Inertial picobalance reveals fast mass fluctuations in mammalian cells

Science.gov (United States)

Martínez-Martín, David; Fläschner, Gotthold; Gaub, Benjamin; Martin, Sascha; Newton, Richard; Beerli, Corina; Mercer, Jason; Gerber, Christoph; Müller, Daniel J.

2017-10-01

The regulation of size, volume and mass in living cells is physiologically important, and dysregulation of these parameters gives rise to many diseases. Cell mass is largely determined by the amount of water, proteins, lipids, carbohydrates and nucleic acids present in a cell, and is tightly linked to metabolism, proliferation and gene expression. Technologies have emerged in recent years that make it possible to track the masses of single suspended cells and adherent cells. However, it has not been possible to track individual adherent cells in physiological conditions at the mass and time resolutions required to observe fast cellular dynamics. Here we introduce a cell balance (a ‘picobalance’), based on an optically excited microresonator, that measures the total mass of single or multiple adherent cells in culture conditions over days with millisecond time resolution and picogram mass sensitivity. Using our technique, we observe that the mass of living mammalian cells fluctuates intrinsically by around one to four per cent over timescales of seconds throughout the cell cycle. Perturbation experiments link these mass fluctuations to the basic cellular processes of ATP synthesis and water transport. Furthermore, we show that growth and cell cycle progression are arrested in cells infected with vaccinia virus, but mass fluctuations continue until cell death. Our measurements suggest that all living cells show fast and subtle mass fluctuations throughout the cell cycle. As our cell balance is easy to handle and compatible with fluorescence microscopy, we anticipate that our approach will contribute to the understanding of cell mass regulation in various cell states and across timescales, which is important in areas including physiology, cancer research, stem-cell differentiation and drug discovery.

Cell-substrate impedance fluctuations of single amoeboid cells encode cell-shape and adhesion dynamics.

Science.gov (United States)

Leonhardt, Helmar; Gerhardt, Matthias; Höppner, Nadine; Krüger, Kirsten; Tarantola, Marco; Beta, Carsten

2016-01-01

We show systematic electrical impedance measurements of single motile cells on microelectrodes. Wild-type cells and mutant strains were studied that differ in their cell-substrate adhesion strength. We recorded the projected cell area by time-lapse microscopy and observed irregular oscillations of the cell shape. These oscillations were correlated with long-term variations in the impedance signal. Superposed to these long-term trends, we observed fluctuations in the impedance signal. Their magnitude clearly correlated with the adhesion strength, suggesting that strongly adherent cells display more dynamic cell-substrate interactions.

Cell-substrate impedance fluctuations of single amoeboid cells encode cell-shape and adhesion dynamics

Science.gov (United States)

Leonhardt, Helmar; Gerhardt, Matthias; Höppner, Nadine; Krüger, Kirsten; Tarantola, Marco; Beta, Carsten

2016-01-01

We show systematic electrical impedance measurements of single motile cells on microelectrodes. Wild-type cells and mutant strains were studied that differ in their cell-substrate adhesion strength. We recorded the projected cell area by time-lapse microscopy and observed irregular oscillations of the cell shape. These oscillations were correlated with long-term variations in the impedance signal. Superposed to these long-term trends, we observed fluctuations in the impedance signal. Their magnitude clearly correlated with the adhesion strength, suggesting that strongly adherent cells display more dynamic cell-substrate interactions.

Stochastic cellular automata model of cell migration, proliferation and differentiation: validation with in vitro cultures of muscle satellite cells.

Science.gov (United States)

Garijo, N; Manzano, R; Osta, R; Perez, M A

2012-12-07

Cell migration and proliferation has been modelled in the literature as a process similar to diffusion. However, using diffusion models to simulate the proliferation and migration of cells tends to create a homogeneous distribution in the cell density that does not correlate to empirical observations. In fact, the mechanism of cell dispersal is not diffusion. Cells disperse by crawling or proliferation, or are transported in a moving fluid. The use of cellular automata, particle models or cell-based models can overcome this limitation. This paper presents a stochastic cellular automata model to simulate the proliferation, migration and differentiation of cells. These processes are considered as completely stochastic as well as discrete. The model developed was applied to predict the behaviour of in vitro cell cultures performed with adult muscle satellite cells. Moreover, non homogeneous distribution of cells has been observed inside the culture well and, using the above mentioned stochastic cellular automata model, we have been able to predict this heterogeneous cell distribution and compute accurate quantitative results. Differentiation was also incorporated into the computational simulation. The results predicted the myotube formation that typically occurs with adult muscle satellite cells. In conclusion, we have shown how a stochastic cellular automata model can be implemented and is capable of reproducing the in vitro behaviour of adult muscle satellite cells. Copyright © 2012 Elsevier Ltd. All rights reserved.

The fluctuation-dissipation theorem for stochastic kinetics—Implications on genetic regulations

Energy Technology Data Exchange (ETDEWEB)

Yan, Ching-Cher Sanders [Institute of Chemistry, Academia Sinica, 128, Section 2, Academia Road, Nankang, Taipei 115, Taiwan (China); Hsu, Chao-Ping, E-mail: cherri@chem.sinica.edu.tw [Institute of Chemistry, Academia Sinica, 128, Section 2, Academia Road, Nankang, Taipei 115, Taiwan (China); Genome and Systems Biology Degree Program, National Taiwan University, 1, Section 4, Roosevelt Road, Taipei 106, Taiwan (China)

2013-12-14

The Fluctuation-Dissipation theorem (FDT) connects the “memory” in the fluctuation in equilibrium to the response of a system after a perturbation, which has been a fundamental ground in many branches of physics. When viewing a cell as a stochastic biochemical system, the cell's response under a perturbation is related to its intrinsic steady-state correlation functions via the FDT, a theorem we derived and present in this work. FDT allows us to use the noise to derive dynamic response and infer dynamic properties in the system. We tested FDT's validity with gene regulation models and found that it is limited to the linear response. For an indirect regulation pathway where unknown components may exist, FDT still works within the linear response region. Thus, FDT may be used for systems with partial knowledge, and it is potentially possible to identify the existence of unobserved components. With FDT, the dynamic response can be composed of steady-state measurements without the complete detailed knowledge for the regulation or kinetics. The response function derived can give important insights into the dynamics and time scales of the system.

Stem cell proliferation and differentiation and stochastic bistability in gene expression

International Nuclear Information System (INIS)

Zhdanov, V. P.

2007-01-01

The process of proliferation and differentiation of stem cells is inherently stochastic in the sense that the outcome of cell division is characterized by probabilities that depend on the intracellular properties, extracellular medium, and cell-cell communication. Despite four decades of intensive studies, the understanding of the physics behind this stochasticity is still limited, both in details and conceptually. Here, we suggest a simple scheme showing that the stochastic behavior of a single stem cell may be related to (i) the existence of a short stage of decision whether it will proliferate or differentiate and (ii) control of this stage by stochastic bistability in gene expression or, more specifically, by transcriptional 'bursts.' Our Monte Carlo simulations indicate that our proposed scheme may operate if the number of mRNA (or protein) molecules generated during the high-reactive periods of gene expression is below or about 50. The stochastic-burst window in the space of kinetic parameters is found to increase with decreasing the mRNA and/or regulatory-protein numbers and increasing the number of regulatory sites. For mRNA production with three regulatory sites, for example, the mRNA degradation rate constant may change in the range ±10%

Spectroscopic imaging of photopotentials and photoinduced potential fluctuations in a bulk heterojunction solar cell film.

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Luria, Justin L; Hoepker, Nikolas; Bruce, Robert; Jacobs, Andrew R; Groves, Chris; Marohn, John A

2012-11-27

We present spatially resolved photovoltage spectra of a bulk heterojunction solar cell film composed of phase-separated poly(9,9'-dioctylfluorene-co-benzothiadiazole) (F8BT) and poly(9,9'-dioctylfluorene-co-bis-N,N'-(4-butylphenyl)-bis-N,N'-phenyl-1,4-phenylenediamine) (PFB) polymers prepared on ITO/PEDOT:PSS and aluminum substrates. Over both PFB- and F8BT-rich domains, the photopotential spectra were found to be proportional to a linear combination of the polymers' absorption spectra. Charge trapping in the film was studied using photopotential fluctuation spectroscopy, in which low-frequency photoinduced electrostatic potential fluctuations were measured by observing noise in the oscillation frequency of a nearby charged atomic force microscope cantilever. Over both F8BT- and PFB-rich regions, the magnitude, distance dependence, frequency dependence, and illumination wavelength dependence of the observed cantilever frequency noise are consistent with photopotential fluctuations arising from stochastic light-driven trapping and detrapping of charges in F8BT. Taken together, our findings suggest a microscopic mechanism by which intermixing of phases leads to charge trapping and thereby to suppressed open-circuit voltage and decreased efficiency in this prototypical bulk heterojunction solar cell film.

On the origin of shape fluctuations of the cell nucleus.

