell wall formation and composition, dictyosomal structure and function, cytoskeleton control of growth and morphogenesis as well as on ionic regulation and signal transduction. It has been also shown in the recent years that Micrasterias is a highly sensitive indicator for environmental stress impact such as heavy metals, high salinity, oxidative stress or starvation. Stress induced organelle degradation, autophagy, adaption and detoxification mechanisms have moved in the center of interest and have been investigated with modern microscopic techniques such as 3-D- and analytical electron microscopy as well as with biochemical, physiological and molecular approaches. This review is intended to summarize and discuss the most important results obtained in Micrasterias in the last 20 years and to compare the results to similar processes in higher plant cells. PMID:27462330

  7. Protein Unfolding by Biological Unfoldases: Insights from Modeling

    Science.gov (United States)

    Wojciechowski, Michał; Szymczak, Piotr; Carrión-Vázquez, Mariano; Cieplak, Marek

    2014-01-01

    The molecular determinants of the high efficiency of biological machines like unfoldases (e.g., the proteasome) are not well understood. We propose a model to study protein translocation into the chamber of biological unfoldases represented as a funnel. It is argued that translocation is a much faster way of unfolding a protein than end-to-end stretching, especially in a low-force regime, because it allows for a conformational freedom while concentrating local tension on consecutive regions of a protein chain and preventing refolding. This results in a serial unfolding of the protein structures dominated by unzipping. Thus, pulling against the unfoldase pore is an efficient catalyst of the unfolding reaction. We also show that the presence of the funnel makes the tension along the backbone of the substrate protein nonuniform even when the protein gets unfolded. Hence, the stalling force measured by single-molecule force spectroscopy techniques may be smaller than the traction force of the unfoldase motor. PMID:25296319

  8. A biologically plausible embodied model of action discovery

    Directory of Open Access Journals (Sweden)

    Rufino eBolado-Gomez

    2013-03-01

    Full Text Available During development, animals can spontaneously discover action-outcomepairings enabling subsequent achievement of their goals. We present abiologically plausible embodied model addressing key aspects of thisprocess. The biomimetic model core comprises the basal ganglia and itsloops through cortex and thalamus. We incorporate reinforcementlearning with phasic dopamine supplying a sensory prediction error,signalling 'surprising' outcomes. Phasic dopamine is used in acorticostriatal learning rule which is consistent with recent data. Wealso hypothesised that objects associated with surprising outcomesacquire 'novelty salience' contingent on the predicability of theoutcome. To test this idea we used a simple model of predictiongoverning the dynamics of novelty salience and phasic dopamine. Thetask of the virtual robotic agent mimicked an in vivo counterpart(Gancarz et al., 2011 and involved interaction with a target objectwhich caused a light flash, or a control object which did not.Learning took place according to two schedules. In one, the phasicoutcome was delivered after interaction with the target in anunpredictable way which emulated the in vivo protocol. Without noveltysalience, the model was unable to account for the experimental data.In the other schedule, the phasic outcome was reliably delivered andthe agent showed a rapid increase in the number of interactions withthe target which then decreased over subsequent sessions. We arguethis is precisely the kind of change in behaviour required torepeatedly present representations of context, action and outcome, toneural networks responsible for learning action-outcome contingency.The model also showed corticostriatal plasticity consistent withlearning a new action in basal ganglia. We conclude that actionlearning is underpinned by a complex interplay of plasticity andstimulus salience, and that our model contains many of the elementsfor biological action discovery to take place.

  9. First Steps in Computational Systems Biology: A Practical Session in Metabolic Modeling and Simulation

    Science.gov (United States)

    Reyes-Palomares, Armando; Sanchez-Jimenez, Francisca; Medina, Miguel Angel

    2009-01-01

    A comprehensive understanding of biological functions requires new systemic perspectives, such as those provided by systems biology. Systems biology approaches are hypothesis-driven and involve iterative rounds of model building, prediction, experimentation, model refinement, and development. Developments in computer science are allowing for ever…

  10. EcoCyc: fusing model organism databases with systems biology.

    Science.gov (United States)

    Keseler, Ingrid M; Mackie, Amanda; Peralta-Gil, Martin; Santos-Zavaleta, Alberto; Gama-Castro, Socorro; Bonavides-Martínez, César; Fulcher, Carol; Huerta, Araceli M; Kothari, Anamika; Krummenacker, Markus; Latendresse, Mario; Muñiz-Rascado, Luis; Ong, Quang; Paley, Suzanne; Schröder, Imke; Shearer, Alexander G; Subhraveti, Pallavi; Travers, Mike; Weerasinghe, Deepika; Weiss, Verena; Collado-Vides, Julio; Gunsalus, Robert P; Paulsen, Ian; Karp, Peter D

    2013-01-01

    EcoCyc (http://EcoCyc.org) is a model organism database built on the genome sequence of Escherichia coli K-12 MG1655. Expert manual curation of the functions of individual E. coli gene products in EcoCyc has been based on information found in the experimental literature for E. coli K-12-derived strains. Updates to EcoCyc content continue to improve the comprehensive picture of E. coli biology. The utility of EcoCyc is enhanced by new tools available on the EcoCyc web site, and the development of EcoCyc as a teaching tool is increasing the impact of the knowledge collected in EcoCyc.

  11. A Model of Biological Attacks on a Realistic Population

    Science.gov (United States)

    Carley, Kathleen M.; Fridsma, Douglas; Casman, Elizabeth; Altman, Neal; Chen, Li-Chiou; Kaminsky, Boris; Nave, Demian; Yahja, Alex

    The capability to assess the impacts of large-scale biological attacks and the efficacy of containment policies is critical and requires knowledge-intensive reasoning about social response and disease transmission within a complex social system. There is a close linkage among social networks, transportation networks, disease spread, and early detection. Spatial dimensions related to public gathering places such as hospitals, nursing homes, and restaurants, can play a major role in epidemics [Klovdahl et. al. 2001]. Like natural epidemics, bioterrorist attacks unfold within spatially defined, complex social systems, and the societal and networked response can have profound effects on their outcome. This paper focuses on bioterrorist attacks, but the model has been applied to emergent and familiar diseases as well.

  12. Causal biological network database: a comprehensive platform of causal biological network models focused on the pulmonary and vascular systems.

    Science.gov (United States)

    Boué, Stéphanie; Talikka, Marja; Westra, Jurjen Willem; Hayes, William; Di Fabio, Anselmo; Park, Jennifer; Schlage, Walter K; Sewer, Alain; Fields, Brett; Ansari, Sam; Martin, Florian; Veljkovic, Emilija; Kenney, Renee; Peitsch, Manuel C; Hoeng, Julia

    2015-01-01

    With the wealth of publications and data available, powerful and transparent computational approaches are required to represent measured data and scientific knowledge in a computable and searchable format. We developed a set of biological network models, scripted in the Biological Expression Language, that reflect causal signaling pathways across a wide range of biological processes, including cell fate, cell stress, cell proliferation, inflammation, tissue repair and angiogenesis in the pulmonary and cardiovascular context. This comprehensive collection of networks is now freely available to the scientific community in a centralized web-based repository, the Causal Biological Network database, which is composed of over 120 manually curated and well annotated biological network models and can be accessed at http://causalbionet.com. The website accesses a MongoDB, which stores all versions of the networks as JSON objects and allows users to search for genes, proteins, biological processes, small molecules and keywords in the network descriptions to retrieve biological networks of interest. The content of the networks can be visualized and browsed. Nodes and edges can be filtered and all supporting evidence for the edges can be browsed and is linked to the original articles in PubMed. Moreover, networks may be downloaded for further visualization and evaluation. Database URL: http://causalbionet.com

  13. Modelling biological invasions: Individual to population scales at interfaces

    KAUST Repository

    Belmonte-Beitia, J.

    2013-10-01

    Extracting the population level behaviour of biological systems from that of the individual is critical in understanding dynamics across multiple scales and thus has been the subject of numerous investigations. Here, the influence of spatial heterogeneity in such contexts is explored for interfaces with a separation of the length scales characterising the individual and the interface, a situation that can arise in applications involving cellular modelling. As an illustrative example, we consider cell movement between white and grey matter in the brain which may be relevant in considering the invasive dynamics of glioma. We show that while one can safely neglect intrinsic noise, at least when considering glioma cell invasion, profound differences in population behaviours emerge in the presence of interfaces with only subtle alterations in the dynamics at the individual level. Transport driven by local cell sensing generates predictions of cell accumulations along interfaces where cell motility changes. This behaviour is not predicted with the commonly used Fickian diffusion transport model, but can be extracted from preliminary observations of specific cell lines in recent, novel, cryo-imaging. Consequently, these findings suggest a need to consider the impact of individual behaviour, spatial heterogeneity and especially interfaces in experimental and modelling frameworks of cellular dynamics, for instance in the characterisation of glioma cell motility. © 2013 Elsevier Ltd.

  14. A new model of the microwave interaction with biological systems

    International Nuclear Information System (INIS)

    The interaction of 30-300 GHz electromagnetic field with biological media is studied . The refraction index and the extinction, reflection and transmission coefficients were shown to depend on concentration of coherent photons created by the biological objects and their dependence was studied on the biological medium excitation level, the non-linear character of this dependence being established. (authors)

  15. Numerical issues for coupling biological models with isopycnal mixing schemes

    Science.gov (United States)

    Gnanadesikan, Anand

    1999-01-01

    In regions of sloping isopycnals, isopycnal mixing acting in conjunction with biological cycling can produce patterns in the nutrient field which have negative values of tracer in light water and unrealistically large values of tracer in dense water. Under certain circumstances, these patterns can start to grow unstably. This paper discusses why such behavior occurs. Using a simple four-box model, it demonstrates that the instability appears when the isopycnal slopes exceed the grid aspect ratio ( Δz/ Δx). In contrast to other well known instabilities of the CFL type, this instability does not depend on the time step or time-stepping scheme. Instead it arises from a fundamental incompatibility between two requirements for isopycnal mixing schemes, namely that they should produce no net flux of passive tracer across an isopycnal and everywhere reduce tracer extrema. In order to guarantee no net flux of tracer across an isopycnal, some upgradient fluxes across certain parts of an isopycnal are required to balance downgradient fluxes across other parts of the isopycnal. However, these upgradient fluxes can cause local maxima in the nutrient field to become self-reinforcing. Although this is less of a problem in larger domains, there is still a strong tendency for isopycnal mixing to overconcentrate tracer in the dense water. The introduction of eddy-induced advection is shown to be capable of counteracting the upgradient fluxes of nutrient which cause problems, stabilizing the solution. The issue is not simply a numerical curiosity. When used in a GCM, different parameterizations of eddy mixing result in noticeably different distributions of nutrient and large differences in biological production. While much of this is attributable to differences in convection and circulation, the numerical errors described here may also play an important role in runs with isopycnal mixing alone.

  16. Modeling of laser ablation and fragmentation of biological calculi

    International Nuclear Information System (INIS)

    Laser ablation and fragmentation of calcified biological materials (e.g. kidney and gall stones, calcified arterial walls, bones and teeth) have been demonstrated in vitro and in vivo. In the interaction, laser light incident upon the target material is of sufficient intensity to produce a plasma. The plasma couples to an acoustic wave which then propagates through the dense material, causing spall and fracture by reflection from material discontinuities or boundaries. Experiments have thus far yielded data on the plasma and the acoustic waves against which models can be tested. Data on: mass removal, light emission, absorption and emission spectra, fragmentation efficiency, and cavitation bubble dynamics have been obtained. Two dimensional simulations of the laser-matter interaction have been performed with the radiation-hydrodynamics code LASNEX to elucidate the important physical mechanisms. This research expands upon earlier 1-D studies. The authors find that good quantitative fits between simulation and experiment are obtained for electron density, plasma pressure, mass loss and cavitation bubble growth. They have not, however, fit the spectroscopic or electron temperature data. It is anticipated that model improvements in the area of laser light absorption and material opacity will enable better quantitative agreement to be obtained

  17. A Transformative Model for Undergraduate Quantitative Biology Education

    OpenAIRE

    Usher, David C.; Driscoll, Tobin A.; Dhurjati, Prasad; Pelesko, John A.; Rossi, Louis F.; Schleiniger, Gilberto; Pusecker, Kathleen; White, Harold B.

    2010-01-01

    The BIO2010 report recommended that students in the life sciences receive a more rigorous education in mathematics and physical sciences. The University of Delaware approached this problem by (1) developing a bio-calculus section of a standard calculus course, (2) embedding quantitative activities into existing biology courses, and (3) creating a new interdisciplinary major, quantitative biology, designed for students interested in solving complex biological problems using advanced mathematic...

  18. "Model Your Genes the Mathematical Way"--A Mathematical Biology Workshop for Secondary School Teachers

    Science.gov (United States)

    Martins, Ana Margarida; Vera-Licona, Paola; Laubenbacher, Reinhard

    2008-01-01

    This article describes a mathematical biology workshop given to secondary school teachers of the Danville area in Virginia, USA. The goal of the workshop was to enable teams of teachers with biology and mathematics expertise to incorporate lesson plans in mathematical modelling into the curriculum. The biological focus of the activities is the…

  19. Biologic data, models, and dosimetric methods for internal emitters

    International Nuclear Information System (INIS)

    The absorbed radiation dose from internal emitters has been and will remain a pivotal factor in assessing risk and therapeutic utility in selecting radiopharmaceuticals for diagnosis and treatment. Although direct measurements of absorbed dose and dose distributions in vivo have been and will continue to be made in limited situations, the measurement of the biodistribution and clearance of radiopharmaceuticals in human subjects and the use of this data is likely to remain the primary means to approach the calculation and estimation of absorbed dose from internal emitters over the next decade. Since several approximations are used in these schema to calculate dose, attention must be given to inspecting and improving the application of this dosimetric method as better techniques are developed to assay body activity and as more experience is gained in applying these schema to calculating absorbed dose. Discussion of the need for considering small scale dosimetry to calculate absorbed dose at the cellular level will be presented in this paper. Other topics include dose estimates for internal emitters, biologic data mathematical models and dosimetric methods employed. 44 refs

  20. Pharmacology and toxicology of diphenyl diselenide in several biological models

    Directory of Open Access Journals (Sweden)

    R.M. Rosa

    2007-10-01

    Full Text Available The pharmacology of synthetic organoselenium compounds indicates that they can be used as antioxidants, enzyme inhibitors, neuroprotectors, anti-tumor and anti-infectious agents, and immunomodulators. In this review, we focus on the effects of diphenyl diselenide (DPDS in various biological model organisms. DPDS possesses antioxidant activity, confirmed in several in vitro and in vivo systems, and thus has a protective effect against hepatic, renal and gastric injuries, in addition to its neuroprotective activity. The activity of the compound on the central nervous system has been studied since DPDS has lipophilic characteristics, increasing adenylyl cyclase activity and inhibiting glutamate and MK-801 binding to rat synaptic membranes. Systemic administration facilitates the formation of long-term object recognition memory in mice and has a protective effect against brain ischemia and on reserpine-induced orofacial dyskinesia in rats. On the other hand, DPDS may be toxic, mainly because of its interaction with thiol groups. In the yeast Saccharomyces cerevisiae, the molecule acts as a pro-oxidant by depleting free glutathione. Administration to mice during cadmium intoxication has the opposite effect, reducing oxidative stress in various tissues. DPDS is a potent inhibitor of d-aminolevulinate dehydratase and chronic exposure to high doses of this compound has central effects on mouse brain, as well as liver and renal toxicity. Genotoxicity of this compound has been assessed in bacteria, haploid and diploid yeast and in a tumor cell line.

  1. Modeling Reactivity to Biological Macromolecules with a Deep Multitask Network.

    Science.gov (United States)

    Hughes, Tyler B; Dang, Na Le; Miller, Grover P; Swamidass, S Joshua

    2016-08-24

    Most small-molecule drug candidates fail before entering the market, frequently because of unexpected toxicity. Often, toxicity is detected only late in drug development, because many types of toxicities, especially idiosyncratic adverse drug reactions (IADRs), are particularly hard to predict and detect. Moreover, drug-induced liver injury (DILI) is the most frequent reason drugs are withdrawn from the market and causes 50% of acute liver failure cases in the United States. A common mechanism often underlies many types of drug toxicities, including both DILI and IADRs. Drugs are bioactivated by drug-metabolizing enzymes into reactive metabolites, which then conjugate to sites in proteins or DNA to form adducts. DNA adducts are often mutagenic and may alter the reading and copying of genes and their regulatory elements, causing gene dysregulation and even triggering cancer. Similarly, protein adducts can disrupt their normal biological functions and induce harmful immune responses. Unfortunately, reactive metabolites are not reliably detected by experiments, and it is also expensive to test drug candidates for potential to form DNA or protein adducts during the early stages of drug development. In contrast, computational methods have the potential to quickly screen for covalent binding potential, thereby flagging problematic molecules and reducing the total number of necessary experiments. Here, we train a deep convolution neural network-the XenoSite reactivity model-using literature data to accurately predict both sites and probability of reactivity for molecules with glutathione, cyanide, protein, and DNA. On the site level, cross-validated predictions had area under the curve (AUC) performances of 89.8% for DNA and 94.4% for protein. Furthermore, the model separated molecules electrophilically reactive with DNA and protein from nonreactive molecules with cross-validated AUC performances of 78.7% and 79.8%, respectively. On both the site- and molecule-level, the

  2. ADAM: Analysis of Discrete Models of Biological Systems Using Computer Algebra

    CERN Document Server

    Hinkelmann, Franziska; Guang, Bonny; McNeill, Rustin; Blekherman, Grigoriy; Veliz-Cuba, Alan; Laubenbacher, Reinhard

    2010-01-01

    Motivation: Many biological systems are modeled qualitatively with discrete models, such as probabilistic Boolean networks, logical models, bounded Petri nets, and agent-based models. Simulation is a common practice for analyzing discrete models, but many systems are far too large to capture all the relevant dynamical features through simulation alone. Results: We convert discrete models into algebraic models and apply tools from computational algebra to analyze their dynamics. The key feature of biological systems that is exploited by our algorithms is their sparsity: while the number of nodes in a biological network may be quite large, each node is affected only by a small number of other nodes. In our experience with models arising in systems biology and random models, this structure leads to fast computations when using algebraic models, and thus efficient analysis. Availability: All algorithms and methods are available in our package Analysis of Dynamic Algebraic Models (ADAM), a user friendly web-interf...

  3. Calibration of a coupled biological-physical model for prediction in a coastal inlet

    Science.gov (United States)

    Guarracino, M.; Dowd, M.; Sheng, J.; Cullen, J. J.

    2011-10-01

    We describe a numerical forecast system designed for prediction of physical and biological dynamics of a coastal inlet. It is based on a coastal ocean observatory that was located at Lunenburg Bay, Nova Scotia, Canada. Biological, chemical, optical, and physical measurements were collected from instrumented moorings, weekly sampling and detailed surveys from 2002 through 2007. Here we present a framework for calibration and evaluation of an ecosystem model using data from the summer of 2007. A three-dimensional hydrodynamic model was coupled to a simple biological (Nutrients-Phytoplankton-Detritus) model; a simple model was used so results could be compared directly to observed biological and chemical variables using skill scores as a first step toward data-assimilation modeling. As a complement to this analysis, variability of model output, e.g., the nutrient limitation term, was examined to understand the modeled biological response to the simulated physical environment. Skill scores based on variances in observed and simulated time-series of biological components were also investigated. Coastal upwelling/downwelling simulated through this model has been found to increase modeled biological activity in the bay. Also model skill in reproducing the observed patterns in nutrients and phytoplankton has been increased due to the restoring conditions for biology set up at the open ocean boundaries of the bay.

  4. 4D-Var data assimilation system for a coupled physical biological model

    Indian Academy of Sciences (India)

    J M Lellouche; M Ouberdous; W Eifler

    2000-12-01

    A 3-compartment model of phytoplankton growth dynamics has been coupled with a primitive-equation circulation model to better understand and quantify physical and biological processes in the Adriatic Sea. This paper presents the development and application of a data assimilation procedure based on optimal control theory. The aim of the procedure is to identify a set of model coefficient values that ensures the best fit between data and model results by minimizing a function that measures model and data discrepancies. In this sense, twin experiments have been successfully implemented in order to have a better estimation of biological model parameters and biological initial conditions.

  5. Biological modeling of gold nanoparticle enhanced radiotherapy for proton therapy

    International Nuclear Information System (INIS)

    Gold nanoparticles (GNPs) have shown potential as a radiosensitizer for radiation therapy using photon beams. Recently, experimental studies have been carried out using proton beams showing the GNP enhanced responses in proton therapy. In this work, we established a biological model to investigate the change in survival of irradiated cells due to the radiosensitizing effect of gold nanoparticles. Results for proton, megavoltage (MV) photon and kilovoltage (kV) photon beams are compared. For each particle source, we assessed various treatment depths, GNP cellular uptakes and sizes. We showed that kilovoltage photons caused the highest enhancement due to the high interaction probability between GNPs and kV photons. The cell survival fraction can be significantly reduced for both proton and MV photon irradiations if GNPs accumulate in the cell. For instance, the sensitizer enhancement ratio (SER) is 1.33 for protons in the middle of a spread out Bragg peak for 1 µM of internalized 50 nm GNPs. If the GNPs can all be internalized into the cell nucleus, the SER for proton therapy increases from 1.33 to 1.81. The results also show that for the same mass of GNPs in the cells, one can expect the greatest sensitization by smaller GNPs, i.e. a SER of 1.33 for 1 µM of internalized 50 nm GNPs and a SER of 3.98 for the same mass of 2 nm GNPs. We concluded that if the GNPs cannot be internalized into the cytoplasm, no GNP enhancement will be observed for proton treatment. Meanwhile, proton radiotherapy can potentially be enhanced with GNPs if they can be internalized into cells, and especially the cell nucleus. (paper)

  6. Using the Unified Modelling Language (UML) to guide the systemic description of biological processes and systems.

    Science.gov (United States)

    Roux-Rouquié, Magali; Caritey, Nicolas; Gaubert, Laurent; Rosenthal-Sabroux, Camille

    2004-07-01

    One of the main issues in Systems Biology is to deal with semantic data integration. Previously, we examined the requirements for a reference conceptual model to guide semantic integration based on the systemic principles. In the present paper, we examine the usefulness of the Unified Modelling Language (UML) to describe and specify biological systems and processes. This makes unambiguous representations of biological systems, which would be suitable for translation into mathematical and computational formalisms, enabling analysis, simulation and prediction of these systems behaviours.

  7. When one model is not enough: Combining epistemic tools in systems biology

    DEFF Research Database (Denmark)

    Green, Sara

    2013-01-01

    . The conceptual repertoire of Rheinberger’s historical epistemology offers important insights for an analysis of the modelling practice. I illustrate this with a case study on network modeling in systems biology where engineering approaches are applied to the study of biological systems. I shall argue...

  8. The Role of Model Integration in Complex Systems Modelling An Example from Cancer Biology

    CERN Document Server

    Patel, Manish

    2010-01-01

    Model integration – the process by which different modelling efforts can be brought together to simulate the target system – is a core technology in the field of Systems Biology. In the work presented here model integration was addressed directly taking cancer systems as an example. An in-depth literature review was carried out to survey the model forms and types currently being utilised. This was used to formalise the main challenges that model integration poses, namely that of paradigm (the formalism on which a model is based), focus (the real-world system the model represents) and scale. A two-tier model integration strategy, including a knowledge-driven approach to address model semantics, was developed to tackle these challenges. In the first step a novel description of models at the level of behaviour, rather than the precise mathematical or computational basis of the model, is developed by distilling a set of abstract classes and properties. These can accurately describe model behaviour and hence d...

  9. Differential Equations Models in Biology, Epidemiology and Ecology

    CERN Document Server

    Martelli, Mario

    1991-01-01

    The past forty years have been the stage for the maturation of mathematical biolo~ as a scientific field. The foundations laid by the pioneers of the field during the first half of this century have been combined with advances in ap­ plied mathematics and the computational sciences to create a vibrant area of scientific research with established research journals, professional societies, deep subspecialty areas, and graduate education programs. Mathematical biology is by its very nature cross-disciplinary, and research papers appear in mathemat­ ics, biology and other scientific journals, as well as in the specialty journals devoted to mathematical and theoretical biology. Multiple author papers are common, and so are collaborations between individuals who have academic bases in different traditional departments. Those who seek to keep abreast of current trends and problems need to interact with research workers from a much broader spectrum of fields than is common in the traditional mono-culture discipline...

  10. A Transformative Model for Undergraduate Quantitative Biology Education

    Science.gov (United States)

    Usher, David C.; Driscoll, Tobin A.; Dhurjati, Prasad; Pelesko, John A.; Rossi, Louis F.; Schleiniger, Gilberto; Pusecker, Kathleen; White, Harold B.

    2010-01-01

    The "BIO2010" report recommended that students in the life sciences receive a more rigorous education in mathematics and physical sciences. The University of Delaware approached this problem by (1) developing a bio-calculus section of a standard calculus course, (2) embedding quantitative activities into existing biology courses, and (3) creating…

  11. EMBAYMENT CHARACTERISTIC TIME AND BIOLOGY VIA TIDAL PRISM MODEL

    Science.gov (United States)

    Transport time scales in water bodies are classically based on their physical and chemical aspects rather than on their ecological and biological character. The direct connection between a physical time scale and ecological effects has to be investigated in order to quantitativel...

  12. Toward University Modeling Instruction—Biology: Adapting Curricular Frameworks from Physics to Biology

    OpenAIRE

    Manthey, Seth; Brewe, Eric

    2013-01-01

    University Modeling Instruction (UMI) is an approach to curriculum and pedagogy that focuses instruction on engaging students in building, validating, and deploying scientific models. Modeling Instruction has been successfully implemented in both high school and university physics courses. Studies within the physics education research (PER) community have identified UMI's positive impacts on learning gains, equity, attitudinal shifts, and self-efficacy. While the success of this pedagogical a...

  13. Markov Chain-Like Quantum Biological Modeling of Mutations, Aging, and Evolution

    Directory of Open Access Journals (Sweden)

    Ivan B. Djordjevic

    2015-08-01

    Full Text Available Recent evidence suggests that quantum mechanics is relevant in photosynthesis, magnetoreception, enzymatic catalytic reactions, olfactory reception, photoreception, genetics, electron-transfer in proteins, and evolution; to mention few. In our recent paper published in Life, we have derived the operator-sum representation of a biological channel based on codon basekets, and determined the quantum channel model suitable for study of the quantum biological channel capacity. However, this model is essentially memoryless and it is not able to properly model the propagation of mutation errors in time, the process of aging, and evolution of genetic information through generations. To solve for these problems, we propose novel quantum mechanical models to accurately describe the process of creation spontaneous, induced, and adaptive mutations and their propagation in time. Different biological channel models with memory, proposed in this paper, include: (i Markovian classical model, (ii Markovian-like quantum model, and (iii hybrid quantum-classical model. We then apply these models in a study of aging and evolution of quantum biological channel capacity through generations. We also discuss key differences of these models with respect to a multilevel symmetric channel-based Markovian model and a Kimura model-based Markovian process. These models are quite general and applicable to many open problems in biology, not only biological channel capacity, which is the main focus of the paper. We will show that the famous quantum Master equation approach, commonly used to describe different biological processes, is just the first-order approximation of the proposed quantum Markov chain-like model, when the observation interval tends to zero. One of the important implications of this model is that the aging phenotype becomes determined by different underlying transition probabilities in both programmed and random (damage Markov chain-like models of aging, which

  14. Electrical Modeling and Impedance Analysis of Biological Cells

    Directory of Open Access Journals (Sweden)

    Gowri Sree V.

    2014-03-01

    Full Text Available It was proved that the external electric field intensity has significant effects on the biological systems. The applied electric field intensity changes the electrical behavior of the cell systems. The impact of electric field intensity on the cell systems should be studied properly to optimize the electric field treatments of biological systems. Based on the cell dimensions and its dielectric properties, an electrical equivalent circuit for an endosperm cell in rice was developed and its total impedance and capacitance were verified with measurement results. The variations of impedance and conductance with respect to applied impulse voltage at different frequencies were plotted. This impedance analysis method can be used to determine the optimum voltage level for electric field treatment and also to determine the cell rupture due to electric field applications.

  15. Diploid biological evolution models with general smooth fitness landscapes and recombination.

    Science.gov (United States)

    Saakian, David B; Kirakosyan, Zara; Hu, Chin-Kun

    2008-06-01

    Using a Hamilton-Jacobi equation approach, we obtain analytic equations for steady-state population distributions and mean fitness functions for Crow-Kimura and Eigen-type diploid biological evolution models with general smooth hypergeometric fitness landscapes. Our numerical solutions of diploid biological evolution models confirm the analytic equations obtained. We also study the parallel diploid model for the simple case of recombination and calculate the variance of distribution, which is consistent with numerical results. PMID:18643300

  16. Near-optimal experimental design for model selection in systems biology

    OpenAIRE

    Busetto, Alberto Giovanni; Hauser, Alain; Krummenacher, Gabriel; Sunnåker, Mikael; Dimopoulos, Sotiris; Ong, Cheng Soon; Stelling, Jörg; Buhmann, Joachim M.

    2013-01-01

    MOTIVATION:  Biological systems are understood through iterations of modeling and experimentation. Not all experiments, however, are equally valuable for predictive modeling. This study introduces an efficient method for experimental design aimed at selecting dynamical models from data. Motivated by biological applications, the method enables the design of crucial experiments: it determines a highly informative selection of measurement readouts and time points. RESULTS:  We demonstrate fo...

  17. Optimal Information Retrieval Model for Molecular Biology Information

    OpenAIRE

    Paulsen, Jon Rune

    2007-01-01

    Search engines for biological information are not a new technology. Since the 1960s computers have emerged as an important tool for biologists. Online Mendelian Inheritance in Man (OMIM) is a comprehensive catalogue containing approximately 14 000 records with information about human genes and genetic disorders. An approach called Latent Semantic Indexing (LSI) was introduced in 1990 that is based on Singular Value Decomposition (SVD). This approach improved the information retrieval and red...

  18. New Approach to Modeling Symbiosis in Biological and Social Systems

    OpenAIRE

    V. I. YUKALOV; Yukalova, E. P.; Sornette, D.

    2014-01-01

    We suggest a novel approach to treating symbiotic relations between biological species or social entities. The main idea is the characterisation of symbiotic relations of coexisting species through their mutual influence on their respective carrying capacities, taking into account that this influence can be quite strong and requires a nonlinear functional framework. We distinguish three variants of mutual influence, representing the main types of relations between species: (i) passive symbios...

  19. How to Build a Course in Mathematical-Biological Modeling: Content and Processes for Knowledge and Skill

    Science.gov (United States)

    Hoskinson, Anne-Marie

    2010-01-01

    Biological problems in the twenty-first century are complex and require mathematical insight, often resulting in mathematical models of biological systems. Building mathematical-biological models requires cooperation among biologists and mathematicians, and mastery of building models. A new course in mathematical modeling presented the opportunity…

  20. Multilevel systems biology modeling characterized the atheroprotective efficiencies of modified dairy fats in a hamster model.

    Science.gov (United States)

    Martin, Jean-Charles; Berton, Amélie; Ginies, Christian; Bott, Romain; Scheercousse, Pierre; Saddi, Alessandra; Gripois, Daniel; Landrier, Jean-François; Dalemans, Daniel; Alessi, Marie-Christine; Delplanque, Bernadette

    2015-09-01

    We assessed the atheroprotective efficiency of modified dairy fats in hyperlipidemic hamsters. A systems biology approach was implemented to reveal and quantify the dietary fat-related components of the disease. Three modified dairy fats (40% energy) were prepared from regular butter by mixing with a plant oil mixture, by removing cholesterol alone, or by removing cholesterol in combination with reducing saturated fatty acids. A plant oil mixture and a regular butter were used as control diets. The atherosclerosis severity (aortic cholesteryl-ester level) was higher in the regular butter-fed hamsters than in the other four groups (P < 0.05). Eighty-seven of the 1,666 variables measured from multiplatform analysis were found to be strongly associated with the disease. When aggregated into 10 biological clusters combined into a multivariate predictive equation, these 87 variables explained 81% of the disease variability. The biological cluster "regulation of lipid transport and metabolism" appeared central to atherogenic development relative to diets. The "vitamin E metabolism" cluster was the main driver of atheroprotection with the best performing transformed dairy fat. Under conditions that promote atherosclerosis, the impact of dairy fats on atherogenesis could be greatly ameliorated by technological modifications. Our modeling approach allowed for identifying and quantifying the contribution of complex factors to atherogenic development in each dietary setup. PMID:26071539

  1. Not just a theory--the utility of mathematical models in evolutionary biology.

    Directory of Open Access Journals (Sweden)

    Maria R Servedio

    2014-12-01

    Full Text Available Progress in science often begins with verbal hypotheses meant to explain why certain biological phenomena exist. An important purpose of mathematical models in evolutionary research, as in many other fields, is to act as “proof-of-concept” tests of the logic in verbal explanations, paralleling the way in which empirical data are used to test hypotheses. Because not all subfields of biology use mathematics for this purpose, misunderstandings of the function of proof-of-concept modeling are common. In the hope of facilitating communication, we discuss the role of proof-of-concept modeling in evolutionary biology.

  2. Neuropsychiatric Model of Biological and Psychological Processes in the Remission of Delusions and Auditory Hallucinations

    NARCIS (Netherlands)

    Gaag, van der M.

    2006-01-01

    This selective review combines cognitive models and biological models of psychosis into a tentative integrated neuropsychiatric model. The aim of the model is to understand better, how pharmacotherapy and cognitive-behavior therapy come forward as partners in the treatment of psychosis and play comp

  3. Systems Modelling and the Development of Coherent Understanding of Cell Biology

    Science.gov (United States)

    Verhoeff, Roald P.; Waarlo, Arend Jan; Boersma, Kerst Th.

    2008-01-01

    This article reports on educational design research concerning a learning and teaching strategy for cell biology in upper-secondary education introducing "systems modelling" as a key competence. The strategy consists of four modelling phases in which students subsequently develop models of free-living cells, a general two-dimensional model of…

  4. Calibration of model constants in a biological reaction model for sewage treatment plants.

    Science.gov (United States)

    Amano, Ken; Kageyama, Kohji; Watanabe, Shoji; Takemoto, Takeshi

    2002-02-01

    Various biological reaction models have been proposed which estimate concentrations of soluble and insoluble components in effluent of sewage treatment plants. These models should be effective to develop a better operation system and plant design, but their formulas consist of nonlinear equations, and there are many model constants, which are not easy to calibrate. A technique has been proposed to decide the model constants by precise experiments, but it is not practical for design engineers or process operators to perform these experiments regularly. Other approaches which calibrate the model constants by mathematical techniques should be used. In this paper, the optimal regulator method of modern control theory is applied as a mathematical technique to calibrate the model constants. This method is applied in a small sewage treatment testing facility. Calibration of the model constants is examined to decrease the deviations between calculated and measured concentrations. Results show that calculated values of component concentrations approach measured values and the method is useful for actual plants. PMID:11848341

  5. Digital learning material for experimental design and model building in molecular biology

    NARCIS (Netherlands)

    Aegerter-Wilmsen, T.

    2005-01-01

    Designing experimental approaches is a major cognitive skill in molecular biology research, and building models, including quantitative ones, is a cognitive skill which is rapidly gaining importance. Since molecular biology education at university level is aimed at educating future researchers, we c

  6. JSBML 1.0: providing a smorgasbord of options to encode systems biology models

    DEFF Research Database (Denmark)

    Rodriguez, Nicolas; Thomas, Alex; Watanabe, Leandro;

    2015-01-01

    JSBML, the official pure Java programming library for the Systems Biology Markup Language (SBML) format, has evolved with the advent of different modeling formalisms in systems biology and their ability to be exchanged and represented via extensions of SBML. JSBML has matured into a major, active...

  7. An Introduction to Biological Modeling Using Coin Flips to Predict the Outcome of a Diffusion Activity

    Science.gov (United States)

    Butcher, Greg Q.; Rodriguez, Juan; Chirhart, Scott; Messina, Troy C.

    2016-01-01

    In order to increase students' awareness for and comfort with mathematical modeling of biological processes, and increase their understanding of diffusion, the following lab was developed for use in 100-level, majors/non-majors biology and neuroscience courses. The activity begins with generation of a data set that uses coin-flips to replicate…

  8. Development of a General Modeling Framework for Investigating Complex Interactions among Biological and Physical Ecosystem Dynamics

    Science.gov (United States)

    Bennett, C.; Poole, G. C.; Kimball, J. S.; Stanford, J. A.; O'Daniel, S. J.; Mertes, L. A.

    2005-05-01

    Historically, physical scientists have developed models with highly accurate governing equations, while biologists have excelled at abstraction (the strategic simplification of system complexity). These different modeling paradigms yield biological (e.g. food web) and physical (e.g. hydrologic) models that can be difficult to integrate. Complex biological dynamics may be impossible to represent with governing equations. Conversely, physical processes may be oversimplified in biological models. Using agent-based modeling, a technique applied widely in social sciences and economics, we are developing a general modeling system to integrate accurate representations of physical dynamics such as water and heat flux with abstracted biological processes such as nutrient transformations. The modeling system represents an ecosystem as a complex integrated network of intelligent physical and biological "agents" that store, transform, and trade ecosystem resources (e.g., water, heat, nutrients, carbon) using equations that describe either abstracted concepts and/or physical laws. The modular design of the system allows resource submodels to be developed independently and installed into the simulation architecture. The modeling system provides a useful heuristic tool to support integrated physical and biological research topics, such as the influence of hydrologic dynamics and spatio-temporal physical heterogeneity on trophic (food web) dynamics and/or nutrient cycling.

  9. Unified Modeling of Filtration and Expression of Biological Sludge

    DEFF Research Database (Denmark)

    Sørensen, Peter Borgen

    Dewatering is a costly operation in both industry, e.g . when dewatering drilling mud, harbor sludge or biomass, and at municipal wastewater treatment plants when dewatering biological sludges. In practice, design and operation of dewatering equipment are mostly based on empirical knowledge, and ......, and normally results are not satisfactory, e.g. in terms of cake solids or capacity of equipment. Thus, there is a need for theoretical and technical developments to improve dewatering performance, based on better scientific knowledge and well defined principles and rules....

  10. Caenorhabditis elegans - A model system for space biology studies

    Science.gov (United States)

    Johnson, Thomas E.; Nelson, Gregory A.

    1991-01-01

    The utility of the nematode Caenorhabditis elegans in studies spanning aspects of development, aging, and radiobiology is reviewed. These topics are interrelated via cellular and DNA repair processes especially in the context of oxidative stress and free-radical metabolism. The relevance of these research topics to problems in space biology is discussed and properties of the space environment are outlined. Exposure to the space-flight environment can induce rapid changes in living systems that are similar to changes occurring during aging; manipulation of these environmental parameters may represent an experimental strategy for studies of development and senescence. The current and future opportunities for such space-flight experimentation are presented.

  11. Rigid Biological Systems as Models for Synthetic Composites

    Science.gov (United States)

    Mayer, George

    2005-11-01

    Advances that have been made in understanding the mechanisms underlying the mechanical behavior of a number of biological materials (namely mollusk shells and sponge spicules) are discussed here. Attempts at biomimicry of the structure of a nacreous layer of a mollusk shell have shown reasonable success. However, they have revealed additional issues that must be addressed if new synthetic composite materials that are based on natural systems are to be constructed. Some of the important advantages and limitations of copying from nature are also described here.

  12. Fluid models and simulations of biological cell phenomena

    Science.gov (United States)

    Greenspan, H. P.

    1982-01-01

    The dynamics of coated droplets are examined within the context of biofluids. Of specific interest is the manner in which the shape of a droplet, the motion within it as well as that of aggregates of droplets can be controlled by the modulation of surface properties and the extent to which such fluid phenomena are an intrinsic part of cellular processes. From the standpoint of biology, an objective is to elucidate some of the general dynamical features that affect the disposition of an entire cell, cell colonies and tissues. Conventionally averaged field variables of continuum mechanics are used to describe the overall global effects which result from the myriad of small scale molecular interactions. An attempt is made to establish cause and effect relationships from correct dynamical laws of motion rather than by what may have been unnecessary invocation of metabolic or life processes. Several topics are discussed where there are strong analogies droplets and cells including: encapsulated droplets/cell membranes; droplet shape/cell shape; adhesion and spread of a droplet/cell motility and adhesion; and oams and multiphase flows/cell aggregates and tissues. Evidence is presented to show that certain concepts of continuum theory such as suface tension, surface free energy, contact angle, bending moments, etc. are relevant and applicable to the study of cell biology.