Science.gov (United States)

Chu, Fang-Yi; Haley, Shannon C; Zidovska, Alexandra

2017-09-26

The nuclear envelope (NE) presents a physical boundary between the cytoplasm and the nucleoplasm, sandwiched in between two highly active systems inside the cell: cytoskeleton and chromatin. NE defines the shape and size of the cell nucleus, which increases during the cell cycle, accommodating for chromosome decondensation followed by genome duplication. In this work, we study nuclear shape fluctuations at short time scales of seconds in human cells. Using spinning disk confocal microscopy, we observe fast fluctuations of the NE, visualized by fluorescently labeled lamin A, and of the chromatin globule surface (CGS) underneath the NE, visualized by fluorescently labeled histone H2B. Our findings reveal that fluctuation amplitudes of both CGS and NE monotonously decrease during the cell cycle, serving as a reliable cell cycle stage indicator. Remarkably, we find that, while CGS and NE typically fluctuate in phase, they do exhibit localized regions of out-of-phase motion, which lead to separation of NE and CGS. To explore the mechanism behind these shape fluctuations, we use biochemical perturbations. We find the shape fluctuations of CGS and NE to be both thermally and actively driven, the latter caused by forces from chromatin and cytoskeleton. Such undulations might affect gene regulation as well as contribute to the anomalously high rates of nuclear transport by, e.g., stirring of molecules next to NE, or increasing flux of molecules through the nuclear pores.

Fuel Cells for Balancing Fluctuation Renewable Energy Sources

DEFF Research Database (Denmark)

Mathiesen, Brian Vad

2007-01-01

In the perspective of using fuel cells for integration of fluctuating renewable energy the SOFCs are the most promising. These cells have the advantage of significantly higher electricity efficiency than competing technologies and fuel flexibility. Fuel cells in general also have the advantage of...... with hydrogen production or electric cars, and on the other hand using biomass and bio fuels [11]. Fuel cells can have an important role in these future energy systems.......In the perspective of using fuel cells for integration of fluctuating renewable energy the SOFCs are the most promising. These cells have the advantage of significantly higher electricity efficiency than competing technologies and fuel flexibility. Fuel cells in general also have the advantage...... flexibility, such as SOFCs, heat pumps and heat storage technologies are more important than storing electricity as hydrogen via electrolysis in energy systems with high amounts of wind [12]. Unnecessary energy conversions should be avoided. However in future energy systems with wind providing more than 50...

Distinguishing between stochasticity and determinism: Examples from cell cycle duration variability.

Science.gov (United States)

Pearl Mizrahi, Sivan; Sandler, Oded; Lande-Diner, Laura; Balaban, Nathalie Q; Simon, Itamar

2016-01-01

We describe a recent approach for distinguishing between stochastic and deterministic sources of variability, focusing on the mammalian cell cycle. Variability between cells is often attributed to stochastic noise, although it may be generated by deterministic components. Interestingly, lineage information can be used to distinguish between variability and determinism. Analysis of correlations within a lineage of the mammalian cell cycle duration revealed its deterministic nature. Here, we discuss the sources of such variability and the possibility that the underlying deterministic process is due to the circadian clock. Finally, we discuss the "kicked cell cycle" model and its implication on the study of the cell cycle in healthy and cancerous tissues. © 2015 WILEY Periodicals, Inc.

Radiobilogical cell survival models

International Nuclear Information System (INIS)

Zackrisson, B.

1992-01-01

A central issue in clinical radiobiological research is the prediction of responses to different radiation qualities. The choice of cell survival and dose-response model greatly influences the results. In this context the relationship between theory and model is emphasized. Generally, the interpretations of experimental data depend on the model. Cell survival models are systematized with respect to their relations to radiobiological theories of cell kill. The growing knowlegde of biological, physical, and chemical mechanisms is reflected in the formulation of new models. The present overview shows that recent modelling has been more oriented towards the stochastic fluctuations connected to radiation energy deposition. This implies that the traditional cell surivival models ought to be complemented by models of stochastic energy deposition processes and repair processes at the intracellular level. (orig.)

Interplanetary Alfvenic fluctuations: A stochastic model

International Nuclear Information System (INIS)

Barnes, A.

1981-01-01

The strong alignment of the average directions of minimum magnetic variance and mean magnetic field in interplanetary Alfvenic fluctuations is inconsistent with the usual wave-propagation models. We investigate the concept of minimum variance for nonplanar Alfvenic fluctuations in which the field direction varies stochastically. It is found that the tendency of the minimum variance and mean field directions to be aligned may be purely a consequence of the randomness of the field direction. In particular, a well-defined direction of minimum variance does not imply that the fluctuations are necessarily planar. The fluctuation power spectrum is a power law for frequencies much higher than the inverse of the correlation time. The probability distribution of directions a randomly fluctuating field of constant magnitude is calculated. A new approach for observational studies of interplanetary fluctuations is suggested

Single-cell-derived mesenchymal stem cells overexpressing Csx/Nkx2.5 and GATA4 undergo the stochastic cardiomyogenic fate and behave like transient amplifying cells

International Nuclear Information System (INIS)

Yamada, Yoji; Sakurada, Kazuhiro; Takeda, Yukiji; Gojo, Satoshi; Umezawa, Akihiro

2007-01-01

Bone marrow-derived stromal cells can give rise to cardiomyocytes as well as adipocytes, osteocytes, and chondrocytes in vitro. The existence of mesenchymal stem cells has been proposed, but it remains unclear if a single-cell-derived stem cell stochastically commits toward a cardiac lineage. By single-cell marking, we performed a follow-up study of individual cells during the differentiation of 9-15c mesenchymal stromal cells derived from bone marrow cells. Three types of cells, i.e., cardiac myoblasts, cardiac progenitors and multipotent stem cells were differentiated from a single cell, implying that cardiomyocytes are generated stochastically from a single-cell-derived stem cell. We also demonstrated that overexpression of Csx/Nkx2.5 and GATA4, precardiac mesodermal transcription factors, enhanced cardiomyogenic differentiation of 9-15c cells, and the frequency of cardiomyogenic differentiation was increased by co-culturing with fetal cardiomyocytes. Single-cell-derived mesenchymal stem cells overexpressing Csx/Nkx2.5 and GATA4 behaved like cardiac transient amplifying cells, and still retained their plasticity in vivo

Computational Investigation of Environment-Noise Interaction in Single-Cell Organisms: The Merit of Expression Stochasticity Depends on the Quality of Environmental Fluctuations.

Science.gov (United States)

Lück, Anja; Klimmasch, Lukas; Großmann, Peter; Germerodt, Sebastian; Kaleta, Christoph

2018-01-10

Organisms need to adapt to changing environments and they do so by using a broad spectrum of strategies. These strategies include finding the right balance between expressing genes before or when they are needed, and adjusting the degree of noise inherent in gene expression. We investigated the interplay between different nutritional environments and the inhabiting organisms' metabolic and genetic adaptations by applying an evolutionary algorithm to an agent-based model of a concise bacterial metabolism. Our results show that constant environments and rapidly fluctuating environments produce similar adaptations in the organisms, making the predictability of the environment a major factor in determining optimal adaptation. We show that exploitation of expression noise occurs only in some types of fluctuating environment and is strongly dependent on the quality and availability of nutrients: stochasticity is generally detrimental in fluctuating environments and beneficial only at equal periods of nutrient availability and above a threshold environmental richness. Moreover, depending on the availability and nutritional value of nutrients, nutrient-dependent and stochastic expression are both strategies used to deal with environmental changes. Overall, we comprehensively characterize the interplay between the quality and periodicity of an environment and the resulting optimal deterministic and stochastic regulation strategies of nutrient-catabolizing pathways.

Stochastic lumping analysis for linear kinetics and its application to the fluctuation relations between hierarchical kinetic networks

Energy Technology Data Exchange (ETDEWEB)

Deng, De-Ming; Chang, Cheng-Hung [Institute of Physics, National Chiao Tung University, Hsinchu 300, Taiwan (China)

2015-05-14

Conventional studies of biomolecular behaviors rely largely on the construction of kinetic schemes. Since the selection of these networks is not unique, a concern is raised whether and under which conditions hierarchical schemes can reveal the same experimentally measured fluctuating behaviors and unique fluctuation related physical properties. To clarify these questions, we introduce stochasticity into the traditional lumping analysis, generalize it from rate equations to chemical master equations and stochastic differential equations, and extract the fluctuation relations between kinetically and thermodynamically equivalent networks under intrinsic and extrinsic noises. The results provide a theoretical basis for the legitimate use of low-dimensional models in the studies of macromolecular fluctuations and, more generally, for exploring stochastic features in different levels of contracted networks in chemical and biological kinetic systems.

Stochastic lumping analysis for linear kinetics and its application to the fluctuation relations between hierarchical kinetic networks.

Science.gov (United States)

Deng, De-Ming; Chang, Cheng-Hung

2015-05-14

Conventional studies of biomolecular behaviors rely largely on the construction of kinetic schemes. Since the selection of these networks is not unique, a concern is raised whether and under which conditions hierarchical schemes can reveal the same experimentally measured fluctuating behaviors and unique fluctuation related physical properties. To clarify these questions, we introduce stochasticity into the traditional lumping analysis, generalize it from rate equations to chemical master equations and stochastic differential equations, and extract the fluctuation relations between kinetically and thermodynamically equivalent networks under intrinsic and extrinsic noises. The results provide a theoretical basis for the legitimate use of low-dimensional models in the studies of macromolecular fluctuations and, more generally, for exploring stochastic features in different levels of contracted networks in chemical and biological kinetic systems.

Stochastic modeling of oligodendrocyte generation in cell culture: model validation with time-lapse data

Directory of Open Access Journals (Sweden)

Noble Mark

2006-05-01

Full Text Available Abstract Background The purpose of this paper is two-fold. The first objective is to validate the assumptions behind a stochastic model developed earlier by these authors to describe oligodendrocyte generation in cell culture. The second is to generate time-lapse data that may help biomathematicians to build stochastic models of cell proliferation and differentiation under other experimental scenarios. Results Using time-lapse video recording it is possible to follow the individual evolutions of different cells within each clone. This experimental technique is very laborious and cannot replace model-based quantitative inference from clonal data. However, it is unrivalled in validating the structure of a stochastic model intended to describe cell proliferation and differentiation at the clonal level. In this paper, such data are reported and analyzed for oligodendrocyte precursor cells cultured in vitro. Conclusion The results strongly support the validity of the most basic assumptions underpinning the previously proposed model of oligodendrocyte development in cell culture. However, there are some discrepancies; the most important is that the contribution of progenitor cell death to cell kinetics in this experimental system has been underestimated.

Simulating biological processes: stochastic physics from whole cells to colonies

Science.gov (United States)

Earnest, Tyler M.; Cole, John A.; Luthey-Schulten, Zaida

2018-05-01

The last few decades have revealed the living cell to be a crowded spatially heterogeneous space teeming with biomolecules whose concentrations and activities are governed by intrinsically random forces. It is from this randomness, however, that a vast array of precisely timed and intricately coordinated biological functions emerge that give rise to the complex forms and behaviors we see in the biosphere around us. This seemingly paradoxical nature of life has drawn the interest of an increasing number of physicists, and recent years have seen stochastic modeling grow into a major subdiscipline within biological physics. Here we review some of the major advances that have shaped our understanding of stochasticity in biology. We begin with some historical context, outlining a string of important experimental results that motivated the development of stochastic modeling. We then embark upon a fairly rigorous treatment of the simulation methods that are currently available for the treatment of stochastic biological models, with an eye toward comparing and contrasting their realms of applicability, and the care that must be taken when parameterizing them. Following that, we describe how stochasticity impacts several key biological functions, including transcription, translation, ribosome biogenesis, chromosome replication, and metabolism, before considering how the functions may be coupled into a comprehensive model of a ‘minimal cell’. Finally, we close with our expectation for the future of the field, focusing on how mesoscopic stochastic methods may be augmented with atomic-scale molecular modeling approaches in order to understand life across a range of length and time scales.

Cosmophysical Factors in the Fluctuation Amplitude Spectrum of Brownian Motion

Directory of Open Access Journals (Sweden)

Kaminsky A. V.

2010-04-01

Full Text Available Phenomenon of the regular variability of the fine structure of the fluctuation in the amplitude distributions (shapes of related histograms for the case of Brownian motion was investigated. We took an advantage of the dynamic light scattering method (DLS to get a stochastically fluctuated signal determined by Brownian motion. Shape of the histograms is most likely to vary, synchronous, in two proximally located independent cells containing Brownian particles. The synchronism persists in the cells distant at 2m from each other, and positioned meridionally. With a parallel-wise positioning of the cells, high probability of the synchronous variation in the shape of the histograms by local time has been observed. This result meets the previous conclusion about the dependency of histogram shapes ("fluctuation amplitudes" of the spectra of stochastic processes upon rotation of the Earth.

Cosmophysical Factors in the Fluctuation Amplitude Spectrum of Brownian Motion

Directory of Open Access Journals (Sweden)

Kaminsky A. V.

2010-07-01

Full Text Available Phenomenon of the regular variability of the fine structure of the fluctuation in the am- plitude distributions (shapes of related histograms for the case of Brownian motion was investigated. We took an advantage of the dynamic light scattering method (DLS to get a stochastically fluctuated signal determined by Brownian motion. Shape of the histograms is most likely to vary, synchronous, in two proximally located independent cells containing Brownian particles. The synchronism persists in the cells distant at 2 m from each other, and positioned meridionally. With a parallel-wise positioning of the cells, high probability of the synchronous variation in the shape of the histograms by local time has been observed. This result meets the previous conclusion about the dependency of histogram shapes (“fluctuation amplitudes” of the spectra of stochastic processes upon rotation of the Earth.

The Beta Cell in Its Cluster: Stochastic Graphs of Beta Cell Connectivity in the Islets of Langerhans.

Directory of Open Access Journals (Sweden)

Deborah A Striegel

2015-08-01

Full Text Available Pancreatic islets of Langerhans consist of endocrine cells, primarily α, β and δ cells, which secrete glucagon, insulin, and somatostatin, respectively, to regulate plasma glucose. β cells form irregular locally connected clusters within islets that act in concert to secrete insulin upon glucose stimulation. Due to the central functional significance of this local connectivity in the placement of β cells in an islet, it is important to characterize it quantitatively. However, quantification of the seemingly stochastic cytoarchitecture of β cells in an islet requires mathematical methods that can capture topological connectivity in the entire β-cell population in an islet. Graph theory provides such a framework. Using large-scale imaging data for thousands of islets containing hundreds of thousands of cells in human organ donor pancreata, we show that quantitative graph characteristics differ between control and type 2 diabetic islets. Further insight into the processes that shape and maintain this architecture is obtained by formulating a stochastic theory of β-cell rearrangement in whole islets, just as the normal equilibrium distribution of the Ornstein-Uhlenbeck process can be viewed as the result of the interplay between a random walk and a linear restoring force. Requiring that rearrangements maintain the observed quantitative topological graph characteristics strongly constrained possible processes. Our results suggest that β-cell rearrangement is dependent on its connectivity in order to maintain an optimal cluster size in both normal and T2D islets.

The Beta Cell in Its Cluster: Stochastic Graphs of Beta Cell Connectivity in the Islets of Langerhans.

Science.gov (United States)

Striegel, Deborah A; Hara, Manami; Periwal, Vipul

2015-08-01

Pancreatic islets of Langerhans consist of endocrine cells, primarily α, β and δ cells, which secrete glucagon, insulin, and somatostatin, respectively, to regulate plasma glucose. β cells form irregular locally connected clusters within islets that act in concert to secrete insulin upon glucose stimulation. Due to the central functional significance of this local connectivity in the placement of β cells in an islet, it is important to characterize it quantitatively. However, quantification of the seemingly stochastic cytoarchitecture of β cells in an islet requires mathematical methods that can capture topological connectivity in the entire β-cell population in an islet. Graph theory provides such a framework. Using large-scale imaging data for thousands of islets containing hundreds of thousands of cells in human organ donor pancreata, we show that quantitative graph characteristics differ between control and type 2 diabetic islets. Further insight into the processes that shape and maintain this architecture is obtained by formulating a stochastic theory of β-cell rearrangement in whole islets, just as the normal equilibrium distribution of the Ornstein-Uhlenbeck process can be viewed as the result of the interplay between a random walk and a linear restoring force. Requiring that rearrangements maintain the observed quantitative topological graph characteristics strongly constrained possible processes. Our results suggest that β-cell rearrangement is dependent on its connectivity in order to maintain an optimal cluster size in both normal and T2D islets.

Transient fluctuations of intracellular zinc ions in cell proliferation

Energy Technology Data Exchange (ETDEWEB)

Li, Yuan [Division of Human Nutrition, Department of Preventive Medicine and Community Health, The University of Texas Medical Branch, Galveston, TX 77555 (United States); Maret, Wolfgang, E-mail: womaret@utmb.edu [Division of Human Nutrition, Department of Preventive Medicine and Community Health, The University of Texas Medical Branch, Galveston, TX 77555 (United States); Department of Anesthesiology, The University of Texas Medical Branch, Galveston, TX 77555 (United States)

2009-08-15

Zinc is essential for cell proliferation, differentiation, and viability. When zinc becomes limited for cultured cells, DNA synthesis ceases and the cell cycle is arrested. The molecular mechanisms of actions of zinc are believed to involve changes in the availability of zinc(II) ions (Zn{sup 2+}). By employing a fluorescent Zn{sup 2+} probe, FluoZin-3 acetoxymethyl ester, intracellular Zn{sup 2+} concentrations were measured in undifferentiated and in nerve growth factor (NGF)-differentiated rat pheochromocytoma (PC12) cells. Intracellular Zn{sup 2+} concentrations are pico- to nanomolar in PC12 cells and are higher in the differentiated than in the undifferentiated cells. When following cellular Zn{sup 2+} concentrations for 48 h after the removal of serum, a condition that is known to cause cell cycle arrest, Zn{sup 2+} concentrations decrease after 30 min but, remarkably, increase after 1 h, and then decrease again to about one half of the initial concentration. Cell proliferation, measured by an MTT [3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide] assay, decreases after both serum starvation and zinc chelation. Two peaks of Zn{sup 2+} concentrations occur within one cell cycle: one early in the G1 phase and the other in the late G1/S phase. Thus, fluctuations of intracellular Zn{sup 2+} concentrations and established modulation of phosphorylation signaling, via an inhibition of protein tyrosine phosphatases at commensurately low Zn{sup 2+} concentrations, suggest a role for Zn{sup 2+} in the control of the cell cycle. Interventions targeted at these picomolar Zn{sup 2+} fluctuations may be a way of controlling cell growth in hyperplasia, neoplasia, and diseases associated with aberrant differentiation.