  13. Fuzzy Stochastic Petri Nets for Modeling Biological Systems with Uncertain Kinetic Parameters.

    Science.gov (United States)

    Liu, Fei; Heiner, Monika; Yang, Ming

    2016-01-01

    Stochastic Petri nets (SPNs) have been widely used to model randomness which is an inherent feature of biological systems. However, for many biological systems, some kinetic parameters may be uncertain due to incomplete, vague or missing kinetic data (often called fuzzy uncertainty), or naturally vary, e.g., between different individuals, experimental conditions, etc. (often called variability), which has prevented a wider application of SPNs that require accurate parameters. Considering the strength of fuzzy sets to deal with uncertain information, we apply a specific type of stochastic Petri nets, fuzzy stochastic Petri nets (FSPNs), to model and analyze biological systems with uncertain kinetic parameters. FSPNs combine SPNs and fuzzy sets, thereby taking into account both randomness and fuzziness of biological systems. For a biological system, SPNs model the randomness, while fuzzy sets model kinetic parameters with fuzzy uncertainty or variability by associating each parameter with a fuzzy number instead of a crisp real value. We introduce a simulation-based analysis method for FSPNs to explore the uncertainties of outputs resulting from the uncertainties associated with input parameters, which works equally well for bounded and unbounded models. We illustrate our approach using a yeast polarization model having an infinite state space, which shows the appropriateness of FSPNs in combination with simulation-based analysis for modeling and analyzing biological systems with uncertain information. PMID:26910830

  14. Anopheles gambiae mosquito isolated neurons : a new biological model for optimizing insecticide/repellent efficacy

    OpenAIRE

    Lavialle-Defaix, C.; Apaire-Marchais, V; Legros, C.; Pennetier, Cédric; Mohamed, A; P. Licznar; Corbel, Vincent; Lapied, B

    2011-01-01

    To understand better the mode of action of insecticides and repellents used in vector-borne diseases control, we developed a new biological model based on mosquito neurons isolated from adults Anopheles gambiae heads. This cellular model is well adapted to multidisciplinary approaches: electrophysiology, pharmacology, molecular biology and biochemical assays. Using RT-PCR, we demonstrated that isolated neurons express the nicotinic acetylcholine receptor subunit alpha 1 (Ag alpha 1 nAchR), tw...

  15. Non-mammalian models in behavioral neuroscience: consequences for biological psychiatry

    OpenAIRE

    Caio eMaximino; Rhayra Xavier do Carmo Silva; Suéllen de Nazaré dos Santos da Silva; Laís do Socorro dos Santos Rodrigues; Hellen eBarbosa; Tayana Silva de Carvalho; Luana Ketlen Reis Leão; Monica Gomes Lima; Karen Renata Matos Oliveira; Anderson Manoel Herculano

    2015-01-01

    Current models in biological psychiatry focus on a handful of model species, and the majority of work relies on data generated in rodents. However, in the same sense that a comparative approach to neuroanatomy allows for the idenfication of patterns of brain organization, the inclusion of other species and an adoption of comparative viewpoints in behavioral neuroscience could also lead to increases in knowledge relevant to biological psychiatry. Specifically, this approach could help to ident...

  16. How computational models can help unlock biological systems.

    Science.gov (United States)

    Brodland, G Wayne

    2015-12-01

    With computation models playing an ever increasing role in the advancement of science, it is important that researchers understand what it means to model something; recognize the implications of the conceptual, mathematical and algorithmic steps of model construction; and comprehend what models can and cannot do. Here, we use examples to show that models can serve a wide variety of roles, including hypothesis testing, generating new insights, deepening understanding, suggesting and interpreting experiments, tracing chains of causation, doing sensitivity analyses, integrating knowledge, and inspiring new approaches. We show that models can bring together information of different kinds and do so across a range of length scales, as they do in multi-scale, multi-faceted embryogenesis models, some of which connect gene expression, the cytoskeleton, cell properties, tissue mechanics, morphogenetic movements and phenotypes. Models cannot replace experiments nor can they prove that particular mechanisms are at work in a given situation. But they can demonstrate whether or not a proposed mechanism is sufficient to produce an observed phenomenon. Although the examples in this article are taken primarily from the field of embryo mechanics, most of the arguments and discussion are applicable to any form of computational modelling.

  17. Biological Effectiveness and Application of Heavy Ions in Radiation Therapy Described by a Physical and Biological Model

    DEFF Research Database (Denmark)

    Olsen, Kjeld J.; Hansen, Johnny W.

    A description is given of the physical basis for applying track structure theory in the determination of the effectiveness of heavy-ion irradiation of single- and multi-hit target systems. It will be shown that for applying the theory to biological systems the effectiveness of heavy-ion irradiation...... is inadequately described by an RBE-factor, whereas the complete formulation of the probability of survival must be used, as survival depends on both radiation quality and dose. The theoretical model of track structure can be used in dose-effect calculations for neutron-, high-LET, and low-LET radiation applied...

  18. Biologically inspired optics: analog semiconductor model of the beetle exoskeleton

    Science.gov (United States)

    Buhl, Kaia; Roth, Zachary; Srinivasan, Pradeep; Rumpf, Raymond; Johnson, Eric

    2008-08-01

    Evolution in nature has produced through adaptation a wide variety of distinctive optical structures in many life forms. For example, pigment differs greatly from the observed color of most beetles because their exoskeletons contain multilayer coatings. The green beetle is disguised in a surrounding leaf by having a comparable reflection spectrum as the leaves. The Manuka and June beetle have a concave structure where light incident at any angle on the concave structures produce matching reflection spectra. In this work, semiconductor processing methods were used to duplicate the structure of the beetle exoskeleton. This was achieved by combining analog lithography with a multilayer deposition process. The artificial exoskeleton, 3D concave multilayer structure, demonstrates a wide field of view with a unique spectral response. Studying and replicating these biologically inspired nanostructures may lead to new knowledge for fabrication and design of new and novel nano-photonic devices, as well as provide valuable insight to how such phenomenon is exploited.

  19. Modelling, abstraction, and computation in systems biology: A view from computer science.

    Science.gov (United States)

    Melham, Tom

    2013-04-01

    Systems biology is centrally engaged with computational modelling across multiple scales and at many levels of abstraction. Formal modelling, precise and formalised abstraction relationships, and computation also lie at the heart of computer science--and over the past decade a growing number of computer scientists have been bringing their discipline's core intellectual and computational tools to bear on biology in fascinating new ways. This paper explores some of the apparent points of contact between the two fields, in the context of a multi-disciplinary discussion on conceptual foundations of systems biology.

  20. ezBioNet: A modeling and simulation system for analyzing biological reaction networks

    Science.gov (United States)

    Yu, Seok Jong; Tung, Thai Quang; Park, Junho; Lim, Jongtae; Yoo, Jaesoo

    2012-10-01

    To achieve robustness against living environments, a living organism is composed of complicated regulatory mechanisms ranging from gene regulations to signal transduction. If such life phenomena are to be understand, an integrated analysis tool that should have modeling and simulation functions for biological reactions, as well as new experimental methods for measuring biological phenomena, is fundamentally required. We have designed and implemented modeling and simulation software (ezBioNet) for analyzing biological reaction networks. The software can simultaneously perform an integrated modeling of various responses occurring in cells, ranging from gene expressions to signaling processes. To support massive analysis of biological networks, we have constructed a server-side simulation system (VCellSim) that can perform ordinary differential equations (ODE) analysis, sensitivity analysis, and parameter estimates. ezBioNet integrates the BioModel database by connecting the european bioinformatics institute (EBI) servers through Web services APIs and supports the handling of systems biology markup language (SBML) files. In addition, we employed eclipse RCP (rich client platform) which is a powerful modularity framework allowing various functional expansions. ezBioNet is intended to be an easy-to-use modeling tool, as well as a simulation system, to understand the control mechanism by monitoring the change of each component in a biological network. A researcher may perform the kinetic modeling and execute the simulation. The simulation result can be managed and visualized on ezBioNet, which is freely available at http://ezbionet.cbnu.ac.kr.

  1. Acclimation, adaptation, traits and trade-offs in plankton functional type models: reconciling terminology for biology and modelling

    DEFF Research Database (Denmark)

    Flynn, Kevin J.; St. John, Michael; Raven, John A.;

    2015-01-01

    ideally only be considered for describing intra-generational interactions; in applications between generations, and certainly between unrelated species, such concepts should be avoided. We suggest that systems biology approaches, through to complex adaptive/acclimative systems modelling, with explicit...

  2. Asymptotic Fitness Distribution in the Bak-Sneppen Model of Biological Evolution with Four Species

    Science.gov (United States)

    Schlemm, Eckhard

    2012-08-01

    We suggest a new method to compute the asymptotic fitness distribution in the Bak-Sneppen model of biological evolution. As applications we derive the full asymptotic distribution in the four-species model, and give an explicit linear recurrence relation for a set of coefficients determining the asymptotic distribution in the five-species model.

  3. Asymptotic fitness distribution in the Bak-Sneppen model of biological evolution with four species

    OpenAIRE

    Schlemm, Eckhard

    2012-01-01

    We suggest a new method to compute the asymptotic fitness distribution in the Bak-Sneppen model of biological evolution. As applications we derive the full asymptotic distribution in the four-species model, and give an explicit linear recurrence relation for a set of coefficients determining the asymptotic distribution in the five-species model.

  4. Understanding a Basic Biological Process: Expert and Novice Models of Science.

    Science.gov (United States)

    Kindfield, A. C. H.

    1994-01-01

    Reports on the meiosis models utilized by five individuals at each of three levels of expertise in genetics as each reasoned about this process in an individual interview setting. Results revealed a set of biologically correct features common to all individuals' models as well as a variety of model flaws (i.e., meiosis misunderstandings) which are…

  5. Modelling of environmental impacts from biological treatment of organic municipal waste in EASEWASTE

    DEFF Research Database (Denmark)

    Boldrin, Alessio; Neidel, Trine Lund; Damgaard, Anders;

    2011-01-01

    The waste-LCA model EASEWASTE quantifies potential environmental effects from biological treatment of organic waste, based on mass and energy flows, emissions to air, water, soil and groundwater as well as effects from upstream and downstream processes. Default technologies for composting......, anaerobic digestion and combinations hereof are available in the model, but the user can change all key parameters in the biological treatment module so that specific local plants and processes can be modelled. EASEWASTE is one of the newest waste LCA models and the biological treatment module was built...... partly on features of earlier waste-LCA models, but offers additional facilities, more flexibility, transparency and user-friendliness. The paper presents the main features of the module and provides some examples illustrating the capability of the model in environmentally assessing and discriminating...

  6. A Novel Method to Verify Multilevel Computational Models of Biological Systems Using Multiscale Spatio-Temporal Meta Model Checking.

    Science.gov (United States)

    Pârvu, Ovidiu; Gilbert, David

    2016-01-01

    Insights gained from multilevel computational models of biological systems can be translated into real-life applications only if the model correctness has been verified first. One of the most frequently employed in silico techniques for computational model verification is model checking. Traditional model checking approaches only consider the evolution of numeric values, such as concentrations, over time and are appropriate for computational models of small scale systems (e.g. intracellular networks). However for gaining a systems level understanding of how biological organisms function it is essential to consider more complex large scale biological systems (e.g. organs). Verifying computational models of such systems requires capturing both how numeric values and properties of (emergent) spatial structures (e.g. area of multicellular population) change over time and across multiple levels of organization, which are not considered by existing model checking approaches. To address this limitation we have developed a novel approximate probabilistic multiscale spatio-temporal meta model checking methodology for verifying multilevel computational models relative to specifications describing the desired/expected system behaviour. The methodology is generic and supports computational models encoded using various high-level modelling formalisms because it is defined relative to time series data and not the models used to generate it. In addition, the methodology can be automatically adapted to case study specific types of spatial structures and properties using the spatio-temporal meta model checking concept. To automate the computational model verification process we have implemented the model checking approach in the software tool Mule (http://mule.modelchecking.org). Its applicability is illustrated against four systems biology computational models previously published in the literature encoding the rat cardiovascular system dynamics, the uterine contractions of labour

  7. A Novel Method to Verify Multilevel Computational Models of Biological Systems Using Multiscale Spatio-Temporal Meta Model Checking.

    Science.gov (United States)

    Pârvu, Ovidiu; Gilbert, David

    2016-01-01

    Insights gained from multilevel computational models of biological systems can be translated into real-life applications only if the model correctness has been verified first. One of the most frequently employed in silico techniques for computational model verification is model checking. Traditional model checking approaches only consider the evolution of numeric values, such as concentrations, over time and are appropriate for computational models of small scale systems (e.g. intracellular networks). However for gaining a systems level understanding of how biological organisms function it is essential to consider more complex large scale biological systems (e.g. organs). Verifying computational models of such systems requires capturing both how numeric values and properties of (emergent) spatial structures (e.g. area of multicellular population) change over time and across multiple levels of organization, which are not considered by existing model checking approaches. To address this limitation we have developed a novel approximate probabilistic multiscale spatio-temporal meta model checking methodology for verifying multilevel computational models relative to specifications describing the desired/expected system behaviour. The methodology is generic and supports computational models encoded using various high-level modelling formalisms because it is defined relative to time series data and not the models used to generate it. In addition, the methodology can be automatically adapted to case study specific types of spatial structures and properties using the spatio-temporal meta model checking concept. To automate the computational model verification process we have implemented the model checking approach in the software tool Mule (http://mule.modelchecking.org). Its applicability is illustrated against four systems biology computational models previously published in the literature encoding the rat cardiovascular system dynamics, the uterine contractions of labour

  8. A data integration approach for cell cycle analysis oriented to model simulation in systems biology

    Directory of Open Access Journals (Sweden)

    Mosca Ettore

    2007-08-01

    Full Text Available Abstract Background The cell cycle is one of the biological processes most frequently investigated in systems biology studies and it involves the knowledge of a large number of genes and networks of protein interactions. A deep knowledge of the molecular aspect of this biological process can contribute to making cancer research more accurate and innovative. In this context the mathematical modelling of the cell cycle has a relevant role to quantify the behaviour of each component of the systems. The mathematical modelling of a biological process such as the cell cycle allows a systemic description that helps to highlight some features such as emergent properties which could be hidden when the analysis is performed only from a reductionism point of view. Moreover, in modelling complex systems, a complete annotation of all the components is equally important to understand the interaction mechanism inside the network: for this reason data integration of the model components has high relevance in systems biology studies. Description In this work, we present a resource, the Cell Cycle Database, intended to support systems biology analysis on the Cell Cycle process, based on two organisms, yeast and mammalian. The database integrates information about genes and proteins involved in the cell cycle process, stores complete models of the interaction networks and allows the mathematical simulation over time of the quantitative behaviour of each component. To accomplish this task, we developed, a web interface for browsing information related to cell cycle genes, proteins and mathematical models. In this framework, we have implemented a pipeline which allows users to deal with the mathematical part of the models, in order to solve, using different variables, the ordinary differential equation systems that describe the biological process. Conclusion This integrated system is freely available in order to support systems biology research on the cell cycle and

  9. Dynamical model for biological functions of DNA molecules

    Institute of Scientific and Technical Information of China (English)

    PANGXiao-fengI; YANGYao

    2004-01-01

    We proposed a dynamic model of DNA to study its nonlinear excitation and duplication and transcription in the basis of molecular structure and changes of conformation of DNA under influence of bioenergy.

  10. Assessing Understanding of Biological Processes: Elucidating Students' Models of Meiosis.

    Science.gov (United States)

    Kindfield, Ann C.

    1994-01-01

    Presents a meiosis reasoning problem that provides direct access to students' current models of chromosomes and meiosis. Also included in the article are tips for classroom implementation and a summary of the solution evaluation. (ZWH)

  11. Benchmarking Biological Nutrient Removal in Wastewater Treatment Plants:Influence of Mathematical Model Assumptions

    OpenAIRE

    Flores-Alsina, Xavier; Gernaey, Krist; Jeppsson, Ulf

    2011-01-01

    This paper examines the effect of different model assumptions when describing biological nutrient removal (BNR) by the activated sludge models (ASM) 1, 2d & 3. The performance of a nitrogen removal (WWTP1) and a combined nitrogen and phosphorus removal (WWTP2) benchmark wastewater treatment plant was compared for a series of model assumptions. Three different model approaches describing BNR are considered. In the reference case, the original model implementations are used to simulate WWTP...

  12. How to Build a Course in Mathematical–Biological Modeling: Content and Processes for Knowledge and Skill

    OpenAIRE

    Hoskinson, Anne-Marie

    2010-01-01

    Biological problems in the twenty-first century are complex and require mathematical insight, often resulting in mathematical models of biological systems. Building mathematical–biological models requires cooperation among biologists and mathematicians, and mastery of building models. A new course in mathematical modeling presented the opportunity to build both content and process learning of mathematical models, the modeling process, and the cooperative process. There was little guidance fro...

  13. Biology of Obesity: Lessons from Animal Models of Obesity

    OpenAIRE

    Keizo Kanasaki; Daisuke Koya

    2011-01-01

    Obesity is an epidemic problem in the world and is associated with several health problems, including diabetes, cardiovascular disease, respiratory failure, muscle weakness, and cancer. The precise molecular mechanisms by which obesity induces these health problems are not yet clear. To better understand the pathomechanisms of human disease, good animal models are essential. In this paper, we will analyze animal models of obesity and their use in the research of obesity-associated human he...

  14. A new shoulder model with a biologically inspired glenohumeral joint.

    Science.gov (United States)

    Quental, C; Folgado, J; Ambrósio, J; Monteiro, J

    2016-09-01

    Kinematically unconstrained biomechanical models of the glenohumeral (GH) joint are needed to study the GH joint function, especially the mechanisms of joint stability. The purpose of this study is to develop a large-scale multibody model of the upper limb that simulates the 6 degrees of freedom (DOF) of the GH joint and to propose a novel inverse dynamics procedure that allows the evaluation of not only the muscle and joint reaction forces of the upper limb but also the GH joint translations. The biomechanical model developed is composed of 7 rigid bodies, constrained by 6 anatomical joints, and acted upon by 21 muscles. The GH joint is described as a spherical joint with clearance. Assuming that the GH joint translates according to the muscle load distribution, the redundant muscle load sharing problem is formulated considering as design variables the 3 translational coordinates associated with the GH joint translations, the joint reaction forces associated with the remaining kinematic constraints, and the muscle activations. For the abduction motion in the frontal plane analysed, the muscle and joint reaction forces estimated by the new biomechanical model proposed are similar to those estimated by a model in which the GH joint is modeled as an ideal spherical joint. Even though this result supports the assumption of an ideal GH joint to study the muscle load sharing problem, only a 6 DOF model of the GH joint, as the one proposed here, provides information regarding the joint translations. In this study, the biomechanical model developed predicts an initial upward and posterior migration of the humeral head, followed by an inferior and anterior movement, which is in good agreement with the literature. PMID:27381499

  15. Biology of Obesity: Lessons from Animal Models of Obesity

    Directory of Open Access Journals (Sweden)

    Keizo Kanasaki

    2011-01-01

    problems, including diabetes, cardiovascular disease, respiratory failure, muscle weakness, and cancer. The precise molecular mechanisms by which obesity induces these health problems are not yet clear. To better understand the pathomechanisms of human disease, good animal models are essential. In this paper, we will analyze animal models of obesity and their use in the research of obesity-associated human health conditions and diseases such as diabetes, cancer, and obstructive sleep apnea syndrome.

  16. On the modeling of internal parameters in hyperelastic biological materials

    CERN Document Server

    Giantesio, Giulia

    2016-01-01

    This paper concerns the behavior of hyperelastic energies depending on an internal parameter. First, the situation in which the internal parameter is a function of the gradient of the deformation is presented. Second, two models where the parameter describes the activation of skeletal muscle tissue are analyzed. In those models, the activation parameter depends on the strain and it is important to consider the derivative of the parameter with respect to the strain in order to capture the proper behavior of the stress.

  17. Kinetic limits for pair-interaction driven master equations and biological swarm models

    CERN Document Server

    Carlen, Eric; Wennberg, Bernt

    2011-01-01

    We consider a class of stochastic processes modeling binary interactions in an N-particle system. Examples of such systems can be found in the modeling of biological swarms. They lead to the definition of a class of master equations that we call pair interaction driven master equations. We prove a propagation of chaos result for this class of master equations which generalizes Mark Kac's well know result for the Kac model in kinetic theory. We use this result to study kinetic limits for two biological swarm models. We show that propagation of chaos may be lost at large times and we exhibit an example where the invariant density is not chaotic.

  18. Dynamic statistical models of biological cognition: insights from communications theory

    Science.gov (United States)

    Wallace, Rodrick

    2014-10-01

    Maturana's cognitive perspective on the living state, Dretske's insight on how information theory constrains cognition, the Atlan/Cohen cognitive paradigm, and models of intelligence without representation, permit construction of a spectrum of dynamic necessary conditions statistical models of signal transduction, regulation, and metabolism at and across the many scales and levels of organisation of an organism and its context. Nonequilibrium critical phenomena analogous to physical phase transitions, driven by crosstalk, will be ubiquitous, representing not only signal switching, but the recruitment of underlying cognitive modules into tunable dynamic coalitions that address changing patterns of need and opportunity at all scales and levels of organisation. The models proposed here, while certainly providing much conceptual insight, should be most useful in the analysis of empirical data, much as are fitted regression equations.

  19. Mathematical models in cell biology and cancer chemotherapy

    CERN Document Server

    Eisen, Martin

    1979-01-01

    The purpose of this book is to show how mathematics can be applied to improve cancer chemotherapy. Unfortunately, most drugs used in treating cancer kill both normal and abnormal cells. However, more cancer cells than normal cells can be destroyed by the drug because tumor cells usually exhibit different growth kinetics than normal cells. To capitalize on this last fact, cell kinetics must be studied by formulating mathematical models of normal and abnormal cell growth. These models allow the therapeutic and harmful effects of cancer drugs to be simulated quantitatively. The combined cell and drug models can be used to study the effects of different methods of administering drugs. The least harmful method of drug administration, according to a given criterion, can be found by applying optimal control theory. The prerequisites for reading this book are an elementary knowledge of ordinary differential equations, probability, statistics, and linear algebra. In order to make this book self-contained, a chapter on...

  20. Von Neumann's growth model: Statistical mechanics and biological applications

    Science.gov (United States)

    De Martino, A.; Marinari, E.; Romualdi, A.

    2012-09-01

    We review recent work on the statistical mechanics of Von Neumann's growth model and discuss its application to cellular metabolic networks. In this context, we present a detailed analysis of the physiological scenario underlying optimality à la Von Neumann in the metabolism of the bacterium E. coli, showing that optimal solutions are characterized by a considerable microscopic flexibility accompanied by a robust emergent picture for the key physiological functions. This suggests that the ideas behind optimal economic growth in Von Neumann's model can be helpful in uncovering functional organization principles of cell energetics.

  1. Study on optimization of parameters in a biological model

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    According to the data observed in a China- Japan Joint Investigation, the parameters of an ecosystem dynamics model (Qiao et al., 2000) were optimized. The values of eighteen parameters for the model were obtained, with nutrient haft saturation constant, Kn = 1.4 μmol/dm3, Kp = 0.129 μmol/dm3 and Ks= 1.16μmol/dm3 for the diatom and Kn=0.345μmol/dm3, Kp=0.113 μmol/dm3 for the flagellate. Three proposals to set up a function for this multiple objective problem were discussed in detail.

  2. Off-Angle Iris Correction using a Biological Model

    Energy Technology Data Exchange (ETDEWEB)

    Thompson, Joseph T [ORNL; Santos-Villalobos, Hector J [ORNL; Karakaya, Mahmut [ORNL; Barstow, Del R [ORNL; Bolme, David S [ORNL; Boehnen, Chris Bensing [ORNL

    2013-01-01

    This work implements an eye model to simulate corneal refraction effects. Using this model, ray tracing is performed to calculate transforms to remove refractive effects in off-angle iris images when reprojected to a frontal view. The correction process is used as a preprocessing step for off-angle iris images for input to a commercial matcher. With this method, a match score distribution mean improvement of 11.65% for 30 degree images, 44.94% for 40 degree images, and 146.1% improvement for 50 degree images is observed versus match score distributions with unmodi ed images.

  3. Off-Angle Iris Correction using a Biological Model

    Energy Technology Data Exchange (ETDEWEB)

    Santos-Villalobos, Hector J [ORNL; Karakaya, Mahmut [ORNL; Barstow, Del R [ORNL; Boehnen, Chris Bensing [ORNL

    2013-01-01

    This work implements an eye model to simulate corneal refraction effects. Using this model, ray tracing is performed to calculate transforms to remove refractive effects in off-angle iris images when reprojected to a frontal view. The correction process is used as a preprocessing step for off-angle iris images for input to a commercial matcher. With this method, a match score distribution mean improvement of 11.65% for 30 degree images, 44.94% for 40 degree images, and 146.1% improvement for 50 degree images is observed versus match score distributions with unmodified images.

  4. A biological hierarchical model based underwater moving object detection.

    Science.gov (United States)

    Shen, Jie; Fan, Tanghuai; Tang, Min; Zhang, Qian; Sun, Zhen; Huang, Fengchen

    2014-01-01

    Underwater moving object detection is the key for many underwater computer vision tasks, such as object recognizing, locating, and tracking. Considering the super ability in visual sensing of the underwater habitats, the visual mechanism of aquatic animals is generally regarded as the cue for establishing bionic models which are more adaptive to the underwater environments. However, the low accuracy rate and the absence of the prior knowledge learning limit their adaptation in underwater applications. Aiming to solve the problems originated from the inhomogeneous lumination and the unstable background, the mechanism of the visual information sensing and processing pattern from the eye of frogs are imitated to produce a hierarchical background model for detecting underwater objects. Firstly, the image is segmented into several subblocks. The intensity information is extracted for establishing background model which could roughly identify the object and the background regions. The texture feature of each pixel in the rough object region is further analyzed to generate the object contour precisely. Experimental results demonstrate that the proposed method gives a better performance. Compared to the traditional Gaussian background model, the completeness of the object detection is 97.92% with only 0.94% of the background region that is included in the detection results. PMID:25140194

  5. Teaching the Big Ideas of Biology with Operon Models

    Science.gov (United States)

    Cooper, Robert A.

    2015-01-01

    This paper presents an activity that engages students in model-based reasoning, requiring them to predict the behavior of the trp and lac operons under different environmental conditions. Students are presented six scenarios for the "trp" operon and five for the "lac" operon. In most of the scenarios, specific mutations have…

  6. A Biological Hierarchical Model Based Underwater Moving Object Detection

    Directory of Open Access Journals (Sweden)

    Jie Shen

    2014-01-01

    Full Text Available Underwater moving object detection is the key for many underwater computer vision tasks, such as object recognizing, locating, and tracking. Considering the super ability in visual sensing of the underwater habitats, the visual mechanism of aquatic animals is generally regarded as the cue for establishing bionic models which are more adaptive to the underwater environments. However, the low accuracy rate and the absence of the prior knowledge learning limit their adaptation in underwater applications. Aiming to solve the problems originated from the inhomogeneous lumination and the unstable background, the mechanism of the visual information sensing and processing pattern from the eye of frogs are imitated to produce a hierarchical background model for detecting underwater objects. Firstly, the image is segmented into several subblocks. The intensity information is extracted for establishing background model which could roughly identify the object and the background regions. The texture feature of each pixel in the rough object region is further analyzed to generate the object contour precisely. Experimental results demonstrate that the proposed method gives a better performance. Compared to the traditional Gaussian background model, the completeness of the object detection is 97.92% with only 0.94% of the background region that is included in the detection results.

  7. Biological Nutrient Removal Model No. 2 (BNRM2): a general model for wastewater treatment plants.

    Science.gov (United States)

    Barat, R; Serralta, J; Ruano, M V; Jiménez, E; Ribes, J; Seco, A; Ferrer, J

    2013-01-01

    This paper presents the plant-wide model Biological Nutrient Removal Model No. 2 (BNRM2). Since nitrite was not considered in the BNRM1, and this previous model also failed to accurately simulate the anaerobic digestion because precipitation processes were not considered, an extension of BNRM1 has been developed. This extension comprises all the components and processes required to simulate nitrogen removal via nitrite and the formation of the solids most likely to precipitate in anaerobic digesters. The solids considered in BNRM2 are: struvite, amorphous calcium phosphate, hidroxyapatite, newberite, vivianite, strengite, variscite, and calcium carbonate. With regard to nitrogen removal via nitrite, apart from nitrite oxidizing bacteria two groups of ammonium oxidizing organisms (AOO) have been considered since different sets of kinetic parameters have been reported for the AOO present in activated sludge systems and SHARON (Single reactor system for High activity Ammonium Removal Over Nitrite) reactors. Due to the new processes considered, BNRM2 allows an accurate prediction of wastewater treatment plant performance in wider environmental and operating conditions. PMID:23552235

  8. Average intensity and spreading of partially coherent model beams propagating in a turbulent biological tissue

    Science.gov (United States)

    Wu, Yuqian; Zhang, Yixin; Wang, Qiu; Hu, Zhengda

    2016-11-01

    For Gaussian beams with three different partially coherent models, including Gaussian-Schell model (GSM), Laguerre-Gaussian Schell-model (LGSM) and Bessel-Gaussian Schell-model (BGSM) beams propagating through a biological turbulent tissue, the expression of the spatial coherence radius of a spherical wave propagating in a turbulent biological tissue, and the average intensity and beam spreading for GSM, LGSM and BGSM beams are derived based on the fractal model of power spectrum of refractive-index variations in biological tissue. Effects of partially coherent model and parameters of biological turbulence on such beams are studied in numerical simulations. Our results reveal that the spreading of GSM beams is smaller than LGSM and BGSM beams on the same conditions, and the beam with larger source coherence width has smaller beam spreading than that with smaller coherence width. The results are useful for any applications involved light beam propagation through tissues, especially the cases where the average intensity and spreading properties of the light should be taken into account to evaluate the system performance and investigations in the structures of biological tissue.

  9. Generalized Beer-Lambert model for near-infrared light propagation in thick biological tissues

    Science.gov (United States)

    Bhatt, Manish; Ayyalasomayajula, Kalyan R.; Yalavarthy, Phaneendra K.

    2016-07-01

    The attenuation of near-infrared (NIR) light intensity as it propagates in a turbid medium like biological tissue is described by modified the Beer-Lambert law (MBLL). The MBLL is generally used to quantify the changes in tissue chromophore concentrations for NIR spectroscopic data analysis. Even though MBLL is effective in terms of providing qualitative comparison, it suffers from its applicability across tissue types and tissue dimensions. In this work, we introduce Lambert-W function-based modeling for light propagation in biological tissues, which is a generalized version of the Beer-Lambert model. The proposed modeling provides parametrization of tissue properties, which includes two attenuation coefficients μ0 and η. We validated our model against the Monte Carlo simulation, which is the gold standard for modeling NIR light propagation in biological tissue. We included numerous human and animal tissues to validate the proposed empirical model, including an inhomogeneous adult human head model. The proposed model, which has a closed form (analytical), is first of its kind in providing accurate modeling of NIR light propagation in biological tissues.

  10. The layer-oriented approach to declarative languages for biological modeling.

    Directory of Open Access Journals (Sweden)

    Ivan Raikov

    Full Text Available We present a new approach to modeling languages for computational biology, which we call the layer-oriented approach. The approach stems from the observation that many diverse biological phenomena are described using a small set of mathematical formalisms (e.g. differential equations, while at the same time different domains and subdomains of computational biology require that models are structured according to the accepted terminology and classification of that domain. Our approach uses distinct semantic layers to represent the domain-specific biological concepts and the underlying mathematical formalisms. Additional functionality can be transparently added to the language by adding more layers. This approach is specifically concerned with declarative languages, and throughout the paper we note some of the limitations inherent to declarative approaches. The layer-oriented approach is a way to specify explicitly how high-level biological modeling concepts are mapped to a computational representation, while abstracting away details of particular programming languages and simulation environments. To illustrate this process, we define an example language for describing models of ionic currents, and use a general mathematical notation for semantic transformations to show how to generate model simulation code for various simulation environments. We use the example language to describe a Purkinje neuron model and demonstrate how the layer-oriented approach can be used for solving several practical issues of computational neuroscience model development. We discuss the advantages and limitations of the approach in comparison with other modeling language efforts in the domain of computational biology and outline some principles for extensible, flexible modeling language design. We conclude by describing in detail the semantic transformations defined for our language.

  11. QNS measurements of water in biological and model systems

    Energy Technology Data Exchange (ETDEWEB)

    Trantham, E.C.; Rorschach, H.E.; Clegg, J.C.; Hazlewood, C.F.; Nicklow, R.M.

    1982-01-01

    Results are presented on the quasi-elastic spectra of 0.95 THz neutrons scattered from pure water, a 20% agarose gel and cysts of the brine shrimp (Artemia) of hydration 1.2 gms H/sub 2/O per gm of dry solids. The lines are interpreted with a two-component model in which the hydration water scatters elastically and the free water is described by a jump-diffusion correlation function. The results for the line widths GAMMA(Q/sup 2/) are in good agreement with previous measurements for the water sample but show deviations from pure water at large Q for agarose and the Artemia cysts that suggest an increased value of the residence time in the jump-diffusion model.

  12. QNS measurements on water in biological and model systems

    Energy Technology Data Exchange (ETDEWEB)

    Trantham, E.C.; Rorschach, H.E.; Clegg, J.C.; Hazlewood, C.F.; Nicklow, R.M.

    1981-01-01

    Results are presented on the quasi-elastic spectra of 0.95 THz neutrons scattered from pure water, a 20% agarose gel and cysts of the brine shrimp (Artemia) of hydration 1.2 gms H/sub 2/O per gm of dry solids. The lines are interpreted with a two-component model in which the hydration water scatters elastically and the free water is described by a jump-diffusion correlation function. The results for the line widths GAMMA(Q/sup 2/) are in good agreement with previous measurements for the water sample but show deviations from pure water at large Q for agarose and the Artemia cysts that suggest an increased value of the residence time in the jump-diffusion model.

  13. Model of heterogeneous material dissolution in simulated biological fluid

    Science.gov (United States)

    Knyazeva, A. G.; Gutmanas, E. Y.

    2015-11-01

    In orthopedic research, increasing attention is being paid to bioresorbable/biodegradable implants as an alternative to permanent metallic bone healing devices. Biodegradable metal based implants possessing high strength and ductility potentially can be used in load bearing sites. Biodegradable Mg and Fe are ductile and Fe possess high strength, but Mg degrades too fast and Fe degrades too slow, Ag is a noble metal and should cause galvanic corrosion of the more active metallic iron - thus, corrosion of Fe can be increased. Nanostructuring should results in higher strength and can result in higher rate of dissolution/degradation from grain boundaries. In this work, a simple dissolution model of heterogeneous three phase nanocomposite material is considered - two phases being metal Fe and Ag and the third - nanopores. Analytical solution for the model is presented. Calculations demonstrate that the changes in the relative amount of each phase depend on mass exchange and diffusion coefficients. Theoretical results agree with preliminary experimental results.

  14. Biochemical physics modeling of biological nano-motors

    Energy Technology Data Exchange (ETDEWEB)

    Santamaría-Holek, I.; López-Alamilla, N. J. [UMDI-Facultad de Ciencias, Universidad Nacional Autónoma de México, Campus Juriquilla, Boulevard Juriquilla 3001, 76230 Querétaro (Mexico)

    2014-01-14

    We present a biochemical physics model accounting for the dynamics and energetics of both translational and rotational protein motors. A modified version of the hand-over-hand mechanism considering competitive inhibition by ADP is presented. Transition state-like theory is used to reconstruct the time dependent free-energy landscape of the cycle catalyst process that allows to predicting the number of steps or rotations that a single motor can perform. In addition, following the usual approach of chemical kinetics, we calculate the average translational velocity and also the stopping time of processes involving a collectivity of motors, such as exocytosis and endocytosis processes. Finally, we formulate a stochastic model reproducing very well single realizations of kinesin and rotary ATPases.

  15. Biological correlates of child and adolescent responses to disaster exposure: a bio-ecological model.

    Science.gov (United States)

    Weems, Carl F

    2015-07-01

    Exposure to both human-caused and natural disasters is associated with a number of postevent reactions in youth including the experience of symptoms of several mental disorders. There is wide variability in these responses, with some youth having very intense exposure to the disaster and yet showing resilience or even personal growth, while others with low exposure sometimes show intensely negative reactions. Research findings are reviewed in this article to identify biological correlates of risk and resilience focusing on potential genetic, neurobiological, and physiological factors linked to the reactions of children exposed to disasters. A bio-ecological model is presented to couch this review of biological correlates of disaster exposure. The model predicts susceptibility to negative reactions after disaster exposure, and the biological correlates of disaster reactions can be understood in terms of this susceptibility as it relates to biological markers of the fear system. PMID:25980506

  16. Integrative multicellular biological modeling: a case study of 3D epidermal development using GPU algorithms

    Directory of Open Access Journals (Sweden)

    Christley Scott

    2010-08-01

    Full Text Available Abstract Background Simulation of sophisticated biological models requires considerable computational power. These models typically integrate together numerous biological phenomena such as spatially-explicit heterogeneous cells, cell-cell interactions, cell-environment interactions and intracellular gene networks. The recent advent of programming for graphical processing units (GPU opens up the possibility of developing more integrative, detailed and predictive biological models while at the same time decreasing the computational cost to simulate those models. Results We construct a 3D model of epidermal development and provide a set of GPU algorithms that executes significantly faster than sequential central processing unit (CPU code. We provide a parallel implementation of the subcellular element method for individual cells residing in a lattice-free spatial environment. Each cell in our epidermal model includes an internal gene network, which integrates cellular interaction of Notch signaling together with environmental interaction of basement membrane adhesion, to specify cellular state and behaviors such as growth and division. We take a pedagogical approach to describing how modeling methods are efficiently implemented on the GPU including memory layout of data structures and functional decomposition. We discuss various programmatic issues and provide a set of design guidelines for GPU programming that are instructive to avoid common pitfalls as well as to extract performance from the GPU architecture. Conclusions We demonstrate that GPU algorithms represent a significant technological advance for the simulation of complex biological models. We further demonstrate with our epidermal model that the integration of multiple complex modeling methods for heterogeneous multicellular biological processes is both feasible and computationally tractable using this new technology. We hope that the provided algorithms and source code will be a

  17. After the Greeting: Realizing the Potential of Physical Models in Cell Biology.

    Science.gov (United States)

    Paluch, Ewa K

    2015-12-01

    Biophysics is increasingly taking center stage in cell biology as the tools for precise quantifications of cellular behaviors expand. Interdisciplinary approaches, combining quantitative physical modeling with cell biology, are of growing interest to journal editors, funding agencies, and hiring committees. However, despite an ever-increasing emphasis on the importance of interdisciplinary research, the student trained in biology may still be at a loss as to what it actually means. I discuss here some considerations on how to achieve meaningful and high-quality interdisciplinary work.

  18. Modeling the Drug Discovery Process: The Isolation and Biological Testing of Eugenol from Clove Oil

    Science.gov (United States)

    Miles, William H.; Smiley, Patricia M.

    2002-01-01

    This experiment describes the isolation and biological testing of eugenol and neutral compounds from commercially available clove oil. By coupling the chemical separation of the components of clove oil (an experiment described in many introductory organic laboratory textbooks) with a simple antibiotic test, the students "discover" the biologically active compound in clove oil. This experiment models one of the primary methods used in the discovery of new pharmaceutical agents.

  19. Local buckling analysis of biological nanocomposites based on a beam-spring model

    OpenAIRE

    Zhiling Bai; Baohua Ji

    2015-01-01

    Biological materials such as bone, tooth, and nacre are load-bearing nanocomposites composed of mineral and protein. Since the mineral crystals often have slender geometry, the nanocomposites are susceptible to buckle under the compressive load. In this paper, we analyze the local buckling behaviors of the nanocomposite structure of the biological materials using a beam-spring model by which we can consider plenty of mineral crystals and their interaction in our analysis compared with existin...

  20. GNU MCSim : bayesian statistical inference for SBML-coded systems biology models

    OpenAIRE

    Bois, Frédéric Y.

    2009-01-01

    International audience Statistical inference about the parameter values of complex models, such as the ones routinely developed in systems biology, is efficiently performed through Bayesian numerical techniques. In that framework, prior information and multiple levels of uncertainty can be seamlessly integrated. GNU MCSim was precisely developed to achieve those aims, in a general non-linear differential context. Starting with version 5.3.0, GNU MCSim reads in and simulates Systems Biology...