Listening to the Noise: Random Fluctuations Reveal Gene Network Parameters

Science.gov (United States)

Munsky, Brian; Trinh, Brooke; Khammash, Mustafa

2010-03-01

The cellular environment is abuzz with noise originating from the inherent random motion of reacting molecules in the living cell. In this noisy environment, clonal cell populations exhibit cell-to-cell variability that can manifest significant prototypical differences. Noise induced stochastic fluctuations in cellular constituents can be measured and their statistics quantified using flow cytometry, single molecule fluorescence in situ hybridization, time lapse fluorescence microscopy and other single cell and single molecule measurement techniques. We show that these random fluctuations carry within them valuable information about the underlying genetic network. Far from being a nuisance, the ever-present cellular noise acts as a rich source of excitation that, when processed through a gene network, carries its distinctive fingerprint that encodes a wealth of information about that network. We demonstrate that in some cases the analysis of these random fluctuations enables the full identification of network parameters, including those that may otherwise be difficult to measure. We use theoretical investigations to establish experimental guidelines for the identification of gene regulatory networks, and we apply these guideline to experimentally identify predictive models for different regulatory mechanisms in bacteria and yeast.

Fluctuations of the peak current of tunnel diodes in multi-junction solar cells

International Nuclear Information System (INIS)

Jandieri, K; Baranovskii, S D; Stolz, W; Gebhard, F; Guter, W; Hermle, M; Bett, A W

2009-01-01

Interband tunnel diodes are widely used to electrically interconnect the individual subcells in multi-junction solar cells. Tunnel diodes have to operate at high current densities and low voltages, especially when used in concentrator solar cells. They represent one of the most critical elements of multi-junction solar cells and the fluctuations of the peak current in the diodes have an essential impact on the performance and reliability of the devices. Recently we have found that GaAs tunnel diodes exhibit extremely high peak currents that can be explained by resonant tunnelling through defects homogeneously distributed in the junction. Experiments evidence rather large fluctuations of the peak current in the diodes fabricated from the same wafer. It is a challenging task to clarify the reason for such large fluctuations in order to improve the performance of the multi-junction solar cells. In this work we show that the large fluctuations of the peak current in tunnel diodes can be caused by relatively small fluctuations of the dopant concentration. We also show that the fluctuations of the peak current become smaller for deeper energy levels of the defects responsible for the resonant tunnelling.

Stochastic model of cell rearrangements in convergent extension of ascidian notochord

Science.gov (United States)

Lubkin, Sharon; Backes, Tracy; Latterman, Russell; Small, Stephen

2007-03-01

We present a discrete stochastic cell based model of convergent extension of the ascidian notochord. Our work derives from research that clarifies the coupling of invagination and convergent extension in ascidian notochord morphogenesis (Odell and Munro, 2002). We have tested the roles of cell-cell adhesion, cell-extracellular matrix adhesion, random motion, and extension of individual cells, as well as the presence or absence of various tissue types, and determined which factors are necessary and/or sufficient for convergent extension.

Transcriptional regulation of lineage commitment--a stochastic model of cell fate decisions.

Directory of Open Access Journals (Sweden)

Jose Teles

Full Text Available Molecular mechanisms employed by individual multipotent cells at the point of lineage commitment remain largely uncharacterized. Current paradigms span from instructive to noise-driven mechanisms. Of considerable interest is also whether commitment involves a limited set of genes or the entire transcriptional program, and to what extent gene expression configures multiple trajectories into commitment. Importantly, the transient nature of the commitment transition confounds the experimental capture of committing cells. We develop a computational framework that simulates stochastic commitment events, and affords mechanistic exploration of the fate transition. We use a combined modeling approach guided by gene expression classifier methods that infers a time-series of stochastic commitment events from experimental growth characteristics and gene expression profiling of individual hematopoietic cells captured immediately before and after commitment. We define putative regulators of commitment and probabilistic rules of transition through machine learning methods, and employ clustering and correlation analyses to interrogate gene regulatory interactions in multipotent cells. Against this background, we develop a Monte Carlo time-series stochastic model of transcription where the parameters governing promoter status, mRNA production and mRNA decay in multipotent cells are fitted to experimental static gene expression distributions. Monte Carlo time is converted to physical time using cell culture kinetic data. Probability of commitment in time is a function of gene expression as defined by a logistic regression model obtained from experimental single-cell expression data. Our approach should be applicable to similar differentiating systems where single cell data is available. Within our system, we identify robust model solutions for the multipotent population within physiologically reasonable values and explore model predictions with regard to

Groundwater management under uncertainty using a stochastic multi-cell model

Science.gov (United States)

Joodavi, Ata; Zare, Mohammad; Ziaei, Ali Naghi; Ferré, Ty P. A.

2017-08-01

The optimization of spatially complex groundwater management models over long time horizons requires the use of computationally efficient groundwater flow models. This paper presents a new stochastic multi-cell lumped-parameter aquifer model that explicitly considers uncertainty in groundwater recharge. To achieve this, the multi-cell model is combined with the constrained-state formulation method. In this method, the lower and upper bounds of groundwater heads are incorporated into the mass balance equation using indicator functions. This provides expressions for the means, variances and covariances of the groundwater heads, which can be included in the constraint set in an optimization model. This method was used to formulate two separate stochastic models: (i) groundwater flow in a two-cell aquifer model with normal and non-normal distributions of groundwater recharge; and (ii) groundwater management in a multiple cell aquifer in which the differences between groundwater abstractions and water demands are minimized. The comparison between the results obtained from the proposed modeling technique with those from Monte Carlo simulation demonstrates the capability of the proposed models to approximate the means, variances and covariances. Significantly, considering covariances between the heads of adjacent cells allows a more accurate estimate of the variances of the groundwater heads. Moreover, this modeling technique requires no discretization of state variables, thus offering an efficient alternative to computationally demanding methods.

Stochasticity in the yeast mating pathway

International Nuclear Information System (INIS)

Hong-Li, Wang; Zheng-Ping, Fu; Xin-Hang, Xu; Qi, Ouyang

2009-01-01

We report stochastic simulations of the yeast mating signal transduction pathway. The effects of intrinsic and external noise, the influence of cell-to-cell difference in the pathway capacity, and noise propagation in the pathway have been examined. The stochastic temporal behaviour of the pathway is found to be robust to the influence of inherent fluctuations, and intrinsic noise propagates in the pathway in a uniform pattern when the yeasts are treated with pheromones of different stimulus strengths and of varied fluctuations. In agreement with recent experimental findings, extrinsic noise is found to play a more prominent role than intrinsic noise in the variability of proteins. The occurrence frequency for the reactions in the pathway are also examined and a more compact network is obtained by dropping most of the reactions of least occurrence

An Optogenetic Platform for Real-Time, Single-Cell Interrogation of Stochastic Transcriptional Regulation.

Science.gov (United States)

Rullan, Marc; Benzinger, Dirk; Schmidt, Gregor W; Milias-Argeitis, Andreas; Khammash, Mustafa

2018-05-17

Transcription is a highly regulated and inherently stochastic process. The complexity of signal transduction and gene regulation makes it challenging to analyze how the dynamic activity of transcriptional regulators affects stochastic transcription. By combining a fast-acting, photo-regulatable transcription factor with nascent RNA quantification in live cells and an experimental setup for precise spatiotemporal delivery of light inputs, we constructed a platform for the real-time, single-cell interrogation of transcription in Saccharomyces cerevisiae. We show that transcriptional activation and deactivation are fast and memoryless. By analyzing the temporal activity of individual cells, we found that transcription occurs in bursts, whose duration and timing are modulated by transcription factor activity. Using our platform, we regulated transcription via light-driven feedback loops at the single-cell level. Feedback markedly reduced cell-to-cell variability and led to qualitative differences in cellular transcriptional dynamics. Our platform establishes a flexible method for studying transcriptional dynamics in single cells. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

Stochastic Eulerian Lagrangian methods for fluid-structure interactions with thermal fluctuations

International Nuclear Information System (INIS)

Atzberger, Paul J.

2011-01-01

We present approaches for the study of fluid-structure interactions subject to thermal fluctuations. A mixed mechanical description is utilized combining Eulerian and Lagrangian reference frames. We establish general conditions for operators coupling these descriptions. Stochastic driving fields for the formalism are derived using principles from statistical mechanics. The stochastic differential equations of the formalism are found to exhibit significant stiffness in some physical regimes. To cope with this issue, we derive reduced stochastic differential equations for several physical regimes. We also present stochastic numerical methods for each regime to approximate the fluid-structure dynamics and to generate efficiently the required stochastic driving fields. To validate the methodology in each regime, we perform analysis of the invariant probability distribution of the stochastic dynamics of the fluid-structure formalism. We compare this analysis with results from statistical mechanics. To further demonstrate the applicability of the methodology, we perform computational studies for spherical particles having translational and rotational degrees of freedom. We compare these studies with results from fluid mechanics. The presented approach provides for fluid-structure systems a set of rather general computational methods for treating consistently structure mechanics, hydrodynamic coupling, and thermal fluctuations.

Emergence of dynamic cooperativity in the stochastic kinetics of fluctuating enzymes

International Nuclear Information System (INIS)

Kumar, Ashutosh; Chatterjee, Sambarta; Nandi, Mintu; Dua, Arti

2016-01-01

Dynamic co-operativity in monomeric enzymes is characterized in terms of a non-Michaelis-Menten kinetic behaviour. The latter is believed to be associated with mechanisms that include multiple reaction pathways due to enzymatic conformational fluctuations. Recent advances in single-molecule fluorescence spectroscopy have provided new fundamental insights on the possible mechanisms underlying reactions catalyzed by fluctuating enzymes. Here, we present a bottom-up approach to understand enzyme turnover kinetics at physiologically relevant mesoscopic concentrations informed by mechanisms extracted from single-molecule stochastic trajectories. The stochastic approach, presented here, shows the emergence of dynamic co-operativity in terms of a slowing down of the Michaelis-Menten (MM) kinetics resulting in negative co-operativity. For fewer enzymes, dynamic co-operativity emerges due to the combined effects of enzymatic conformational fluctuations and molecular discreteness. The increase in the number of enzymes, however, suppresses the effect of enzymatic conformational fluctuations such that dynamic co-operativity emerges solely due to the discrete changes in the number of reacting species. These results confirm that the turnover kinetics of fluctuating enzyme based on the parallel-pathway MM mechanism switches over to the single-pathway MM mechanism with the increase in the number of enzymes. For large enzyme numbers, convergence to the exact MM equation occurs in the limit of very high substrate concentration as the stochastic kinetics approaches the deterministic behaviour.

Emergence of dynamic cooperativity in the stochastic kinetics of fluctuating enzymes

Energy Technology Data Exchange (ETDEWEB)

Kumar, Ashutosh; Chatterjee, Sambarta; Nandi, Mintu; Dua, Arti, E-mail: arti@iitm.ac.in [Department of Chemistry, Indian Institute of Technology, Madras, Chennai 600036 (India)

2016-08-28

Dynamic co-operativity in monomeric enzymes is characterized in terms of a non-Michaelis-Menten kinetic behaviour. The latter is believed to be associated with mechanisms that include multiple reaction pathways due to enzymatic conformational fluctuations. Recent advances in single-molecule fluorescence spectroscopy have provided new fundamental insights on the possible mechanisms underlying reactions catalyzed by fluctuating enzymes. Here, we present a bottom-up approach to understand enzyme turnover kinetics at physiologically relevant mesoscopic concentrations informed by mechanisms extracted from single-molecule stochastic trajectories. The stochastic approach, presented here, shows the emergence of dynamic co-operativity in terms of a slowing down of the Michaelis-Menten (MM) kinetics resulting in negative co-operativity. For fewer enzymes, dynamic co-operativity emerges due to the combined effects of enzymatic conformational fluctuations and molecular discreteness. The increase in the number of enzymes, however, suppresses the effect of enzymatic conformational fluctuations such that dynamic co-operativity emerges solely due to the discrete changes in the number of reacting species. These results confirm that the turnover kinetics of fluctuating enzyme based on the parallel-pathway MM mechanism switches over to the single-pathway MM mechanism with the increase in the number of enzymes. For large enzyme numbers, convergence to the exact MM equation occurs in the limit of very high substrate concentration as the stochastic kinetics approaches the deterministic behaviour.

Emergence of dynamic cooperativity in the stochastic kinetics of fluctuating enzymes

Science.gov (United States)

Kumar, Ashutosh; Chatterjee, Sambarta; Nandi, Mintu; Dua, Arti

2016-08-01

Dynamic co-operativity in monomeric enzymes is characterized in terms of a non-Michaelis-Menten kinetic behaviour. The latter is believed to be associated with mechanisms that include multiple reaction pathways due to enzymatic conformational fluctuations. Recent advances in single-molecule fluorescence spectroscopy have provided new fundamental insights on the possible mechanisms underlying reactions catalyzed by fluctuating enzymes. Here, we present a bottom-up approach to understand enzyme turnover kinetics at physiologically relevant mesoscopic concentrations informed by mechanisms extracted from single-molecule stochastic trajectories. The stochastic approach, presented here, shows the emergence of dynamic co-operativity in terms of a slowing down of the Michaelis-Menten (MM) kinetics resulting in negative co-operativity. For fewer enzymes, dynamic co-operativity emerges due to the combined effects of enzymatic conformational fluctuations and molecular discreteness. The increase in the number of enzymes, however, suppresses the effect of enzymatic conformational fluctuations such that dynamic co-operativity emerges solely due to the discrete changes in the number of reacting species. These results confirm that the turnover kinetics of fluctuating enzyme based on the parallel-pathway MM mechanism switches over to the single-pathway MM mechanism with the increase in the number of enzymes. For large enzyme numbers, convergence to the exact MM equation occurs in the limit of very high substrate concentration as the stochastic kinetics approaches the deterministic behaviour.

Stochastic E2F activation and reconciliation of phenomenological cell-cycle models.

Science.gov (United States)

Lee, Tae J; Yao, Guang; Bennett, Dorothy C; Nevins, Joseph R; You, Lingchong

2010-09-21

The transition of the mammalian cell from quiescence to proliferation is a highly variable process. Over the last four decades, two lines of apparently contradictory, phenomenological models have been proposed to account for such temporal variability. These include various forms of the transition probability (TP) model and the growth control (GC) model, which lack mechanistic details. The GC model was further proposed as an alternative explanation for the concept of the restriction point, which we recently demonstrated as being controlled by a bistable Rb-E2F switch. Here, through a combination of modeling and experiments, we show that these different lines of models in essence reflect different aspects of stochastic dynamics in cell cycle entry. In particular, we show that the variable activation of E2F can be described by stochastic activation of the bistable Rb-E2F switch, which in turn may account for the temporal variability in cell cycle entry. Moreover, we show that temporal dynamics of E2F activation can be recast into the frameworks of both the TP model and the GC model via parameter mapping. This mapping suggests that the two lines of phenomenological models can be reconciled through the stochastic dynamics of the Rb-E2F switch. It also suggests a potential utility of the TP or GC models in defining concise, quantitative phenotypes of cell physiology. This may have implications in classifying cell types or states.

Stochastic NANOG fluctuations allow mouse embryonic stem cells to explore pluripotency

Czech Academy of Sciences Publication Activity Database

Abranches, E.; Guedes, A.M.V.; Moravec, Martin; Maamar, H.; Svoboda, Petr; Raj, A.; Henrique, D.

2014-01-01

Roč. 141, č. 14 (2014), s. 2770-2779 ISSN 0950-1991 R&D Projects: GA ČR(CZ) GBP305/12/G034 Grant - others:Fundacao para a Ciencia e Tecnologia(PT) SFRH/ BPD/78313/2011; Fundacao para a Ciencia e Tecnologia(PT) SFRH/BD/80191/2011; Fundacao para a Ciencia e Tecnologia(PT) PTDC/SAUOBD/100664/2008; GA AV ČR M200521202 Institutional support: RVO:68378050 Keywords : Gene expression heterogeneity * Pluripotency * Lineage priming * Nanog * Stem cells Subject RIV: EB - Genetics ; Molecular Biology Impact factor: 6.462, year: 2014

Stochastic dark energy from inflationary quantum fluctuations

Science.gov (United States)

Glavan, Dražen; Prokopec, Tomislav; Starobinsky, Alexei A.

2018-05-01

We study the quantum backreaction from inflationary fluctuations of a very light, non-minimally coupled spectator scalar and show that it is a viable candidate for dark energy. The problem is solved by suitably adapting the formalism of stochastic inflation. This allows us to self-consistently account for the backreaction on the background expansion rate of the Universe where its effects are large. This framework is equivalent to that of semiclassical gravity in which matter vacuum fluctuations are included at the one loop level, but purely quantum gravitational fluctuations are neglected. Our results show that dark energy in our model can be characterized by a distinct effective equation of state parameter (as a function of redshift) which allows for testing of the model at the level of the background.

Quantifying intrinsic and extrinsic variability in stochastic gene expression models.