  1. Introductory Biology Students' Conceptual Models and Explanations of the Origin of Variation

    Science.gov (United States)

    Bray Speth, Elena; Shaw, Neil; Momsen, Jennifer; Reinagel, Adam; Le, Paul; Taqieddin, Ranya; Long, Tammy

    2014-01-01

    Mutation is the key molecular mechanism generating phenotypic variation, which is the basis for evolution. In an introductory biology course, we used a model-based pedagogy that enabled students to integrate their understanding of genetics and evolution within multiple case studies. We used student-generated conceptual models to assess…

  2. Modeling Biological Invasion: The Case Of Dengue And Mangrove

    Science.gov (United States)

    Lye, Koh Hock; Yean, Teh Su; Ismail, Ahmad Izani Md.; DeAngelis, Donald L.

    2008-01-01

    It is well known that organism invades territory in the form of wave fronts whose characteristics are determined primarily by environmental conditions such as hydrology, salinity, climate, carrying capacity and resource. In this invited paper, we will consider two ecosystems, one comprising mosquitoes of the species Aedes aegypti that are the vector of dengue fever while the second consists of coastal ecosystem composed of mixtures of mangrove and hardwood hammocks in south Florida. Their dispersal dynamics modeled by simulations DEER and MANHAM will be discussed. Implications regarding approaches for the eradication of A. aegypti and the replanting or recovery of coastal mangrove forests will be presented.

  3. Computational modeling of chemo-bio-mechanical coupling: a systems-biology approach toward wound healing.

    Science.gov (United States)

    Buganza Tepole, A; Kuhl, E

    2016-01-01

    Wound healing is a synchronized cascade of chemical, biological, and mechanical phenomena, which act in concert to restore the damaged tissue. An imbalance between these events can induce painful scarring. Despite intense efforts to decipher the mechanisms of wound healing, the role of mechanics remains poorly understood. Here, we establish a computational systems biology model to identify the chemical, biological, and mechanical mechanisms of scar formation. First, we introduce the generic problem of coupled chemo-bio-mechanics. Then, we introduce the model problem of wound healing in terms of a particular chemical signal, inflammation, a particular biological cell type, fibroblasts, and a particular mechanical model, isotropic hyperelasticity. We explore the cross-talk between chemical, biological, and mechanical signals and show that all three fields have a significant impact on scar formation. Our model is the first step toward rigorous multiscale, multifield modeling in wound healing. Our formulation has the potential to improve effective wound management and optimize treatment on an individualized patient-specific basis.

  4. Goldfish morphology as a model for evolutionary developmental biology.

    Science.gov (United States)

    Ota, Kinya G; Abe, Gembu

    2016-01-01

    Morphological variation of the goldfish is known to have been established by artificial selection for ornamental purposes during the domestication process. Chinese texts that date to the Song dynasty contain descriptions of goldfish breeding for ornamental purposes, indicating that the practice originated over one thousand years ago. Such a well-documented goldfish breeding process, combined with the phylogenetic and embryological proximities of this species with zebrafish, would appear to make the morphologically diverse goldfish strains suitable models for evolutionary developmental (evodevo) studies. However, few modern evodevo studies of goldfish have been conducted. In this review, we provide an overview of the historical background of goldfish breeding, and the differences between this teleost and zebrafish from an evolutionary perspective. We also summarize recent progress in the field of molecular developmental genetics, with a particular focus on the twin-tail goldfish morphology. Furthermore, we discuss unanswered questions relating to the evolution of the genome, developmental robustness, and morphologies in the goldfish lineage, with the goal of blazing a path toward an evodevo study paradigm using this teleost species as a new model species. For further resources related to this article, please visit the WIREs website. PMID:26952007

  5. Biological ensemble modeling to evaluate potential futures of living marine resources

    DEFF Research Database (Denmark)

    Gårdmark, Anna; Lindegren, Martin; Neuenfeldt, Stefan;

    2013-01-01

    Natural resource management requires approaches to understand and handle sources of uncertainty in future responses of complex systems to human activities. Here we present one such approach, the “biological ensemble modeling approach,” using the Eastern Baltic cod (Gadus morhua callarias) as an...... single-species to food web models. These models were analyzed using the “biological ensemble modeling approach” by which we (1) identified a key ecological mechanism explaining the differences in simulated cod responses between models, (2) disentangled the uncertainty caused by differences in ecological...... model assumptions from the statistical uncertainty of future climate, and (3) identified results common for the whole model ensemble. Species interactions greatly influenced the simulated response of cod to fishing and climate, as well as the degree to which the statistical uncertainty of climate...

  6. Modeling Wireless Sensor Networks for Monitoring in Biological Processes

    DEFF Research Database (Denmark)

    Nadimi, Esmaeil

    parameters, as the use of wired sensors is impractical. In this thesis, a ZigBee based wireless sensor network was employed and only a part of the herd was monitored, as monitoring each individual animal in a large herd under practical conditions is inefficient. Investigations to show that the monitored...... (MMAE) approach to the data resulted in the highest classification success rate, due to the use of precise forth-order mathematical models which relate the feed offer to the pitch angle of the neck. This thesis shows that wireless sensor networks can be successfully employed to monitor the behavior...... animals can indeed represent the whole herd were carried out. The tagged animals in the herd were equipped with wireless nodes around the neck capable of measuring two behavioral parameters: the pitch angle of the neck (using accelerometer) and the velocity of the movement of the animal (using received...

  7. Human development VII: a spiral fractal model of fine structure of physical energy could explain central aspects of biological information, biological organization and biological creativity.

    Science.gov (United States)

    Ventegodt, Søren; Hermansen, Tyge Dahl; Flensborg-Madsen, Trine; Rald, Erik; Nielsen, Maj Lyck; Merrick, Joav

    2006-01-01

    In this paper we have made a draft of a physical fractal essence of the universe, a sketch of a new cosmology, which we believe to lay at the root of our new holistic biological paradigm. We present the fractal roomy spiraled structures and the energy-rich dancing "infinite strings" or lines of the universe that our hypothesis is based upon. The geometric language of this cosmology is symbolic and both pre-mathematical and pre-philosophical. The symbols are both text and figures, and using these we step by step explain the new model that at least to some extent is able to explain the complex informational system behind morphogenesis, ontogenesis, regeneration and healing. We suggest that it is from this highly dynamic spiraled structure that organization of cells, organs, and the wholeness of the human being including consciousness emerge. The model of "dancing fractal spirals" carries many similarities to premodern cultures descriptions of the energy of the life and universe. Examples are the Native American shamanistic descriptions of their perception of energy and the old Indian Yogis descriptions of the life-energy within the body and outside. Similar ideas of energy and matter are found in the modern superstring theories. The model of the informational system of the organism gives new meaning to Bateson's definition of information: "A difference that makes a difference", and indicates how information-directed self-organization can exist on high structural levels in living organisms, giving birth to their subjectivity and consciousness. PMID:17115083

  8. Pig Brain Mitochondria as a Biological Model for Study of Mitochondrial Respiration.

    Science.gov (United States)

    Fišar, Z; Hroudová, J

    2016-01-01

    Oxidative phosphorylation is a key process of intracellular energy transfer by which mitochondria produce ATP. Isolated mitochondria serve as a biological model for understanding the mitochondrial respiration control, effects of various biologically active substances, and pathophysiology of mitochondrial diseases. The aim of our study was to evaluate pig brain mitochondria as a proper biological model for investigation of activity of the mitochondrial electron transport chain. Oxygen consumption rates of isolated pig brain mitochondria were measured using high-resolution respirometry. Mitochondrial respiration of crude mitochondrial fraction, mitochondria purified in sucrose gradient, and mitochondria purified in Percoll gradient were assayed as a function of storage time. Oxygen flux and various mitochondrial respiratory control ratios were not changed within two days of mitochondria storage on ice. Leak respiration was found higher and Complex I-linked respiration lower in purified mitochondria compared to the crude mitochondrial fraction. Damage to both outer and inner mitochondrial membrane caused by the isolation procedure was the greatest after purification in a sucrose gradient. We confirmed that pig brain mitochondria can serve as a biological model for investigation of mitochondrial respiration. The advantage of this biological model is the stability of respiratory parameters for more than 48 h and the possibility to isolate large amounts of mitochondria from specific brain areas without the need to kill laboratory animals. We suggest the use of high-resolution respirometry of pig brain mitochondria for research of the neuroprotective effects and/or mitochondrial toxicity of new medical drugs.

  9. Simple Empirical Model for Identifying Rheological Properties of Soft Biological Tissues

    CERN Document Server

    Kobayashi, Yo; Miyashita, Tomoyuki; Fujie, Masakatsu G

    2015-01-01

    Understanding the rheological properties of soft biological tissue is a key issue for mechanical systems used in the healthcare field. We propose a simple empirical model using Fractional Dynamics and Exponential Nonlinearity (FDEN) to identify the rheological properties of soft biological tissue. The model is derived from detailed material measurements using samples isolated from porcine liver. We conducted dynamic viscoelastic and creep tests on liver samples using a rheometer. The experimental results indicated that biological tissue has specific properties: i) power law increases in storage elastic modulus and loss elastic modulus with the same slope; ii) power law gain decrease and constant phase delay in the frequency domain over two decades; iii) log-log scale linearity between time and strain relationships under constant force; and iv) linear and log scale linearity between strain and stress relationships. Our simple FDEN model uses only three dependent parameters and represents the specific propertie...

  10. GSK-3: functional insights from cell biology and animal models

    Directory of Open Access Journals (Sweden)

    Oksana eKaidanovich-Beilin

    2011-11-01

    Full Text Available Glycogen synthase kinase-3 (GSK-3 is a widely expressed and highly conserved serine/threonine protein kinase encoded in mammals by two genes that generate two related proteins: GSK-3α and GSK-3β. GSK-3 is active in cells under resting conditions and is primarily regulated through inhibition or diversion of its activity. While GSK-3 is one of the few protein kinases that can be inactivated by phosphorylation, the mechanisms of GSK-3 regulation are more varied and not fully understood. Precise control appears to be achieved by a combination of phosphorylation, localization, and sequestration by a number of GSK-3-binding proteins. GSK-3 lies downstream of several major signaling pathways including the phosphatidylinositol 3’ kinase pathway, the Wnt pathway, Hedgehog signaling and Notch. Specific pools of GSK-3, which differ in intracellular localization, binding partner affinity and relative amount are differentially sensitized to several distinct signaling pathways and these sequestration mechanisms contribute to pathway insulation and signal specificity. Dysregulation of signaling pathways involving GSK-3 is associated with the pathogenesis of numerous neurological and psychiatric disorders and there are data suggesting GSK-3 isoform-selective roles in several of these. Here, we review the current knowledge of GSK-3 regulation and targets and discuss the various animal models that have been employed to dissect the functions of GSK-3 in brain development and function through the use of conventional or conditional knock-out mice as well as transgenic mice. These studies have revealed fundamental roles for these protein kinases in memory, behavior and neuronal fate determination and provide insights into possible therapeutic interventions.

  11. A biological plausible Generalized Leaky Integrate-and-Fire neuron model.

    Science.gov (United States)

    Wang, Zhenzhong; Guo, Lilin; Adjouadi, Malek

    2014-01-01

    This study introduces a new Generalized Leaky Integrate-and-Fire (GLIF) neuron model. Unlike Normal Leaky Integrate-and-Fire (NLIF) models, the leaking resistor in the GLIF model equation is assumed to be variable, and an additional term would have the bias current added to the model equation in order to improve the accuracy. Adjusting the parameters defined for the leaking resistor and bias current, a GLIF model could be accurately matched to any Hodgkin-Huxley (HH) model and be able to reproduce plausible biological neuron behaviors.

  12. Tav4SB: integrating tools for analysis of kinetic models of biological systems

    Directory of Open Access Journals (Sweden)

    Rybiński Mikołaj

    2012-04-01

    Full Text Available Abstract Background Progress in the modeling of biological systems strongly relies on the availability of specialized computer-aided tools. To that end, the Taverna Workbench eases integration of software tools for life science research and provides a common workflow-based framework for computational experiments in Biology. Results The Taverna services for Systems Biology (Tav4SB project provides a set of new Web service operations, which extend the functionality of the Taverna Workbench in a domain of systems biology. Tav4SB operations allow you to perform numerical simulations or model checking of, respectively, deterministic or stochastic semantics of biological models. On top of this functionality, Tav4SB enables the construction of high-level experiments. As an illustration of possibilities offered by our project we apply the multi-parameter sensitivity analysis. To visualize the results of model analysis a flexible plotting operation is provided as well. Tav4SB operations are executed in a simple grid environment, integrating heterogeneous software such as Mathematica, PRISM and SBML ODE Solver. The user guide, contact information, full documentation of available Web service operations, workflows and other additional resources can be found at the Tav4SB project’s Web page: http://bioputer.mimuw.edu.pl/tav4sb/. Conclusions The Tav4SB Web service provides a set of integrated tools in the domain for which Web-based applications are still not as widely available as for other areas of computational biology. Moreover, we extend the dedicated hardware base for computationally expensive task of simulating cellular models. Finally, we promote the standardization of models and experiments as well as accessibility and usability of remote services.

  13. mlegp: statistical analysis for computer models of biological systems using R

    OpenAIRE

    Dancik, Garrett M.; Dorman, Karin S

    2008-01-01

    Summary: Gaussian processes (GPs) are flexible statistical models commonly used for predicting output from complex computer codes. As such, GPs are well suited for the analysis of computer models of biological systems, which have been traditionally difficult to analyze due to their high-dimensional, non-linear and resource-intensive nature. We describe an R package, mlegp, that fits GPs to computer model outputs and performs sensitivity analysis to identify and characterize the effects of imp...

  14. Modeling marrow damage from response data: evolution from radiation biology to benzene toxicity.

    OpenAIRE

    Jones, D T; Morris, M D; Hasan, J S

    1996-01-01

    Consensus principles from radiation biology were used to describe a generic set of nonlinear, first-order differential equations for modeling toxicity-induced compensatory cell kinetics in terms of sublethal injury, repair, direct killing, killing of cells with unrepaired sublethal injury, and repopulation. This cellular model was linked to a probit model of hematopoietic mortality that describes death from infection and/or hemorrhage between 5 and 30 days. Mortality data from 27 experiments ...

  15. The marmoset monkey: a multi-purpose preclinical and translational model of human biology and disease.

    Science.gov (United States)

    't Hart, Bert A; Abbott, David H; Nakamura, Katsuki; Fuchs, Eberhard

    2012-11-01

    The development of biologic molecules (monoclonal antibodies, cytokines, soluble receptors) as specific therapeutics for human disease creates a need for animal models in which safety and efficacy can be tested. Models in lower animal species are precluded when the reagents fail to recognize their targets, which is often the case in rats and mice. In this Feature article we will highlight the common marmoset, a small-bodied nonhuman primate (NHP), as a useful model in biomedical and preclinical translational research.

  16. Community-Reviewed Biological Network Models for Toxicology and Drug Discovery Applications

    Science.gov (United States)

    Namasivayam, Aishwarya Alex; Morales, Alejandro Ferreiro; Lacave, Ángela María Fajardo; Tallam, Aravind; Simovic, Borislav; Alfaro, David Garrido; Bobbili, Dheeraj Reddy; Martin, Florian; Androsova, Ganna; Shvydchenko, Irina; Park, Jennifer; Calvo, Jorge Val; Hoeng, Julia; Peitsch, Manuel C.; Racero, Manuel González Vélez; Biryukov, Maria; Talikka, Marja; Pérez, Modesto Berraquero; Rohatgi, Neha; Díaz-Díaz, Noberto; Mandarapu, Rajesh; Ruiz, Rubén Amián; Davidyan, Sergey; Narayanasamy, Shaman; Boué, Stéphanie; Guryanova, Svetlana; Arbas, Susana Martínez; Menon, Swapna; Xiang, Yang

    2016-01-01

    Biological network models offer a framework for understanding disease by describing the relationships between the mechanisms involved in the regulation of biological processes. Crowdsourcing can efficiently gather feedback from a wide audience with varying expertise. In the Network Verification Challenge, scientists verified and enhanced a set of 46 biological networks relevant to lung and chronic obstructive pulmonary disease. The networks were built using Biological Expression Language and contain detailed information for each node and edge, including supporting evidence from the literature. Network scoring of public transcriptomics data inferred perturbation of a subset of mechanisms and networks that matched the measured outcomes. These results, based on a computable network approach, can be used to identify novel mechanisms activated in disease, quantitatively compare different treatments and time points, and allow for assessment of data with low signal. These networks are periodically verified by the crowd to maintain an up-to-date suite of networks for toxicology and drug discovery applications.

  17. IMPLEMENTASI MODEL TUTORIAL BERBASIS KOMPUTER FISIOLOGI HEWAN UNTUK MEMBEKALI KEMAMPUAN REKONSTRUKSI KONSEP MAHASISWA CALON GURU BIOLOGI

    Directory of Open Access Journals (Sweden)

    Adeng Slamet

    2015-03-01

    Full Text Available Penelitian ini bertujuan untuk membandingkan model perkuliahan fisiologi hewan yang diharapkan mampu membekali kemampuan rekonstruksi konsep bagi mahasiswa calon guru biologi. Strategi perkuliahan ditempuh melalui implementasi model tutorial berbasis komputer. Sebanyak 80 orang mahasiswa S1 calon guru biologi dibagi ke dalam dua kelompok, 41 mahasiswa mengikuti perkuliahan model tutorial komputer, dan 39 mahasiswa mengikuti perkuliahan konvensional. Kemampuan rekonstruksi konsep diukur dengan membandingkan skor sebelum pembelajaran (pretes dengan setelah implementasi model (postes di antara kedua kelompok belajar. Selain itu, untuk mengungkap pandangan mahasiswa mengenai pengalaman belajarnya, seperangkat angket disebarkan kepada mahasiswa yang mengikuti model perkuliahan.  Efektivitas program perkuliahan dievaluasi dengan tes tertulis bentuk respon terbatas pada mahasiswa yang mengikuti program perkuliahan model tutorial komputer dibandingkan dengan mahasiswa dari kelompok konvensional. Hasil penelitian menunjukkan secara keseluruhan terjadi peningkatan  kemampuan rekonstruksi konsep pada kedua kelompok belajar, namun mahasiswa yang mengikuti perkuliahan model tutorial berbasis komputer menunjukkan peningkatan yang lebih tinggi dibandingkan kelompok mahasiswa peserta perkuliahan konvensional. Mahasiswa menanggapi positif implementasi model tutorial berbasis komputer dalam perkuliahan fisiologi hewan. Dapat disimpulkan bahwa penerapan model tutorial berbasis komputer pada penelitian ini dinyatakan lebih efektif dan mampu  membekali mahasiwa calon guru biologi dalam meningkatkan kemampuan  rekonstruksi konsep.

  18. A model of heavy ion detection in physical and biological systems

    International Nuclear Information System (INIS)

    Track structure theory (the Katz model) and its application to the detection of heavy ions in physical and biological systems are reviewed. Following the use of a new corrected formula describing the radial distribution of average dose around the path of a heavy ion, based on results of Monte Carlo calculations and on results of experimental measurements, better agreement is achieved between model calculations and experimentally measured relative effectiveness, for enzymatic and viral systems, for the Fricke dosemeter and for alanine and thermoluminescent (TDL-700) dosemeters irradiated with beams of heavy charged particles. From experimentally measured RBE dependences for survival and frequency of neoplastic transformations in a mammalian cell culture irradiated with beams of energetic heavy ions, values of model parameters for these biological endpoints have been extracted, and a model extrapolation to the low-dose region performed. Results of model calculations are then compared with evaluations of the lung cancer hazard in populations exposed to radon and its progeny. The model can be applied to practical phenomenological analysis of radiation damage in solid-state systems and to dosimetry of charged particle and fast neutron beams using a variety of detectors. The model can also serve as a guide in building more basic models of the action of ionizing radiation with physical and biological systems and guide of development of models of radiation risk more relevant than that used presently. 185 refs., 31 figs., 3 tabs. (author)

  19. ADAM: Analysis of Discrete Models of Biological Systems Using Computer Algebra

    Directory of Open Access Journals (Sweden)

    Blekherman Grigoriy

    2011-07-01

    Full Text Available Abstract Background Many biological systems are modeled qualitatively with discrete models, such as probabilistic Boolean networks, logical models, Petri nets, and agent-based models, to gain a better understanding of them. The computational complexity to analyze the complete dynamics of these models grows exponentially in the number of variables, which impedes working with complex models. There exist software tools to analyze discrete models, but they either lack the algorithmic functionality to analyze complex models deterministically or they are inaccessible to many users as they require understanding the underlying algorithm and implementation, do not have a graphical user interface, or are hard to install. Efficient analysis methods that are accessible to modelers and easy to use are needed. Results We propose a method for efficiently identifying attractors and introduce the web-based tool Analysis of Dynamic Algebraic Models (ADAM, which provides this and other analysis methods for discrete models. ADAM converts several discrete model types automatically into polynomial dynamical systems and analyzes their dynamics using tools from computer algebra. Specifically, we propose a method to identify attractors of a discrete model that is equivalent to solving a system of polynomial equations, a long-studied problem in computer algebra. Based on extensive experimentation with both discrete models arising in systems biology and randomly generated networks, we found that the algebraic algorithms presented in this manuscript are fast for systems with the structure maintained by most biological systems, namely sparseness and robustness. For a large set of published complex discrete models, ADAM identified the attractors in less than one second. Conclusions Discrete modeling techniques are a useful tool for analyzing complex biological systems and there is a need in the biological community for accessible efficient analysis tools. ADAM provides

  20. Modelling the behaviour of mechanical biological treatment outputs in landfills using the GasSim model.

    Science.gov (United States)

    Donovan, S M; Bateson, T; Gronow, J R; Voulvoulis, N

    2010-03-15

    The pretreatment of the biodegradable components of municipal solid waste (MSW) has been suggested as a method of reducing landfill gas emissions. Mechanical biological treatment (MBT) is the technology being developed to provide this reduction in biodegradability, either as an alternative to source segregated collection or for dealing with residual MSW which still contains high levels of biodegradable waste. The compost like outputs (CLOs) from MBT plants can be applied to land as a soil conditioner; treated to produce a solid recovered fuel (SRF) or landfilled. In this study the impact that landfilling of these CLOs will have on gaseous emissions is investigated. It is important that the gas production behaviour of landfilled waste is well understood, especially in European member states where the mitigation of gaseous emissions is a legal requirement. Results of an experiment carried out to characterise the biodegradable components of pretreated biowastes have been used with the GasSim model to predict the long term emissions behaviour of landfills accepting these wastes, in varying quantities. The landfill directive also enforces the mitigation of potential methane emissions from landfills, and the ability of landfill operators to capture gaseous emissions from low emitting landfills of the future is discussed, as well as new techniques that could be used for the mitigation of methane generation. PMID:20092874

  1. Modeling Genetic and Environmental Factors in Biological Systems Using Structural Equation Modeling: An Application to Energy Balance

    OpenAIRE

    Nock, Nora L.; Li, Li; Elston, Robert C.

    2009-01-01

    To improve our understanding of the role(s) that genes and environmental factors play in a complex disease, we need statistical approaches that model multiple factors simultaneously in a hierarchical manner that aims to reflect the underlying biological system(s). We present an approach that models genes as latent constructs, defined by multiple variants (single nucleotide polymorphisms, SNPs) within each gene, using the multivariate statistical framework of structural equation modeling (SEM)...

  2. Modelling approach for biological control of insect pest by releasing infected pest

    Energy Technology Data Exchange (ETDEWEB)

    Tan Yuanshun [Department of Mathematics and Physics, Chongqing Jiaotong University, Chongqing 400074 (China); Department of Applied Mathematics, Dalian University of Technology, Liaoning 116024 (China)], E-mail: ystan625@yahoo.com.cn; Chen Lansun [Department of Applied Mathematics, Dalian University of Technology, Liaoning 116024 (China); Minnan Science and Technology Institute, Fujian Normal University, 362332 Fujian (China)

    2009-01-15

    Models of biological control have a long history of theoretical development that have focused on the interactions between a predator and a prey. Here we have extended the classical epidemic model to include a continuous and impulsive pest control strategies by releasing the infected pests bred in laboratory. For the continuous model, the results imply that the susceptible pest goes to extinct if the threshold condition R{sub 0} < 1. While R{sub 0} > 1, the positive equilibrium of continuous model is globally asymptotically stable. Similarly, the threshold condition which guarantees the global stability of the susceptible pest-eradication periodic solution is obtained for the model with impulsive control strategy. Consequently, based on the results obtained in this paper, the control strategies which maintain the pests below an acceptably low level are discussed by controlling the release rate and impulsive period. Finally, the biological implications of the results and the efficiency of two control strategies are also discussed.

  3. Fuzzy Logic as a Computational Tool for Quantitative Modelling of Biological Systems with Uncertain Kinetic Data.

    Science.gov (United States)

    Bordon, Jure; Moskon, Miha; Zimic, Nikolaj; Mraz, Miha

    2015-01-01

    Quantitative modelling of biological systems has become an indispensable computational approach in the design of novel and analysis of existing biological systems. However, kinetic data that describe the system's dynamics need to be known in order to obtain relevant results with the conventional modelling techniques. These data are often hard or even impossible to obtain. Here, we present a quantitative fuzzy logic modelling approach that is able to cope with unknown kinetic data and thus produce relevant results even though kinetic data are incomplete or only vaguely defined. Moreover, the approach can be used in the combination with the existing state-of-the-art quantitative modelling techniques only in certain parts of the system, i.e., where kinetic data are missing. The case study of the approach proposed here is performed on the model of three-gene repressilator. PMID:26451831

  4. Fuzzy Logic as a Computational Tool for Quantitative Modelling of Biological Systems with Uncertain Kinetic Data.

    Science.gov (United States)

    Bordon, Jure; Moskon, Miha; Zimic, Nikolaj; Mraz, Miha

    2015-01-01

    Quantitative modelling of biological systems has become an indispensable computational approach in the design of novel and analysis of existing biological systems. However, kinetic data that describe the system's dynamics need to be known in order to obtain relevant results with the conventional modelling techniques. These data are often hard or even impossible to obtain. Here, we present a quantitative fuzzy logic modelling approach that is able to cope with unknown kinetic data and thus produce relevant results even though kinetic data are incomplete or only vaguely defined. Moreover, the approach can be used in the combination with the existing state-of-the-art quantitative modelling techniques only in certain parts of the system, i.e., where kinetic data are missing. The case study of the approach proposed here is performed on the model of three-gene repressilator.

  5. A biologically plausible model of time-scale invariant interval timing.

    Science.gov (United States)

    Almeida, Rita; Ledberg, Anders

    2010-02-01

    The temporal durations between events often exert a strong influence over behavior. The details of this influence have been extensively characterized in behavioral experiments in different animal species. A remarkable feature of the data collected in these experiments is that they are often time-scale invariant. This means that response measurements obtained under intervals of different durations coincide when plotted as functions of relative time. Here we describe a biologically plausible model of an interval timing device and show that it is consistent with time-scale invariant behavior over a substantial range of interval durations. The model consists of a set of bistable units that switch from one state to the other at random times. We first use an abstract formulation of the model to derive exact expressions for some key quantities and to demonstrate time-scale invariance for any range of interval durations. We then show how the model could be implemented in the nervous system through a generic and biologically plausible mechanism. In particular, we show that any system that can display noise-driven transitions from one stable state to another can be used to implement the timing device. Our work demonstrates that a biologically plausible model can qualitatively account for a large body of data and thus provides a link between the biology and behavior of interval timing.

  6. Biclustering with Flexible Plaid Models to Unravel Interactions between Biological Processes.

    Science.gov (United States)

    Henriques, Rui; Madeira, Sara C

    2015-01-01

    Genes can participate in multiple biological processes at a time and thus their expression can be seen as a composition of the contributions from the active processes. Biclustering under a plaid assumption allows the modeling of interactions between transcriptional modules or biclusters (subsets of genes with coherence across subsets of conditions) by assuming an additive composition of contributions in their overlapping areas. Despite the biological interest of plaid models, few biclustering algorithms consider plaid effects and, when they do, they place restrictions on the allowed types and structures of biclusters, and suffer from robustness problems by seizing exact additive matchings. We propose BiP (Biclustering using Plaid models), a biclustering algorithm with relaxations to allow expression levels to change in overlapping areas according to biologically meaningful assumptions (weighted and noise-tolerant composition of contributions). BiP can be used over existing biclustering solutions (seizing their benefits) as it is able to recover excluded areas due to unaccounted plaid effects and detect noisy areas non-explained by a plaid assumption, thus producing an explanatory model of overlapping transcriptional activity. Experiments on synthetic data support BiP's efficiency and effectiveness. The learned models from expression data unravel meaningful and non-trivial functional interactions between biological processes associated with putative regulatory modules. PMID:26357312

  7. Models of risk assessments for biologicals or related products in the European Union.

    Science.gov (United States)

    Moos, M

    1995-12-01

    In the context of veterinary biologicals, environmental risk assessment means the evaluation of the risk to human health and the environment (which includes plants and animals) connected with the release of such products. The following categories or types of veterinary biologicals can be distinguished: non-genetically modified organisms (non-GMOs) (inactivated/live) GMOs (inactivated/live) carrier products related products (e.g. non-specific "inducers'). Suitable models used in risk assessment for these products should aim to identify all possible adverse effects. A good working model should lead, at least, to a qualitative judgement on the environmental risk of the biological product (e.g. negligible, low, medium, severe, unacceptable). Quantifiable outcomes are rare; therefore, the producer of a biological product and the European control authorities should accept only models which are based on testable points and which are relevant to the type of product and its instructions for use. In view of animal welfare aspects, models working without animals should be preferred. In recent years, some of these methods have been integrated into safety tests described in European Union Directives and in monographs of the European Pharmacopoeia. By reviewing vaccine/registration problems (e.g. Aujeszky's disease live vaccine for pigs, and vaccinia-vectored rabies vaccine), several models used in risk assessment are demonstrated and discussed. PMID:8639943

  8. Stochastic and deterministic multiscale models for systems biology: an auxin-transport case study

    Directory of Open Access Journals (Sweden)

    King John R

    2010-03-01

    Full Text Available Abstract Background Stochastic and asymptotic methods are powerful tools in developing multiscale systems biology models; however, little has been done in this context to compare the efficacy of these methods. The majority of current systems biology modelling research, including that of auxin transport, uses numerical simulations to study the behaviour of large systems of deterministic ordinary differential equations, with little consideration of alternative modelling frameworks. Results In this case study, we solve an auxin-transport model using analytical methods, deterministic numerical simulations and stochastic numerical simulations. Although the three approaches in general predict the same behaviour, the approaches provide different information that we use to gain distinct insights into the modelled biological system. We show in particular that the analytical approach readily provides straightforward mathematical expressions for the concentrations and transport speeds, while the stochastic simulations naturally provide information on the variability of the system. Conclusions Our study provides a constructive comparison which highlights the advantages and disadvantages of each of the considered modelling approaches. This will prove helpful to researchers when weighing up which modelling approach to select. In addition, the paper goes some way to bridging the gap between these approaches, which in the future we hope will lead to integrative hybrid models.

  9. Information-theoretic analysis of the dynamics of an executable biological model.

    Directory of Open Access Journals (Sweden)

    Avital Sadot

    Full Text Available To facilitate analysis and understanding of biological systems, large-scale data are often integrated into models using a variety of mathematical and computational approaches. Such models describe the dynamics of the biological system and can be used to study the changes in the state of the system over time. For many model classes, such as discrete or continuous dynamical systems, there exist appropriate frameworks and tools for analyzing system dynamics. However, the heterogeneous information that encodes and bridges molecular and cellular dynamics, inherent to fine-grained molecular simulation models, presents significant challenges to the study of system dynamics. In this paper, we present an algorithmic information theory based approach for the analysis and interpretation of the dynamics of such executable models of biological systems. We apply a normalized compression distance (NCD analysis to the state representations of a model that simulates the immune decision making and immune cell behavior. We show that this analysis successfully captures the essential information in the dynamics of the system, which results from a variety of events including proliferation, differentiation, or perturbations such as gene knock-outs. We demonstrate that this approach can be used for the analysis of executable models, regardless of the modeling framework, and for making experimentally quantifiable predictions.

  10. Identification problem for stochastic models with application to carcinogenesis, cancer detection and radiation biology

    Directory of Open Access Journals (Sweden)

    L. G. Hanin

    2002-01-01

    Full Text Available A general framework for solving identification problem for a broad class of deterministic and stochastic models is discussed. This methodology allows for a unified approach to studying identifiability of various stochastic models arising in biology and medicine including models of spontaneous and induced Carcinogenesis, tumor progression and detection, and randomized hit and target models of irradiated cell survival. A variety of known results on parameter identification for stochastic models is reviewed and several new results are presented with an emphasis on rigorous mathematical development.

  11. Global stability and optimisation of a general impulsive biological control model

    CERN Document Server

    Mailleret, Ludovic

    2008-01-01

    An impulsive model of augmentative biological control consisting of a general continuous predator-prey model in ordinary differential equations augmented by a discrete part describing periodic introductions of predators is considered. It is shown that there exists an invariant periodic solution that corresponds to prey eradication and a condition ensuring its global asymptotic stability is given. An optimisation problem related to the preemptive use of augmentative biological control is then considered. It is assumed that the per time unit budget of biological control (i.e. the number of predators to be released) is fixed and the best deployment of this budget is sought after in terms of release frequency. The cost function to be minimised is the time taken to reduce an unforeseen prey (pest) invasion under some harmless level. The analysis shows that the optimisation problem admits a countable infinite number of solutions. An argumentation considering the required robustness of the optimisation result is the...

  12. Agent-based Models in Synthetic Biology: Tools for Simulation and Prospects

    Directory of Open Access Journals (Sweden)

    E.V.Krishnamurthy

    2012-03-01

    Full Text Available We describe a multiset of agents based modeling and simulation paradigm for synthetic biology. The multiset of agents –based programming paradigm, can be interpreted as the outcome arising out of deterministic, nondeterministic or stochastic interaction among elements in a multiset object space, that includes the environment. These interactions are like chemical reactions and the evolution of the multiset can emulate the system biological functions. Since the reaction rules are inherently parallel, any number of actions can be performed cooperatively or competitively among the subsets of elements, so that the elements evolve toward equilibrium or emergent state. Practical realization of this paradigm for system biological simulation is achieved through the concept of transactional style programming with agents, as well as soft computing (neural- network principles. Also we briefly describe currently available tools for agent-based-modeling, simulation and animation.

  13. Human Development VII: A Spiral Fractal Model of Fine Structure of Physical Energy Could Explain Central Aspects of Biological Information, Biological Organization and Biological Creativity

    Directory of Open Access Journals (Sweden)

    Søren Ventegodt

    2006-01-01

    Full Text Available In this paper we have made a draft of a physical fractal essence of the universe, a sketch of a new cosmology, which we believe to lay at the root of our new holistic biological paradigm. We present the fractal roomy spiraled structures and the energy-rich dancing “infinite strings” or lines of the universe that our hypothesis is based upon. The geometric language of this cosmology is symbolic and both pre-mathematical and pre-philosophical. The symbols are both text and figures, and using these we step by step explain the new model that at least to some extent is able to explain the complex informational system behind morphogenesis, ontogenesis, regeneration and healing. We suggest that it is from this highly dynamic spiraled structure that organization of cells, organs, and the wholeness of the human being including consciousness emerge. The model of ““dancing fractal spirals” carries many similarities to premodern cultures descriptions of the energy of the life and universe. Examples are the Native American shamanistic descriptions of their perception of energy and the old Indian Yogis descriptions of the life-energy within the body and outside. Similar ideas of energy and matter are found in the modern superstring theories. The model of the informational system of the organism gives new meaning to Bateson’s definition of information: “A difference that makes a difference”, and indicates how information-directed self-organization can exist on high structural levels in living organisms, giving birth to their subjectivity and consciousness.

  14. The effect of bacterial infection on the biomechanical properties of biological mesh in a rat model.

    Directory of Open Access Journals (Sweden)

    Charles F Bellows

    Full Text Available BACKGROUND: The use of biologic mesh to repair abdominal wall defects in contaminated surgical fields is becoming the standard of practice. However, failure rates and infections of these materials persist clinically. The purpose of this study was to determine the mechanical properties of biologic mesh in response to a bacterial encounter. METHODS: A rat model of Staphylococcus aureus colonization and infection of subcutaneously implanted biologic mesh was used. Samples of biologic meshes (acellular human dermis (ADM and porcine small intestine submucosa (SIS were inoculated with various concentrations of methicillin-resistant Staphylococcus aureus [10(5, 10(9 colony-forming units] or saline (control prior to wound closure (n = 6 per group. After 10 or 20 days, meshes were explanted, and cultured for bacteria. Histological changes and bacterial recovery together with biomechanical properties were assessed. Data were compared using a 1-way ANOVA or a Mann-Whitney test, with p0.05. After inoculation with MRSA, a time, dose and material dependent decrease in the ultimate tensile strength and modulus of elasticity of SIS and ADM were noted compared to control values. CONCLUSION: The biomechanical properties of biologic mesh significantly decline after colonization with MRSA. Surgeons selecting a repair material should be aware of its biomechanical fate relative to other biologic materials when placed in a contaminated environment.

  15. Computational approaches and metrics required for formulating biologically realistic nanomaterial pharmacokinetic models

    International Nuclear Information System (INIS)

    The field of nanomaterial pharmacokinetics is in its infancy, with major advances largely restricted by a lack of biologically relevant metrics, fundamental differences between particles and small molecules of organic chemicals and drugs relative to biological processes involved in disposition, a scarcity of sufficiently rich and characterized in vivo data and a lack of computational approaches to integrating nanomaterial properties to biological endpoints. A central concept that links nanomaterial properties to biological disposition, in addition to their colloidal properties, is the tendency to form a biocorona which modulates biological interactions including cellular uptake and biodistribution. Pharmacokinetic models must take this crucial process into consideration to accurately predict in vivo disposition, especially when extrapolating from laboratory animals to humans since allometric principles may not be applicable. The dynamics of corona formation, which modulates biological interactions including cellular uptake and biodistribution, is thereby a crucial process involved in the rate and extent of biodisposition. The challenge will be to develop a quantitative metric that characterizes a nanoparticle's surface adsorption forces that are important for predicting biocorona dynamics. These types of integrative quantitative approaches discussed in this paper for the dynamics of corona formation must be developed before realistic engineered nanomaterial risk assessment can be accomplished. (paper)

  16. Computational approaches and metrics required for formulating biologically realistic nanomaterial pharmacokinetic models

    Science.gov (United States)

    Riviere, Jim E.; Scoglio, Caterina; Sahneh, Faryad D.; Monteiro-Riviere, Nancy A.

    2013-01-01

    The field of nanomaterial pharmacokinetics is in its infancy, with major advances largely restricted by a lack of biologically relevant metrics, fundamental differences between particles and small molecules of organic chemicals and drugs relative to biological processes involved in disposition, a scarcity of sufficiently rich and characterized in vivo data and a lack of computational approaches to integrating nanomaterial properties to biological endpoints. A central concept that links nanomaterial properties to biological disposition, in addition to their colloidal properties, is the tendency to form a biocorona which modulates biological interactions including cellular uptake and biodistribution. Pharmacokinetic models must take this crucial process into consideration to accurately predict in vivo disposition, especially when extrapolating from laboratory animals to humans since allometric principles may not be applicable. The dynamics of corona formation, which modulates biological interactions including cellular uptake and biodistribution, is thereby a crucial process involved in the rate and extent of biodisposition. The challenge will be to develop a quantitative metric that characterizes a nanoparticle's surface adsorption forces that are important for predicting biocorona dynamics. These types of integrative quantitative approaches discussed in this paper for the dynamics of corona formation must be developed before realistic engineered nanomaterial risk assessment can be accomplished.

  17. Effective identification of conserved pathways in biological networks using hidden Markov models.

    Directory of Open Access Journals (Sweden)

    Xiaoning Qian

    Full Text Available BACKGROUND: The advent of various high-throughput experimental techniques for measuring molecular interactions has enabled the systematic study of biological interactions on a global scale. Since biological processes are carried out by elaborate collaborations of numerous molecules that give rise to a complex network of molecular interactions, comparative analysis of these biological networks can bring important insights into the functional organization and regulatory mechanisms of biological systems. METHODOLOGY/PRINCIPAL FINDINGS: In this paper, we present an effective framework for identifying common interaction patterns in the biological networks of different organisms based on hidden Markov models (HMMs. Given two or more networks, our method efficiently finds the top matching paths in the respective networks, where the matching paths may contain a flexible number of consecutive insertions and deletions. CONCLUSIONS/SIGNIFICANCE: Based on several protein-protein interaction (PPI networks obtained from the Database of Interacting Proteins (DIP and other public databases, we demonstrate that our method is able to detect biologically significant pathways that are conserved across different organisms. Our algorithm has a polynomial complexity that grows linearly with the size of the aligned paths. This enables the search for very long paths with more than 10 nodes within a few minutes on a desktop computer. The software program that implements this algorithm is available upon request from the authors.

  18. A coupled physical-biological-chemical model for the Indian Ocean

    Indian Academy of Sciences (India)

    P S Swathi; M K Sharada; K S Yajnik

    2000-12-01

    A coupled physical-biological-chemical model has been developed at C-MMACS. for studying the time- variation of primary productivity and air-sea carbon-dioxide exchange in the Indian Ocean. The physical model is based on the Modular Ocean Model, Version 2 (MOM2) and the biological model describes the nonlinear dynamics of a 7-component marine ecosystem. The chemical model includes dynamical equation for the evolution of dissolved inorganic carbon and total alkalinity. The interaction between the biological and chemical model is through the Redfield ratio. The partial pressure of carbon dioxide pCO2 of the surface layer is obtained from the chemical equilibrium equations of Peng et al 1987. Transfer coefficients for air-sea exchange of CO2 are computed dynamically based on the wind speeds. The coupled model reproduces the high productivity observed in the Arabian Sea off the Somali and Omani coasts during the Southwest (SW) monsoon. The entire Arabian Sea is an outgassing region for CO2 in spite of high productivity with transfer rates as high as 80 m-mol C/m2/day during SW monsoon near the Somali Coast on account of strong winds.

  19. Comparative systems biology between human and animal models based on next-generation sequencing methods

    Institute of Scientific and Technical Information of China (English)

    Yu-Qi ZHAO; Gong-Hua LI; Jing-Fei HUANG

    2013-01-01

    Animal models provide myriad benefits to both experimental and clinical research.Unfortunately,in many situations,they fall short of expected results or provide contradictory results.In part,this can be the result of traditional molecular biological approaches that are relatively inefficient in elucidating underlying molecular mechanism.To improve the efficacy of animal models,a technological breakthrough is required.The growing availability and application of the high-throughput methods make systematic comparisons between human and animal models easier to perform.In the present study,we introduce the concept of the comparative systems biology,which we define as "comparisons of biological systems in different states or species used to achieve an integrated understanding of life forms with all their characteristic complexity of interactions at multiple levels".Furthermore,we discuss the applications of RNA-seq and ChIP-seq technologies to comparative systems biology between human and animal models and assess the potential applications for this approach in the future studies.

  20. Solution of the Master Equation for Bak-Sneppen Model of Biological Evolution in Finite Ecosystem

    OpenAIRE

    Pis'mak, Yu. M.

    1996-01-01

    The master equations describing processes of biological evolution in the framework of the random neighbor Bak-Sneppen model are studied. For the eqosystem of $N$ species they are solved exactly and asymptotical behavior of this solution for large $N$ is analyzed.

  1. 13C-NMR detection of lipid polymorphism in model and biological membranes

    NARCIS (Netherlands)

    Kruijff, B. de; Rietveld, A.; Echteld, C.J.A. van

    1980-01-01

    1. 1. The application of the 13C-NMR technique to the study of lipid polymorphism is described for various model and biological membranes. 2. 2. The 13C-NMR line-width of various resonances of the lipid molecule are sensitive to the bilayer hexagonal and the bilayer ‘isotropic’ phase transition.

  2. Identifying biological concepts from a protein-related corpus with a probabilistic topic model

    Directory of Open Access Journals (Sweden)

    Lu Xinghua

    2006-02-01

    Full Text Available Abstract Background Biomedical literature, e.g., MEDLINE, contains a wealth of knowledge regarding functions of proteins. Major recurring biological concepts within such text corpora represent the domains of this body of knowledge. The goal of this research is to identify the major biological topics/concepts from a corpus of protein-related MEDLINE© titles and abstracts by applying a probabilistic topic model. Results The latent Dirichlet allocation (LDA model was applied to the corpus. Based on the Bayesian model selection, 300 major topics were extracted from the corpus. The majority of identified topics/concepts was found to be semantically coherent and most represented biological objects or concepts. The identified topics/concepts were further mapped to the controlled vocabulary of the Gene Ontology (GO terms based on mutual information. Conclusion The major and recurring biological concepts within a collection of MEDLINE documents can be extracted by the LDA model. The identified topics/concepts provide parsimonious and semantically-enriched representation of the texts in a semantic space with reduced dimensionality and can be used to index text.

  3. Mobile Applications in Cell Biology Present New Approaches for Cell Modelling

    Science.gov (United States)

    de Oliveira, Mayara Lustosa; Galembeck, Eduardo

    2016-01-01

    Cell biology apps were surveyed in order to identify whether there are new approaches for modelling cells allowed by the new technologies implemented in tablets and smartphones. A total of 97 apps were identified in 3 stores surveyed (Apple, Google Play and Amazon), they are presented as: education 48.4%, games 26.8% and medicine 15.4%. The apps…

  4. Monte Carlo evaluation of biological variation: Random generation of correlated non-Gaussian model parameters

    Science.gov (United States)

    Hertog, Maarten L. A. T. M.; Scheerlinck, Nico; Nicolaï, Bart M.

    2009-01-01

    When modelling the behaviour of horticultural products, demonstrating large sources of biological variation, we often run into the issue of non-Gaussian distributed model parameters. This work presents an algorithm to reproduce such correlated non-Gaussian model parameters for use with Monte Carlo simulations. The algorithm works around the problem of non-Gaussian distributions by transforming the observed non-Gaussian probability distributions using a proposed SKN-distribution function before applying the covariance decomposition algorithm to generate Gaussian random co-varying parameter sets. The proposed SKN-distribution function is based on the standard Gaussian distribution function and can exhibit different degrees of both skewness and kurtosis. This technique is demonstrated using a case study on modelling the ripening of tomato fruit evaluating the propagation of biological variation with time.

  5. The reduction of biological production induced by mesoscale mixing: a modelling study in the Benguela upwelling

    CERN Document Server

    Hernández-Carrasco, Ismael; Hernández-García, Emilio; Garçon, Veronique; López, Cristóbal

    2013-01-01

    Recent studies, both based on remote sensed data and coupled models, showed a reduction of biological productivity due to vigorous horizontal mixing in upwelling systems. In order to better understand this phenomenon, we have considered a system of oceanic flow in the Benguela area coupled with a simple biogeochemical model of Nutrient-Phyto-Zooplankton (NPZ) type. For the flow three different surface velocity fields are considered: one derived from satellite altimetry data, and the other two from a regional numerical model at two different spatial resolutions. We computed horizontal particle dispersion in terms of Lyapunov Exponents, and analyzed their correlations with phytoplankton concentrations. Our modelling approach confirms that in the south Benguela, there is a reduction of biological activity when stirring is increased. Two-dimensional offshore advection seems to be the dominant process involved. In the northern area, other factors not taken into account in our simulation are influencing the ecosyst...

  6. Model Order and Identifiability of Non-Linear Biological Systems in Stable Oscillation.

    Science.gov (United States)

    Wigren, Torbjörn

    2015-01-01

    The paper presents a theoretical result that clarifies when it is at all possible to determine the nonlinear dynamic equations of a biological system in stable oscillation, from measured data. As it turns out the minimal order needed for this is dependent on the minimal dimension in which the stable orbit of the system does not intersect itself. This is illustrated with a simulated fourth order Hodgkin-Huxley spiking neuron model, which is identified using a non-linear second order differential equation model. The simulated result illustrates that the underlying higher order model of the spiking neuron cannot be uniquely determined given only the periodic measured data. The result of the paper is of general validity when the dynamics of biological systems in stable oscillation is identified, and illustrates the need to carefully address non-linear identifiability aspects when validating models based on periodic data.

  7. Exploring behaviors of stochastic differential equation models of biological systems using change of measures

    Directory of Open Access Journals (Sweden)

    Jha Sumit

    2012-04-01

    Full Text Available Abstract Stochastic Differential Equations (SDE are often used to model the stochastic dynamics of biological systems. Unfortunately, rare but biologically interesting behaviors (e.g., oncogenesis can be difficult to observe in stochastic models. Consequently, the analysis of behaviors of SDE models using numerical simulations can be challenging. We introduce a method for solving the following problem: given a SDE model and a high-level behavioral specification about the dynamics of the model, algorithmically decide whether the model satisfies the specification. While there are a number of techniques for addressing this problem for discrete-state stochastic models, the analysis of SDE and other continuous-state models has received less attention. Our proposed solution uses a combination of Bayesian sequential hypothesis testing, non-identically distributed samples, and Girsanov's theorem for change of measures to examine rare behaviors. We use our algorithm to analyze two SDE models of tumor dynamics. Our use of non-identically distributed samples sampling contributes to the state of the art in statistical verification and model checking of stochastic models by providing an effective means for exposing rare events in SDEs, while retaining the ability to compute bounds on the probability that those events occur.

  8. Tuneable resolution as a systems biology approach for multi-scale, multi-compartment computational models.

    Science.gov (United States)

    Kirschner, Denise E; Hunt, C Anthony; Marino, Simeone; Fallahi-Sichani, Mohammad; Linderman, Jennifer J

    2014-01-01

    The use of multi-scale mathematical and computational models to study complex biological processes is becoming increasingly productive. Multi-scale models span a range of spatial and/or temporal scales and can encompass multi-compartment (e.g., multi-organ) models. Modeling advances are enabling virtual experiments to explore and answer questions that are problematic to address in the wet-lab. Wet-lab experimental technologies now allow scientists to observe, measure, record, and analyze experiments focusing on different system aspects at a variety of biological scales. We need the technical ability to mirror that same flexibility in virtual experiments using multi-scale models. Here we present a new approach, tuneable resolution, which can begin providing that flexibility. Tuneable resolution involves fine- or coarse-graining existing multi-scale models at the user's discretion, allowing adjustment of the level of resolution specific to a question, an experiment, or a scale of interest. Tuneable resolution expands options for revising and validating mechanistic multi-scale models, can extend the longevity of multi-scale models, and may increase computational efficiency. The tuneable resolution approach can be applied to many model types, including differential equation, agent-based, and hybrid models. We demonstrate our tuneable resolution ideas with examples relevant to infectious disease modeling, illustrating key principles at work.

  9. Systems biology by the rules: hybrid intelligent systems for pathway modeling and discovery

    OpenAIRE

    Bosl William J

    2007-01-01

    Abstract Background Expert knowledge in journal articles is an important source of data for reconstructing biological pathways and creating new hypotheses. An important need for medical research is to integrate this data with high throughput sources to build useful models that span several scales. Researchers traditionally use mental models of pathways to integrate information and development new hypotheses. Unfortunately, the amount of information is often overwhelming and these are inadequa...

  10. Punctuated equilibria and 1/f noise in a biological coevolution model with individual-based dynamics

    OpenAIRE

    Rikvold, Per Arne; Zia, R. K. P.

    2003-01-01

    We present a study by linear stability analysis and large-scale Monte Carlo simulations of a simple model of biological coevolution. Selection is provided through a reproduction probability that contains quenched, random interspecies interactions, while genetic variation is provided through a low mutation rate. Both selection and mutation act on individual organisms. Consistent with some current theories of macroevolutionary dynamics, the model displays intermittent, statistically self-simila...

  11. Robust Models for Optic Flow Coding in Natural Scenes Inspired by Insect Biology

    OpenAIRE

    Russell S A Brinkworth; David C O'Carroll

    2009-01-01

    The extraction of accurate self-motion information from the visual world is a difficult problem that has been solved very efficiently by biological organisms utilizing non-linear processing. Previous bio-inspired models for motion detection based on a correlation mechanism have been dogged by issues that arise from their sensitivity to undesired properties of the image, such as contrast, which vary widely between images. Here we present a model with multiple levels of non-linear dynamic adapt...

  12. AMIGO, a toolbox for advanced model identification in systems biology using global optimization

    OpenAIRE

    Balsa-Canto, Eva; Julio R Banga

    2011-01-01

    Motivation: Mathematical models of complex biological systems usually consist of sets of differential equations which depend on several parameters which are not accessible to experimentation. These parameters must be estimated by fitting the model to experimental data. This estimation problem is very challenging due to the non-linear character of the dynamics, the large number of parameters and the frequently poor information content of the experimental data (poor practical identifiability). ...

  13. Large-scale executable biology using rapid integration of computational models

    OpenAIRE

    Vladimir Rogojin; Ion Petre

    2016-01-01

    We plan to develop a systematic framework for assembling large-scale computational biological models by reusing and combining already existing modelling efforts. Our goal is to build a software platform that will compile large-scale biomodels through successive integrations of smaller modules. The modules can be arbitrary executable programs accompanied by a set of (I/O) interface variables; they may also have an internal structure (such as a metabolic network, interaction network, etc.) that...

  14. Daphnia as a model organism in limnology and aquatic biology: introductory remarks

    OpenAIRE

    Petrusek, Adam; Jaromir SEDA

    2011-01-01

    Cladocerans of the genus Daphnia are keystone pelagic filter feeders in many temperate ponds and lakes. They have also become popular model organisms in various biological disciplines, from aquatic ecology to biomedical sciences. The crucial features that make these organisms excellent experimental models are their cyclical parthenogenetic life cycle together with easy culturing and handling. Thanks to these characteristics, the number of publications dealing with Daphnia is rapidly growing. ...

  15. Systems biology by the rules: hybrid intelligent systems for pathway modeling and discovery

    Directory of Open Access Journals (Sweden)

    Bosl William J

    2007-02-01

    Full Text Available Abstract Background Expert knowledge in journal articles is an important source of data for reconstructing biological pathways and creating new hypotheses. An important need for medical research is to integrate this data with high throughput sources to build useful models that span several scales. Researchers traditionally use mental models of pathways to integrate information and development new hypotheses. Unfortunately, the amount of information is often overwhelming and these are inadequate for predicting the dynamic response of complex pathways. Hierarchical computational models that allow exploration of semi-quantitative dynamics are useful systems biology tools for theoreticians, experimentalists and clinicians and may provide a means for cross-communication. Results A novel approach for biological pathway modeling based on hybrid intelligent systems or soft computing technologies is presented here. Intelligent hybrid systems, which refers to several related computing methods such as fuzzy logic, neural nets, genetic algorithms, and statistical analysis, has become ubiquitous in engineering applications for complex control system modeling and design. Biological pathways may be considered to be complex control systems, which medicine tries to manipulate to achieve desired results. Thus, hybrid intelligent systems may provide a useful tool for modeling biological system dynamics and computational exploration of new drug targets. A new modeling approach based on these methods is presented in the context of hedgehog regulation of the cell cycle in granule cells. Code and input files can be found at the Bionet website: www.chip.ord/~wbosl/Software/Bionet. Conclusion This paper presents the algorithmic methods needed for modeling complicated biochemical dynamics using rule-based models to represent expert knowledge in the context of cell cycle regulation and tumor growth. A notable feature of this modeling approach is that it allows biologists

  16. Random many-particle systems: applications from biology, and propagation of chaos in abstract models

    CERN Document Server

    Wennberg, Bernt

    2011-01-01

    The paper discusses a family of Markov processes that represent many particle systems, and their limiting behaviour when the number of particles go to infinity. The first part concerns model of biological systems: a model for sympatric speciation, i.e. the process in which a genetically homogeneous population is split in two or more different species sharing the same habitat, and models for swarming animals. The second part of the paper deals with abstract many particle systems, and methods for rigorously deriving mean field models.

  17. Research on models of biological systems that can be integrated into mechatronic systems

    Science.gov (United States)

    Pop, P. P.; Pop-Vadean, A.; Barz, C.; Latinovic, T.; Chiver, O.

    2016-02-01

    The models of biological systems that we find on Earth can be the subject of research to develop a few mechatronic systems. Such models are offered by bees, ants, crows, cranes, etc. Article aims to investigate these models and their manifestations. Imitating this behavior and studied him offer ideas for develop models that can be integrated into mechatronic systems. They can be integrated into mechatronic system as algorithms for finding local optimum, to search, to detect an optimal way travel on a network, to find best decision, etc.

  18. Application of source-receptor models to determine source areas of biological components (pollen and butterflies

    Directory of Open Access Journals (Sweden)

    M. Alarcón

    2010-01-01

    Full Text Available The source-receptor models allow the establishment of relationships between a receptor point (sampling point and the probable source areas (regions of emission through the association of concentration values at the receptor point with the corresponding atmospheric back-trajectories, and, together with other techniques, to interpret transport phenomena on a synoptic scale. These models are generally used in air pollution studies to determine the areas of origin of chemical compounds measured at a sampling point, and thus be able to target actions to reduce pollutants. However, until now, few studies have applied these types of models to describe the source areas of biological organisms. In Catalonia there are very complete records of pollen (data from the Xarxa Aerobiològica de Catalunya, Aerobiology Network of Catalonia and butterflies (data from the Catalan Butterfly Monitoring Scheme, a biological material that is also liable to be transported long distances and whose areas of origin could be interesting to know. This work presents the results of the use of the Seibert et al. model applied to the study of the source regions of: (1 certain pollen of an allergic nature, observed in Catalonia and the Canary Islands, and (2 the migratory butterfly Vanessa cardui, observed in Catalonia. Based on the results obtained we can corroborate the suitability of these models to determine the area of origin of several species, both chemical and biological, therefore expanding the possibilities of applying the original model to the wider field of Aerobiology.

  19. The development of yoctowells as a basis for modeling biological systems.

    Science.gov (United States)

    Bhosale, Sheshanath V; Langford, Steven J

    2007-12-01

    Yoctolitre-sized vessels (1 yL = 10(-24)L) defined by encompassing porphyrin islands by rigid molecular monolayers of diamido bolaamphiphiles (with different characteristics) on smooth surfaces-the so-called yoctowells-have the ability to store (house) molecules in ways reminiscent of a molecular container. Such containers fill by kinetic or thermodynamic trapping processes allowing in some instances for the sorting and spatial positioning of molecular entities within the container. In this Emerging Area we describe the usefulness and versatility of yoctowells as the basis for modeling biological systems with a view to addressing some of the challenges in chemistry, molecular biology and biochemistry. PMID:18004452

  20. Computational modeling of STED microscopy through multiple biological cells under one- and two-photon excitation

    Science.gov (United States)

    Mark, Andrew E.; Davis, Mitchell A.; Starosta, Matthew S.; Dunn, Andrew K.

    2015-03-01

    While superresolution optical microscopy techniques afford enhanced resolution for biological applications, they have largely been used to study structures in isolated cells. We use the FDTD method to simulate the propagation of focused beams for STED microscopy through multiple biological cells. We model depletion beams that provide 2D and 3D confinement of the fluorescence spot and assess the effective PSF of the system as a function of focal depth. We compare the relative size of the STED effective PSF under one- and two-photon excitation. PSF calculations suggest that imaging is possible up to the maximum simulation depth if the fluorescence emission remains detectable.

  1. Biological computation

    CERN Document Server

    Lamm, Ehud

    2011-01-01

    Introduction and Biological BackgroundBiological ComputationThe Influence of Biology on Mathematics-Historical ExamplesBiological IntroductionModels and Simulations Cellular Automata Biological BackgroundThe Game of Life General Definition of Cellular Automata One-Dimensional AutomataExamples of Cellular AutomataComparison with a Continuous Mathematical Model Computational UniversalitySelf-Replication Pseudo Code Evolutionary ComputationEvolutionary Biology and Evolutionary ComputationGenetic AlgorithmsExample ApplicationsAnalysis of the Behavior of Genetic AlgorithmsLamarckian Evolution Genet

  2. Efficient Analysis of Systems Biology Markup Language Models of Cellular Populations Using Arrays.

    Science.gov (United States)

    Watanabe, Leandro; Myers, Chris J

    2016-08-19

    The Systems Biology Markup Language (SBML) has been widely used for modeling biological systems. Although SBML has been successful in representing a wide variety of biochemical models, the core standard lacks the structure for representing large complex regular systems in a standard way, such as whole-cell and cellular population models. These models require a large number of variables to represent certain aspects of these types of models, such as the chromosome in the whole-cell model and the many identical cell models in a cellular population. While SBML core is not designed to handle these types of models efficiently, the proposed SBML arrays package can represent such regular structures more easily. However, in order to take full advantage of the package, analysis needs to be aware of the arrays structure. When expanding the array constructs within a model, some of the advantages of using arrays are lost. This paper describes a more efficient way to simulate arrayed models. To illustrate the proposed method, this paper uses a population of repressilator and genetic toggle switch circuits as examples. Results show that there are memory benefits using this approach with a modest cost in runtime. PMID:26912276

  3. Biological science learning model based on Turgo's local wisdom on managing biodiversity

    Science.gov (United States)

    Anwari, Nahdi, Maizer Said; Sulistyowati, Eka

    2016-02-01

    Local wisdom as product of local knowledge has been giving a local context in science development. Local wisdom is important to connect scientific theories and local conditions; hence science could be accessed by common people. Using local wisdom as a model for learning science enables students to build contextual learning, hence learning science becomes more meaningful and becomes more accessible for students in a local community. Based on this consideration, therefore, this research developed a model for learning biology based on Turgo's local wisdom on managing biodiversity. For this purpose, Turgo's biodiversity was mapped, and any local values that are co-existing with the biodiversity were recorded. All of these informations were, then, used as a hypohetical model for developing materials for teaching biology in a senior high school adjacent to Turgo. This research employed a qualitative method. We combined questionnaries, interviews and observation to gather the data. We found that Turgo community has been practicing local wisdom on using traditional plants for many uses, including land management and practicing rituals and traditional ceremonies. There were local values that they embrace which enable them to manage the nature wisely. After being cross-referenced with literature regarding educational philoshophy, educational theories and teachings, and biology curriculum for Indonesia's senior high school, we concluded that Turgo's local wisdom on managing biodiversity can be recommended to be used as learning materials and sources for biological learning in schools.

  4. Logarithmic rate based elasto-viscoplastic cyclic constitutive model for soft biological tissues.

    Science.gov (United States)

    Zhu, Yilin; Kang, Guozheng; Yu, Chao; Poh, Leong Hien

    2016-08-01

    Based on the logarithmic rate and piecewise linearization theory, a thermodynamically consistent elasto-viscoplastic constitutive model is developed in the framework of finite deformations to describe the nonlinear time-dependent biomechanical performances of soft biological tissues, such as nonlinear anisotropic monotonic stress-strain responses, stress relaxation, creep and ratchetting. In the proposed model, the soft biological tissue is assumed as a typical composites consisting of an isotropic matrix and anisotropic fiber aggregation. Accordingly, the free energy function and stress tensor are divided into two parts related to the matrix and fiber aggregation, respectively. The nonlinear biomechanical responses of the tissues are described by the piecewise linearization theory with hypo-elastic relations of fiber aggregation. The evolution equations of viscoplasticity are formulated from the dissipation inequalities by the co-directionality hypotheses. The anisotropy is considered in the hypo-elastic relations and viscoplastic flow rules by introducing some material parameters dependent on the loading direction. Then the capability of the proposed model to describe the nonlinear time-dependent deformation of soft biological tissues is verified by comparing the predictions with the corresponding experimental results of three tissues. It is seen that the predicted monotonic stress-strain responses, stress relaxation, creep and ratchetting of soft biological tissues are in good agreement with the corresponding experimental ones. PMID:27108349

  5. A physical-biological coupled model for algal dynamics in lakes.

    Science.gov (United States)

    Franke, U; Hutter, K; Jöhnk, K

    1999-03-01

    A coupled model is presented for simulating physical and biological dynamics in fresh water lakes. The physical model rests upon the assumption that the turbulent kinetic energy in a water column of the lake is fully contained in a mixed layer of variable depth. Below this layer the mechanical energy content is assumed to vanish. Additionally, the horizontal currents are ignored. This one-dimensional two-layered model describes the internal conversion of the mechanical and thermal energy input from the atmosphere into an evolution of the mixed layer depth by entrainment and detrainment mechanisms. It is supposed to form the physical domain in which the simulation of the biological processes takes place. The biological model describes mathematically the typical properties of phyto- and zooplankton, their interactions and their response to the physical environment. This description then allows the study of the behaviour of Lagrangian clusters of virtual plankton that are subjected to such environments. The essence of the model is the dynamical simulation of an arbitrary number of nutrient limited phytoplankton species and one species of zooplankton. The members of the food web above and below affect the model only statically. The model is able to reproduce the typical progression of a predator-prey interaction between phyto- and zooplankton as well as the exploitative competition for nutrients between two phytoplankton species under grazing pressure of Daphnia. It suggests that the influence of the biological system on the physical system results in a weak increase of the surface temperature for coupled simulations, but a considerably higher seasonal thermocline in spring and a lower one in autumn.

  6. A four-dimensional validation of a coupled physical-biological model of the Arabian Sea

    Science.gov (United States)

    Hood, Raleigh R.; Kohler, Kevin E.; McCreary, Julian P.; Smith, Sharon L.

    2003-11-01

    In this paper, we use a coupled biological/physical model to synthesize and understand observations taken during the US JGOFS Arabian Sea Process Study (ASPS). Its physical component is a variable-density, 4 1/2-layer model; its biological component consists of a set of advective-diffusive equations in each layer that determine nitrogen concentrations in four compartments, namely, nutrients, phytoplankton, zooplankton, and detritus. Solutions are compared to time series and cruise sections from the ASPS data set, including observations of mixed-layer thickness, chlorophyll concentrations, inorganic nitrogen concentrations, particulate nitrogen export flux, zooplankton biomass, and primary production. Through these comparisons, we adjust model parameters to obtain a "best-fit" main-run solution, identify key biological and physical processes, and assess model strengths and weaknesses. Substantial improvements in the model/data comparison are obtained by: (1) adjusting the turbulence-production coefficients in the mixed-layer model to thin the mixed layer; (2) increasing the detrital sinking and remineralization rates to improve the timing and amplitude of the model's export flux; and (3) introducing a parameterization of particle aggregation to lower phytoplankton concentrations in coastal upwelling regions. With these adjustments, the model captures many key aspects of the observed physical and biogeochemical variability in offshore waters, including the near-surface DIN and phytoplankton P concentrations, mesozooplankton biomass, and primary production. Nevertheless, there are still significant model/data discrepancies of P for most of the cruises. Most of them can be attributed to forcing or process errors in the physical model: inaccurate mixed-layer thicknesses, lack of mesoscale eddies and filaments, and differences in the timing and spatial extent of coastal upwelling. Relatively few are clearly related to the simplicity of the biological model, the model

  7. The quest for a new modelling framework in mathematical biology. Comment on "On the interplay between mathematics and biology: Hallmarks towards a new systems biology" by N. Bellomo et al.

    Science.gov (United States)

    Eftimie, Raluca

    2015-03-01

    One of the main unsolved problems of modern physics is finding a "theory of everything" - a theory that can explain, with the help of mathematics, all physical aspects of the universe. While the laws of physics could explain some aspects of the biology of living systems (e.g., the phenomenological interpretation of movement of cells and animals), there are other aspects specific to biology that cannot be captured by physics models. For example, it is generally accepted that the evolution of a cell-based system is influenced by the activation state of cells (e.g., only activated and functional immune cells can fight diseases); on the other hand, the evolution of an animal-based system can be influenced by the psychological state (e.g., distress) of animals. Therefore, the last 10-20 years have seen also a quest for a "theory of everything"-approach extended to biology, with researchers trying to propose mathematical modelling frameworks that can explain various biological phenomena ranging from ecology to developmental biology and medicine [1,2,6]. The basic idea behind this approach can be found in a few reviews on ecology and cell biology [6,7,9-11], where researchers suggested that due to the parallel between the micro-scale dynamics and the emerging macro-scale phenomena in both cell biology and in ecology, many mathematical methods used for ecological processes could be adapted to cancer modelling [7,9] or to modelling in immunology [11]. However, this approach generally involved the use of different models to describe different biological aspects (e.g., models for cell and animal movement, models for competition between cells or animals, etc.).

  8. Mediating objects: scientific and public functions of models in nineteenth-century biology.

    Science.gov (United States)

    Ludwig, David

    2013-01-01

    The aim of this article is to examine the scientific and public functions of two- and three-dimensional models in the context of three episodes from nineteenth-century biology. I argue that these models incorporate both data and theory by presenting theoretical assumptions in the light of concrete data or organizing data through theoretical assumptions. Despite their diverse roles in scientific practice, they all can be characterized as mediators between data and theory. Furthermore, I argue that these different mediating functions often reflect their different audiences that included specialized scientists, students, and the general public. In this sense, models in nineteenth-century biology can be understood as mediators between theory, data, and their diverse audiences.

  9. A model of electrostatically actuated MEMS and carbon nanotubes resonators for biological mass detection

    KAUST Repository

    Bouchaala, Adam M.

    2015-01-01

    We investigate the dynamics of electrically actuated Micro and Nano (Carbon nanotube (CNT)) cantilever beams implemented as resonant sensors for mass detection of biological elements. The beams are modeled using an Euler-Bernoulli beam theory including the nonlinear electrostatic forces and the added biological elements, which are modeled as a discrete point mass. A multi-mode Galerkin procedure is utilized to derive a reduced-order model, which is used for the dynamic simulations. The frequency shifts due to added mass of Escherichia coli (E. coli) and Prostate Specific Antigen (PSA) are calculated for the primary and higher order modes of vibrations. Also, analytical expressions of the natural frequency shift under dc voltage and added mass have been developed. We found that using higher-order modes of vibration of MEMS beams or miniaturizing the size of the beam to Nano scale leads to significant improved sensitivity. © Springer International Publishing Switzerland 2015.

  10. Explanatory models are needed to integrate RCT and observational data with the patient's unique biology.

    Science.gov (United States)

    Sharma, Vijay; Minhas, Rubin

    2012-01-01

    In this review, we make the case for evidence-based medicine (EBM) to include models of disease underscored by evidence in order to integrate evidence, as it is currently defined, with the patient's unique biology. This would allow clinicians to use a pathophysiologic rationale, but underscoring the pathophysiological model with evidence would create an objective evidence base for extrapolating randomized controlled trial evidence. EBM encourages practitioners not to be passive receivers of information, but to question the information. By the same token, practitioners should not be passive executors of the process by which information is generated, appraised and applied, but should question the process. We use the historical examples of the evolution of EBM to show that its subordination of a pathophysiological perspective was unintentional, and of essential hypertension to illustrate the importance of disease models and the fact that evidence supporting them comes from many sources. We follow this with an illustration of the benefits a pathophysiological perspective can bring and a suggested model of how inclusion of pathophysiological models in the EBM approach would work. From a practical perspective, information cannot be integrated with the patient's unique biology without knowledge of that biology; this is why EBM is currently so silent on how to carry out its fourth stage. It is also clear that, regardless of whether a philosophical or practical definition of evidence is used, pathophysiology is evidence and should be regarded as such.

  11. Combining partially ranked data in plant breeding and biology: II. Analysis with Rasch model

    Directory of Open Access Journals (Sweden)

    Ivan Simko

    2010-07-01

    Full Text Available Many years of breeding experiments, germplasm screening, and molecular biologic experimentation have generated volumes of sequence, genotype, and phenotype information that have been stored in public data repositories. These resources afford genetic and genomic researchers the opportunity to handle and analyze raw data from multiple laboratories and study groups whose research interests revolve around a common or closely related trait. However, although such data sets are widely available for secondary analysis, their heterogeneous nature often precludes their direct combination and joint exploration. Integration of phenotype information across multiple studies and databases is challenging due to variations in the measurement instruments, endpoint classifications, and biological material employed by each investigator. In the present work, we demonstrate how Rasch measurement model can surmount these problems. The model allows incorporating data sets with partially overlapping variables, large numbers of missing data points and dissimilar ratings of phenotypic endpoints. The model also enables quantifying the extent of heterogeneity between data sets. Biologists can use the model in a data-mining process to obtain combined ratings from various databases and other sources. Subsequently, these ratings can be used for selecting desirable material or (in combination with genotypic information for mapping genes involved in the particular trait. The model is not limited to genetics and breeding and can be applied in many other areas of biology and agriculture.

  12. Comment on a mathematical model for estimating biological damage caused by radiation. Whack-A-Mole model

    International Nuclear Information System (INIS)

    We propose a mathematical model, Whack-A-Mole (WAM) model which can estimate the biological effect caused by radiation. The WAM model assumes that the basic element is a cell and the number of cells increases or decreases due to response to stimulus, damage and recovery, cause by radiation and so on. WAM takes into account dose rate as an important physical quantity, which marks a fundamental difference from target theory or LQ model. WAM model reproduces various experimental data of mutation frequency induced by radiation, for example, mega-mouse project. Furthermore, we can predict unknown experimental results. From WAM model, it is learnt that the mutation frequency caused by radiation is not proportional to dose but also have saturation value. There is some possibility that the WAM model will drastically change risk estimation of radiation as it brings us different results from those of target theory or LQ model. (author)

  13. Heterogeneity of intracellular polymer storage states in enhanced biological phosphorus removal (EBPR)--observation and modeling.

    Science.gov (United States)

    Bucci, Vanni; Majed, Nehreen; Hellweger, Ferdi L; Gu, April Z

    2012-03-20

    A number of agent-based models (ABMs) for biological wastewater treatment processes have been developed, but their skill in predicting heterogeneity of intracellular storage states has not been tested against observations due to the lack of analytical methods for measuring single-cell intracellular properties. Further, several mechanisms can produce and maintain heterogeneity (e.g., different histories, uneven division) and their relative importance has not been explored. This article presents an ABM for the enhanced biological phosphorus removal (EBPR) treatment process that resolves heterogeneity in three intracellular polymer storage compounds (i.e., polyphosphate, polyhydroxybutyrate, and glycogen) in three functional microbial populations (i.e., polyphosphate-accumulating, glycogen-accumulating, and ordinary heterotrophic organisms). Model predicted distributions were compared to those based on single-cell estimates obtained using a Raman microscopy method for a laboratory-scale sequencing batch reactor (SBR) system. The model can reproduce many features of the observed heterogeneity. Two methods for introducing heterogeneity were evaluated. First, biological variability in individual cell behavior was simulated by randomizing model parameters (e.g., maximum acetate uptake rate) at division. This method produced the best fit to the data. An optimization algorithm was used to determine the best variability (i.e., coefficient of variance) for each parameter, which suggests large variability in acetate uptake. Second, biological variability in individual cell states was simulated by randomizing state variables (e.g., internal nutrient) at division, which was not able to maintain heterogeneity because the memory in the internal states is too short. These results demonstrate the ability of ABM to predict heterogeneity and provide insights into the factors that contribute to it. Comparison of the ABM with an equivalent population-level model illustrates the effect

  14. Review of "System Modeling in Cellular Biology: From Concepts to Nuts and Bolts" by Szallasi, Stelling and Periwal

    OpenAIRE

    McSharry Patrick E

    2007-01-01

    Abstract "System Modeling in Cellular Biology: From Concepts to Nuts and Bolts" by Szallasi, Stelling and Periwal introduces the relevant concepts, terminology, and techniques of this field of science. It emphasises the modelling and computational challenges of taking a multidisciplinary approach to biology. This book provides a comprehensive introduction to systems biology and will form a valuable resource for students, teachers and researchers from both experimental and theoretical discipli...

  15. Persistence of biological traces in gun barrels--an approach to an experimental model.

    Science.gov (United States)

    Courts, Cornelius; Madea, Burkhard; Schyma, Christian

    2012-05-01

    Traces of backspatter in gun barrels after homicidal or suicidal contact shots may be a valuable source of forensic evidence. Yet, a systematic investigation of the persistence and durability of DNA from biological traces in gun barrels is lacking. Our aim was to generate a realistic model to emulate blood and tissue spatters in gun barrels generated by contact gunshots at biological targets and to analyse the persistence and typability of DNA recovered from such stains. Herein, we devise and evaluate three different models for the emulation of backspatter from contact shots: a gelatine-based model with embedded blood bags, a model based on a spongious matrix soaked with blood and covered with a thin plastic membrane and a head model consisting of an acrylic half sphere filled with ballistic gelatine and with blood bags attached to the sphere under a 3-mm silicone layer. The sampling procedure for all three models: a first shot was fired with several types of guns at each model construction and subsequently a second shot was fired at a backstop. Blood samples were collected after each shot by probing the inner surface of the front and rear end of the respective gun barrel with a sterile swab. DNA was then extracted and quantified and up to 20 different short tandem repeat (STR) systems were amplified to generate DNA profiles. Although DNA quantity and STR typing results were heterogenous between the models, all models succeeded in delivering full STR profiles even after more than one shot. We conclude that biological traces in gun barrels are robust and accessible to forensic analysis and that systematic examination of the inside of gun barrels may be advisable for forensic casework.

  16. A termination criterion for parameter estimation in stochastic models in systems biology.

    Science.gov (United States)

    Zimmer, Christoph; Sahle, Sven

    2015-11-01

    Parameter estimation procedures are a central aspect of modeling approaches in systems biology. They are often computationally expensive, especially when the models take stochasticity into account. Typically parameter estimation involves the iterative optimization of an objective function that describes how well the model fits some measured data with a certain set of parameter values. In order to limit the computational expenses it is therefore important to apply an adequate stopping criterion for the optimization process, so that the optimization continues at least until a reasonable fit is obtained, but not much longer. In the case of stochastic modeling, at least some parameter estimation schemes involve an objective function that is itself a random variable. This means that plain convergence tests are not a priori suitable as stopping criteria. This article suggests a termination criterion suited to optimization problems in parameter estimation arising from stochastic models in systems biology. The termination criterion is developed for optimization algorithms that involve populations of parameter sets, such as particle swarm or evolutionary algorithms. It is based on comparing the variance of the objective function over the whole population of parameter sets with the variance of repeated evaluations of the objective function at the best parameter set. The performance is demonstrated for several different algorithms. To test the termination criterion we choose polynomial test functions as well as systems biology models such as an Immigration-Death model and a bistable genetic toggle switch. The genetic toggle switch is an especially challenging test case as it shows a stochastic switching between two steady states which is qualitatively different from the model behavior in a deterministic model.

  17. Modified biological training model for percutaneous renal surgery with ultrasound and fluoroscopy guidance

    Institute of Scientific and Technical Information of China (English)

    QIU Zhi; YANG Yong; ZHANG Yi; SUN Yu-cheng

    2011-01-01

    Background The 12th rib is an important anatomic marker in the process of percutaneous renal surgery; while the previous models without ribs can not provide close simulation conditions to human upper abdomen. To facilitate the learning and training of percutaneous renal access and intrarenal procedures under ultrasound and fluoroscopy guidance, we reported a biological bench model for percutaneous renal surgery. Methods The model was developed using an ex vivo porcine kidney with a longer than 3 cm ureter, a flap of full thickness of thoracic wall with skin, subcutaneous fascia, muscle and two ribs, as well as the standard equipment for percutaneous nephrolithotomy. The porcine kidney with a catheterized ureter was placed within the porcine flap and fixed to a wooden board with two long steel nails. Afterward, contrast medium or physiological saline (0.9% sodium chloride solution) was injected through the ureter, and the urinary system was examined with a fluoroscopy unit or an ultrasound. Artificial stone material was implanted in the renal pelvis. After practicing, the model could be dissected for kidney examination and a technical analysis. Results The advantage of this model was simple to set up and inexpensive, by using widely available material. The biological bench model can be employed for percutanous renal access, tract dilation, nephroscopy, and stone disintegration in the training and learning of clinical practice. Imaging is feasible under fluoroscopic and ultrasound guidance. The kidney models were utilized in hands on courses with over 100 people, and 90.5% attendants rated the porcine kidney model for simulation of percutaneous renal surgery as 'very helpful" or "helpful". Conclusion This biological training model simulates realistically the clinical procedure of percutaneous nephrolithotomy under fluoroscopic and ultrasound guidance.