Science.gov (United States)

Singh, Abhyudai; Soltani, Mohammad

2013-01-01

Genetically identical cell populations exhibit considerable intercellular variation in the level of a given protein or mRNA. Both intrinsic and extrinsic sources of noise drive this variability in gene expression. More specifically, extrinsic noise is the expression variability that arises from cell-to-cell differences in cell-specific factors such as enzyme levels, cell size and cell cycle stage. In contrast, intrinsic noise is the expression variability that is not accounted for by extrinsic noise, and typically arises from the inherent stochastic nature of biochemical processes. Two-color reporter experiments are employed to decompose expression variability into its intrinsic and extrinsic noise components. Analytical formulas for intrinsic and extrinsic noise are derived for a class of stochastic gene expression models, where variations in cell-specific factors cause fluctuations in model parameters, in particular, transcription and/or translation rate fluctuations. Assuming mRNA production occurs in random bursts, transcription rate is represented by either the burst frequency (how often the bursts occur) or the burst size (number of mRNAs produced in each burst). Our analysis shows that fluctuations in the transcription burst frequency enhance extrinsic noise but do not affect the intrinsic noise. On the contrary, fluctuations in the transcription burst size or mRNA translation rate dramatically increase both intrinsic and extrinsic noise components. Interestingly, simultaneous fluctuations in transcription and translation rates arising from randomness in ATP abundance can decrease intrinsic noise measured in a two-color reporter assay. Finally, we discuss how these formulas can be combined with single-cell gene expression data from two-color reporter experiments for estimating model parameters.

Protein Signaling Networks from Single Cell Fluctuations and Information Theory Profiling

Science.gov (United States)

Shin, Young Shik; Remacle, F.; Fan, Rong; Hwang, Kiwook; Wei, Wei; Ahmad, Habib; Levine, R.D.; Heath, James R.

2011-01-01

Protein signaling networks among cells play critical roles in a host of pathophysiological processes, from inflammation to tumorigenesis. We report on an approach that integrates microfluidic cell handling, in situ protein secretion profiling, and information theory to determine an extracellular protein-signaling network and the role of perturbations. We assayed 12 proteins secreted from human macrophages that were subjected to lipopolysaccharide challenge, which emulates the macrophage-based innate immune responses against Gram-negative bacteria. We characterize the fluctuations in protein secretion of single cells, and of small cell colonies (n = 2, 3,···), as a function of colony size. Measuring the fluctuations permits a validation of the conditions required for the application of a quantitative version of the Le Chatelier's principle, as derived using information theory. This principle provides a quantitative prediction of the role of perturbations and allows a characterization of a protein-protein interaction network. PMID:21575571

Particle-in-cell simulations on spontaneous thermal magnetic field fluctuations

Energy Technology Data Exchange (ETDEWEB)

Simões, F. J. R. Jr.; Pavan, J. [Instituto de Física e Matemática, UFPel, Pelotas, RS (Brazil); Gaelzer, R.; Ziebell, L. F. [Instituto de Física, UFRGS, Porto Alegre, RS (Brazil); Yoon, P. H. [Institute for Physical Science and Technology, University of Maryland, College Park, Maryland 20742 (United States)

2013-10-15

In this paper an electromagnetic particle code is used to investigate the spontaneous thermal emission. Specifically we perform particle-in-cell simulations employing a non-relativistic isotropic Maxwellian particle distribution to show that thermal fluctuations are related to the origin of spontaneous magnetic field fluctuation. These thermal fluctuations can become seed for further amplification mechanisms and thus be considered at the origin of the cosmological magnetic field, at microgauss levels. Our numerical results are in accordance with theoretical results presented in the literature.

Inverse Stochastic Resonance in Cerebellar Purkinje Cells.

Directory of Open Access Journals (Sweden)

Anatoly Buchin

2016-08-01

Full Text Available Purkinje neurons play an important role in cerebellar computation since their axons are the only projection from the cerebellar cortex to deeper cerebellar structures. They have complex internal dynamics, which allow them to fire spontaneously, display bistability, and also to be involved in network phenomena such as high frequency oscillations and travelling waves. Purkinje cells exhibit type II excitability, which can be revealed by a discontinuity in their f-I curves. We show that this excitability mechanism allows Purkinje cells to be efficiently inhibited by noise of a particular variance, a phenomenon known as inverse stochastic resonance (ISR. While ISR has been described in theoretical models of single neurons, here we provide the first experimental evidence for this effect. We find that an adaptive exponential integrate-and-fire model fitted to the basic Purkinje cell characteristics using a modified dynamic IV method displays ISR and bistability between the resting state and a repetitive activity limit cycle. ISR allows the Purkinje cell to operate in different functional regimes: the all-or-none toggle or the linear filter mode, depending on the variance of the synaptic input. We propose that synaptic noise allows Purkinje cells to quickly switch between these functional regimes. Using mutual information analysis, we demonstrate that ISR can lead to a locally optimal information transfer between the input and output spike train of the Purkinje cell. These results provide the first experimental evidence for ISR and suggest a functional role for ISR in cerebellar information processing.

A hypothetical stochastic mechanism of radiation effects in single cells: some further thoughts and results

International Nuclear Information System (INIS)

Puri, P.S.

1982-01-01

The author briefly describes a stochastic model for radiation effects in single cells, discusses some results based on this model and then raises several questions which need further attention. While the ultimate goal is to develop appropriate stochastic models of phenomena arising in irradiated experimental animals, the present concern however is limited to irradiation effects on cells of some homogeneous tissue. The basic assumptions underlying the stochastic model are presented. Some of the model implications are compared with empirical findings in the literature. The question of whether or not a primary particle of UV radiation generates any secondary particles is also considered. (Auth.)

Stochastic level-set variational implicit-solvent approach to solute-solvent interfacial fluctuations

Energy Technology Data Exchange (ETDEWEB)

Zhou, Shenggao, E-mail: sgzhou@suda.edu.cn, E-mail: bli@math.ucsd.edu [Department of Mathematics and Mathematical Center for Interdiscipline Research, Soochow University, 1 Shizi Street, Jiangsu, Suzhou 215006 (China); Sun, Hui; Cheng, Li-Tien [Department of Mathematics, University of California, San Diego, La Jolla, California 92093-0112 (United States); Dzubiella, Joachim [Soft Matter and Functional Materials, Helmholtz-Zentrum Berlin, 14109 Berlin, Germany and Institut für Physik, Humboldt-Universität zu Berlin, 12489 Berlin (Germany); Li, Bo, E-mail: sgzhou@suda.edu.cn, E-mail: bli@math.ucsd.edu [Department of Mathematics and Quantitative Biology Graduate Program, University of California, San Diego, La Jolla, California 92093-0112 (United States); McCammon, J. Andrew [Department of Chemistry and Biochemistry, Department of Pharmacology, Howard Hughes Medical Institute, University of California, San Diego, La Jolla, California 92093-0365 (United States)

2016-08-07

Recent years have seen the initial success of a variational implicit-solvent model (VISM), implemented with a robust level-set method, in capturing efficiently different hydration states and providing quantitatively good estimation of solvation free energies of biomolecules. The level-set minimization of the VISM solvation free-energy functional of all possible solute-solvent interfaces or dielectric boundaries predicts an equilibrium biomolecular conformation that is often close to an initial guess. In this work, we develop a theory in the form of Langevin geometrical flow to incorporate solute-solvent interfacial fluctuations into the VISM. Such fluctuations are crucial to biomolecular conformational changes and binding process. We also develop a stochastic level-set method to numerically implement such a theory. We describe the interfacial fluctuation through the “normal velocity” that is the solute-solvent interfacial force, derive the corresponding stochastic level-set equation in the sense of Stratonovich so that the surface representation is independent of the choice of implicit function, and develop numerical techniques for solving such an equation and processing the numerical data. We apply our computational method to study the dewetting transition in the system of two hydrophobic plates and a hydrophobic cavity of a synthetic host molecule cucurbit[7]uril. Numerical simulations demonstrate that our approach can describe an underlying system jumping out of a local minimum of the free-energy functional and can capture dewetting transitions of hydrophobic systems. In the case of two hydrophobic plates, we find that the wavelength of interfacial fluctuations has a strong influence to the dewetting transition. In addition, we find that the estimated energy barrier of the dewetting transition scales quadratically with the inter-plate distance, agreeing well with existing studies of molecular dynamics simulations. Our work is a first step toward the

Fluctuations of the transcription factor ATML1 generate the pattern of giant cells in the Arabidopsis sepal

Science.gov (United States)

Meyer, Heather M; Teles, José; Formosa-Jordan, Pau; Refahi, Yassin; San-Bento, Rita; Ingram, Gwyneth; Jönsson, Henrik; Locke, James C W; Roeder, Adrienne H K

2017-01-01

Multicellular development produces patterns of specialized cell types. Yet, it is often unclear how individual cells within a field of identical cells initiate the patterning process. Using live imaging, quantitative image analyses and modeling, we show that during Arabidopsis thaliana sepal development, fluctuations in the concentration of the transcription factor ATML1 pattern a field of identical epidermal cells to differentiate into giant cells interspersed between smaller cells. We find that ATML1 is expressed in all epidermal cells. However, its level fluctuates in each of these cells. If ATML1 levels surpass a threshold during the G2 phase of the cell cycle, the cell will likely enter a state of endoreduplication and become giant. Otherwise, the cell divides. Our results demonstrate a fluctuation-driven patterning mechanism for how cell fate decisions can be initiated through a random yet tightly regulated process. DOI: http://dx.doi.org/10.7554/eLife.19131.001 PMID:28145865

The response analysis of fractional-order stochastic system via generalized cell mapping method.