  18. Micro-to-nano biomechanical modeling for assisted biological cell injection.

    Science.gov (United States)

    Ladjal, Hamid; Hanus, Jean-Luc; Ferreira, Antoine

    2013-09-01

    To facilitate training of biological cell injection operations, we are developing an interactive virtual environment to simulate needle insertion into biological cells. This paper presents methodologies for dynamic modeling, visual/haptic display, and model validation of cell injection. We first investigate the challenging issues in the modeling of the biomechanical properties of living cells. We propose two dynamic models to simulate cell deformation and puncture. The first approach is based on the assumptions that the mechanical response of living cells is mainly determined by the cytoskeleton and that the cytoskeleton is organized as a tensegrity structure including microfilaments, microtubules, and intermediate filaments. Equivalent microtubules struts are represented with a linear mass-tensor finite-element model and equivalent microfilaments and intermediate filaments with viscoelastic Kelvin-Voigt elements. The second modeling method assumes the overall cell as an homogeneous hyperelastic model (St, Venant-Kirchhoff). Both graphic and haptic rendering are provided in real time to the operator through a 3-D virtual environment. Simulated responses are compared to experimental data to show the effectiveness of the proposed physically based model. PMID:23613019

  19. Introductory biology students' conceptual models and explanations of the origin of variation.

    Science.gov (United States)

    Speth, Elena Bray; Shaw, Neil; Momsen, Jennifer; Reinagel, Adam; Le, Paul; Taqieddin, Ranya; Long, Tammy

    2014-01-01

    Mutation is the key molecular mechanism generating phenotypic variation, which is the basis for evolution. In an introductory biology course, we used a model-based pedagogy that enabled students to integrate their understanding of genetics and evolution within multiple case studies. We used student-generated conceptual models to assess understanding of the origin of variation. By midterm, only a small percentage of students articulated complete and accurate representations of the origin of variation in their models. Targeted feedback was offered through activities requiring students to critically evaluate peers' models. At semester's end, a substantial proportion of students significantly improved their representation of how variation arises (though one-third still did not include mutation in their models). Students' written explanations of the origin of variation were mostly consistent with their models, although less effective than models in conveying mechanistic reasoning. This study contributes evidence that articulating the genetic origin of variation is particularly challenging for learners and may require multiple cycles of instruction, assessment, and feedback. To support meaningful learning of the origin of variation, we advocate instruction that explicitly integrates multiple scales of biological organization, assessment that promotes and reveals mechanistic and causal reasoning, and practice with explanatory models with formative feedback.

  20. Computational simulation of a new system modelling ions electromigration through biological membranes

    Science.gov (United States)

    2013-01-01

    Background The interest in cell membrane has grown drastically for their important role as controllers of biological functions in health and illness. In fact most important physiological processes are intimately related to the transport ability of the membrane, such as cell adhesion, cell signaling and immune defense. Furthermore, ion migration is connected with life-threatening pathologies such as metastases and atherosclerosis. Consequently, a large amount of research is consecrated to this topic. To better understand cell membranes, more accurate models of ionic flux are required and also their computational simulations. Results This paper is presenting the numerical simulation of a more general system modelling ion migration through biological membranes. The model includes both the effects of biochemical reaction between ions and fixed charges. The model is a nonlinear coupled system. In the first we describe the mathematical model. To realize the numerical simulation of our model, we proceed by a finite element discretisation and then by choosing an appropriate resolution algorithm to the nonlinearities. Conclusions We give numerical simulations obtained for different popular models of enzymatic reaction which were compared to those obtained in literature on systems of ordinary differential equations. The results obtained show a complete agreement between the two modellings. Furthermore, various numerical experiments are presented to confirm the accuracy, efficiency and stability of the proposed method. In particular, we show that the scheme is unconditionally stable and second-order accurate in space. PMID:24010551

  1. Exposure factors for marine eutrophication impacts assessment based on a mechanistic biological model

    DEFF Research Database (Denmark)

    Cosme, Nuno Miguel Dias; Koski, Marja; Hauschild, Michael Zwicky

    2015-01-01

    ). This pathway is typical of marine eutrophication. A model is proposed to mechanistically estimate the response of coastal marine ecosystems to N inputs. It addresses the biological processes of nutrient-limited primary production (PP), metazoan consumption, and bacterial degradation, in four distinct sinking...... is essential to estimate a marine eutrophication impacts indicator in Life Cycle Impact Assessment (LCIA) of anthropogenic-N emissions. Every relevant process was modelled and the uncertainty of the driving parameters considered low suggesting valid applicability in characterisation modelling in LCIA....

  2. Kinetic limits for pair-interaction driven master equations and biological swarm models

    OpenAIRE

    Carlen, Eric; Degond, Pierre; Wennberg, Bernt

    2011-01-01

    We consider a class of stochastic processes modeling binary interactions in an N-particle system. Examples of such systems can be found in the modeling of biological swarms. They lead to the definition of a class of master equations that we call pair interaction driven master equations. We prove a propagation of chaos result for this class of master equations which generalizes Mark Kac's well know result for the Kac model in kinetic theory. We use this result to study kinetic limits for two b...

  3. Hybrid neural modelling of an anaerobic digester with respect to biological constraints.

    Science.gov (United States)

    Karama, A; Bernard, O; Gouzé, J L; Benhammou, A; Dochain, D

    2001-01-01

    A hybrid model for an anaerobic digestion process is proposed. The fermentation is assumed to be performed in two steps, acidogenesis and methanogenesis, by two bacterial populations. The model is based on mass balance equations, and the bacterial growth rates are represented by neural networks. In order to guarantee the biological meaning of the hybrid model (positivity of the concentrations, boundedness, saturation or inhibition of the growth rates) outside the training data set, a method that imposes constraints in the neural network is proposed. The method is applied to experimental data from a fixed bed reactor.

  4. Probability-based model of protein-protein interactions on biological timescales

    Directory of Open Access Journals (Sweden)

    Bates Paul A

    2006-12-01

    Full Text Available Abstract Background Simulation methods can assist in describing and understanding complex networks of interacting proteins, providing fresh insights into the function and regulation of biological systems. Recent studies have investigated such processes by explicitly modelling the diffusion and interactions of individual molecules. In these approaches, two entities are considered to have interacted if they come within a set cutoff distance of each other. Results In this study, a new model of bimolecular interactions is presented that uses a simple, probability-based description of the reaction process. This description is well-suited to simulations on timescales relevant to biological systems (from seconds to hours, and provides an alternative to the previous description given by Smoluchowski. In the present approach (TFB the diffusion process is explicitly taken into account in generating the probability that two freely diffusing chemical entities will interact within a given time interval. It is compared to the Smoluchowski method, as modified by Andrews and Bray (AB. Conclusion When implemented, the AB & TFB methods give equivalent results in a variety of situations relevant to biology. Overall, the Smoluchowski method as modified by Andrews and Bray emerges as the most simple, robust and efficient method for simulating biological diffusion-reaction processes currently available.

  5. On the Modelling of Biological Patterns with Mechanochemical Models: Insights from Analysis and Computation

    KAUST Repository

    Moreo, P.

    2009-11-14

    The diversity of biological form is generated by a relatively small number of underlying mechanisms. Consequently, mathematical and computational modelling can, and does, provide insight into how cellular level interactions ultimately give rise to higher level structure. Given cells respond to mechanical stimuli, it is therefore important to consider the effects of these responses within biological self-organisation models. Here, we consider the self-organisation properties of a mechanochemical model previously developed by three of the authors in Acta Biomater. 4, 613-621 (2008), which is capable of reproducing the behaviour of a population of cells cultured on an elastic substrate in response to a variety of stimuli. In particular, we examine the conditions under which stable spatial patterns can emerge with this model, focusing on the influence of mechanical stimuli and the interplay of non-local phenomena. To this end, we have performed a linear stability analysis and numerical simulations based on a mixed finite element formulation, which have allowed us to study the dynamical behaviour of the system in terms of the qualitative shape of the dispersion relation. We show that the consideration of mechanotaxis, namely changes in migration speeds and directions in response to mechanical stimuli alters the conditions for pattern formation in a singular manner. Furthermore without non-local effects, responses to mechanical stimuli are observed to result in dispersion relations with positive growth rates at arbitrarily large wavenumbers, in turn yielding heterogeneity at the cellular level in model predictions. This highlights the sensitivity and necessity of non-local effects in mechanically influenced biological pattern formation models and the ultimate failure of the continuum approximation in their absence. © 2009 Society for Mathematical Biology.

  6. Modelling the biological performance of a side-stream membrane bioreactor using ASM1

    Institute of Scientific and Technical Information of China (English)

    TIAN Ke-jun; LIU Xin-ai; JIANG Tao; M.D. Kennedy; J.C. Schippers; P.A. Vanrolleghem

    2004-01-01

    Membrane bioreactors(MBRs) are attracting global interest but the mathematical modeling of the biological performance of MBRs remains very limited. This study focuses on the modelling of a side-stream MBR system using Activated Sludge Model No.1(ASM1), and comparing the results with the modelling of traditional activated sludge processes. ASM1 parameters relevant for the long-term biological behaviour in MBR systems were calibrated(i.e. YH=0.72gCOD/gCOD, YA=0.25gCOD/gN, bH=0.25 d-1, bA=0.080 d-1 and fP=0.06), and generally agreed with the parameters in traditional activated sludge processes, with the exception that a higher autotrophic biomass decay rate was observed in the MBR. A sensitivity analysis for steady state operation and DO dynamics suggested that the biological performance of the MBR system(the sludge concentration, effluent quality and the DO dynamics) are very sensitive to the parameters(i.e. YH, YA, bH, bA, (maxH and (maxA), and influent wastewater components(XI, Ss, Xs, SNH).

  7. The biological carbon pump in the ocean: Reviewing model representations and its feedbacks on climate perturbations.

    Science.gov (United States)

    Hülse, Dominik; Arndt, Sandra; Ridgwell, Andy; Wilson, Jamie

    2016-04-01

    The ocean-sediment system, as the biggest carbon reservoir in the Earth's carbon cycle, plays a crucial role in regulating atmospheric carbon dioxide concentrations and climate. Therefore, it is essential to constrain the importance of marine carbon cycle feedbacks on global warming and ocean acidification. Arguably, the most important single component of the ocean's carbon cycle is the so-called "biological carbon pump". It transports carbon that is fixed in the light-flooded surface layer of the ocean to the deep ocean and the surface sediment, where it is degraded/dissolved or finally buried in the deep sediments. Over the past decade, progress has been made in understanding different factors that control the efficiency of the biological carbon pump and their feedbacks on the global carbon cycle and climate (i.e. ballasting = ocean acidification feedback; temperature dependant organic matter degradation = global warming feedback; organic matter sulphurisation = anoxia/euxinia feedback). Nevertheless, many uncertainties concerning the interplay of these processes and/or their relative significance remain. In addition, current Earth System Models tend to employ empirical and static parameterisations of the biological pump. As these parametric representations are derived from a limited set of present-day observations, their ability to represent carbon cycle feedbacks under changing climate conditions is limited. The aim of my research is to combine past carbon cycling information with a spatially resolved global biogeochemical model to constrain the functioning of the biological pump and to base its mathematical representation on a more mechanistic approach. Here, I will discuss important aspects that control the efficiency of the ocean's biological carbon pump, review how these processes of first order importance are mathematically represented in existing Earth system Models of Intermediate Complexity (EMIC) and distinguish different approaches to approximate

  8. A Computational Systems Biology Software Platform for Multiscale Modeling and Simulation: Integrating Whole-Body Physiology, Disease Biology, and Molecular Reaction Networks

    Science.gov (United States)

    Eissing, Thomas; Kuepfer, Lars; Becker, Corina; Block, Michael; Coboeken, Katrin; Gaub, Thomas; Goerlitz, Linus; Jaeger, Juergen; Loosen, Roland; Ludewig, Bernd; Meyer, Michaela; Niederalt, Christoph; Sevestre, Michael; Siegmund, Hans-Ulrich; Solodenko, Juri; Thelen, Kirstin; Telle, Ulrich; Weiss, Wolfgang; Wendl, Thomas; Willmann, Stefan; Lippert, Joerg

    2011-01-01

    Today, in silico studies and trial simulations already complement experimental approaches in pharmaceutical R&D and have become indispensable tools for decision making and communication with regulatory agencies. While biology is multiscale by nature, project work, and software tools usually focus on isolated aspects of drug action, such as pharmacokinetics at the organism scale or pharmacodynamic interaction on the molecular level. We present a modeling and simulation software platform consisting of PK-Sim® and MoBi® capable of building and simulating models that integrate across biological scales. A prototypical multiscale model for the progression of a pancreatic tumor and its response to pharmacotherapy is constructed and virtual patients are treated with a prodrug activated by hepatic metabolization. Tumor growth is driven by signal transduction leading to cell cycle transition and proliferation. Free tumor concentrations of the active metabolite inhibit Raf kinase in the signaling cascade and thereby cell cycle progression. In a virtual clinical study, the individual therapeutic outcome of the chemotherapeutic intervention is simulated for a large population with heterogeneous genomic background. Thereby, the platform allows efficient model building and integration of biological knowledge and prior data from all biological scales. Experimental in vitro model systems can be linked with observations in animal experiments and clinical trials. The interplay between patients, diseases, and drugs and topics with high clinical relevance such as the role of pharmacogenomics, drug–drug, or drug–metabolite interactions can be addressed using this mechanistic, insight driven multiscale modeling approach. PMID:21483730

  9. A computational systems biology software platform for multiscale modeling and simulation: Integrating whole-body physiology, disease biology, and molecular reaction networks

    Directory of Open Access Journals (Sweden)

    Thomas eEissing

    2011-02-01

    Full Text Available Today, in silico studies and trial simulations already complement experimental approaches in pharmaceutical R&D and have become indispensable tools for decision making and communication with regulatory agencies. While biology is multi-scale by nature, project work and software tools usually focus on isolated aspects of drug action, such as pharmacokinetics at the organism scale or pharmacodynamic interaction on the molecular level. We present a modeling and simulation software platform consisting of PK-Sim® and MoBi® capable of building and simulating models that integrate across biological scales. A prototypical multiscale model for the progression of a pancreatic tumor and its response to pharmacotherapy is constructed and virtual patients are treated with a prodrug activated by hepatic metabolization. Tumor growth is driven by signal transduction leading to cell cycle transition and proliferation. Free tumor concentrations of the active metabolite inhibit Raf kinase in the signaling cascade and thereby cell cycle progression. In a virtual clinical study, the individual therapeutic outcome of the chemotherapeutic intervention is simulated for a large population with heterogeneous genomic background. Thereby, the platform allows efficient model building and integration of biological knowledge and prior data from all biological scales. Experimental in vitro model systems can be linked with observations in animal experiments and clinical trials. The interplay between patients, diseases, and drugs and topics with high clinical relevance such as the role of pharmacogenomics, drug-drug or drug-metabolite interactions can be addressed using this mechanistic, insight driven multiscale modeling approach.

  10. Conceptual modeling in systems biology fosters empirical findings: the mRNA lifecycle.

    Directory of Open Access Journals (Sweden)

    Dov Dori

    Full Text Available One of the main obstacles to understanding complex biological systems is the extent and rapid evolution of information, way beyond the capacity individuals to manage and comprehend. Current modeling approaches and tools lack adequate capacity to model concurrently structure and behavior of biological systems. Here we propose Object-Process Methodology (OPM, a holistic conceptual modeling paradigm, as a means to model both diagrammatically and textually biological systems formally and intuitively at any desired number of levels of detail. OPM combines objects, e.g., proteins, and processes, e.g., transcription, in a way that is simple and easily comprehensible to researchers and scholars. As a case in point, we modeled the yeast mRNA lifecycle. The mRNA lifecycle involves mRNA synthesis in the nucleus, mRNA transport to the cytoplasm, and its subsequent translation and degradation therein. Recent studies have identified specific cytoplasmic foci, termed processing bodies that contain large complexes of mRNAs and decay factors. Our OPM model of this cellular subsystem, presented here, led to the discovery of a new constituent of these complexes, the translation termination factor eRF3. Association of eRF3 with processing bodies is observed after a long-term starvation period. We suggest that OPM can eventually serve as a comprehensive evolvable model of the entire living cell system. The model would serve as a research and communication platform, highlighting unknown and uncertain aspects that can be addressed empirically and updated consequently while maintaining consistency.

  11. Large-scale executable biology using rapid integration of computational models

    Directory of Open Access Journals (Sweden)

    Vladimir Rogojin

    2016-04-01

    Full Text Available We plan to develop a systematic framework for assembling large-scale computational biological models by reusing and combining already existing modelling efforts. Our goal is to build a software platform that will compile large-scale biomodels through successive integrations of smaller modules. The modules can be arbitrary executable programs accompanied by a set of (I/O interface variables; they may also have an internal structure (such as a metabolic network, interaction network, etc. that yields its executable part in a well defined way. Firstly, wherever possible, modules with the compatible internal structure will be joined by combining their structure and by producing new larger executable modules (like, combining two metabolic networks, etc.. Then, irrespective of the underlying internal structure and modelling formalisms, all the modules will be integrated through connecting their overlapping interface variables. The resulting composed model will be regarded as an executable program itself and it will be simulated by running its submodules in parallel and synchronizing them via their I/O variables. This composed model in its turn can also act as a sub-module for some other even large composite model. The major goal of this project is to deliver a powerful large-scale modeling methodology for the primary use in the fields of Computational Systems Biology and Bioinformatics.

  12. Particle-based model to simulate the micromechanics of biological cells

    Science.gov (United States)

    van Liedekerke, P.; Tijskens, E.; Ramon, H.; Ghysels, P.; Samaey, G.; Roose, D.

    2010-06-01

    This paper is concerned with addressing how biological cells react to mechanical impulse. We propose a particle based model to numerically study the mechanical response of these cells with subcellular detail. The model focuses on a plant cell in which two important features are present: (1) the cell’s interior liquidlike phase inducing hydrodynamic phenomena, and (2) the cell wall, a viscoelastic solid membrane that encloses the protoplast. In this particle modeling framework, the cell fluid is modeled by a standard smoothed particle hydrodynamics (SPH) technique. For the viscoelastic solid phase (cell wall), a discrete element method (DEM) is proposed. The cell wall hydraulic conductivity (permeability) is built in through a constitutive relation in the SPH formulation. Simulations show that the SPH-DEM model is in reasonable agreement with compression experiments on an in vitro cell and with analytical models for the basic dynamical modes of a spherical liquid filled shell. We have performed simulations to explore more complex situations such as relaxation and impact, thereby considering two cell types: a stiff plant type and a soft animal-like type. Their particular behavior (force transmission) as a function of protoplasm and cell wall viscosity is discussed. We also show that the mechanics during and after cell failure can be modeled adequately. This methodology has large flexibility and opens possibilities to quantify problems dealing with the response of biological cells to mechanical impulses, e.g., impact, and the prediction of damage on a (sub)cellular scale.

  13. Development of a coupled physical-biological ecosystem model ECOSMO - Part I: Model description and validation for the North Sea

    DEFF Research Database (Denmark)

    Schrum, Corinna; Alekseeva, I.; St. John, Michael

    2006-01-01

    A 3-D coupled biophysical model ECOSMO (ECOSystem MOdel) has been developed. The biological module of ECOSMO is based on lower trophic level interactions between two phyto- and two zooplankton components. The dynamics of the different phytoplankton components are governed by the availability...... showed that the model, based on consideration of limiting processes, is able to reproduce the observed spatial and seasonal variability of the North Sea ecosystem e.g. the spring bloom, summer sub-surface production and the fall bloom. Distinct differences in regional characteristics of diatoms...

  14. Modeling Nitrous Oxide Production during Biological Nitrogen Removal via Nitrification and Denitrification: Extensions to the General ASM Models

    DEFF Research Database (Denmark)

    Ni, Bing-Jie; Ruscalleda, Maël; Pellicer i Nàcher, Carles;

    2011-01-01

    Nitrous oxide (N2O) can be formed during biological nitrogen (N) removal processes. In this work, a mathematical model is developed that describes N2O production and consumption during activated sludge nitrification and denitrification. The well-known ASM process models are extended to capture N2O...... the four denitrification steps, the last one (N2O reduction to N2) seems to be inhibited first when O2 is present. Overall, N2O production can account for 0.1–25% of the consumed N in different nitrification and denitrification systems, which can be well simulated by the proposed model. In conclusion, we...

  15. Ice Formation in Model Biological Membranes in the Presence of Cryoprotectors

    CERN Document Server

    Kiselev, M A; Kisselev, A M; Ollivon, M

    2000-01-01

    Ice formation in model biological membranes is studied by SAXS and WAXS in the presence of cryoprotectors: dimethyl sulfoxide and glycerol. Three types of phospholipid membranes: DPPC, DMPC, DSPC are chosen for the investigation as well-studied model biological membranes. A special cryostat is used for sample cooling from 14.1C to -55.4C. The ice formation is only detected by WAXS in binary phospholipid/water and ternary phospholipid/cryoprotector/water systems in the condition of excess solvent. Ice formation in a binary phospholipid/water system creates an abrupt decrease of the membrane repeat distance by delta-d, so-called ice-induced dehydration of intermembrane space. The value of delta-d decreases as the cryoprotector concentration increases. The formation of ice does not influence the membrane structure (delta-d = 0) for cryoprotector mole fractions higher than 0.05.

  16. The planarian flatworm: an in vivo model for stem cell biology and nervous system regeneration

    Directory of Open Access Journals (Sweden)

    Luca Gentile

    2011-01-01

    Full Text Available Planarian flatworms are an exception among bilaterians in that they possess a large pool of adult stem cells that enables them to promptly regenerate any part of their body, including the brain. Although known for two centuries for their remarkable regenerative capabilities, planarians have only recently emerged as an attractive model for studying regeneration and stem cell biology. This revival is due in part to the availability of a sequenced genome and the development of new technologies, such as RNA interference and next-generation sequencing, which facilitate studies of planarian regeneration at the molecular level. Here, we highlight why planarians are an exciting tool in the study of regeneration and its underlying stem cell biology in vivo, and discuss the potential promises and current limitations of this model organism for stem cell research and regenerative medicine.

  17. Solving the Advection-Diffusion Equations in Biological Contexts using the Cellular Potts Model

    CERN Document Server

    Dan, D; Chen, K; Glazier, J A; Dan, Debasis; Mueller, Chris; Chen, Kun; Glazier, James A.

    2005-01-01

    The Cellular Potts Model (CPM) is a robust, cell-level methodology for simulation of biological tissues and morphogenesis. Both tissue physiology and morphogenesis depend on diffusion of chemical morphogens in the extra-cellular fluid or matrix (ECM). Standard diffusion solvers applied to the cellular potts model use finite difference methods on the underlying CPM lattice. However, these methods produce a diffusing field tied to the underlying lattice, which is inaccurate in many biological situations in which cell or ECM movement causes advection rapid compared to diffusion. Finite difference schemes suffer numerical instabilities solving the resulting advection-diffusion equations. To circumvent these problems we simulate advection-diffusion within the framework of the CPM using off-lattice finite-difference methods. We define a set of generalized fluid particles which detach advection and diffusion from the lattice. Diffusion occurs between neighboring fluid particles by local averaging rules which approxi...

  18. Biologically motivated tumor-models used for risk estimates at low doses of radiation

    International Nuclear Information System (INIS)

    Biologically motivated tumour models are necessary for estimating the radiation risk at low doses, as epidemiological studies cannot give significant results for sufficiently small risks as a matter of principle. The tumour models combine knowledge about the mechanisms of tumour development with epidemiological data and results of animal experiments. The are usefuls for testing hypothesis on radiation carcinogenesis. In the framework of EU-projects European partners work on the difficult task of quantifying the relevant biological parameters, and the radiation risk at low doses. Various data sets are described well by assuming an initiating and a promoting action of radiation. As an example a new analysis of radon-induced lung tumours in the Colorado plateau miners is discussed. The estimated lifetime relative risk extrapolated to exposures as they hold in indoor situations is substantially lower than estimated in the BEIR VI report. (orig.)

  19. Inferring Pairwise Interactions from Biological Data Using Maximum-Entropy Probability Models.

    Directory of Open Access Journals (Sweden)

    Richard R Stein

    2015-07-01

    Full Text Available Maximum entropy-based inference methods have been successfully used to infer direct interactions from biological datasets such as gene expression data or sequence ensembles. Here, we review undirected pairwise maximum-entropy probability models in two categories of data types, those with continuous and categorical random variables. As a concrete example, we present recently developed inference methods from the field of protein contact prediction and show that a basic set of assumptions leads to similar solution strategies for inferring the model parameters in both variable types. These parameters reflect interactive couplings between observables, which can be used to predict global properties of the biological system. Such methods are applicable to the important problems of protein 3-D structure prediction and association of gene-gene networks, and they enable potential applications to the analysis of gene alteration patterns and to protein design.

  20. Predicting spiral wave patterns from cell properties in a model of biological self-organization

    Science.gov (United States)

    Geberth, Daniel; Hütt, Marc-Thorsten

    2008-09-01

    In many biological systems, biological variability (i.e., systematic differences between the system components) can be expected to outrank statistical fluctuations in the shaping of self-organized patterns. In principle, the distribution of single-element properties should thus allow predicting features of such patterns. For a mathematical model of a paradigmatic and well-studied pattern formation process, spiral waves of cAMP signaling in colonies of the slime mold Dictyostelium discoideum, we explore this possibility and observe a pronounced anticorrelation between spiral waves and cell properties (namely, the firing rate) and particularly a clustering of spiral wave tips in regions devoid of spontaneously firing (pacemaker) cells. Furthermore, we observe local inhomogeneities in the distribution of spiral chiralities, again induced by the pacemaker distribution. We show that these findings can be explained by a simple geometrical model of spiral wave generation.

  1. Geomorphic controls on biological soil crust distribution: A conceptual model from the Mojave Desert (USA)

    Science.gov (United States)

    Williams, Amanda J.; Buck, Brenda J.; Soukup, Deborah A.; Merkler, Douglas J.

    2013-08-01

    Biological soil crusts (BSCs) are bio-sedimentary features that play critical geomorphic and ecological roles in arid environments. Extensive mapping, surface characterization, GIS overlays, and statistical analyses explored relationships among BSCs, geomorphology, and soil characteristics in a portion of the Mojave Desert (USA). These results were used to develop a conceptual model that explains the spatial distribution of BSCs. In this model, geologic and geomorphic processes control the ratio of fine sand to rocks, which constrains the development of three surface cover types and biogeomorphic feedbacks across intermontane basins. (1) Cyanobacteria crusts grow where abundant fine sand and negligible rocks form saltating sand sheets. Cyanobacteria facilitate moderate sand sheet activity that reduces growth potential of mosses and lichens. (2) Extensive tall moss-lichen pinnacled crusts are favored on early to late Holocene surfaces composed of mixed rock and fine sand. Moss-lichen crusts induce a dust capture feedback mechanism that promotes further crust propagation and forms biologically-mediated vesicular (Av) horizons. The presence of thick biogenic vesicular horizons supports the interpretation that BSCs are long-lived surface features. (3) Low to moderate density moss-lichen crusts grow on early Holocene and older geomorphic surfaces that display high rock cover and negligible surficial fine sand. Desert pavement processes and abiotic vesicular horizon formation dominate these surfaces and minimize bioturbation potential. The biogeomorphic interactions that sustain these three surface cover trajectories support unique biological communities and soil conditions, thereby sustaining ecological stability. The proposed conceptual model helps predict BSC distribution within intermontane basins to identify biologically sensitive areas, set reference conditions for ecological restoration, and potentially enhance arid landscape models, as scientists address impacts

  2. Positive Almost Periodic Solution on a Nonlinear Logistic Biological Model with Grazing Rates

    Institute of Scientific and Technical Information of China (English)

    NI Hua; TIAN Li-xin

    2013-01-01

    In this paper,we study the following nonlinear biological model dx(t)/dt =x(t)[a(t)-b(t)xα(t)] + f(t,xt),by using fixed pointed theorem,the sufficient conditions of the existence of unique positive almost periodic solution for the above system are obtained,by using the theories of stability,the sufficient conditions which guarantee the stability of the positive almost periodic solution are derived.

  3. Application of a calculational model for thermal neutrons through biological shields

    International Nuclear Information System (INIS)

    In this work a computational program, based on the Boltzmann transport integrodifferential equation, is applied. The scattering kernel is represented by the synthetic scattering model. The behaviour of thermal neutron in hydrogenous materials, which can be used as biological shields, are studied. These materials are water, polyethylene, Oak-Ridge concrete, ordinary concrete and manganese concrete. The data obtained are presented in tables. The results are analysed and compared with similar experimental values. Safety evaluation and environmental impact are discussed. 2 tabs

  4. Simple model of self-organized biological evolution as completely integrable dissipative system

    OpenAIRE

    Pis'mak, Yu. M.

    1998-01-01

    The Bak-Sneppen model of self-organized biological evolution of an infinite ecosystem of randomly interacting species is represented in terms of an infinite set of variables which can be considered as an analog to the set of integrals of motion of completely integrable system. Each of this variables remains to be constant but its influence on the evolution process is restricted in time and after definite moment its value is excluded from description of the system dynamics.

  5. THE SEMANTIC INFORMATION MODEL FOR CLUSTER "BIOLOGICAL ACTIVE SUBSTANCES IN FEEDING AND COSMETICS"

    OpenAIRE

    Stefan Velikov

    2015-01-01

    The article presents a unified data model for cluster "Biologically active substances in feeding and cosmetics”. The basic information components and their relationship are indicated. The information system provides the data in a structured format thereby realize the concept of interoperability and allows the integration of different systems, storages, processing and re-using of information. The best practices for combining and adapting information resources to support semantic interoperabili...

  6. What controls biological productivity in coastal upwelling systems? Insights from a comparative modeling study

    OpenAIRE

    Z. Lachkar; Gruber, N.

    2011-01-01

    The magnitude of the biological productivity in Eastern Boundary Upwelling Systems (EBUS) is traditionally viewed as directly reflecting the upwelling intensity. Yet, different EBUS show different sensitivities of productivity to upwelling-favorable winds (Carr and Kearns, 2003). Here, using a comparative modeling study of the California Current System (California CS) and Canary Current System (Canary CS), we show how physical and environmental factors, such as light, temperature and c...

  7. The female gametophyte: an emerging model for cell type-specific systems biology in plant development

    Directory of Open Access Journals (Sweden)

    Marc William Schmid

    2015-11-01

    Full Text Available Systems biology, a holistic approach describing a system emerging from the interactions of its molecular components, critically depends on accurate qualitative determination and quantitative measurements of these components. Development and improvement of large-scale profiling methods (omics now facilitates comprehensive measurements of many relevant molecules. For multicellular organisms, such as animals, fungi, algae, and plants, the complexity of the system is augmented by the presence of specialized cell types and organs, and a complex interplay within and between them. Cell type-specific analyses are therefore crucial for the understanding of developmental processes and environmental responses. This review first gives an overview of current methods used for large-scale profiling of specific cell types exemplified by recent advances in plant biology. The focus then lies on suitable model systems to study plant development and cell type specification. We introduce the female gametophyte of flowering plants as an ideal model to study fundamental developmental processes. Moreover, the female reproductive lineage is of importance for the emergence of evolutionary novelties such as an unequal parental contribution to the tissue nurturing the embryo or the clonal production of seeds by asexual reproduction (apomixis. Understanding these processes is not only interesting from a developmental or evolutionary perspective, but bears great potential for further crop improvement and the simplification of breeding efforts. We finally highlight novel methods, which are already available or which will likely soon facilitate large-scale profiling of the specific cell types of the female gametophyte in both model and non-model species. We conclude that it may take only few years until an evolutionary systems biology approach toward female gametogenesis may decipher some of its biologically most interesting and economically most valuable processes.

  8. Modelling biological processes in WWTP; Modelado de procesos biologicos en las EDAR

    Energy Technology Data Exchange (ETDEWEB)

    Carpes, G.

    2009-07-01

    Biological technologies by active sludges are the most used in wastewater treatments. Multiple variants are affected in the characterization of this process, like wastewater treatment plant (WWTP) design, features and concentration of sludge, dissolved oxygen concentration and characteristics of the wastewater, including temperature and nutrients. Mathematical formula applied to WWTP modelling are presented to design its operation and to test the most important parameters, too. It is necessary to optimize the process in WWTP. (Author) 19 refs.

  9. Porcine incisional hernia model: Evaluation of biologically derived intact extracellular matrix repairs

    OpenAIRE

    Monteiro, Gary A; Delossantos, Aubrey I.; Rodriguez, Neil L.; Patel, Paarun; Franz, Michael G.; Wagner, Christopher T.

    2013-01-01

    We compared fascial wounds repaired with non-cross-linked intact porcine-derived acellular dermal matrix versus primary closure in a large-animal hernia model. Incisional hernias were created in Yucatan pigs and repaired after 3 weeks via open technique with suture-only primary closure or intraperitoneally placed porcine-derived acellular dermal matrix. Progressive changes in mechanical and biological properties of porcine-derived acellular dermal matrix and repair sites were assessed. Porcin...

  10. A Simpler Energy Transfer Efficiency Model to Predict Relative Biological Effect for Protons and Heavier Ions

    OpenAIRE

    Jones, Bleddyn

    2015-01-01

    The aim of this work is to predict relative biological effectiveness (RBE) for protons and clinically relevant heavier ions, by using a simplified semi-empirical process based on rational expectations and published experimental results using different ion species. The model input parameters are: Z (effective nuclear charge) and radiosensitivity parameters αL and βL of the control low linear energy transfer (LET) radiation. Sequential saturation processes are assumed for: (a) the position of t...

  11. Comparison of Model Calculations of Biological Damage from Exposure to Heavy Ions with Measurements

    Science.gov (United States)

    Kim, Myung-Hee Y.; Hada, Megumi; Cucinotta, Francis A.; Wu, Honglu

    2014-01-01

    The space environment consists of a varying field of radiation particles including high-energy ions, with spacecraft shielding material providing the major protection to astronauts from harmful exposure. Unlike low-LET gamma or X rays, the presence of shielding does not always reduce the radiation risks for energetic charged-particle exposure. Dose delivered by the charged particle increases sharply at the Bragg peak. However, the Bragg curve does not necessarily represent the biological damage along the particle path since biological effects are influenced by the track structures of both primary and secondary particles. Therefore, the ''biological Bragg curve'' is dependent on the energy and the type of the primary particle and may vary for different biological end points. Measurements of the induction of micronuclei (MN) have made across the Bragg curve in human fibroblasts exposed to energetic silicon and iron ions in vitro at two different energies, 300 MeV/nucleon and 1 GeV/nucleon. Although the data did not reveal an increased yield of MN at the location of the Bragg peak, the increased inhibition of cell progression, which is related to cell death, was found at the Bragg peak location. These results are compared to the calculations of biological damage using a stochastic Monte-Carlo track structure model, Galactic Cosmic Ray Event-based Risk Model (GERM) code (Cucinotta, et al., 2011). The GERM code estimates the basic physical properties along the passage of heavy ions in tissue and shielding materials, by which the experimental set-up can be interpreted. The code can also be used to describe the biophysical events of interest in radiobiology, cancer therapy, and space exploration. The calculation has shown that the severely damaged cells at the Bragg peak are more likely to go through reproductive death, the so called "overkill".

  12. A model for estimating body shape biological age based on clinical parameters associated with body composition

    Directory of Open Access Journals (Sweden)

    Bae CY

    2012-12-01

    Full Text Available Chul-Young Bae,1 Young Gon Kang,2 Young-Sung Suh,3 Jee Hye Han,4 Sung-Soo Kim,5 Kyung Won Shim61MediAge Research Center, Seoul, Korea; 2Chaum Power Aging Center, College of Medicine, CHA University, Seoul, Korea; 3Health Promotion Center, Keimyung University Dongsam Medical Center, Daegu, Korea; 4Department of Family Medicine, College of Medicine, Eulji University, Seoul, Korea; 5Department of Family Medicine, College of Medicine, Chungnam National University, Daejeon, Korea; 6Department of Family Medicine, Ewha Womans University Mokdong Hospital, Ewha Womans University, Seoul, KoreaBackground: To date, no studies have attempted to estimate body shape biological age using clinical parameters associated with body composition for the purposes of examining a person's body shape based on their age.Objective: We examined the relations between clinical parameters associated with body composition and chronological age, and proposed a model for estimating the body shape biological age.Methods: The study was conducted in 243,778 subjects aged between 20 and 90 years who received a general medical checkup at health promotion centers at university and community hospitals in Korea from 2004 to 2011.Results: In men, the clinical parameters with the highest correlation to age included the waist-to-hip ratio (r = 0.786, P < 0.001, hip circumference (r = −0.448, P < 0.001, and height (r = −0.377, P < 0.001. In women, the clinical parameters with the highest correlation to age include the waist-to-hip ratio (r = 0.859, P < 0.001, waist circumference (r = 0.580, P < 0.001, and hip circumference (r = 0.520, P < 0.001. To estimate the optimal body shape biological age based on clinical parameters associated with body composition, we performed a multiple regression analysis. In a model estimating the body shape biological age, the coefficient of determination (R2 was 0.71 in men and 0.76 in women.Conclusion: Our model for estimating body shape biological age

  13. The Biological Big Bang model for the major transitions in evolution

    Science.gov (United States)

    Koonin, Eugene V

    2007-01-01

    Background Major transitions in biological evolution show the same pattern of sudden emergence of diverse forms at a new level of complexity. The relationships between major groups within an emergent new class of biological entities are hard to decipher and do not seem to fit the tree pattern that, following Darwin's original proposal, remains the dominant description of biological evolution. The cases in point include the origin of complex RNA molecules and protein folds; major groups of viruses; archaea and bacteria, and the principal lineages within each of these prokaryotic domains; eukaryotic supergroups; and animal phyla. In each of these pivotal nexuses in life's history, the principal "types" seem to appear rapidly and fully equipped with the signature features of the respective new level of biological organization. No intermediate "grades" or intermediate forms between different types are detectable. Usually, this pattern is attributed to cladogenesis compressed in time, combined with the inevitable erosion of the phylogenetic signal. Hypothesis I propose that most or all major evolutionary transitions that show the "explosive" pattern of emergence of new types of biological entities correspond to a boundary between two qualitatively distinct evolutionary phases. The first, inflationary phase is characterized by extremely rapid evolution driven by various processes of genetic information exchange, such as horizontal gene transfer, recombination, fusion, fission, and spread of mobile elements. These processes give rise to a vast diversity of forms from which the main classes of entities at the new level of complexity emerge independently, through a sampling process. In the second phase, evolution dramatically slows down, the respective process of genetic information exchange tapers off, and multiple lineages of the new type of entities emerge, each of them evolving in a tree-like fashion from that point on. This biphasic model of evolution incorporates the

  14. FastGGM: An Efficient Algorithm for the Inference of Gaussian Graphical Model in Biological Networks.

    Science.gov (United States)

    Wang, Ting; Ren, Zhao; Ding, Ying; Fang, Zhou; Sun, Zhe; MacDonald, Matthew L; Sweet, Robert A; Wang, Jieru; Chen, Wei

    2016-02-01

    Biological networks provide additional information for the analysis of human diseases, beyond the traditional analysis that focuses on single variables. Gaussian graphical model (GGM), a probability model that characterizes the conditional dependence structure of a set of random variables by a graph, has wide applications in the analysis of biological networks, such as inferring interaction or comparing differential networks. However, existing approaches are either not statistically rigorous or are inefficient for high-dimensional data that include tens of thousands of variables for making inference. In this study, we propose an efficient algorithm to implement the estimation of GGM and obtain p-value and confidence interval for each edge in the graph, based on a recent proposal by Ren et al., 2015. Through simulation studies, we demonstrate that the algorithm is faster by several orders of magnitude than the current implemented algorithm for Ren et al. without losing any accuracy. Then, we apply our algorithm to two real data sets: transcriptomic data from a study of childhood asthma and proteomic data from a study of Alzheimer's disease. We estimate the global gene or protein interaction networks for the disease and healthy samples. The resulting networks reveal interesting interactions and the differential networks between cases and controls show functional relevance to the diseases. In conclusion, we provide a computationally fast algorithm to implement a statistically sound procedure for constructing Gaussian graphical model and making inference with high-dimensional biological data. The algorithm has been implemented in an R package named "FastGGM". PMID:26872036

  15. Determination of Biological Treatability Processes of Textile Wastewater and Implementation of a Fuzzy Logic Model

    Directory of Open Access Journals (Sweden)

    Harun Akif Kabuk

    2015-01-01

    Full Text Available This study investigated the biological treatability of textile wastewater. For this purpose, a membrane bioreactor (MBR was utilized for biological treatment after the ozonation process. Due to the refractory organic contents of textile wastewater that has a low biodegradability capacity, ozonation was implemented as an advanced oxidation process prior to the MBR system to increase the biodegradability of the wastewater. Textile wastewater, oxidized by ozonation, was fed to the MBR at different hydraulic retention times (HRT. During the process, color, chemical oxygen demand (COD, and biochemical oxygen demand (BOD removal efficiencies were monitored for 24-hour, 12-hour, 6-hour, and 3-hour retention times. Under these conditions, 94% color, 65% COD, and 55% BOD removal efficiencies were obtained in the MBR system. The experimental outputs were modeled with multiple linear regressions (MLR and fuzzy logic. MLR results suggested that color removal is more related to COD removal relative to BOD removal. A surface map of this issue was prepared with a fuzzy logic model. Furthermore, fuzzy logic was employed to the whole modeling of the biological system treatment. Determination coefficients for COD, BOD, and color removal efficiencies were 0.96, 0.97, and 0.92, respectively.

  16. The Role of Stochastic Models in Interpreting the Origins of Biological Chirality

    Directory of Open Access Journals (Sweden)

    Gábor Lente

    2010-04-01

    Full Text Available This review summarizes recent stochastic modeling efforts in the theoretical research aimed at interpreting the origins of biological chirality. Stochastic kinetic models, especially those based on the continuous time discrete state approach, have great potential in modeling absolute asymmetric reactions, experimental examples of which have been reported in the past decade. An overview of the relevant mathematical background is given and several examples are presented to show how the significant numerical problems characteristic of the use of stochastic models can be overcome by non-trivial, but elementary algebra. In these stochastic models, a particulate view of matter is used rather than the concentration-based view of traditional chemical kinetics using continuous functions to describe the properties system. This has the advantage of giving adequate description of single-molecule events, which were probably important in the origin of biological chirality. The presented models can interpret and predict the random distribution of enantiomeric excess among repetitive experiments, which is the most striking feature of absolute asymmetric reactions. It is argued that the use of the stochastic kinetic approach should be much more widespread in the relevant literature.

  17. Polarizable Mean-Field Model of Water for Biological Simulations with Amber and Charmm force fields

    CERN Document Server

    Leontyev, Igor

    2015-01-01

    Although a great number of computational models of water are available today, the majority of current biological simulations are done with simple models, such as TIP3P and SPC, developed almost thirty years ago and only slightly modified since then. The reason is that the non-polarizable force fields that are mostly used to describe proteins and other biological molecules are incompatible with more sophisticated modern polarizable models of water. The issue is electronic polarizability: in liquid state, in protein, and in vacuum the water molecule is polarized differently, and therefore has different properties; thus the only way to describe all these different media with the same model is to use a polarizable water model. However, to be compatible with the force field of the rest of the system, e.g. a protein, the latter should be polarizable as well. Here we describe a novel model of water that is in effect polarizable, and yet compatible with the standard non-polarizable force fields such as AMBER, CHARMM,...

  18. Hidden Markov models and other machine learning approaches in computational molecular biology

    Energy Technology Data Exchange (ETDEWEB)

    Baldi, P. [California Inst. of Tech., Pasadena, CA (United States)

    1995-12-31

    This tutorial was one of eight tutorials selected to be presented at the Third International Conference on Intelligent Systems for Molecular Biology which was held in the United Kingdom from July 16 to 19, 1995. Computational tools are increasingly needed to process the massive amounts of data, to organize and classify sequences, to detect weak similarities, to separate coding from non-coding regions, and reconstruct the underlying evolutionary history. The fundamental problem in machine learning is the same as in scientific reasoning in general, as well as statistical modeling: to come up with a good model for the data. In this tutorial four classes of models are reviewed. They are: Hidden Markov models; artificial Neural Networks; Belief Networks; and Stochastic Grammars. When dealing with DNA and protein primary sequences, Hidden Markov models are one of the most flexible and powerful alignments and data base searches. In this tutorial, attention is focused on the theory of Hidden Markov Models, and how to apply them to problems in molecular biology.

  19. Structural identifiability of systems biology models: a critical comparison of methods.

    Directory of Open Access Journals (Sweden)

    Oana-Teodora Chis

    Full Text Available Analysing the properties of a biological system through in silico experimentation requires a satisfactory mathematical representation of the system including accurate values of the model parameters. Fortunately, modern experimental techniques allow obtaining time-series data of appropriate quality which may then be used to estimate unknown parameters. However, in many cases, a subset of those parameters may not be uniquely estimated, independently of the experimental data available or the numerical techniques used for estimation. This lack of identifiability is related to the structure of the model, i.e. the system dynamics plus the observation function. Despite the interest in knowing a priori whether there is any chance of uniquely estimating all model unknown parameters, the structural identifiability analysis for general non-linear dynamic models is still an open question. There is no method amenable to every model, thus at some point we have to face the selection of one of the possibilities. This work presents a critical comparison of the currently available techniques. To this end, we perform the structural identifiability analysis of a collection of biological models. The results reveal that the generating series approach, in combination with identifiability tableaus, offers the most advantageous compromise among range of applicability, computational complexity and information provided.

  20. BSim: an agent-based tool for modeling bacterial populations in systems and synthetic biology.

    Science.gov (United States)

    Gorochowski, Thomas E; Matyjaszkiewicz, Antoni; Todd, Thomas; Oak, Neeraj; Kowalska, Kira; Reid, Stephen; Tsaneva-Atanasova, Krasimira T; Savery, Nigel J; Grierson, Claire S; di Bernardo, Mario

    2012-01-01

    Large-scale collective behaviors such as synchronization and coordination spontaneously arise in many bacterial populations. With systems biology attempting to understand these phenomena, and synthetic biology opening up the possibility of engineering them for our own benefit, there is growing interest in how bacterial populations are best modeled. Here we introduce BSim, a highly flexible agent-based computational tool for analyzing the relationships between single-cell dynamics and population level features. BSim includes reference implementations of many bacterial traits to enable the quick development of new models partially built from existing ones. Unlike existing modeling tools, BSim fully considers spatial aspects of a model allowing for the description of intricate micro-scale structures, enabling the modeling of bacterial behavior in more realistic three-dimensional, complex environments. The new opportunities that BSim opens are illustrated through several diverse examples covering: spatial multicellular computing, modeling complex environments, population dynamics of the lac operon, and the synchronization of genetic oscillators. BSim is open source software that is freely available from http://bsim-bccs.sf.net and distributed under the Open Source Initiative (OSI) recognized MIT license. Developer documentation and a wide range of example simulations are also available from the website. BSim requires Java version 1.6 or higher. PMID:22936991

  1. Bayesian model comparison and parameter inference in systems biology using nested sampling.

    Science.gov (United States)

    Pullen, Nick; Morris, Richard J

    2014-01-01

    Inferring parameters for models of biological processes is a current challenge in systems biology, as is the related problem of comparing competing models that explain the data. In this work we apply Skilling's nested sampling to address both of these problems. Nested sampling is a Bayesian method for exploring parameter space that transforms a multi-dimensional integral to a 1D integration over likelihood space. This approach focuses on the computation of the marginal likelihood or evidence. The ratio of evidences of different models leads to the Bayes factor, which can be used for model comparison. We demonstrate how nested sampling can be used to reverse-engineer a system's behaviour whilst accounting for the uncertainty in the results. The effect of missing initial conditions of the variables as well as unknown parameters is investigated. We show how the evidence and the model ranking can change as a function of the available data. Furthermore, the addition of data from extra variables of the system can deliver more information for model comparison than increasing the data from one variable, thus providing a basis for experimental design. PMID:24523891

  2. Bayesian model comparison and parameter inference in systems biology using nested sampling.

    Directory of Open Access Journals (Sweden)

    Nick Pullen

    Full Text Available Inferring parameters for models of biological processes is a current challenge in systems biology, as is the related problem of comparing competing models that explain the data. In this work we apply Skilling's nested sampling to address both of these problems. Nested sampling is a Bayesian method for exploring parameter space that transforms a multi-dimensional integral to a 1D integration over likelihood space. This approach focuses on the computation of the marginal likelihood or evidence. The ratio of evidences of different models leads to the Bayes factor, which can be used for model comparison. We demonstrate how nested sampling can be used to reverse-engineer a system's behaviour whilst accounting for the uncertainty in the results. The effect of missing initial conditions of the variables as well as unknown parameters is investigated. We show how the evidence and the model ranking can change as a function of the available data. Furthermore, the addition of data from extra variables of the system can deliver more information for model comparison than increasing the data from one variable, thus providing a basis for experimental design.

  3. BSim: an agent-based tool for modeling bacterial populations in systems and synthetic biology.

    Directory of Open Access Journals (Sweden)

    Thomas E Gorochowski

    Full Text Available Large-scale collective behaviors such as synchronization and coordination spontaneously arise in many bacterial populations. With systems biology attempting to understand these phenomena, and synthetic biology opening up the possibility of engineering them for our own benefit, there is growing interest in how bacterial populations are best modeled. Here we introduce BSim, a highly flexible agent-based computational tool for analyzing the relationships between single-cell dynamics and population level features. BSim includes reference implementations of many bacterial traits to enable the quick development of new models partially built from existing ones. Unlike existing modeling tools, BSim fully considers spatial aspects of a model allowing for the description of intricate micro-scale structures, enabling the modeling of bacterial behavior in more realistic three-dimensional, complex environments. The new opportunities that BSim opens are illustrated through several diverse examples covering: spatial multicellular computing, modeling complex environments, population dynamics of the lac operon, and the synchronization of genetic oscillators. BSim is open source software that is freely available from http://bsim-bccs.sf.net and distributed under the Open Source Initiative (OSI recognized MIT license. Developer documentation and a wide range of example simulations are also available from the website. BSim requires Java version 1.6 or higher.

  4. Whack-A-Mole Model: Towards unified description of biological effect caused by radiation-exposure

    CERN Document Server

    Manabe, Yuichiro; Tsunoyama, Yuichi; Nakajima, Hiroo; Nakamura, Issei; Bando, Masako

    2014-01-01

    We present a novel model to estimate biological effects caused by artificial radiation exposure, Whack-a-mole (WAM) model. It is important to take account of the recovery effects during the time course of the cellular reactions. The inclusion of the dose-rate dependence is essential in the risk estimation of low dose radiation, while nearly all the existing theoretical models relies on the total dose dependence only. By analyzing the experimental data of the relation between the radiation dose and the induced mutation frequency of 5 organisms, mouse, drosophila, chrysanthemum, maize and tradescantia, we found that all the data can be reproduced by WAM model. Most remarkably, a scaling function, which is derived from WAM model, consistently accounts for the observed mutation frequencies of 5 organisms. This is the first rationale to account for the dose rate dependence as well as to give a unified understanding of a general feature of organisms.

  5. Modeling a full scale oxidation ditch system, coupling hydrodynamics and biological kinetics using ASM1 model

    International Nuclear Information System (INIS)

    Optimising the aeration in oxidation ditch aims on one hand, a better wastewater quality and on the other hand, a reduction of the energy expenses of the treatment. given that the energy expenses relative to the aeration represents 60 to 80% of the operating costs of a wastewater treatment plant and given that the biological activity is strictly dependent on dissolved oxygen, the transfer of oxygen is considered as one of the key parameters of the process. (Author) 8 refs.

  6. Embodied Learning of a Generative Neural Model for Biological Motion Perception and Inference

    Directory of Open Access Journals (Sweden)

    Fabian eSchrodt

    2015-07-01

    Full Text Available Although an action observation network and mirror neurons for understanding the actions and intentions of others have been under deep, interdisciplinary consideration over recent years, it remains largely unknown how the brain manages to map visually perceived biological motion of others onto its own motor system. This paper shows how such a mapping may be established, even if the biologically motion is visually perceived from a new vantage point. We introduce a learning artificial neural network model and evaluate it on full body motion tracking recordings. The model implements an embodied, predictive inference approach. It first learns to correlate and segment multimodal sensory streams of own bodily motion. In doing so, it becomes able to anticipate motion progression, to complete missing modal information, and to self-generate learned motion sequences. When biological motion of another person is observed, this self-knowledge is utilized to recognize similar motion patterns and predict their progress. Due to the relative encodings, the model shows strong robustness in recognition despite observing rather large varieties of body morphology and posture dynamics. By additionally equipping the model with the capability to rotate its visual frame of reference, it is able to deduce the visual perspective onto the observed person, establishing full consistency to the embodied self-motion encodings by means of active inference. In further support of its neuro-cognitive plausibility, we also model typical bistable perceptions when crucial depth information is missing. In sum, the introduced neural model proposes a solution to the problem of how the human brain may establish correspondence between observed bodily motion and its own motor system, thus offering a mechanism that supports the development of mirror neurons.

  7. Neurospora as a model fungus for studies in cytogenetics and sexual biology at Stanford

    Indian Academy of Sciences (India)

    Namboori B Raju

    2009-03-01

    Dodge’s early work (1927–1940) on Neurospora genetics and sexual biology inspired Beadle and Tatum at Stanford to use N. crassa for their landmark discovery that genes specify enzymes. Neurospora has since become a model organism for numerous genetic, cytogenetic, biochemical, molecular and population biology studies. Neurospora is haploid in the vegetative phase with a transient diploid sexual phase. Its meiotic cells (asci) are large, allowing easy examination of dividing nuclei and chromosomes under a light microscope. The haploid meiotic products are themselves the sexual progeny that grow into vegetative cultures, thus avoiding the cumbersome testcrosses and complex dominance–recessive relationships, as in diploid organisms. The Perkins’ laboratory at Stanford (1949–2007) played a pivotal role in advancing our knowledge of Neurospora genetics, sexual biology, cytogenetics and population biology. Since 1974, I have taken advantage of various chromosome-staining methods to examine ascus and ascospore development in wild type and in numerous mutant strains. In addition, I have used GFP-tagged genes to visualize the expression or silencing of unpaired genes in a post-transcriptional gene silencing process (meiotic silencing by unpaired DNA) that operates specifically during meiosis. The genome of N. crassa contains over 10 000 protein-coding genes. Gene knockouts or mutations in specific sequences may now be readily correlated with the observed cytological defects in the sexual stage, thus advancing our molecular understanding of complex processes during ascus and ascospore development.

  8. What controls biological productivity in coastal upwelling systems? Insights from a comparative modeling study

    Directory of Open Access Journals (Sweden)

    Z. Lachkar

    2011-06-01

    Full Text Available The magnitude of the biological productivity in Eastern Boundary Upwelling Systems (EBUS is traditionally viewed as directly reflecting the upwelling intensity. Yet, different EBUS show different sensitivities of productivity to upwelling-favorable winds (Carr and Kearns, 2003. Here, using a comparative modeling study of the California Current System (California CS and Canary Current System (Canary CS, we show how physical and environmental factors, such as light, temperature and cross-shore circulation modulate the response of biological productivity to upwelling strength. To this end, we made a series of eddy-resolving simulations of the California CS and Canary CS using the Regional Ocean Modeling System (ROMS, coupled to a nitrogen based Nutrient-Phytoplankton-Zooplankton-Detritus (NPZD ecosystem model. We find the nutrient content of the euphotic zone to be 20 % smaller in the Canary CS relative to the California CS. Yet, the biological productivity is 50 % smaller in the latter. This is due to: (1 a faster nutrient-replete growth in the Canary CS relative to the California CS, related to a more favorable light and temperature conditions in the Canary CS, and (2 the longer nearshore water residence times in the Canary CS which lead to larger buildup of biomass in the upwelling zone, thereby enhancing the productivity. The longer residence times in the Canary CS appear to be associated with the wider continental shelves and the lower eddy activity characterizing this upwelling system. This results in a weaker offshore export of nutrients and organic matter, thereby increasing local nutrient recycling and enhancing the coupling between new and export production in the Northwest African system. Our results suggest that climate change induced perturbations such as upwelling favorable wind intensification might lead to contrasting biological responses in the California CS and the Canary CS, with major implications for the biogeochemical cycles

  9. Bioinformatics for transporter pharmacogenomics and systems biology: data integration and modeling with UML.

    Science.gov (United States)

    Yan, Qing

    2010-01-01

    Bioinformatics is the rational study at an abstract level that can influence the way we understand biomedical facts and the way we apply the biomedical knowledge. Bioinformatics is facing challenges in helping with finding the relationships between genetic structures and functions, analyzing genotype-phenotype associations, and understanding gene-environment interactions at the systems level. One of the most important issues in bioinformatics is data integration. The data integration methods introduced here can be used to organize and integrate both public and in-house data. With the volume of data and the high complexity, computational decision support is essential for integrative transporter studies in pharmacogenomics, nutrigenomics, epigenetics, and systems biology. For the development of such a decision support system, object-oriented (OO) models can be constructed using the Unified Modeling Language (UML). A methodology is developed to build biomedical models at different system levels and construct corresponding UML diagrams, including use case diagrams, class diagrams, and sequence diagrams. By OO modeling using UML, the problems of transporter pharmacogenomics and systems biology can be approached from different angles with a more complete view, which may greatly enhance the efforts in effective drug discovery and development. Bioinformatics resources of membrane transporters and general bioinformatics databases and tools that are frequently used in transporter studies are also collected here. An informatics decision support system based on the models presented here is available at http://www.pharmtao.com/transporter . The methodology developed here can also be used for other biomedical fields.

  10. Efficient estimation of the robustness region of biological models with oscillatory behavior.

    Directory of Open Access Journals (Sweden)

    Mochamad Apri

    Full Text Available Robustness is an essential feature of biological systems, and any mathematical model that describes such a system should reflect this feature. Especially, persistence of oscillatory behavior is an important issue. A benchmark model for this phenomenon is the Laub-Loomis model, a nonlinear model for cAMP oscillations in Dictyostelium discoideum. This model captures the most important features of biomolecular networks oscillating at constant frequencies. Nevertheless, the robustness of its oscillatory behavior is not yet fully understood. Given a system that exhibits oscillating behavior for some set of parameters, the central question of robustness is how far the parameters may be changed, such that the qualitative behavior does not change. The determination of such a "robustness region" in parameter space is an intricate task. If the number of parameters is high, it may be also time consuming. In the literature, several methods are proposed that partially tackle this problem. For example, some methods only detect particular bifurcations, or only find a relatively small box-shaped estimate for an irregularly shaped robustness region. Here, we present an approach that is much more general, and is especially designed to be efficient for systems with a large number of parameters. As an illustration, we apply the method first to a well understood low-dimensional system, the Rosenzweig-MacArthur model. This is a predator-prey model featuring satiation of the predator. It has only two parameters and its bifurcation diagram is available in the literature. We find a good agreement with the existing knowledge about this model. When we apply the new method to the high dimensional Laub-Loomis model, we obtain a much larger robustness region than reported earlier in the literature. This clearly demonstrates the power of our method. From the results, we conclude that the biological system underlying is much more robust than was realized until now.

  11. Dual-porosity model of solute diffusion in biological tissue modified by electroporation.

    Science.gov (United States)

    Mahnič-Kalamiza, Samo; Miklavčič, Damijan; Vorobiev, Eugène

    2014-07-01

    In many electroporation applications mass transport in biological tissue is of primary concern. This paper presents a theoretical advancement in the field and gives some examples of model use in electroporation applications. The study focuses on post-treatment solute diffusion. We use a dual-porosity approach to describe solute diffusion in electroporated biological tissue. The cellular membrane presents a hindrance to solute transport into the extracellular space and is modeled as electroporation-dependent porosity, assigned to the intracellular space (the finite rate of mass transfer within an individual cell is not accounted for, for reasons that we elaborate on). The second porosity is that of the extracellular space, through which solute vacates a block of tissue. The model can be used to study extraction out of or introduction of solutes into tissue, and we give three examples of application, a full account of model construction, validation with experiments, and a parametrical analysis. To facilitate easy implementation and experimentation by the reader, the complete derivation of the analytical solution for a simplified example is presented. Validation is done by comparing model results to experimentally-obtained data; we modeled kinetics of sucrose extraction by diffusion from sugar beet tissue in laboratory-scale experiments. The parametrical analysis demonstrates the importance of selected physicochemical and geometrical properties of the system, illustrating possible outcomes of applying the model to different electroporation applications. The proposed model is a new platform that supports rapid extension by state-of-the-art models of electroporation phenomena, developed as latest achievements in the field of electroporation.

  12. Biological modelling of the radiation dose escalation effect of regional hyperthermia in cervical cancer

    International Nuclear Information System (INIS)

    Locoregional hyperthermia combined with radiotherapy significantly improves locoregional control and overall survival for cervical tumors compared to radiotherapy alone. In this study biological modelling is applied to quantify the effect of radiosensitization for three cervical cancer patients to evaluate the improvement in equivalent dose for the combination treatment with radiotherapy and hyperthermia. The Linear-Quadratic (LQ) model extended with temperature-dependent LQ-parameters α and β was used to model radiosensitization by hyperthermia and to calculate the conventional radiation dose that is equivalent in biological effect to the combined radiotherapy and hyperthermia treatment. External beam radiotherapy planning was performed based on a prescription dose of 46Gy in 23 fractions of 2Gy. Hyperthermia treatment using the AMC-4 system was simulated based on the actual optimized system settings used during treatment. The simulated hyperthermia treatments for the 3 patients yielded a T50 of 40.1 °C, 40.5 °C, 41.1 °C and a T90 of 39.2 °C, 39.7 °C, 40.4 °C, respectively. The combined radiotherapy and hyperthermia treatment resulted in a D95 of 52.5Gy, 55.5Gy, 56.9Gy in the GTV, a dose escalation of 7.3–11.9Gy compared to radiotherapy alone (D95 = 45.0–45.5Gy). This study applied biological modelling to evaluate radiosensitization by hyperthermia as a radiation-dose escalation for cervical cancer patients. This model is very useful to compare the effectiveness of different treatment schedules for combined radiotherapy and hyperthermia treatments and to guide the design of clinical studies on dose escalation using hyperthermia in a multi-modality setting

  13. Chemical kinetic mechanistic models to investigate cancer biology and impact cancer medicine

    Science.gov (United States)

    Stites, Edward C.

    2013-04-01

    Traditional experimental biology has provided a mechanistic understanding of cancer in which the malignancy develops through the acquisition of mutations that disrupt cellular processes. Several drugs developed to target such mutations have now demonstrated clinical value. These advances are unequivocal testaments to the value of traditional cellular and molecular biology. However, several features of cancer may limit the pace of progress that can be made with established experimental approaches alone. The mutated genes (and resultant mutant proteins) function within large biochemical networks. Biochemical networks typically have a large number of component molecules and are characterized by a large number of quantitative properties. Responses to a stimulus or perturbation are typically nonlinear and can display qualitative changes that depend upon the specific values of variable system properties. Features such as these can complicate the interpretation of experimental data and the formulation of logical hypotheses that drive further research. Mathematical models based upon the molecular reactions that define these networks combined with computational studies have the potential to deal with these obstacles and to enable currently available information to be more completely utilized. Many of the pressing problems in cancer biology and cancer medicine may benefit from a mathematical treatment. As work in this area advances, one can envision a future where such models may meaningfully contribute to the clinical management of cancer patients.

  14. Estimation of the Biological Half-Life of Methylmercury Using a Population Toxicokinetic Model

    Directory of Open Access Journals (Sweden)

    Seongil Jo

    2015-07-01

    Full Text Available Methylmercury is well known for causing adverse health effects in the brain and nervous system. Estimating the elimination constant derived from the biological half-life of methylmercury in the blood or hair is an important part of calculating guidelines for methylmercury intake. Thus, this study was conducted to estimate the biological half-life of methylmercury in Korean adults. We used a one-compartment model with a direct relationship between methylmercury concentrations in the blood and daily dietary intake of methylmercury. We quantified the between-person variability of the methylmercury half-life in the population, and informative priors were used to estimate the parameters in the model. The population half-life of methylmercury was estimated to be 80.2 ± 8.6 days. The population mean of the methylmercury half-life was 81.6 ± 8.4 days for men and 78.9 ± 8.6 days for women. The standard deviation of the half-life was estimated at 25.0 ± 8.6 days. Using the direct relationship between methylmercury concentrations in blood and methylmercury intake, the biological half-life in this study was estimated to be longer than indicated by the earlier studies that have been used to set guideline values.

  15. A model for the biological precipitation of Precambrian iron-formation

    Science.gov (United States)

    Laberge, G. L.

    1986-01-01

    A biological model for the precipitation of Precambrian iron formations is presented. Assuming an oxygen deficient atmosphere and water column to allow sufficient Fe solubility, it is proposed that local oxidizing environments, produced biologically, led to precipitation of iron formations. It is further suggested that spheroidal structures about 30 mm in diameter, which are widespread in low grade cherty rion formations, are relict forms of the organic walled microfossil Eosphaera tylerii. The presence of these structures suggests that the organism may have had a siliceous test, which allowed sufficient rigidity for accumulation and preservation. The model involves precipitation of ferric hydrates by oxidation of iron in the photic zone by a variety of photosynthetic organisms. Silica may have formed in the frustules of silica secreting organisms, including Eosphaera tylerii. Iron formates formed, therefore, by a sediment rain of biologically produced ferric hydrates and silica and other organic material. Siderite and hematite formed diagenetically on basin floors, and subsequent metamorphism produced magnetite and iron silicates.

  16. A Two-Layer Mathematical Modelling of Drug Delivery to Biological Tissues

    CERN Document Server

    Chakravarty, Koyel

    2016-01-01

    Local drug delivery has received much recognition in recent years, yet it is still unpredictable how drug efficacy depends on physicochemical properties and delivery kinetics. The purpose of the current study is to provide a useful mathematical model for drug release from a drug delivery device and consecutive drug transport in biological tissue, thereby aiding the development of new therapeutic drug by a systemic approach. In order to study the complete process, a two-layer spatio-temporal model depicting drug transport between the coupled media is presented. Drug release is described by considering solubilisation dynamics of drug particle, diffusion of the solubilised drug through porous matrix and also some other processes like reversible dissociation / recrystallization, drug particle-receptor binding and internalization phenomena. The model has led to a system of partial differential equations describing the important properties of drug kinetics. This model contributes towards the perception of the roles...

  17. Biological Principles in Self-Organization of Young Brain - Viewed from Kohonen Model

    CERN Document Server

    Pallaver, T; Parizeau, M; Pallaver, Tanguy; Kroger, Helmut; Parizeau, Marc

    2006-01-01

    Variants of the Kohonen model are proposed to study biological principles of self-organization in a model of young brain. We suggest a function to measure aquired knowledge and use it to auto-adapt the topology of neuronal connectivity, yielding substantial organizational improvement relative to the standard model. In the early phase of organization with most intense learning, we observe that neural connectivity is of Small World type, which is very efficient to organize neurons in response to stimuli. In analogy to human brain where pruning of neural connectivity (and neuron cell death) occurs in early life, this feature is present also in our model, which is found to stabilize neuronal response to stimuli.

  18. Self-optimization, community stability, and fluctuations in two individual-based models of biological coevolution

    CERN Document Server

    Rikvold, Per Arne

    2007-01-01

    We compare and contrast the long-time dynamical properties of two individual-based models of biological coevolution. Selection occurs via multispecies, stochastic population dynamics with reproduction probabilities that depend nonlinearly on the population densities of all species resident in the community. New species are introduced through mutation. Both models are amenable to exact linear stability analysis, and we compare the analytic results with large-scale kinetic Monte Carlo simulations, obtaining the population size as a function of an average interspecies interaction strength. Over time, the models self-optimize through mutation and selection to approximately maximize a community fitness function, subject only to constraints internal to the particular model. If the interspecies interactions are randomly distributed on an interval including positive values, the system evolves toward self-sustaining, mutualistic communities. In contrast, for the predator-prey case the matrix of interactions is antisym...

  19. The UV-irradiated mouse as a model for testing biological response modifiers

    International Nuclear Information System (INIS)

    In addition to inducing primary cancers of the skin, ultraviolet (UV) radiation produces specific impairments in the immune system that contribute to the growth and pathogenesis of these skin cancers. The cellular basis for the immunological alterations induced in mice by UV radiation has been studied and characterized over the past ten years. It is now possible to make use of this system to study the activity and mode of action of biological response modifiers. The advantages of this system are that it employs primary hosts, which may respond quite differently from normal animals bearing a transplanted tumor, it closely parallels several specific situations relevant to human cancer, and it may be useful in establishing the mechanism of action of certain agents. Studies in which biological response modifiers have been used in conjunction with the UV carcinogenesis model are reviewed. (Auth.)

  20. Synthesis, molecular modeling and biological evaluation of dithiocarbamates as novel antitubulin agents.

    Science.gov (United States)

    Qian, Yong; Ma, Gao-Yuan; Yang, Ying; Cheng, Kui; Zheng, Qing-Zhong; Mao, Wen-Jun; Shi, Lei; Zhao, Jing; Zhu, Hai-Liang

    2010-06-15

    A series of novel dithiocarbamate compounds with the chalcone scaffold have been designed and synthesized, and their biological activities were also evaluated as potential antiproliferation and antitubulin polymerization inhibitors. Compound 2n showed the most potent biological activity in vitro, which inhibited the growth of MCF-7 cells with IC(50) of 0.04+/-0.01 microM and the polymerization of tubulin with IC(50) of 6.8+/-0.6 microM. To understand the tubulin-inhibitor interaction and the selectivity of the most active compound towards tubulin, molecular modeling studies were performed to dock compound 2n into the colchicine binding site, which suggested probable inhibition mechanism. PMID:20493717

  1. Modelling the Influence of Shielding on Physical and Biological Organ Doses

    CERN Document Server

    Ballarini, Francesca; Ferrari, Alfredo; Ottolenghi, Andrea; Pelliccioni, Maurizio; Scannicchio, Domenico

    2002-01-01

    Distributions of "physical" and "biological" dose in different organs were calculated by coupling the FLUKA MC transport code with a geometrical human phantom inserted into a shielding box of variable shape, thickness and material. While the expression "physical dose" refers to the amount of deposited energy per unit mass (in Gy), "biological dose" was modelled with "Complex Lesions" (CL), clustered DNA strand breaks calculated in a previous work based on "event-by-event" track-structure simulations. The yields of complex lesions per cell and per unit dose were calculated for different radiation types and energies, and integrated into a version of FLUKA modified for this purpose, allowing us to estimate the effects of mixed fields. As an initial test simulation, the phantom was inserted into an aluminium parallelepiped and was isotropically irradiated with 500 MeV protons. Dose distributions were calculated for different values of the shielding thickness. The results were found to be organ-dependent. In most ...

  2. [The importance of model organisms to study cilia and flagella biology].

    Science.gov (United States)

    Vincensini, Laetitia; Blisnick, Thierry; Bastin, Philippe

    2011-01-01

    Cilia and flagella are ubiquitous organelles that protrude from the surfaces of many cells, and whose architecture is highly conserved from protists to humans. These complex organelles, composed of over 500 proteins, can be either immotile or motile. They are involved in a myriad of biological processes, including sensing (non-motile cilia) and/or cell motility or movement of extracellular fluids (motile cilia). The ever-expanding list of human diseases linked to defective cilia illustrates the functional importance of cilia and flagella. These ciliopathies are characterised by an impressive diversity of symptoms and an often complex genetic etiology. A precise knowledge of cilia and flagella biology is thus critical to better understand these pathologies. However, multi-ciliated cells are terminally differentiated and difficult to manipulate, and a primary cilium is assembled only when the cell exits from the cell cycle. In this context the use of model organisms, that relies on the high degree of structural but also of molecular conservation of these organelles across evolution, is instrumental to decipher the many facets of cilia and flagella biology. In this review, we highlight the specific strengths of the main model organisms to investigate the molecular composition, mode of assembly, sensing and motility mechanisms and functions of cilia and flagella. Pioneering studies carried out in the green alga Chlamydomonas established the link between cilia and several genetic diseases. Moreover, multicellular organisms such as mouse, zebrafish, Xenopus, C. elegans or Drosophila, and protists like Paramecium, Tetrahymena and Trypanosoma or Leishmania each bring specific advantages to the study of cilium biology. For example, the function of genes involved in primary ciliary dyskinesia (due to defects in ciliary motility) can be efficiently assessed in trypanosomes.

  3. Biologically inspired information theory: Adaptation through construction of external reality models by living systems.

    Science.gov (United States)

    Nakajima, Toshiyuki

    2015-12-01

    Higher animals act in the world using their external reality models to cope with the uncertain environment. Organisms that have not developed such information-processing organs may also have external reality models built in the form of their biochemical, physiological, and behavioral structures, acquired by natural selection through successful models constructed internally. Organisms subject to illusions would fail to survive in the material universe. How can organisms, or living systems in general, determine the external reality from within? This paper starts with a phenomenological model, in which the self constitutes a reality model developed through the mental processing of phenomena. Then, the it-from-bit concept is formalized using a simple mathematical model. For this formalization, my previous work on an algorithmic process is employed to constitute symbols referring to the external reality, called the inverse causality, with additional improvements to the previous work. Finally, as an extension of this model, the cognizers system model is employed to describe the self as one of many material entities in a world, each of which acts as a subject by responding to the surrounding entities. This model is used to propose a conceptual framework of information theory that can deal with both the qualitative (semantic) and quantitative aspects of the information involved in biological processes.

  4. Biologically inspired information theory: Adaptation through construction of external reality models by living systems.

    Science.gov (United States)

    Nakajima, Toshiyuki

    2015-12-01

    Higher animals act in the world using their external reality models to cope with the uncertain environment. Organisms that have not developed such information-processing organs may also have external reality models built in the form of their biochemical, physiological, and behavioral structures, acquired by natural selection through successful models constructed internally. Organisms subject to illusions would fail to survive in the material universe. How can organisms, or living systems in general, determine the external reality from within? This paper starts with a phenomenological model, in which the self constitutes a reality model developed through the mental processing of phenomena. Then, the it-from-bit concept is formalized using a simple mathematical model. For this formalization, my previous work on an algorithmic process is employed to constitute symbols referring to the external reality, called the inverse causality, with additional improvements to the previous work. Finally, as an extension of this model, the cognizers system model is employed to describe the self as one of many material entities in a world, each of which acts as a subject by responding to the surrounding entities. This model is used to propose a conceptual framework of information theory that can deal with both the qualitative (semantic) and quantitative aspects of the information involved in biological processes. PMID:26196087

  5. PENGEMBANGAN MODEL PEMBELAJARAN HYPNOTEACHING UNTUK MENINGKATKAN MOTIVASI DAN HASIL BELAJAR BIOLOGI SISWA SMA/MA

    Directory of Open Access Journals (Sweden)

    Hepta Bungsu Agung Jayawardana

    2015-10-01

    Full Text Available Penelitian ini bertujuan untuk mengetahui: (1 validitas model pembelajaran hypnoteaching beserta perangkat pembelajarannya; (2 pengaruh model pembelajaran hypnoteaching terhadap peningkatan motivasi belajar siswa; dan (3 pengaruh model pembelajaran hypnoteaching terhadap peningkatan hasil belajar biologi siswa kelas XI SMA/MA. Prosedur pengembangan dalam penelitian ini diadaptasi dari Model Borg & Gall yang meliputi studi pendahuluan dan pengumpulan informasi, perencanaan dan perancangan, penyusunan draf model pembelajaran dan validasi, uji coba terbatas dan revisi, uji coba diperluas dan penyempurnaan produk, serta diseminasi dan implementasi produk. Teknik analisis data yang digunakan yaitu statistik deskriptif dan statistik inferensial dengan menggunakan uji multivariat (MANOVA. Hasil penelitian adalah sebagai berikut: (1 Model pembelajaran hypnoteaching beserta perangkatnya, secara empiris valid dan reliabel dengan kategori “Sangat Baik”. (2 Model pembelajaran hypnoteaching mempunyai pengaruh yang signifikan terhadap peningkatan motivasi belajar siswa kelas XI SMAN 2 Banguntapan dan MAN Yogyakarta III, hal ini dibuktikan dengan uji MANOVA dengan nilai signifikansi (Sig. = 0,000 pada taraf kepercayaan 95%. (3 Model pembelajaran hypnoteaching mempunyai pengaruh yang signifikan terhadap peningkatan hasil belajar siswa kelas XI SMAN 2 Banguntapan dan MAN Yogyakarta III, yang dibuktikan dengan uji MANOVA dengan nilai signifikansi (Sig. = 0,000 pada taraf kepercayaan 95%. Kata Kunci: model pembelajaran, hypnoteaching, motivasi belajar, hasil belajar kognitif   DEVELOPING OF HYPNOTEACHING LEARNING MODEL TO IMPROVE THE MOTIVATION AND STUDENT'S LEARNING ACHIEVEMENT OF BIOLOGY OF SENIOR HIGH SCHOOL Abstract This research aims to find out: (1 the validity of hypnoteaching learning model including the learning tools; (2 the effect of hypnoteaching learning model for the increasing of student’s learning motivation; and (3 the effect of

  6. A Gaussian mixture model based cost function for parameter estimation of chaotic biological systems

    Science.gov (United States)

    Shekofteh, Yasser; Jafari, Sajad; Sprott, Julien Clinton; Hashemi Golpayegani, S. Mohammad Reza; Almasganj, Farshad

    2015-02-01

    As we know, many biological systems such as neurons or the heart can exhibit chaotic behavior. Conventional methods for parameter estimation in models of these systems have some limitations caused by sensitivity to initial conditions. In this paper, a novel cost function is proposed to overcome those limitations by building a statistical model on the distribution of the real system attractor in state space. This cost function is defined by the use of a likelihood score in a Gaussian mixture model (GMM) which is fitted to the observed attractor generated by the real system. Using that learned GMM, a similarity score can be defined by the computed likelihood score of the model time series. We have applied the proposed method to the parameter estimation of two important biological systems, a neuron and a cardiac pacemaker, which show chaotic behavior. Some simulated experiments are given to verify the usefulness of the proposed approach in clean and noisy conditions. The results show the adequacy of the proposed cost function.

  7. Unit testing, model validation, and biological simulation [version 1; referees: 2 approved, 1 approved with reservations

    Directory of Open Access Journals (Sweden)

    Gopal P. Sarma

    2016-08-01

    Full Text Available The growth of the software industry has gone hand in hand with the development of tools and cultural practices for ensuring the reliability of complex pieces of software. These tools and practices are now acknowledged to be essential to the management of modern software. As computational models and methods have become increasingly common in the biological sciences, it is important to examine how these practices can accelerate biological software development and improve research quality. In this article, we give a focused case study of our experience with the practices of unit testing and test-driven development in OpenWorm, an open-science project aimed at modeling Caenorhabditis elegans. We identify and discuss the challenges of incorporating test-driven development into a heterogeneous, data-driven project, as well as the role of model validation tests, a category of tests unique to software which expresses scientific models.

  8. Development of Biological Movement Recognition by Interaction between Active Basis Model and Fuzzy Optical Flow Division

    Directory of Open Access Journals (Sweden)

    Bardia Yousefi

    2014-01-01

    Full Text Available Following the study on computational neuroscience through functional magnetic resonance imaging claimed that human action recognition in the brain of mammalian pursues two separated streams, that is, dorsal and ventral streams. It follows up by two pathways in the bioinspired model, which are specialized for motion and form information analysis (Giese and Poggio 2003. Active basis model is used to form information which is different from orientations and scales of Gabor wavelets to form a dictionary regarding object recognition (human. Also biologically movement optic-flow patterns utilized. As motion information guides share sketch algorithm in form pathway for adjustment plus it helps to prevent wrong recognition. A synergetic neural network is utilized to generate prototype templates, representing general characteristic form of every class. Having predefined templates, classifying performs based on multitemplate matching. As every human action has one action prototype, there are some overlapping and consistency among these templates. Using fuzzy optical flow division scoring can prevent motivation for misrecognition. We successfully apply proposed model on the human action video obtained from KTH human action database. Proposed approach follows the interaction between dorsal and ventral processing streams in the original model of the biological movement recognition. The attained results indicate promising outcome and improvement in robustness using proposed approach.

  9. The study of biological effects of electromagnetic mobile phone radiation on experimental animals by combining numerical modeling and experimental research

    Directory of Open Access Journals (Sweden)

    Dejan Krstić

    2012-12-01

    Full Text Available In order to study biological effects of electromagneticradiation, it is essential to know the real values of field componentsthat penetrated the tissue. The study of biological effects is usuallyperformed on experimental animals. The biological effects observedon experimental animals should be linked with penetrating field inthe tissue. The penetrating electromagnetic field is almost impossibleto measure; therefore, modeling process must be carried out and thefield components in models of experimental animals could becalculated. This paper presents an approach to modeling of fieldpenetration and gives contribution to understanding the real effects of the fields and the sensitivity of tissues to electromagnetic radiation generated by mobile phone.