Science.gov (United States)

Wang, Liang; Xue, Lili; Sun, Chunyan; Yue, Xiaole; Xu, Wei

2018-01-01

This paper is concerned with the response of a fractional-order stochastic system. The short memory principle is introduced to ensure that the response of the system is a Markov process. The generalized cell mapping method is applied to display the global dynamics of the noise-free system, such as attractors, basins of attraction, basin boundary, saddle, and invariant manifolds. The stochastic generalized cell mapping method is employed to obtain the evolutionary process of probability density functions of the response. The fractional-order ϕ 6 oscillator and the fractional-order smooth and discontinuous oscillator are taken as examples to give the implementations of our strategies. Studies have shown that the evolutionary direction of the probability density function of the fractional-order stochastic system is consistent with the unstable manifold. The effectiveness of the method is confirmed using Monte Carlo results.

Stochastic Threshold Microdose Model for Cell Killing by Insoluble Metallic Nanomaterial Particles

Science.gov (United States)

Scott, Bobby R.

2010-01-01

This paper introduces a novel microdosimetric model for metallic nanomaterial-particles (MENAP)-induced cytotoxicity. The focus is on the engineered insoluble MENAP which represent a significant breakthrough in the design and development of new products for consumers, industry, and medicine. Increased production is rapidly occurring and may cause currently unrecognized health effects (e.g., nervous system dysfunction, heart disease, cancer); thus, dose-response models for MENAP-induced biological effects are needed to facilitate health risk assessment. The stochastic threshold microdose (STM) model presented introduces novel stochastic microdose metrics for use in constructing dose-response relationships for the frequency of specific cellular (e.g., cell killing, mutations, neoplastic transformation) or subcellular (e.g., mitochondria dysfunction) effects. A key metric is the exposure-time-dependent, specific burden (MENAP count) for a given critical target (e.g., mitochondria, nucleus). Exceeding a stochastic threshold specific burden triggers cell death. For critical targets in the cytoplasm, the autophagic mode of death is triggered. For the nuclear target, the apoptotic mode of death is triggered. Overall cell survival is evaluated for the indicated competing modes of death when both apply. The STM model can be applied to cytotoxicity data using Bayesian methods implemented via Markov chain Monte Carlo. PMID:21191483

Stochastic responses of tumor–immune system with periodic treatment

International Nuclear Information System (INIS)

Li Dong-Xi; Li Ying

2017-01-01

We investigate the stochastic responses of a tumor–immune system competition model with environmental noise and periodic treatment. Firstly, a mathematical model describing the interaction between tumor cells and immune system under external fluctuations and periodic treatment is established based on the stochastic differential equation. Then, sufficient conditions for extinction and persistence of the tumor cells are derived by constructing Lyapunov functions and Ito’s formula. Finally, numerical simulations are introduced to illustrate and verify the results. The results of this work provide the theoretical basis for designing more effective and precise therapeutic strategies to eliminate cancer cells, especially for combining the immunotherapy and the traditional tools. (paper)

Stochastic adaptation and fold-change detection: from single-cell to population behavior

Directory of Open Access Journals (Sweden)

Leier André

2011-02-01

Full Text Available Abstract Background In cell signaling terminology, adaptation refers to a system's capability of returning to its equilibrium upon a transient response. To achieve this, a network has to be both sensitive and precise. Namely, the system must display a significant output response upon stimulation, and later on return to pre-stimulation levels. If the system settles at the exact same equilibrium, adaptation is said to be 'perfect'. Examples of adaptation mechanisms include temperature regulation, calcium regulation and bacterial chemotaxis. Results We present models of the simplest adaptation architecture, a two-state protein system, in a stochastic setting. Furthermore, we consider differences between individual and collective adaptive behavior, and show how our system displays fold-change detection properties. Our analysis and simulations highlight why adaptation needs to be understood in terms of probability, and not in strict numbers of molecules. Most importantly, selection of appropriate parameters in this simple linear setting may yield populations of cells displaying adaptation, while single cells do not. Conclusions Single cell behavior cannot be inferred from population measurements and, sometimes, collective behavior cannot be determined from the individuals. By consequence, adaptation can many times be considered a purely emergent property of the collective system. This is a clear example where biological ergodicity cannot be assumed, just as is also the case when cell replication rates are not homogeneous, or depend on the cell state. Our analysis shows, for the first time, how ergodicity cannot be taken for granted in simple linear examples either. The latter holds even when cells are considered isolated and devoid of replication capabilities (cell-cycle arrested. We also show how a simple linear adaptation scheme displays fold-change detection properties, and how rupture of ergodicity prevails in scenarios where transitions between

Sizing for fuel cell/supercapacitor hybrid vehicles based on stochastic driving cycles

International Nuclear Information System (INIS)

Feroldi, Diego; Carignano, Mauro

2016-01-01

Highlights: • A sizing procedure based on the fulfilment of real driving conditions is proposed. • A methodology to generate long-term stochastic driving cycles is proposed. • A parametric optimization of the real-time EMS is conducted. • A trade-off design is adopted from a Pareto front. • A comparison with optimal consumption via Dynamic Programming is performed. - Abstract: In this article, a methodology for the sizing and analysis of fuel cell/supercapacitor hybrid vehicles is presented. The proposed sizing methodology is based on the fulfilment of power requirements, including sustained speed tests and stochastic driving cycles. The procedure to generate driving cycles is also presented in this paper. The sizing algorithm explicitly accounts for the Equivalent Consumption Minimization Strategy (ECMS). The performance is compared with optimal consumption, which is found using an off-line strategy via Dynamic Programming. The sizing methodology provides guidance for sizing the fuel cell and the supercapacitor number. The results also include analysis on oversizing the fuel cell and varying the parameters of the energy management strategy. The simulation results highlight the importance of integrating sizing and energy management into fuel cell hybrid vehicles.

Stochasticity in the enterococcal sex pheromone response revealed by quantitative analysis of transcription in single cells.

Science.gov (United States)

Breuer, Rebecca J; Bandyopadhyay, Arpan; O'Brien, Sofie A; Barnes, Aaron M T; Hunter, Ryan C; Hu, Wei-Shou; Dunny, Gary M

2017-07-01

In Enterococcus faecalis, sex pheromone-mediated transfer of antibiotic resistance plasmids can occur under unfavorable conditions, for example, when inducing pheromone concentrations are low and inhibiting pheromone concentrations are high. To better understand this paradox, we adapted fluorescence in situ hybridization chain reaction (HCR) methodology for simultaneous quantification of multiple E. faecalis transcripts at the single cell level. We present direct evidence for variability in the minimum period, maximum response level, and duration of response of individual cells to a specific inducing condition. Tracking of induction patterns of single cells temporally using a fluorescent reporter supported HCR findings. It also revealed subpopulations of rapid responders, even under low inducing pheromone concentrations where the overall response of the entire population was slow. The strong, rapid induction of small numbers of cells in cultures exposed to low pheromone concentrations is in agreement with predictions of a stochastic model of the enterococcal pheromone response. The previously documented complex regulatory circuitry controlling the pheromone response likely contributes to stochastic variation in this system. In addition to increasing our basic understanding of the biology of a horizontal gene transfer system regulated by cell-cell signaling, demonstration of the stochastic nature of the pheromone response also impacts any future efforts to develop therapeutic agents targeting the system. Quantitative single cell analysis using HCR also has great potential to elucidate important bacterial regulatory mechanisms not previously amenable to study at the single cell level, and to accelerate the pace of functional genomic studies.

Cytoskeleton dynamics: Fluctuations within the network

International Nuclear Information System (INIS)

Bursac, Predrag; Fabry, Ben; Trepat, Xavier; Lenormand, Guillaume; Butler, James P.; Wang, Ning; Fredberg, Jeffrey J.; An, Steven S.

2007-01-01

Out-of-equilibrium systems, such as the dynamics of a living cytoskeleton (CSK), are inherently noisy with fluctuations arising from the stochastic nature of the underlying biochemical and molecular events. Recently, such fluctuations within the cell were characterized by observing spontaneous nano-scale motions of an RGD-coated microbead bound to the cell surface [Bursac et al., Nat. Mater. 4 (2005) 557-561]. While these reported anomalous bead motions represent a molecular level reorganization (remodeling) of microstructures in contact with the bead, a precise nature of these cytoskeletal constituents and forces that drive their remodeling dynamics are largely unclear. Here, we focused upon spontaneous motions of an RGD-coated bead and, in particular, assessed to what extent these motions are attributable to (i) bulk cell movement (cell crawling), (ii) dynamics of focal adhesions, (iii) dynamics of lipid membrane, and/or (iv) dynamics of the underlying actin CSK driven by myosin motors

Bayesian parameter estimation for stochastic models of biological cell migration

Science.gov (United States)

Dieterich, Peter; Preuss, Roland

2013-08-01

Cell migration plays an essential role under many physiological and patho-physiological conditions. It is of major importance during embryonic development and wound healing. In contrast, it also generates negative effects during inflammation processes, the transmigration of tumors or the formation of metastases. Thus, a reliable quantification and characterization of cell paths could give insight into the dynamics of these processes. Typically stochastic models are applied where parameters are extracted by fitting models to the so-called mean square displacement of the observed cell group. We show that this approach has several disadvantages and problems. Therefore, we propose a simple procedure directly relying on the positions of the cell's trajectory and the covariance matrix of the positions. It is shown that the covariance is identical with the spatial aging correlation function for the supposed linear Gaussian models of Brownian motion with drift and fractional Brownian motion. The technique is applied and illustrated with simulated data showing a reliable parameter estimation from single cell paths.