  10. Applying complex models to poultry production in the future--economics and biology.

    Science.gov (United States)

    Talpaz, H; Cohen, M; Fancher, B; Halley, J

    2013-09-01

    The ability to determine the optimal broiler feed nutrient density that maximizes margin over feeding cost (MOFC) has obvious economic value. To determine optimal feed nutrient density, one must consider ingredient prices, meat values, the product mix being marketed, and the projected biological performance. A series of 8 feeding trials was conducted to estimate biological responses to changes in ME and amino acid (AA) density. Eight different genotypes of sex-separate reared broilers were fed diets varying in ME (2,723-3,386 kcal of ME/kg) and AA (0.89-1.65% digestible lysine with all essential AA acids being indexed to lysine) levels. Broilers were processed to determine carcass component yield at many different BW (1.09-4.70 kg). Trial data generated were used in model constructed to discover the dietary levels of ME and AA that maximize MOFC on a per broiler or per broiler annualized basis (bird × number of cycles/year). The model was designed to estimate the effects of dietary nutrient concentration on broiler live weight, feed conversion, mortality, and carcass component yield. Estimated coefficients from the step-wise regression process are subsequently used to predict the optimal ME and AA concentrations that maximize MOFC. The effects of changing feed or meat prices across a wide spectrum on optimal ME and AA levels can be evaluated via parametric analysis. The model can rapidly compare both biological and economic implications of changing from current practice to the simulated optimal solution. The model can be exploited to enhance decision making under volatile market conditions.

  11. Applying complex models to poultry production in the future--economics and biology.

    Science.gov (United States)

    Talpaz, H; Cohen, M; Fancher, B; Halley, J

    2013-09-01

    The ability to determine the optimal broiler feed nutrient density that maximizes margin over feeding cost (MOFC) has obvious economic value. To determine optimal feed nutrient density, one must consider ingredient prices, meat values, the product mix being marketed, and the projected biological performance. A series of 8 feeding trials was conducted to estimate biological responses to changes in ME and amino acid (AA) density. Eight different genotypes of sex-separate reared broilers were fed diets varying in ME (2,723-3,386 kcal of ME/kg) and AA (0.89-1.65% digestible lysine with all essential AA acids being indexed to lysine) levels. Broilers were processed to determine carcass component yield at many different BW (1.09-4.70 kg). Trial data generated were used in model constructed to discover the dietary levels of ME and AA that maximize MOFC on a per broiler or per broiler annualized basis (bird × number of cycles/year). The model was designed to estimate the effects of dietary nutrient concentration on broiler live weight, feed conversion, mortality, and carcass component yield. Estimated coefficients from the step-wise regression process are subsequently used to predict the optimal ME and AA concentrations that maximize MOFC. The effects of changing feed or meat prices across a wide spectrum on optimal ME and AA levels can be evaluated via parametric analysis. The model can rapidly compare both biological and economic implications of changing from current practice to the simulated optimal solution. The model can be exploited to enhance decision making under volatile market conditions. PMID:23960140

  12. Progress in Modeling Biological Invasion%生物入侵模型研究进展

    Institute of Scientific and Technical Information of China (English)

    王勤龙; 李百炼

    2011-01-01

    Biological invasion is now one of the six hot research topics in modern ecology.Modeling the invasion processes,the diffusions and the spreading mechanisms of invasive species including diseases is important not only for theoretical puposes,but also for practical purposes,such as in ecological risk assessments,and optimal control and management of those invasive species.In this paper the widely adopted models of biological invasion are systematically reviewed such as diffusion-reaction equations,integral-difference models,space-discrete models,and stochastic models.The fundamental issues of modeling biological invasions are discussed,and the characteristics and analytic conditions of various models are analyzed with typical exampies and recent research results.Various modeling techniques are compared,and some commonly used methods in solving biological invasion models are outlined,including numerical computing and simulating,exact solutions,methods for functional differential equation and qualitative analysis of differential equations Some key and unsolved problems in building mathematical models of biological invasion,and future research directions are also Hiscussed.%生物入侵的机制与控制已经成为生态学关注的重点和研究的热点问题.建立数学模型是解释生物入侵机制的主要手段,对外来生物包括疾病的入侵扩散过程进行模型分析不仅具有重要的理论意义,还有助于风险评估,特别对入侵进行早期预测、控制、科学管理与防治.然而,目前有关生物入侵的数理模型方法还远不能满足实际的需要,本文系统地介绍和分析了生物入侵模型的研究现状.首先分析了对于生物入侵过程建模所要考虑的基本问题;然后讨论了几类典型的入侵数学模型,分析了各自的特点及条件,同时给出了有代表性的实例和最近的研究成果;其次比较列出了入侵模型分析的各类方法:最后探讨了生物入侵建模与求

  13. Traveling wave solutions of a biological reaction-convection-diffusion equation model by using $(G'/G$ expansion method

    Directory of Open Access Journals (Sweden)

    Shahnam Javadi

    2013-07-01

    Full Text Available In this paper, the $(G'/G$-expansion method is applied to solve a biological reaction-convection-diffusion model arising in mathematical biology. Exact traveling wave solutions are obtained by this method. This scheme can be applied to a wide class of nonlinear partial differential equations.

  14. Analyzing Change in Students' Gene-to-Evolution Models in College-Level Introductory Biology

    Science.gov (United States)

    Dauer, Joseph T.; Momsen, Jennifer L.; Speth, Elena Bray; Makohon-Moore, Sasha C.; Long, Tammy M.

    2013-01-01

    Research in contemporary biology has become increasingly complex and organized around understanding biological processes in the context of systems. To better reflect the ways of thinking required for learning about systems, we developed and implemented a pedagogical approach using box-and-arrow models (similar to concept maps) as a foundational…

  15. Comparison of Model Calculations of Biological Damage from Exposure to Heavy Ions with Measurements

    Science.gov (United States)

    Kim, Myung-Hee Y.; Wu, Honglu; Hada, Megumi; Cucinotta, Francis

    The space environment consists of a varying field of radiation particles including high-energy ions, with spacecraft shielding material providing the major protection to astronauts from harmful exposure. Unlike low-LET g or X rays, the presence of shielding does not always reduce the radiation risks for energetic charged-particle exposure. Dose delivered by the charged particle increases sharply at the Bragg peak. However, the Bragg curve does not necessarily represent the biological damage along the particle path since biological effects are influenced by the track structures of both primary and secondary particles. Therefore, the ‘‘biological Bragg curve’’ is dependent on the energy and the type of the primary particle and may vary for different biological end points. Measurements of the induction of micronuclei (MN) have made across the Bragg curve in human fibroblasts exposed to energetic silicon and iron ions in vitro at two different energies, 300 MeV/nucleon and 1 GeV/nucleon. Although the data did not reveal an increased yield of MN at the location of the Bragg peak, the increased inhibition of cell progression, which is related to cell death, was found at the Bragg peak location. These results are compared to the calculations of biological damage using a stochastic Monte-Carlo track structure model, Galactic Cosmic Ray Event-based Risk Model (GERM) code (Cucinotta et al., 2011). The GERM code estimates the basic physical properties along the passage of heavy ions in tissue and shielding materials, by which the experimental set-up can be interpreted. The code can also be used to describe the biophysical events of interest in radiobiology, cancer therapy, and space exploration. The calculation has shown that the severely damaged cells at the Bragg peak are more likely to go through reproductive death, the so called “overkill”. F. A. Cucinotta, I. Plante, A. L. Ponomarev, and M. Y. Kim, Nuclear Interactions in Heavy Ion Transport and Event

  16. A cooperative strategy for parameter estimation in large scale systems biology models

    Directory of Open Access Journals (Sweden)

    Villaverde Alejandro F

    2012-06-01

    Full Text Available Abstract Background Mathematical models play a key role in systems biology: they summarize the currently available knowledge in a way that allows to make experimentally verifiable predictions. Model calibration consists of finding the parameters that give the best fit to a set of experimental data, which entails minimizing a cost function that measures the goodness of this fit. Most mathematical models in systems biology present three characteristics which make this problem very difficult to solve: they are highly non-linear, they have a large number of parameters to be estimated, and the information content of the available experimental data is frequently scarce. Hence, there is a need for global optimization methods capable of solving this problem efficiently. Results A new approach for parameter estimation of large scale models, called Cooperative Enhanced Scatter Search (CeSS, is presented. Its key feature is the cooperation between different programs (“threads” that run in parallel in different processors. Each thread implements a state of the art metaheuristic, the enhanced Scatter Search algorithm (eSS. Cooperation, meaning information sharing between threads, modifies the systemic properties of the algorithm and allows to speed up performance. Two parameter estimation problems involving models related with the central carbon metabolism of E. coli which include different regulatory levels (metabolic and transcriptional are used as case studies. The performance and capabilities of the method are also evaluated using benchmark problems of large-scale global optimization, with excellent results. Conclusions The cooperative CeSS strategy is a general purpose technique that can be applied to any model calibration problem. Its capability has been demonstrated by calibrating two large-scale models of different characteristics, improving the performance of previously existing methods in both cases. The cooperative metaheuristic presented here

  17. Indoor Air Nuclear, Biological, and Chemical Health Modeling and Assessment System

    Energy Technology Data Exchange (ETDEWEB)

    Stenner, Robert D.; Hadley, Donald L.; Armstrong, Peter R.; Buck, John W.; Hoopes, Bonnie L.; Janus, Michael C.

    2001-03-01

    Indoor air quality effects on human health are of increasing concern to public health agencies and building owners. The prevention and treatment of 'sick building' syndrome and the spread of air-borne diseases in hospitals, for example, are well known priorities. However, increasing attention is being directed to the vulnerability of our public buildings/places, public security and national defense facilities to terrorist attack or the accidental release of air-borne biological pathogens, harmful chemicals, or radioactive contaminants. The Indoor Air Nuclear, Biological, and Chemical Health Modeling and Assessment System (IA-NBC-HMAS) was developed to serve as a health impact analysis tool for use in addressing these concerns. The overall goal was to develop a user-friendly fully functional prototype Health Modeling and Assessment system, which will operate under the PNNL FRAMES system for ease of use and to maximize its integration with other modeling and assessment capabilities accessible within the FRAMES system (e.g., ambient air fate and transport models, water borne fate and transport models, Physiologically Based Pharmacokinetic models, etc.). The prototype IA-NBC-HMAS is designed to serve as a functional Health Modeling and Assessment system that can be easily tailored to meet specific building analysis needs of a customer. The prototype system was developed and tested using an actual building (i.e., the Churchville Building located at the Aberdeen Proving Ground) and release scenario (i.e., the release and measurement of tracer materials within the building) to ensure realism and practicality in the design and development of the prototype system. A user-friendly "demo" accompanies this report to allow the reader the opportunity for a "hands on" review of the prototype system's capability.

  18. N-gram analysis of 970 microbial organisms reveals presence of biological language models

    Directory of Open Access Journals (Sweden)

    Ganapathiraju Madhavi K

    2011-01-01

    Full Text Available Abstract Background It has been suggested previously that genome and proteome sequences show characteristics typical of natural-language texts such as "signature-style" word usage indicative of authors or topics, and that the algorithms originally developed for natural language processing may therefore be applied to genome sequences to draw biologically relevant conclusions. Following this approach of 'biological language modeling', statistical n-gram analysis has been applied for comparative analysis of whole proteome sequences of 44 organisms. It has been shown that a few particular amino acid n-grams are found in abundance in one organism but occurring very rarely in other organisms, thereby serving as genome signatures. At that time proteomes of only 44 organisms were available, thereby limiting the generalization of this hypothesis. Today nearly 1,000 genome sequences and corresponding translated sequences are available, making it feasible to test the existence of biological language models over the evolutionary tree. Results We studied whole proteome sequences of 970 microbial organisms using n-gram frequencies and cross-perplexity employing the Biological Language Modeling Toolkit and Patternix Revelio toolkit. Genus-specific signatures were observed even in a simple unigram distribution. By taking statistical n-gram model of one organism as reference and computing cross-perplexity of all other microbial proteomes with it, cross-perplexity was found to be predictive of branch distance of the phylogenetic tree. For example, a 4-gram model from proteome of Shigellae flexneri 2a, which belongs to the Gammaproteobacteria class showed a self-perplexity of 15.34 while the cross-perplexity of other organisms was in the range of 15.59 to 29.5 and was proportional to their branching distance in the evolutionary tree from S. flexneri. The organisms of this genus, which happen to be pathotypes of E.coli, also have the closest perplexity values with

  19. Assessing Impacts of Climate Change on Forests: The State of Biological Modeling

    Science.gov (United States)

    Dale, V. H.; Rauscher, H. M.

    1993-04-06

    Models that address the impacts to forests of climate change are reviewed by four levels of biological organization: global, regional or landscape, community, and tree. The models are compared as to their ability to assess changes in greenhouse gas flux, land use, maps of forest type or species composition, forest resource productivity, forest health, biodiversity, and wildlife habitat. No one model can address all of these impacts, but landscape transition models and regional vegetation and land-use models consider the largest number of impacts. Developing landscape vegetation dynamics models of functional groups is suggested as a means to integrate the theory of both landscape ecology and individual tree responses to climate change. Risk assessment methodologies can be adapted to deal with the impacts of climate change at various spatial and temporal scales. Four areas of research development are identified: (1) linking socioeconomic and ecologic models, (2) interfacing forest models at different scales, (3) obtaining data on susceptibility of trees and forest to changes in climate and disturbance regimes, and (4) relating information from different scales.

  20. Assessing impacts of climate change on forests: The state of biological modeling

    Energy Technology Data Exchange (ETDEWEB)

    Dale, V.H. [Oak Ridge National Lab., TN (United States); Rauscher, H.M. [Forest Service, Grand Rapids, MI (United States). North Central Forest Experiment Station

    1993-04-06

    Models that address the impacts to forests of climate change are reviewed by four levels of biological organization: global, regional or landscape, community, and tree. The models are compared as to their ability to assess changes in greenhouse gas flux, land use, maps of forest type or species composition, forest resource productivity, forest health, biodiversity, and wildlife habitat. No one model can address all of these impacts, but landscape transition models and regional vegetation and land-use models consider the largest number of impacts. Developing landscape vegetation dynamics models of functional groups is suggested as a means to integrate the theory of both landscape ecology and individual tree responses to climate change. Risk assessment methodologies can be adapted to deal with the impacts of climate change at various spatial and temporal scales. Four areas of research development are identified: (1) linking socioeconomic and ecologic models, (2) interfacing forest models at different scales, (3) obtaining data on susceptibility of trees and forest to changes in climate and disturbance regimes, and (4) relating information from different scales.

  1. Modelling GAC adsorption of biologically pre-treated process water from hydrothermal carbonization.

    Science.gov (United States)

    Fettig, J; Liebe, H

    2015-01-01

    Granular-activated carbon (GAC) adsorption of biologically pre-treated process waters from hydrothermal carbonization (HTC) of different materials was investigated. Overall, isotherms showed that most of the dissolved organic substances are strongly adsorbable while the non-adsorbable fractions are small. The equilibrium data were modelled by using five fictive components to represent the organic matter. Mean film transfer coefficients and mean intraparticle diffusivities were derived from short-column and batch kinetic test data, respectively. Breakthrough curves in GAC columns could be predicted satisfactorily by applying the film-homogeneous diffusion model and using the equilibrium and kinetic parameters determined from batch tests. Thus, the approach is suited to model GAC adsorption of HTC process water under technical-scale conditions. PMID:26114274

  2. Modeling Complex Biological Flows in Multi-Scale Systems using the APDEC Framework

    Energy Technology Data Exchange (ETDEWEB)

    Trebotich, D

    2006-06-24

    We have developed advanced numerical algorithms to model biological fluids in multiscale flow environments using the software framework developed under the SciDAC APDEC ISIC. The foundation of our computational effort is an approach for modeling DNA-laden fluids as ''bead-rod'' polymers whose dynamics are fully coupled to an incompressible viscous solvent. The method is capable of modeling short range forces and interactions between particles using soft potentials and rigid constraints. Our methods are based on higher-order finite difference methods in complex geometry with adaptivity, leveraging algorithms and solvers in the APDEC Framework. Our Cartesian grid embedded boundary approach to incompressible viscous flow in irregular geometries has also been interfaced to a fast and accurate level-sets method within the APDEC Framework for extracting surfaces from volume renderings of medical image data and used to simulate cardio-vascular and pulmonary flows in critical anatomies.

  3. Three "quantum" models of competition and cooperation in interacting biological populations and social groups

    CERN Document Server

    Vol, E D

    2012-01-01

    In present paper we propose the consistent statistical approach which appropriate for a number of models describing both behavior of biological populations and various social groups interacting with each other.The approach proposed based on the ideas of quantum theory of open systems (QTOS) and allows one to account explicitly both discreteness of a system variables and their fluctuations near mean values.Therefore this approach can be applied also for the description of small populations where standard dynamical methods are failed. We study in detail three typical models of interaction between populations and groups: 1) antagonistic struggle between two populations 2) cooperation (or, more precisely, obligatory mutualism) between two species 3) the formation of coalition between two feeble groups in their conflict with third one that is more powerful . The models considered in a sense are mutually complementary and include the most types of interaction between populations and groups. Besides this method can ...

  4. Benchmarking biological nutrient removal in wastewater treatment plants: influence of mathematical model assumptions

    DEFF Research Database (Denmark)

    Flores-Alsina, Xavier; Gernaey, Krist V.; Jeppsson, Ulf

    2012-01-01

    This paper examines the effect of different model assumptions when describing biological nutrient removal (BNR) by the activated sludge models (ASM) 1, 2d & 3. The performance of a nitrogen removal (WWTP1) and a combined nitrogen and phosphorus removal (WWTP2) benchmark wastewater treatment plant...... of the non-reactive TSS sedimentation and transport in the reference case with the full set of ASM processes. Finally, the third set of models is based on including electron acceptor dependency of biomass decay rates for ASM1 (WWTP1) and ASM2d (WWTP2). The results show that incorporation of a reactive...... settler: (1) increases the hydrolysis of particulates; (2) increases the overall plant's denitrification efficiency by reducing the SNOx concentration at the bottom of the clarifier; (3) increases the oxidation of COD compounds; (4) increases XOHO and XANO decay; and, finally, (5) increases the growth...

  5. Macroscopic model for biological fixation and its uncover-ing idea in Chinese Mongolian traditional osteopathy

    Institute of Scientific and Technical Information of China (English)

    ZHAO Namula; LI Xue-en; WANG Mei; HU Da-lai

    2009-01-01

    Splintage external fixation in Chinese Mongolian oste-opathy is a biological macroscopic model. In this model, the ideas of self-life "unity of mind and body" and vital natural "correspondence of nature and human" combine the physi-ological and psychological self-fixation with supplementary external fixation of fracture using small splints. This model implies macroscopic ideas of uncovering fixation and healing: structural stability integrating geometrical "dy-namic" stability with mechanical "dynamic" equilibrium and the stability of state integrating statics with dynamics, and osteoblasts with osteoclasts, and psychological stability in-tegrating closed and open systems of human and nature. These ideas indicate a trend of development in modem osteopathy.

  6. Modelling chemical and biological reactions during unsaturated flow in silty arable soils

    Science.gov (United States)

    Michel, Kerstin; Herrmann, Sandra; Ludwig, Bernard

    2010-05-01

    Ion dynamics in arable soils are strongly affected by the chemical and biological transformations triggered by fertilizer input. Hydrogeochemical models may improve our understanding of underlying processes. Our objective was to test the ability of the hydrogeochemical model PHREEQC2 in combination with the parameter optimization programme PEST to describe and predict chemical and biological processes in silty soils triggered by fertilizer application or acidification and to investigate the usefulness of different parameterization approaches. Three different experiments were carried out using undisturbed columns of two topsoils (0-25 cm) from Germany (Göttingen, GO) and from the Oman (Qasha', QA). The columns were irrigated at 10 oC with 3 mm day-1 for one year using 1 mM HCl (HCl experiment) and two fertilizer solutions with low (0.1 to 0.9 mmol L-1) and high concentrations (1.3 to 14.7 mmol L-1) of N (as NH4NO3), K, Ca and Mg. In the fertilization experiments (Fert1, Fert2), the columns were alternately irrigated with the two different solutions for variable time periods. One-dimensional transport and homogenous and heterogenous reactions were calculated using PHREEQC2. The Fert1 experiment was used for calibration. The models were validated using the Fert2 and HCl experiments. The models tested were model variant m1 with no adjustable parameters, model variant m2 in which nitrate concentrations in input solutions and cation exchange capacity were optimized for Fert1, and m3 in which additionally all cation exchange coefficients and ion concentrations in the initial solution were optimized. Model variant m1 failed to predict the concentrations of several cations for both soils (modelling efficiencies (EF) ≤ 0), since N dynamics were not considered adequately. Model variants m2 and m3 described (Fert1 treatment) and predicted (Fert2 and HCl treatment) pH, cation and NO3- concentrations generally more accurately for both soils. For nutrient cations, EF values

  7. A model of the ocean iron cycle and its influence on biological production

    Science.gov (United States)

    Dutkiewicz, S.; Parekh, P.; Follows, M.

    2003-04-01

    Biological productivity in large regions of the ocean, specifically high nutrient, low chlorophyll regions, is limited by the deficit in iron relative to other nutrients. We have developed a parameterization of the iron cycle of the world's oceans which attempts to explicitly represent the processes by which this deficit in iron occurs. We have implemented this parameterization in the context of the MIT three dimensional global ocean model and examined the consequences for nutrient distributions, new production and primary production. The iron model parameterizes the mechanisms of scavenging of iron onto sinking particles and complexation with an organic ligand and is driven by specified aeolian flux patterns. First, using an idealized representation of export production, limited by light, phosphate and iron, the model reproduces the broad features of the observed ocean phosphate and iron distributions. We replace the simplified export parameterization with an explicit, but highly idealized, ecosystem model. The model represents a simplified food web with two phytoplankton size classes and a single grazer. The base currency for this model is phosphorus, but the larger phytoplankton class (i.e. diatoms) is also limited by silica. Both classes are limited by the availability of iron. The results of this model are also generally consistent with the observed patterns of phosphate and iron. In addition, the model captures the broad features of the distributions and cycles of silica, chlorophyll and primary production. We will also explore the sensitivities of this model to the forcing fields (e.g. aeolian iron flux) and parameter choices of the ecosystem model. This model represents a step towards the explicit representation of the ocean iron cycle, and its biogeochemical influences, in global biogeochemical models.

  8. Local buckling analysis of biological nanocomposites based on a beam-spring model

    Directory of Open Access Journals (Sweden)

    Zhiling Bai

    2015-07-01

    Full Text Available Biological materials such as bone, tooth, and nacre are load-bearing nanocomposites composed of mineral and protein. Since the mineral crystals often have slender geometry, the nanocomposites are susceptible to buckle under the compressive load. In this paper, we analyze the local buckling behaviors of the nanocomposite structure of the biological materials using a beam-spring model by which we can consider plenty of mineral crystals and their interaction in our analysis compared with existing studies. We show that there is a transition of the buckling behaviors from a local buckling mode to a global one when we continuously increase the aspect ratio of mineral, leading to an increase of the buckling strength which levels off to the strength of the composites reinforced with continuous crystals. We find that the contact condition at the mineral tips has a striking effect on the local buckling mode at small aspect ratio, but the effect diminishes when the aspect ratio is large. Our analyses also show that the staggered arrangement of mineral plays a central role in the stability of the biological nanocomposites.

  9. A Novel CPU/GPU Simulation Environment for Large-Scale Biologically-Realistic Neural Modeling

    Directory of Open Access Journals (Sweden)

    Roger V Hoang

    2013-10-01

    Full Text Available Computational Neuroscience is an emerging field that provides unique opportunities to studycomplex brain structures through realistic neural simulations. However, as biological details are added tomodels, the execution time for the simulation becomes longer. Graphics Processing Units (GPUs are now being utilized to accelerate simulations due to their ability to perform computations in parallel. As such, they haveshown significant improvement in execution time compared to Central Processing Units (CPUs. Most neural simulators utilize either multiple CPUs or a single GPU for better performance, but still show limitations in execution time when biological details are not sacrificed. Therefore, we present a novel CPU/GPU simulation environment for large-scale biological networks,the NeoCortical Simulator version 6 (NCS6. NCS6 is a free, open-source, parallelizable, and scalable simula-tor, designed to run on clusters of multiple machines, potentially with high performance computing devicesin each of them. It has built-in leaky-integrate-and-fire (LIF and Izhikevich (IZH neuron models, but usersalso have the capability to design their own plug-in interface for different neuron types as desired. NCS6is currently able to simulate one million cells and 100 million synapses in quasi real time by distributing dataacross these heterogeneous clusters of CPUs and GPUs.

  10. Metabolic Free Energy and Biological Codes: A 'Data Rate Theorem' Aging Model.

    Science.gov (United States)

    Wallace, Rodrick

    2015-06-01

    A famous argument by Maturana and Varela (Autopoiesis and cognition. Reidel, Dordrecht, 1980) holds that the living state is cognitive at every scale and level of organization. Since it is possible to associate many cognitive processes with 'dual' information sources, pathologies can sometimes be addressed using statistical models based on the Shannon Coding, the Shannon-McMillan Source Coding, the Rate Distortion, and the Data Rate Theorems, which impose necessary conditions on information transmission and system control. Deterministic-but-for-error biological codes do not directly invoke cognition, but may be essential subcomponents within larger cognitive processes. A formal argument, however, places such codes within a similar framework, with metabolic free energy serving as a 'control signal' stabilizing biochemical code-and-translator dynamics in the presence of noise. Demand beyond available energy supply triggers punctuated destabilization of the coding channel, affecting essential biological functions. Aging, normal or prematurely driven by psychosocial or environmental stressors, must interfere with the routine operation of such mechanisms, initiating the chronic diseases associated with senescence. Amyloid fibril formation, intrinsically disordered protein logic gates, and cell surface glycan/lectin 'kelp bed' logic gates are reviewed from this perspective. The results generalize beyond coding machineries having easily recognizable symmetry modes, and strip a layer of mathematical complication from the study of phase transitions in nonequilibrium biological systems. PMID:25185747

  11. A stochastic multicellular model identifies biological watermarks from disorders in self-organized patterns of phyllotaxis

    Science.gov (United States)

    Refahi, Yassin; Brunoud, Géraldine; Farcot, Etienne; Jean-Marie, Alain; Pulkkinen, Minna; Vernoux, Teva; Godin, Christophe

    2016-01-01

    Exploration of developmental mechanisms classically relies on analysis of pattern regularities. Whether disorders induced by biological noise may carry information on building principles of developmental systems is an important debated question. Here, we addressed theoretically this question using phyllotaxis, the geometric arrangement of plant aerial organs, as a model system. Phyllotaxis arises from reiterative organogenesis driven by lateral inhibitions at the shoot apex. Motivated by recurrent observations of disorders in phyllotaxis patterns, we revisited in depth the classical deterministic view of phyllotaxis. We developed a stochastic model of primordia initiation at the shoot apex, integrating locality and stochasticity in the patterning system. This stochastic model recapitulates phyllotactic patterns, both regular and irregular, and makes quantitative predictions on the nature of disorders arising from noise. We further show that disorders in phyllotaxis instruct us on the parameters governing phyllotaxis dynamics, thus that disorders can reveal biological watermarks of developmental systems. DOI: http://dx.doi.org/10.7554/eLife.14093.001 PMID:27380805

  12. Combination of Neuro-Fuzzy Network Models with Biological Knowledge for Reconstructing Gene Regulatory Networks

    Institute of Scientific and Technical Information of China (English)

    Guixia Liu; Lei Liu; Chunyu Liu; Ming Zheng; Lanying Su; Chunguang Zhou

    2011-01-01

    Inferring gene regulatory networks from large-scale expression data is an important topic in both cellular systems and computational biology. The inference of regulators might be the core factor for understanding actual regulatory conditions in gene regulatory networks, especially when strong regulators do work significantly, in this paper, we propose a novel approach based on combining neuro-fuzzy network models with biological knowledge to infer strong regulators and interrelated fuzzy rules. The hybrid neuro-fuzzy architecture can not only infer the fuzzy rules, which are suitable for describing the regulatory conditions in regulatory networks, but also explain the meaning of nodes and weight value in the neural network. It can get useful rules automatically without factitious judgments. At the same time, it does not add recursive layers to the model, and the model can also strengthen the relationships among genes and reduce calculation. We use the proposed approach to reconstruct a partial gene regulatory network of yeast. The results show that this approach can work effectively.

  13. The continuous Sir Philip Sidney game: a simple model of biological signalling.

    Science.gov (United States)

    Johnstone, R A; Grafen, A

    1992-05-21

    An analysis of Maynard Smith's two-player, ESS model of biological signalling, the "Sir Philip Sidney game", is presented. The stable strategies of the players in this game are shown to satisfy the conditions of Zahavi's handicap principle. At equilibrium, signals are honest, costly, and costly in a way that is related to the true quality revealed. Further analysis reveals that the level of cost required to maintain stability is inversely related to the degree of relatedness between the players. It therefore seems likely that stable biological signalling systems will feature lower signalling costs when communication occurs between relatives. A three-player, extended version of the model is investigated, in which signals are passed via an intermediate, or "messenger". It is shown that this destabilizes the signalling system, and leads to increased signalling costs. This result suggests that "kin conflict" theories of the evolution of the endosperm in flowering plants require further refinement. The introduction of a novel resource acquisition tissue, which mediates parent-offspring interaction during development, cannot be assumed to limit parent-offspring conflict simply because it carries an extra copy of the maternally inherited genes. The ability to add such complications to the Sir Philip Sidney game and still obtain solutions makes it a very useful modelling tool. PMID:1640723

  14. Biological network module-based model for the analysis of differential expression in shotgun proteomics.

    Science.gov (United States)

    Xu, Jia; Wang, Lily; Li, Jing

    2014-12-01

    Protein differential expression analysis plays an important role in the understanding of molecular mechanisms as well as the pathogenesis of complex diseases. With the rapid development of mass spectrometry, shotgun proteomics using spectral counts has become a prevailing method for the quantitative analysis of complex protein mixtures. Existing methods in differential proteomics expression typically carry out analysis at the single-protein level. However, it is well-known that proteins interact with each other when they function in biological processes. In this study, focusing on biological network modules, we proposed a negative binomial generalized linear model for differential expression analysis of spectral count data in shotgun proteomics. In order to show the efficacy of the model in protein expression analysis at the level of protein modules, we conducted two simulation studies using synthetic data sets generated from theoretical distribution of count data and a real data set with shuffled counts. Then, we applied our method to a colorectal cancer data set and a nonsmall cell lung cancer data set. When compared with single-protein analysis methods, the results showed that module-based statistical model which takes account of the interactions among proteins led to more effective identification of subtle but coordinated changes at the systems level. PMID:25327611

  15. Network-based analysis of affected biological processes in type 2 diabetes models.

    Directory of Open Access Journals (Sweden)

    Manway Liu

    2007-06-01

    Full Text Available Type 2 diabetes mellitus is a complex disorder associated with multiple genetic, epigenetic, developmental, and environmental factors. Animal models of type 2 diabetes differ based on diet, drug treatment, and gene knockouts, and yet all display the clinical hallmarks of hyperglycemia and insulin resistance in peripheral tissue. The recent advances in gene-expression microarray technologies present an unprecedented opportunity to study type 2 diabetes mellitus at a genome-wide scale and across different models. To date, a key challenge has been to identify the biological processes or signaling pathways that play significant roles in the disorder. Here, using a network-based analysis methodology, we identified two sets of genes, associated with insulin signaling and a network of nuclear receptors, which are recurrent in a statistically significant number of diabetes and insulin resistance models and transcriptionally altered across diverse tissue types. We additionally identified a network of protein-protein interactions between members from the two gene sets that may facilitate signaling between them. Taken together, the results illustrate the benefits of integrating high-throughput microarray studies, together with protein-protein interaction networks, in elucidating the underlying biological processes associated with a complex disorder.

  16. Bayesian parameter inference and model selection by population annealing in systems biology.

    Science.gov (United States)

    Murakami, Yohei

    2014-01-01

    Parameter inference and model selection are very important for mathematical modeling in systems biology. Bayesian statistics can be used to conduct both parameter inference and model selection. Especially, the framework named approximate Bayesian computation is often used for parameter inference and model selection in systems biology. However, Monte Carlo methods needs to be used to compute Bayesian posterior distributions. In addition, the posterior distributions of parameters are sometimes almost uniform or very similar to their prior distributions. In such cases, it is difficult to choose one specific value of parameter with high credibility as the representative value of the distribution. To overcome the problems, we introduced one of the population Monte Carlo algorithms, population annealing. Although population annealing is usually used in statistical mechanics, we showed that population annealing can be used to compute Bayesian posterior distributions in the approximate Bayesian computation framework. To deal with un-identifiability of the representative values of parameters, we proposed to run the simulations with the parameter ensemble sampled from the posterior distribution, named "posterior parameter ensemble". We showed that population annealing is an efficient and convenient algorithm to generate posterior parameter ensemble. We also showed that the simulations with the posterior parameter ensemble can, not only reproduce the data used for parameter inference, but also capture and predict the data which was not used for parameter inference. Lastly, we introduced the marginal likelihood in the approximate Bayesian computation framework for Bayesian model selection. We showed that population annealing enables us to compute the marginal likelihood in the approximate Bayesian computation framework and conduct model selection depending on the Bayes factor.

  17. Formal modeling and analysis of ER-α associated Biological Regulatory Network in breast cancer

    Science.gov (United States)

    Tareen, Samar H.K.; Siddiqa, Amnah; Bibi, Zurah; Ahmad, Jamil

    2016-01-01

    Background Breast cancer (BC) is one of the leading cause of death among females worldwide. The increasing incidence of BC is due to various genetic and environmental changes which lead to the disruption of cellular signaling network(s). It is a complex disease in which several interlinking signaling cascades play a crucial role in establishing a complex regulatory network. The logical modeling approach of René Thomas has been applied to analyze the behavior of estrogen receptor-alpha (ER-α) associated Biological Regulatory Network (BRN) for a small part of complex events that leads to BC metastasis. Methods A discrete model was constructed using the kinetic logic formalism and its set of logical parameters were obtained using the model checking technique implemented in the SMBioNet software which is consistent with biological observations. The discrete model was further enriched with continuous dynamics by converting it into an equivalent Petri Net (PN) to analyze the logical parameters of the involved entities. Results In-silico based discrete and continuous modeling of ER-α associated signaling network involved in BC provides information about behaviors and gene-gene interaction in detail. The dynamics of discrete model revealed, imperative behaviors represented as cyclic paths and trajectories leading to pathogenic states such as metastasis. Results suggest that the increased expressions of receptors ER-α, IGF-1R and EGFR slow down the activity of tumor suppressor genes (TSGs) such as BRCA1, p53 and Mdm2 which can lead to metastasis. Therefore, IGF-1R and EGFR are considered as important inhibitory targets to control the metastasis in BC. Conclusion The in-silico approaches allow us to increase our understanding of the functional properties of living organisms. It opens new avenues of investigations of multiple inhibitory targets (ER-α, IGF-1R and EGFR) for wet lab experiments as well as provided valuable insights in the treatment of cancers such as BC.

  18. Electrical pulse induced biological effects using dielectric spectroscopy and mathematical models

    Science.gov (United States)

    Garner, Allen Lawrence

    This dissertation studies the effects of pulsed electric fields (PEFs) on biological cells by measuring the changes in the electrical properties of the pulsed cells and mathematically modeling avascular tumor growth, cell population dynamics, and Ohmic heating. These issues are critical because of the recent use of intense ultrashort PEFs for various biological and medical applications. Recent research using PEFs for tumor treatment motivated an investigation of a simple model for the growth of an avascular tumor. We modeled tumor growth before and after necrotic core formation by incorporating spatial dependence into a one dimensional scaling law. This model emphasized the importance of cell metabolic rate in determining the final steady state size of the tumor. Experimental results showing changes in cell survival and cell cycle due to PEFs led to an investigation of a simple mathematical model for cell population dynamics that considered the cells to be proliferating (dividing) or quiescent (resting). Although some cell populations apparently reached steady state quickly, the proliferating cell population fell below one, meaning that the overall cell population would eventually decay away. This result, which was unaltered by including a transition from the quiescent to proliferating state, emphasized the importance of targeting proliferating cells for successful cancer treatments. Time domain dielectric spectroscopy was used to measure the electrical properties of a biological cell suspension over a wide frequency range with a single pulse following multiple PEFs. Fitting the dielectric properties of a cancer cell (Jurkat) suspension to a double shell model yielded the dielectric parameters of the cell membrane, cytoplasm, nuclear envelope, and nucleoplasm. Decreased cytoplasm and nucleoplasm conductivity and increased suspension conductivity suggestion transport from the cell interior to the exterior consistent with electroporation. Reduced cell membrane

  19. Logic-statistic modeling and analysis of biological sequence data

    DEFF Research Database (Denmark)

    Christiansen, Henning

    2007-01-01

    We describe here the intentions and plans of a newly started, funded research project in order to further the dialogue with the international research in the field. The purpose is to obtain experiences for realistic applications of flexible and powerful modeling tools that integrate logic and sta...... and statistics, as exemplified by the PRISM system. As part of this, we will develop systematic and automatic optimizations, and the overall goal is to see how far it is possible to promote such techniques in computational biology....

  20. Sialoglycoconjugates in Trypanosoma cruzi-host cell interaction: possible biological model - a review

    Directory of Open Access Journals (Sweden)

    Alane Beatriz Vermelho

    1994-03-01

    Full Text Available A number of glycoconjugates, including glycolipids and glycoproteins, participate in the process of host-cell invasion by Trypanosoma cruzi and one of the most important carbohydrates involved on this interaction is sialic acid. It is known that parasite trans-sialidase participates with sialic acid in a coordinated fashion in the initial stages of invasion. Given the importance of these sialogycoconjugates, this review sets out various possible biological models for the interaction between the parasite and mammalian cells that possess a sialylated receptor/ligand system.

  1. Constructing Soliton and Kink Solutions of PDE Models in Transport and Biology

    Directory of Open Access Journals (Sweden)

    Vsevolod A. Vladimirov

    2006-06-01

    Full Text Available We present a review of our recent works directed towards discovery of a periodic, kink-like and soliton-like travelling wave solutions within the models of transport phenomena and the mathematical biology. Analytical description of these wave patterns is carried out by means of our modification of the direct algebraic balance method. In the case when the analytical description fails, we propose to approximate invariant travelling wave solutions by means of an infinite series of exponential functions. The effectiveness of the method of approximation is demonstrated on a hyperbolic modification of Burgers equation.

  2. Symbiosis in the Bak Sneppen model for biological evolution with economic applications

    Science.gov (United States)

    Bartolozzi, M.; Leinweber, D. B.; Thomas, A. W.

    2006-06-01

    In the present work we extend the Bak-Sneppen model for biological evolution by introducing local interactions between species. This “environmental” perturbation modifies the intrinsic fitness of each element of the ecology, leading to higher survival probability, even for the less fit. While the system still self-organizes toward a critical state, the distribution of fitness broadens, losing the classical step-function shape. A possible application in economics is discussed, where firms are represented as evolving species linked by mutual interests.

  3. Rabi model as a quantum coherent heat engine: From quantum biology to superconducting circuits

    OpenAIRE

    Hardal, Ali U. C.; Müstecaplıoğlu, Özgür Esat; Altıntaş, Ferdi

    2014-01-01

    PHYSICAL REVIEW A 91, 023816 (2015) Rabi model as a quantum coherent heat engine: From quantum biology to superconducting circuits Ferdi Altintas,1 Ali U¨ . C. Hardal,2 and O¨ zgu¨r E. Mu¨stecaplıog˘lu2,* 1Department of Physics, Abant Izzet Baysal University, Bolu, 14280, Turkey 2Department of Physics, Koc¸ University, Sarıyer, ˙Istanbul, 34450, Turkey (Received 10 November 2014; published 12 February 2015) We propose a multilevel quantum heat engine with a working medium de...

  4. Automated Bayesian model development for frequency detection in biological time series

    Directory of Open Access Journals (Sweden)

    Oldroyd Giles ED

    2011-06-01

    Full Text Available Abstract Background A first step in building a mathematical model of a biological system is often the analysis of the temporal behaviour of key quantities. Mathematical relationships between the time and frequency domain, such as Fourier Transforms and wavelets, are commonly used to extract information about the underlying signal from a given time series. This one-to-one mapping from time points to frequencies inherently assumes that both domains contain the complete knowledge of the system. However, for truncated, noisy time series with background trends this unique mapping breaks down and the question reduces to an inference problem of identifying the most probable frequencies. Results In this paper we build on the method of Bayesian Spectrum Analysis and demonstrate its advantages over conventional methods by applying it to a number of test cases, including two types of biological time series. Firstly, oscillations of calcium in plant root cells in response to microbial symbionts are non-stationary and noisy, posing challenges to data analysis. Secondly, circadian rhythms in gene expression measured over only two cycles highlights the problem of time series with limited length. The results show that the Bayesian frequency detection approach can provide useful results in specific areas where Fourier analysis can be uninformative or misleading. We demonstrate further benefits of the Bayesian approach for time series analysis, such as direct comparison of different hypotheses, inherent estimation of noise levels and parameter precision, and a flexible framework for modelling the data without pre-processing. Conclusions Modelling in systems biology often builds on the study of time-dependent phenomena. Fourier Transforms are a convenient tool for analysing the frequency domain of time series. However, there are well-known limitations of this method, such as the introduction of spurious frequencies when handling short and noisy time series, and

  5. Applying Intelligent Computing Techniques to Modeling Biological Networks from Expression Data

    Institute of Scientific and Technical Information of China (English)

    Wei-Po Lee; Kung-Cheng Yang

    2008-01-01

    Constructing biological networks is one of the most important issues in system sbiology. However, constructing a network from data manually takes a considerable large amount of time, therefore an automated procedure is advocated. To automate the procedure of network construction, in this work we use two intelligent computing techniques, genetic programming and neural computation, to infer two kinds of network models that use continuous variables. To verify the presented approaches, experiments have been conducted and the preliminary results show that both approaches can be used to infer networks successfully.

  6. Mesos-scale modeling of irradiation in pressurized water reactor concrete biological shields

    Energy Technology Data Exchange (ETDEWEB)

    Le Pape, Yann [ORNL; Huang, Hai [Idaho National Laboratory (INL)

    2016-01-01

    Neutron irradiation exposure causes aggregate expansion, namely radiation-induced volumetric expansion (RIVE). The structural significance of RIVE on a portion of a prototypical pressurized water reactor (PWR) concrete biological shield (CBS) is investigated by using a meso- scale nonlinear concrete model with inputs from an irradiation transport code and a coupled moisture transport-heat transfer code. RIVE-induced severe cracking onset appears to be triggered by the ini- tial shrinkage-induced cracking and propagates to a depth of > 10 cm at extended operation of 80 years. Relaxation of the cement paste stresses results in delaying the crack propagation by about 10 years.

  7. CoreFlow: A computational platform for integration, analysis and modeling of complex biological data

    OpenAIRE

    Pasculescu, Adrian; Schoof, Erwin; Creixell, Pau; Zheng, Yong; Olhovsky, Marina; Tian, Ruijun; So, Jonathan; Vanderlaan, Rachel D.; Pawson, Tony; Linding, Rune; Colwill, Karen

    2014-01-01

    A major challenge in mass spectrometry and other large-scale applications is how to handle, integrate, and model the data that is produced. Given the speed at which technology advances and the need to keep pace with biological experiments, we designed a computational platform, CoreFlow, which provides programmers with a framework to manage data in real-time. It allows users to upload data into a relational database (MySQL), and to create custom scripts in high-level languages such as R, Pytho...

  8. Regarding on the prototype solutions for the nonlinear fractional-order biological population model

    Science.gov (United States)

    Baskonus, Haci Mehmet; Bulut, Hasan

    2016-06-01

    In this study, we have submitted to literature a method newly extended which is called as Improved Bernoulli sub-equation function method based on the Bernoulli Sub-ODE method. The proposed analytical scheme has been expressed with steps. We have obtained some new analytical solutions to the nonlinear fractional-order biological population model by using this technique. Two and three dimensional surfaces of analytical solutions have been drawn by wolfram Mathematica 9. Finally, a conclusion has been submitted by mentioning important acquisitions founded in this study.

  9. Molecular modeling studies, synthesis and biological evaluation of dabigatran analogues as thrombin inhibitors.

    Science.gov (United States)

    Dong, Ming-Hui; Chen, Hai-Feng; Ren, Yu-Jie; Shao, Fang-Ming

    2016-01-15

    In this work, 48 thrombin inhibitors based on the structural scaffold of dabigatran were analyzed using a combination of molecular modeling techniques. We generated three-dimensional quantitative structure-activity relationship (3D-QSAR) models based on three alignments for both comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) to highlight the structural requirements for thrombin protein inhibition. In addition to the 3D-QSAR study, Topomer CoMFA model also was established with a higher leave-one-out cross-validation q(2) and a non-cross-validation r(2), which suggest that the three models have good predictive ability. The results indicated that the steric, hydrophobic and electrostatic fields play key roles in QSAR model. Furthermore, we employed molecular docking and re-docking simulation explored the binding relationship of the ligand and the receptor protein in detail. Molecular docking simulations identified several key interactions that were also indicated through 3D-QSAR analysis. On the basis of the obtained results, two compounds were designed and predicted by three models, the biological evaluation in vitro (IC50) demonstrated that these molecular models were effective for the development of novel potent thrombin inhibitors.

  10. Chaste: A test-driven approach to software development for biological modelling

    KAUST Repository

    Pitt-Francis, Joe

    2009-12-01

    Chaste (\\'Cancer, heart and soft-tissue environment\\') is a software library and a set of test suites for computational simulations in the domain of biology. Current functionality has arisen from modelling in the fields of cancer, cardiac physiology and soft-tissue mechanics. It is released under the LGPL 2.1 licence. Chaste has been developed using agile programming methods. The project began in 2005 when it was reasoned that the modelling of a variety of physiological phenomena required both a generic mathematical modelling framework, and a generic computational/simulation framework. The Chaste project evolved from the Integrative Biology (IB) e-Science Project, an inter-institutional project aimed at developing a suitable IT infrastructure to support physiome-level computational modelling, with a primary focus on cardiac and cancer modelling. Program summary: Program title: Chaste. Catalogue identifier: AEFD_v1_0. Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AEFD_v1_0.html. Program obtainable from: CPC Program Library, Queen\\'s University, Belfast, N. Ireland. Licensing provisions: LGPL 2.1. No. of lines in distributed program, including test data, etc.: 5 407 321. No. of bytes in distributed program, including test data, etc.: 42 004 554. Distribution format: tar.gz. Programming language: C++. Operating system: Unix. Has the code been vectorised or parallelized?: Yes. Parallelized using MPI. RAM:< 90   Megabytes for two of the scenarios described in Section 6 of the manuscript (Monodomain re-entry on a slab or Cylindrical crypt simulation). Up to 16 Gigabytes (distributed across processors) for full resolution bidomain cardiac simulation. Classification: 3. External routines: Boost, CodeSynthesis XSD, CxxTest, HDF5, METIS, MPI, PETSc, Triangle, Xerces. Nature of problem: Chaste may be used for solving coupled ODE and PDE systems arising from modelling biological systems. Use of Chaste in two application areas are described in this paper: cardiac

  11. Seasonal nutrient and plankton dynamics in a physical-biological model of Crater Lake

    Science.gov (United States)

    Fennel, K.; Collier, R.; Larson, G.; Crawford, G.; Boss, E.

    2007-01-01

    A coupled 1D physical-biological model of Crater Lake is presented. The model simulates the seasonal evolution of two functional phytoplankton groups, total chlorophyll, and zooplankton in good quantitative agreement with observations from a 10-year monitoring study. During the stratified period in summer and early fall the model displays a marked vertical structure: the phytoplankton biomass of the functional group 1, which represents diatoms and dinoflagellates, has its highest concentration in the upper 40 m; the phytoplankton biomass of group 2, which represents chlorophyta, chrysophyta, cryptomonads and cyanobacteria, has its highest concentrations between 50 and 80 m, and phytoplankton chlorophyll has its maximum at 120 m depth. A similar vertical structure is a reoccurring feature in the available data. In the model the key process allowing a vertical separation between biomass and chlorophyll is photoacclimation. Vertical light attenuation (i.e., water clarity) and the physiological ability of phytoplankton to increase their cellular chlorophyll-to-biomass ratio are ultimately determining the location of the chlorophyll maximum. The location of the particle maxima on the other hand is determined by the balance between growth and losses and occurs where growth and losses equal. The vertical particle flux simulated by our model agrees well with flux measurements from a sediment trap. This motivated us to revisit a previously published study by Dymond et al. (1996). Dymond et al. used a box model to estimate the vertical particle flux and found a discrepancy by a factor 2.5-10 between their model-derived flux and measured fluxes from a sediment trap. Their box model neglected the exchange flux of dissolved and suspended organic matter, which, as our model and available data suggests is significant for the vertical exchange of nitrogen. Adjustment of Dymond et al.'s assumptions to account for dissolved and suspended nitrogen yields a flux estimate that is

  12. The role of mathematical models in understanding pattern formation in developmental biology.

    Science.gov (United States)

    Umulis, David M; Othmer, Hans G

    2015-05-01

    In a Wall Street Journal article published on April 5, 2013, E. O. Wilson attempted to make the case that biologists do not really need to learn any mathematics-whenever they run into difficulty with numerical issues, they can find a technician (aka mathematician) to help them out of their difficulty. He formalizes this in Wilsons Principle No. 1: "It is far easier for scientists to acquire needed collaboration from mathematicians and statisticians than it is for mathematicians and statisticians to find scientists able to make use of their equations." This reflects a complete misunderstanding of the role of mathematics in all sciences throughout history. To Wilson, mathematics is mere number crunching, but as Galileo said long ago, "The laws of Nature are written in the language of mathematics[Formula: see text] the symbols are triangles, circles and other geometrical figures, without whose help it is impossible to comprehend a single word." Mathematics has moved beyond the geometry-based model of Galileo's time, and in a rebuttal to Wilson, E. Frenkel has pointed out the role of mathematics in synthesizing the general principles in science (Both point and counter-point are available in Wilson and Frenkel in Notices Am Math Soc 60(7):837-838, 2013). We will take this a step further and show how mathematics has been used to make new and experimentally verified discoveries in developmental biology and how mathematics is essential for understanding a problem that has puzzled experimentalists for decades-that of how organisms can scale in size. Mathematical analysis alone cannot "solve" these problems since the validation lies at the molecular level, but conversely, a growing number of questions in biology cannot be solved without mathematical analysis and modeling. Herein, we discuss a few examples of the productive intercourse between mathematics and biology. PMID:25280665

  13. a Geo-Visual Analytics Approach to Biological Shepherding: Modelling Animal Movements and Impacts

    Science.gov (United States)

    Benke, K. K.; Sheth, F.; Betteridge, K.; Pettit, C. J.; Aurambout, J.-P.

    2012-07-01

    The lamb industry in Victoria is a significant component of the state economy with annual exports in the vicinity of 1 billion. GPS and visualisation tools can be used to monitor grazing animal movements at the farm scale and observe interactions with the environment. Modelling the spatial-temporal movements of grazing animals in response to environmental conditions provides input for the design of paddocks with the aim of improving management procedures, animal performance and animal welfare. The term "biological shepherding" is associated with the re-design of environmental conditions and the analysis of responses from grazing animals. The combination of biological shepherding with geo-visual analytics (geo-spatial data analysis with visualisation) provides a framework for improving landscape design and supports research in grazing behaviour in variable landscapes, heat stress avoidance behaviour during summer months, and modelling excreta distributions (with respect to nitrogen emissions and nitrogen return for fertilising the paddock). Nitrogen losses due to excreta are mainly in the form of gaseous emissions to the atmosphere and leaching into the groundwater. In this study, background and context are provided in the case of biological shepherding and tracking animal movements. Examples are provided of recent applications in regional Australia and New Zealand. Based on experimental data and computer simulation, and using data visualisation and feature extraction, it was demonstrated that livestock excreta are not always randomly located, but concentrated around localised gathering points, sometimes separated by the nature of the excretion. Farmers require information on the nitrogen losses in order to reduce emissions to meet local and international nitrogen leaching and greenhouse gas targets and to improve the efficiency of nutrient management.

  14. Combined model of intrinsic and extrinsic variability for computational network design with application to synthetic biology.

    Directory of Open Access Journals (Sweden)

    Tina Toni

    Full Text Available Biological systems are inherently variable, with their dynamics influenced by intrinsic and extrinsic sources. These systems are often only partially characterized, with large uncertainties about specific sources of extrinsic variability and biochemical properties. Moreover, it is not yet well understood how different sources of variability combine and affect biological systems in concert. To successfully design biomedical therapies or synthetic circuits with robust performance, it is crucial to account for uncertainty and effects of variability. Here we introduce an efficient modeling and simulation framework to study systems that are simultaneously subject to multiple sources of variability, and apply it to make design decisions on small genetic networks that play a role of basic design elements of synthetic circuits. Specifically, the framework was used to explore the effect of transcriptional and post-transcriptional autoregulation on fluctuations in protein expression in simple genetic networks. We found that autoregulation could either suppress or increase the output variability, depending on specific noise sources and network parameters. We showed that transcriptional autoregulation was more successful than post-transcriptional in suppressing variability across a wide range of intrinsic and extrinsic magnitudes and sources. We derived the following design principles to guide the design of circuits that best suppress variability: (i high protein cooperativity and low miRNA cooperativity, (ii imperfect complementarity between miRNA and mRNA was preferred to perfect complementarity, and (iii correlated expression of mRNA and miRNA--for example, on the same transcript--was best for suppression of protein variability. Results further showed that correlations in kinetic parameters between cells affected the ability to suppress variability, and that variability in transient states did not necessarily follow the same principles as variability in

  15. SBMLSimulator: A Java Tool for Model Simulation and Parameter Estimation in Systems Biology

    Directory of Open Access Journals (Sweden)

    Alexander Dörr

    2014-12-01

    Full Text Available The identification of suitable model parameters for biochemical reactions has been recognized as a quite difficult endeavor. Parameter values from literature or experiments can often not directly be combined in complex reaction systems. Nature-inspired optimization techniques can find appropriate sets of parameters that calibrate a model to experimentally obtained time series data. We present SBMLsimulator, a tool that combines the Systems Biology Simulation Core Library for dynamic simulation of biochemical models with the heuristic optimization framework EvA2. SBMLsimulator provides an intuitive graphical user interface with various options as well as a fully-featured command-line interface for large-scale and script-based model simulation and calibration. In a parameter estimation study based on a published model and artificial data we demonstrate the capability of SBMLsimulator to identify parameters. SBMLsimulator is useful for both, the interactive simulation and exploration of the parameter space and for the large-scale model calibration and estimation of uncertain parameter values.

  16. Modelling of biogas extraction at an Italian landfill accepting mechanically and biologically treated municipal solid waste.

    Science.gov (United States)

    Calabrò, Paolo S; Orsi, Sirio; Gentili, Emiliano; Carlo, Meoni

    2011-12-01

    This paper presents the results of the modelling of the biogas extraction in a full-scale Italian landfill by the USEPA LandGEM model and the Andreottola-Cossu approach. The landfill chosen for this research ('Il Fossetto' plant, Monsummano Terme, Italy) had accepted mixed municipal raw waste for about 15 years. In the year 2003 a mechanical biological treatment (MBT) was implemented and starting from the end of the year 2006, the recirculation in the landfill of the concentrated leachate coming from the internal membrane leachate treatment plant was put into practice. The USEPA LandGEM model and the Andreottola-Cossu approach were chosen since they require only input data routinely acquired during landfill management (waste amount and composition) and allow a simplified calibration, therefore they are potentially useful for practical purposes such as landfill gas management. The results given by the models are compared with measured data and analysed in order to verify the impact of MBT on biogas production; moreover, the possible effects of the recirculation of the concentrated leachate are discussed. The results clearly show how both models can adequately fit measured data even after MBT implementation. Model performance was significantly reduced for the period after the beginning of recirculation of concentrated leachate when the probable inhibition of methane production, due to the competition between methanogens and sulfate-reducing bacteria, significantly influenced the biogas production and composition. PMID:21930528

  17. The Effect of Blended Learning Model on High School Students' Biology Achievement and on Their Attitudes towards the Internet

    Science.gov (United States)

    Yapici, I.Umit; Akbayin, Hasan

    2012-01-01

    The present study aims to determine the effect of the blended learning model on high school students' biology achievement and on their attitudes towards the Internet. Among the experimental models, the pretest-posttest control group model was used in the study. The study was carried out with 107 students (47 of whom were in the experimental group,…

  18. Evaluation of iodide deficiency in the lactating rat and pup using a biologically based dose-response model

    Science.gov (United States)

    A biologically-based dose response (BBDR) model for the hypothalamic-pituitary thyroid (BPT) axis in the lactating rat and nursing pup was developed to describe the perturbations caused by iodide deficiency on the HPT axis. Model calibrations, carried out by adjusting key model p...

  19. Evaluation of iodide deficiency in the lactating rat and pup using a biologically based dose response (BBDR) Model***

    Science.gov (United States)

    A biologically-based dose response (BBDR) model for the hypothalamic-pituitary thyroid (HPT) axis in the lactating rat and nursing pup was developed to describe the perturbations caused by iodide deficiency on the 1-IPT axis. Model calibrations, carried out by adjusting key model...

  20. A Computational Systems Biology Software Platform for Multiscale Modeling and Simulation: Integrating Whole-Body Physiology, Disease Biology, and Molecular Reaction Networks

    OpenAIRE

    ThomasEissing

    2011-01-01

    Today, in silico studies and trial simulations already complement experimental approaches in pharmaceutical R&D and have become indispensable tools for decision making and communication with regulatory agencies. While biology is multi-scale by nature, project work and software tools usually focus on isolated aspects of drug action, such as pharmacokinetics at the organism scale or pharmacodynamic interaction on the molecular level. We present a modeling and simulation software platform co...

  1. Daphnia as a model organism in limnology and aquatic biology: introductory remarks

    Directory of Open Access Journals (Sweden)

    Adam PETRUSEK

    2011-08-01

    Full Text Available Cladocerans of the genus Daphnia are keystone pelagic filter feeders in many temperate ponds and lakes. They have also become popular model organisms in various biological disciplines, from aquatic ecology to biomedical sciences. The crucial features that make these organisms excellent experimental models are their cyclical parthenogenetic life cycle together with easy culturing and handling. Thanks to these characteristics, the number of publications dealing with Daphnia is rapidly growing. The special insert to the Journal of Limnology on Daphnia biology contains contributions that deal directly or indirectly with the reproduction and development of these water fleas, in relation to various ecological factors. These include predator-prey interactions and their impact on morphology, population dynamics, or senescence-related traits, growth of daphnids on a diet consisting of invasively spreading cyanobacteria, and also the impact of extreme floods on a Daphnia population (and particularly on its dormant ephippial egg bank in a reservoir. Here, we discuss these presented works, and point out the potential lines of research that may improve the generalisation of their findings.

  2. ScaleNet: a literature-based model of scale insect biology and systematics.

    Science.gov (United States)

    García Morales, Mayrolin; Denno, Barbara D; Miller, Douglass R; Miller, Gary L; Ben-Dov, Yair; Hardy, Nate B

    2016-01-01

    Scale insects (Hemiptera: Coccoidea) are small herbivorous insects found on all continents except Antarctica. They are extremely invasive, and many species are serious agricultural pests. They are also emerging models for studies of the evolution of genetic systems, endosymbiosis and plant-insect interactions. ScaleNet was launched in 1995 to provide insect identifiers, pest managers, insect systematists, evolutionary biologists and ecologists efficient access to information about scale insect biological diversity. It provides comprehensive information on scale insects taken directly from the primary literature. Currently, it draws from 23,477 articles and describes the systematics and biology of 8194 valid species. For 20 years, ScaleNet ran on the same software platform. That platform is no longer viable. Here, we present a new, open-source implementation of ScaleNet. We have normalized the data model, begun the process of correcting invalid data, upgraded the user interface, and added online administrative tools. These improvements make ScaleNet easier to use and maintain and make the ScaleNet data more accurate and extendable. Database URL: http://scalenet.info.

  3. Biologically-directed modeling reflects cytolytic clearance of SIV-infected cells in vivo in macaques.

    Directory of Open Access Journals (Sweden)

    W David Wick

    Full Text Available The disappointing outcomes of cellular immune-based vaccines against HIV-1 despite strong evidence for the protective role of CD8⁺ T lymphocytes (CTLs has prompted revisiting the mechanisms of cellular immunity. Prior data from experiments examining the kinetics of Simian Immunodeficiency Virus (SIV clearance in infected macaques with or without in vivo CD8 depletion were interpreted as refuting the concept that CTLs suppress SIV/HIV by direct killing of infected cells. Here we briefly review the biological evidence for CTL cytolytic activity in viral infections, and utilize biologically-directed modeling to assess the possibility of a killing mechanism for the antiviral effect of CTLs, taking into account the generation, proliferation, and survival of activated CD4⁺ and CD8⁺ T lymphocytes, as well as the life cycle of the virus. Our analyses of the published macaque data using these models support a killing mechanism, when one considers T lymphocyte and HIV-1 lifecycles, and factors such as the eclipse period before release of virions by infected cells, an exponential pattern of virion production by infected cells, and a variable lifespan for acutely infected cells. We conclude that for SIV/HIV pathogenesis, CTLs deserve their reputation as being cytolytic.

  4. Evaluation of biological activity of Uncaria tomentosa (Willd.) DC. using the chicken embryo model.

    Science.gov (United States)

    Pilarski, Radosław; Bednarczyk, Marek; Gulewicz, Krzysztof

    2009-01-01

    The biological activity of Uncaria tomentosa (Willd.) DC. (cat's claw) was evaluated by application of the chicken embryo model. Among three groups of eggs (n = 360) with twelve-day old embryos, two were injected with different doses of cat's claw extracts (0.0492 and 0.492 mg/200 lambda). To the third control group 200 lambda of physiological salt was applied. All eggs were incubated in conventional forced-air apparatus until hatched. Hatchability, chicken weight and wholesomeness were analyzed. Selected parameters of blood including number of erythrocytes (RBC), number of leukocytes (WBC), mean red cell volume (MCV), hematocrit (HCT), hemoglobin concentration (HGB), mean amount of cell hemoglobin (MCH), mean cell hemoglobin concentration (MCHC) and embryo weight (MAS) were assayed and compared. Significant differences with ANOVA were observed for MCV (P = 0.002), MCHC (P = 0.00001) and MCH (P = 0.02). Applying the chicken embryo model brought new information about the biological activity of U. tomentosa showing an unfavourable effect on some morphological blood parameters.

  5. Stromal microenvironment processes unveiled by biological component analysis of gene expression in xenograft tumor models

    Directory of Open Access Journals (Sweden)

    Chen James L

    2010-10-01

    Full Text Available Abstract Background Mouse xenograft models, in which human cancer cells are implanted in immune-suppressed mice, have been popular for studying the mechanisms of novel therapeutic targets, tumor progression and metastasis. We hypothesized that we could exploit the interspecies genetic differences in these experiments. Our purpose is to elucidate stromal microenvironment signals from probes on human arrays unintentionally cross-hybridizing with mouse homologous genes in xenograft tumor models. Results By identifying cross-species hybridizing probes from sequence alignment and cross-species hybridization experiment for the human whole-genome arrays, deregulated stromal genes can be identified and then their biological significance were predicted from enrichment studies. Comparing these results with those found by the laser capture microdissection of stromal cells from tumor specimens resulted in the discovery of significantly enriched stromal biological processes. Conclusions Using this method, in addition to their primary endpoints, researchers can leverage xenograft experiments to better characterize the tumor microenvironment without additional costs. The Xhyb probes and R script are available at http://www.lussierlab.org/publications/Stroma

  6. Modeling LIDAR Detection of Biological Aerosols to Determine Optimum Implementation Strategy

    Energy Technology Data Exchange (ETDEWEB)

    Sheen, David M.; Aker, Pam M.

    2007-09-19

    This report summarizes work performed for a larger multi-laboratory project named the Background Interferent Measurement and Standards project. While originally tasked to develop algorithms to optimize biological warfare agent detection using UV fluorescence LIDAR, the current uncertainties in the reported fluorescence profiles and cross sections the development of any meaningful models. It was decided that a better approach would be to model the wavelength-dependent elastic backscattering from a number of ambient background aerosol types, and compare this with that generated from representative sporulated and vegetative bacterial systems. Calculations in this report show that a 266, 355, 532 and 1064 nm elastic backscatter LIDAR experiment will allow an operator to immediately recognize when sulfate, VOC-based or road dust (silicate) aerosols are approaching, independent of humidity changes. It will be more difficult to distinguish soot aerosols from biological aerosols, or vegetative bacteria from sporulated bacteria. In these latter cases, the elastic scattering data will most likely have to be combined with UV fluorescence data to enable a more robust categorization.

  7. Interconnection between biological abnormalities in borderline personality disorder: use of the Bayesian networks model.

    Science.gov (United States)

    De la Fuente, José Manuel; Bengoetxea, Endika; Navarro, Felipe; Bobes, Julio; Alarcón, Renato Daniel

    2011-04-30

    There is agreement in that strengthening the sets of neurobiological data would reinforce the diagnostic objectivity of many psychiatric entities. This article attempts to use this approach in borderline personality disorder (BPD). Assuming that most of the biological findings in BPD reflect common underlying pathophysiological processes we hypothesized that most of the data involved in the findings would be statistically interconnected and interdependent, indicating biological consistency for this diagnosis. Prospectively obtained data on scalp and sleep electroencephalography (EEG), clinical neurologic soft signs, the dexamethasone suppression and thyrotropin-releasing hormone stimulation tests of 20 consecutive BPD patients were used to generate a Bayesian network model, an artificial intelligence paradigm that visually illustrates eventual associations (or inter-dependencies) between otherwise seemingly unrelated variables. The Bayesian network model identified relationships among most of the variables. EEG and TSH were the variables that influence most of the others, especially sleep parameters. Neurological soft signs were linked with EEG, TSH, and sleep parameters. The results suggest the possibility of using objective neurobiological variables to strengthen the validity of future diagnostic criteria and nosological characterization of BPD.

  8. Advantages and challenges of using physics curricula as a model for reforming an undergraduate biology course.

    Science.gov (United States)

    Donovan, D A; Atkins, L J; Salter, I Y; Gallagher, D J; Kratz, R F; Rousseau, J V; Nelson, G D

    2013-06-01

    We report on the development of a life sciences curriculum, targeted to undergraduate students, which was modeled after a commercially available physics curriculum and based on aspects of how people learn. Our paper describes the collaborative development process and necessary modifications required to apply a physics pedagogical model in a life sciences context. While some approaches were easily adapted, others provided significant challenges. Among these challenges were: representations of energy, introducing definitions, the placement of Scientists' Ideas, and the replicability of data. In modifying the curriculum to address these challenges, we have come to see them as speaking to deeper differences between the disciplines, namely that introductory physics--for example, Newton's laws, magnetism, light--is a science of pairwise interaction, while introductory biology--for example, photosynthesis, evolution, cycling of matter in ecosystems--is a science of linked processes, and we suggest that this is how the two disciplines are presented in introductory classes. We illustrate this tension through an analysis of our adaptations of the physics curriculum for instruction on the cycling of matter and energy; we show that modifications of the physics curriculum to address the biological framework promotes strong gains in student understanding of these topics, as evidenced by analysis of student work.

  9. Biological models in vivo for boron neutronic capture studies as tumors therapy

    International Nuclear Information System (INIS)

    The use of experimental models for Boron Neutronic Capture studies as Tumors Therapy have as two main objectives: 1) To contribute to the basic knowledge of the biological mechanisms involved to increase the method therapeutical advantage, and 2) To explore the possible application of this therapeutic method to other pathologies. In this frame it was studied the carcinogenesis model of hamster cheek pouch, a type of human buccal cancer. Biodistribution studies of boron compound were performed in tumor, blood and in different precancerous and normal tissues as well as BNCT studies. Results validated this method for BNCT studies and show the capacity of the oral mucosa tumors of selectively concentrate the boron compound, showing a deleterious clear effect on the tumor after 24 hours with BNCT treatment. (author)

  10. An individual-based predator-prey model for biological coevolution: Fluctuations, stability, and community structure

    CERN Document Server

    Rikvold, Per Arne

    2007-01-01

    We study an individual-based predator-prey model of biological coevolution, using linear stability analysis and large-scale kinetic Monte Carlo simulations. The model exhibits approximate 1/f noise in diversity and population-size fluctuations, and it generates a sequence of quasi-steady communities in the form of simple food webs. These communities are quite resilient toward the loss of one or a few species, which is reflected in different power-law exponents for the durations of communities and the lifetimes of species. The exponent for the former is near -1, while the latter is close to -2. Statistical characteristics of the evolving communities, including degree (predator and prey) distributions and proportions of basal, intermediate, and top species, compare reasonably with data for real food webs.

  11. Evaluation of blade-strike models for estimating the biological performance of large Kaplan hydro turbines

    Energy Technology Data Exchange (ETDEWEB)

    Deng, Z. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Carlson, T. J. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Ploskey, G. R. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Richmond, M. C. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States)

    2005-11-01

    Bio-indexing of hydro turbines has been identified as an important means to optimize passage conditions for fish by identifying operations for existing and new design turbines that minimize the probability of injury. Cost-effective implementation of bio-indexing requires the use of tools such as numerical and physical turbine models to generate hypotheses for turbine operations that can be tested at prototype scales using live fish. Blade strike has been proposed as an index variable for the biological performance of turbines. Report reviews an evaluation of the use of numerical blade-strike models as a means with which to predict the probability of blade strike and injury of juvenile salmon smolt passing through large Kaplan turbines on the mainstem Columbia River.

  12. Robust biological nitrogen fixation in a model grass-bacterial association.

    Science.gov (United States)

    Pankievicz, Vânia C S; do Amaral, Fernanda P; Santos, Karina F D N; Agtuca, Beverly; Xu, Youwen; Schueller, Michael J; Arisi, Ana Carolina M; Steffens, Maria B R; de Souza, Emanuel M; Pedrosa, Fábio O; Stacey, Gary; Ferrieri, Richard A

    2015-03-01

    Nitrogen-fixing rhizobacteria can promote plant growth; however, it is controversial whether biological nitrogen fixation (BNF) from associative interaction contributes to growth promotion. The roots of Setaria viridis, a model C4 grass, were effectively colonized by bacterial inoculants resulting in a significant enhancement of growth. Nitrogen-13 tracer studies provided direct evidence for tracer uptake by the host plant and incorporation into protein. Indeed, plants showed robust growth under nitrogen-limiting conditions when inoculated with an ammonium-excreting strain of Azospirillum brasilense. (11)C-labeling experiments showed that patterns in central carbon metabolism and resource allocation exhibited by nitrogen-starved plants were largely reversed by bacterial inoculation, such that they resembled plants grown under nitrogen-sufficient conditions. Adoption of S. viridis as a model should promote research into the mechanisms of associative nitrogen fixation with the ultimate goal of greater adoption of BNF for sustainable crop production.

  13. CHANGES IN BIOLOGICAL PROPERTIES OF ORDINARY BLACK SOILS AT GLEYISATION (MODEL EXPERIMENT

    Directory of Open Access Journals (Sweden)

    Kandashova K. A.

    2015-10-01

    Full Text Available The article presents the results of laboratory modeling of gleyisation and its effect on the biological properties of soils with stagnant regime in ordinary black soils. Gleyisation is a complex biochemical process that occurs under oxygen reduction conditions. Anaerobic microorganisms, the presence of organic substances, and the constant or prolonged waterlogging of individual horizons or the entire soil profile promote gleyisation. Model experiments revealed that gleyisation increase the total number of bacteria and suppresses number of actinomycetes, micromycetes and growth of fungal mycelium. Gleyisation decreases the activity of oxidoreductases and increases the hydrolases activity. In addition, the second content of humus slightly increases and active acidity (pH changes to neutral. Accumulation of large amounts of iron oxide (II in soil is revealed

  14. Modelling and interpreting biologically crusted dryland soil sub-surface structure using automated micropenetrometry

    Science.gov (United States)

    Hoon, Stephen R.; Felde, Vincent J. M. N. L.; Drahorad, Sylvie L.; Felix-Henningsen, Peter

    2015-04-01

    Soil penetrometers are used routinely to determine the shear strength of soils and deformable sediments both at the surface and throughout a depth profile in disciplines as diverse as soil science, agriculture, geoengineering and alpine avalanche-safety (e.g. Grunwald et al. 2001, Van Herwijnen et al. 2009). Generically, penetrometers comprise two principal components: An advancing probe, and a transducer; the latter to measure the pressure or force required to cause the probe to penetrate or advance through the soil or sediment. The force transducer employed to determine the pressure can range, for example, from a simple mechanical spring gauge to an automatically data-logged electronic transducer. Automated computer control of the penetrometer step size and probe advance rate enables precise measurements to be made down to a resolution of 10's of microns, (e.g. the automated electronic micropenetrometer (EMP) described by Drahorad 2012). Here we discuss the determination, modelling and interpretation of biologically crusted dryland soil sub-surface structures using automated micropenetrometry. We outline a model enabling the interpretation of depth dependent penetration resistance (PR) profiles and their spatial differentials using the model equations, σ {}(z) ={}σ c0{}+Σ 1n[σ n{}(z){}+anz + bnz2] and dσ /dz = Σ 1n[dσ n(z) /dz{} {}+{}Frn(z)] where σ c0 and σ n are the plastic deformation stresses for the surface and nth soil structure (e.g. soil crust, layer, horizon or void) respectively, and Frn(z)dz is the frictional work done per unit volume by sliding the penetrometer rod an incremental distance, dz, through the nth layer. Both σ n(z) and Frn(z) are related to soil structure. They determine the form of σ {}(z){} measured by the EMP transducer. The model enables pores (regions of zero deformation stress) to be distinguished from changes in layer structure or probe friction. We have applied this method to both artificial calibration soils in the

  15. How can selection of biologically inspired features improve the performance of a robust object recognition model?

    Directory of Open Access Journals (Sweden)

    Masoud Ghodrati

    Full Text Available Humans can effectively and swiftly recognize objects in complex natural scenes. This outstanding ability has motivated many computational object recognition models. Most of these models try to emulate the behavior of this remarkable system. The human visual system hierarchically recognizes objects in several processing stages. Along these stages a set of features with increasing complexity is extracted by different parts of visual system. Elementary features like bars and edges are processed in earlier levels of visual pathway and as far as one goes upper in this pathway more complex features will be spotted. It is an important interrogation in the field of visual processing to see which features of an object are selected and represented by the visual cortex. To address this issue, we extended a hierarchical model, which is motivated by biology, for different object recognition tasks. In this model, a set of object parts, named patches, extracted in the intermediate stages. These object parts are used for training procedure in the model and have an important role in object recognition. These patches are selected indiscriminately from different positions of an image and this can lead to the extraction of non-discriminating patches which eventually may reduce the performance. In the proposed model we used an evolutionary algorithm approach to select a set of informative patches. Our reported results indicate that these patches are more informative than usual random patches. We demonstrate the strength of the proposed model on a range of object recognition tasks. The proposed model outperforms the original model in diverse object recognition tasks. It can be seen from the experiments that selected features are generally particular parts of target images. Our results suggest that selected features which are parts of target objects provide an efficient set for robust object recognition.

  16. Development of in vitro models for investigating spatially fractionated irradiation: physics and biological results

    Science.gov (United States)

    Blockhuys, S; Vanhoecke, B; Paelinck, L; Bracke, M; DeWagter, C

    2009-03-01

    We present different in vitro experimental models which allow us to evaluate the effect of spatially fractionated dose distributions on metabolic activity. We irradiated a monolayer of MCF-7/6 human breast cancer cells with a steep and a smooth 6 MV x-ray dose gradient. In the steep gradient model, we irradiated the cells with three separate small fields. We also developed two smooth gradient models. In the first model, the cells are cultured in a T25 flask and irradiated with a smooth dose gradient over the length of the flask, while in the second one, the cells are cultured in a 96-well plate and also irradiated over the length of the plate. In an attempt to correlate the spatially fractionated dose distributions with metabolic activity, the effect of irradiation was evaluated by means of the MTT assay. This assay is used to determine the metabolic activity by measuring the amount of formazan formed after the conversion of MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) by cellular dehydrogenases. The results obtained with our different models suggest a dose-specific effect on metabolic activity, characterized by an increased formazan optical density occurring in the dose range 1.0-4.0 Gy in the steep dose gradient model and in the dose ranges 4.2-6.5 Gy and 2.3-5.1 Gy in the two smooth dose gradient models. The corresponding times for maximal formazan accumulation were 5-7 days in the steep dose gradient model and day 9-13 and day 9-11 in the smooth dose gradient models. Altogether, our results suggest that the MTT assay may be used as a biological dose-response meter to monitor the radiotherapeutic effectiveness.

  17. Students' Levels of Understanding Models and Modelling in Biology: Global or Aspect-Dependent?

    Science.gov (United States)

    Krell, Moritz; Upmeier zu Belzen, Annette; Krüger, Dirk

    2014-01-01

    It is argued that knowledge about models is an important part of a profound understanding of Nature of Science. Consequently, researchers have developed different "levels of understanding" to analyse students', teachers', or experts' comprehension of this topic. In some approaches, "global" levels of…

  18. Modeling marrow damage from response data: Morphallaxis from radiation biology to benzene toxicity

    Energy Technology Data Exchange (ETDEWEB)

    Jones, T.D.; Morris, M.D.; Hasan, J.S.

    1995-12-01

    Consensus principles from radiation biology were used to describe a generic set of nonlinear, first-order differential equations for modeling of toxicity-induced compensatory cell kinetics in terms of sublethal injury, repair, direct killing, killing of cells with unrepaired sublethal injury, and repopulation. This cellular model was linked to a probit model of hematopoietic mortality that describes death from infection and/or hemorrhage between {approximately} 5 and 30 days. Mortality data from 27 experiments with 851 doseresponse groups, in which doses were protracted by rate and/or fractionation, were used to simultaneously estimate all rate constants by maximum-likelihood methods. Data used represented 18,940 test animals distributed according to: (mice, 12,827); (rats, 2,925); (sheep, 1,676); (swine, 829); (dogs, 479); and (burros, 204). Although a long-term, repopulating hematopoietic stem cell is ancestral to all lineages needed to restore normal homeostasis, the dose-response data from the protracted irradiations indicate clearly that the particular lineage that is ``critical`` to hematopoietic recovery does not resemble stem-like cells with regard to radiosensitivity and repopulation rates. Instead, the weakest link in the chain of hematopoiesis was found to have an intrinsic radioresistance equal to or greater than stromal cells and to repopulate at the same rates. Model validation has been achieved by predicting the LD{sub 50} and/or fractional group mortality in 38 protracted-dose experiments (rats and mice) that were not used in the fitting of model coefficients.

  19. Optimal experiment selection for parameter estimation in biological differential equation models

    Directory of Open Access Journals (Sweden)

    Transtrum Mark K

    2012-07-01

    Full Text Available Abstract Background Parameter estimation in biological models is a common yet challenging problem. In this work we explore the problem for gene regulatory networks modeled by differential equations with unknown parameters, such as decay rates, reaction rates, Michaelis-Menten constants, and Hill coefficients. We explore the question to what extent parameters can be efficiently estimated by appropriate experimental selection. Results A minimization formulation is used to find the parameter values that best fit the experiment data. When the data is insufficient, the minimization problem often has many local minima that fit the data reasonably well. We show that selecting a new experiment based on the local Fisher Information of one local minimum generates additional data that allows one to successfully discriminate among the many local minima. The parameters can be estimated to high accuracy by iteratively performing minimization and experiment selection. We show that the experiment choices are roughly independent of which local minima is used to calculate the local Fisher Information. Conclusions We show that by an appropriate choice of experiments, one can, in principle, efficiently and accurately estimate all the parameters of gene regulatory network. In addition, we demonstrate that appropriate experiment selection can also allow one to restrict model predictions without constraining the parameters using many fewer experiments. We suggest that predicting model behaviors and inferring parameters represent two different approaches to model calibration with different requirements on data and experimental cost.

  20. A systems biology framework for modeling metabolic enzyme inhibition of Mycobacterium tuberculosis

    Directory of Open Access Journals (Sweden)

    Reifman Jaques

    2009-09-01

    Full Text Available Abstract Background Because metabolism is fundamental in sustaining microbial life, drugs that target pathogen-specific metabolic enzymes and pathways can be very effective. In particular, the metabolic challenges faced by intracellular pathogens, such as Mycobacterium tuberculosis, residing in the infected host provide novel opportunities for therapeutic intervention. Results We developed a mathematical framework to simulate the effects on the growth of a pathogen when enzymes in its metabolic pathways are inhibited. Combining detailed models of enzyme kinetics, a complete metabolic network description as modeled by flux balance analysis, and a dynamic cell population growth model, we quantitatively modeled and predicted the dose-response of the 3-nitropropionate inhibitor on the growth of M. tuberculosis in a medium whose carbon source was restricted to fatty acids, and that of the 5'-O-(N-salicylsulfamoyl adenosine inhibitor in a medium with low-iron concentration. Conclusion The predicted results quantitatively reproduced the experimentally measured dose-response curves, ranging over three orders of magnitude in inhibitor concentration. Thus, by allowing for detailed specifications of the underlying enzymatic kinetics, metabolic reactions/constraints, and growth media, our model captured the essential chemical and biological factors that determine the effects of drug inhibition on in vitro growth of M. tuberculosis cells.