Contributions of cell growth and biochemical reactions to nongenetic variability of cells.

NARCIS (Netherlands)

Schwabe, A.; Bruggeman, F.J.

2014-01-01

Cell-to-cell variability in the molecular composition of isogenic, steady-state growing cells arises spontaneously from the inherent stochasticity of intracellular biochemical reactions and cell growth. Here, we present a general decomposition of the total variance in the copy number per cell of a

Bridging the Timescales of Single-Cell and Population Dynamics

Science.gov (United States)

Jafarpour, Farshid; Wright, Charles S.; Gudjonson, Herman; Riebling, Jedidiah; Dawson, Emma; Lo, Klevin; Fiebig, Aretha; Crosson, Sean; Dinner, Aaron R.; Iyer-Biswas, Srividya

2018-04-01

How are granular details of stochastic growth and division of individual cells reflected in smooth deterministic growth of population numbers? We provide an integrated, multiscale perspective of microbial growth dynamics by formulating a data-validated theoretical framework that accounts for observables at both single-cell and population scales. We derive exact analytical complete time-dependent solutions to cell-age distributions and population growth rates as functionals of the underlying interdivision time distributions, for symmetric and asymmetric cell division. These results provide insights into the surprising implications of stochastic single-cell dynamics for population growth. Using our results for asymmetric division, we deduce the time to transition from the reproductively quiescent (swarmer) to the replication-competent (stalked) stage of the Caulobacter crescentus life cycle. Remarkably, population numbers can spontaneously oscillate with time. We elucidate the physics leading to these population oscillations. For C. crescentus cells, we show that a simple measurement of the population growth rate, for a given growth condition, is sufficient to characterize the condition-specific cellular unit of time and, thus, yields the mean (single-cell) growth and division timescales, fluctuations in cell division times, the cell-age distribution, and the quiescence timescale.

Continuum modeling of ion-beam eroded surfaces under normal incidence: Impact of stochastic fluctuations

International Nuclear Information System (INIS)

Dreimann, Karsten; Linz, Stefan J.

2010-01-01

Graphical abstract: Deterministic surface pattern (left) and its stochastic counterpart (right) arising in a stochastic damped Kuramoto-Sivashinsky equation that serves as a model equation for ion-beam eroded surfaces and is systematically investigated. - Abstract: Using a recently proposed field equation for the surface evolution of ion-beam eroded semiconductor target materials under normal incidence, we systematically explore the impact of additive stochastic fluctuations that are permanently present during the erosion process. Specifically, we investigate the dependence of the surface roughness, the underlying pattern forming properties and the bifurcation behavior on the strength of the fluctuations.

Combining magnetic sorting of mother cells and fluctuation tests to analyze genome instability during mitotic cell aging in Saccharomyces cerevisiae.

Science.gov (United States)

Patterson, Melissa N; Maxwell, Patrick H

2014-10-16

Saccharomyces cerevisiae has been an excellent model system for examining mechanisms and consequences of genome instability. Information gained from this yeast model is relevant to many organisms, including humans, since DNA repair and DNA damage response factors are well conserved across diverse species. However, S. cerevisiae has not yet been used to fully address whether the rate of accumulating mutations changes with increasing replicative (mitotic) age due to technical constraints. For instance, measurements of yeast replicative lifespan through micromanipulation involve very small populations of cells, which prohibit detection of rare mutations. Genetic methods to enrich for mother cells in populations by inducing death of daughter cells have been developed, but population sizes are still limited by the frequency with which random mutations that compromise the selection systems occur. The current protocol takes advantage of magnetic sorting of surface-labeled yeast mother cells to obtain large enough populations of aging mother cells to quantify rare mutations through phenotypic selections. Mutation rates, measured through fluctuation tests, and mutation frequencies are first established for young cells and used to predict the frequency of mutations in mother cells of various replicative ages. Mutation frequencies are then determined for sorted mother cells, and the age of the mother cells is determined using flow cytometry by staining with a fluorescent reagent that detects bud scars formed on their cell surfaces during cell division. Comparison of predicted mutation frequencies based on the number of cell divisions to the frequencies experimentally observed for mother cells of a given replicative age can then identify whether there are age-related changes in the rate of accumulating mutations. Variations of this basic protocol provide the means to investigate the influence of alterations in specific gene functions or specific environmental conditions on

Stochastic fluctuations and the detectability limit of network communities.

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Floretta, Lucio; Liechti, Jonas; Flammini, Alessandro; De Los Rios, Paolo

2013-12-01

We have analyzed the detectability limits of network communities in the framework of the popular Girvan and Newman benchmark. By carefully taking into account the inevitable stochastic fluctuations that affect the construction of each and every instance of the benchmark, we come to the conclusion that the native, putative partition of the network is completely lost even before the in-degree/out-degree ratio becomes equal to that of a structureless Erdös-Rényi network. We develop a simple iterative scheme, analytically well described by an infinite branching process, to provide an estimate of the true detectability limit. Using various algorithms based on modularity optimization, we show that all of them behave (semiquantitatively) in the same way, with the same functional form of the detectability threshold as a function of the network parameters. Because the same behavior has also been found by further modularity-optimization methods and for methods based on different heuristics implementations, we conclude that indeed a correct definition of the detectability limit must take into account the stochastic fluctuations of the network construction.

Intrinsic periodic and aperiodic stochastic resonance in an electrochemical cell

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Tiwari, Ishant; Phogat, Richa; Parmananda, P.; Ocampo-Espindola, J. L.; Rivera, M.

2016-08-01

In this paper we show the interaction of a composite of a periodic or aperiodic signal and intrinsic electrochemical noise with the nonlinear dynamics of an electrochemical cell configured to study the corrosion of iron in an acidic media. The anodic voltage setpoint (V0) in the cell is chosen such that the anodic current (I ) exhibits excitable fixed point behavior in the absence of noise. The subthreshold periodic (aperiodic) signal consists of a train of rectangular pulses with a fixed amplitude and width, separated by regular (irregular) time intervals. The irregular time intervals chosen are of deterministic and stochastic origins. The amplitude of the intrinsic internal noise, regulated by the concentration of chloride ions, is then monotonically increased, and the provoked dynamics are analyzed. The signal to noise ratio and the cross-correlation coefficient versus the chloride ions' concentration curves have a unimodal shape indicating the emergence of an intrinsic periodic or aperiodic stochastic resonance. The abscissa for the maxima of these unimodal curves correspond to the optimum value of intrinsic noise where maximum regularity of the invoked dynamics is observed. In the particular case of the intrinsic periodic stochastic resonance, the scanning electron microscope images for the electrode metal surfaces are shown for certain values of chloride ions' concentrations. These images, qualitatively, corroborate the emergence of order as a result of the interaction between the nonlinear dynamics and the composite signal.

Determinants of cell-to-cell variability in protein kinase signaling.

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Jeschke, Matthias; Baumgärtner, Stephan; Legewie, Stefan

2013-01-01

Cells reliably sense environmental changes despite internal and external fluctuations, but the mechanisms underlying robustness remain unclear. We analyzed how fluctuations in signaling protein concentrations give rise to cell-to-cell variability in protein kinase signaling using analytical theory and numerical simulations. We characterized the dose-response behavior of signaling cascades by calculating the stimulus level at which a pathway responds ('pathway sensitivity') and the maximal activation level upon strong stimulation. Minimal kinase cascades with gradual dose-response behavior show strong variability, because the pathway sensitivity and the maximal activation level cannot be simultaneously invariant. Negative feedback regulation resolves this trade-off and coordinately reduces fluctuations in the pathway sensitivity and maximal activation. Feedbacks acting at different levels in the cascade control different aspects of the dose-response curve, thereby synergistically reducing the variability. We also investigated more complex, ultrasensitive signaling cascades capable of switch-like decision making, and found that these can be inherently robust to protein concentration fluctuations. We describe how the cell-to-cell variability of ultrasensitive signaling systems can be actively regulated, e.g., by altering the expression of phosphatase(s) or by feedback/feedforward loops. Our calculations reveal that slow transcriptional negative feedback loops allow for variability suppression while maintaining switch-like decision making. Taken together, we describe design principles of signaling cascades that promote robustness. Our results may explain why certain signaling cascades like the yeast pheromone pathway show switch-like decision making with little cell-to-cell variability.

Critical composition fluctuations in artificial and cell-derived lipid membranes

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Honerkamp-Smith, Aurelia

2014-03-01

Cell plasma membranes contain a mixture of lipid types which can segregate into coexisting liquids, a thermodynamic phenomenon which may contribute to biological functions. Simplified, artificial three-component lipid vesicles can be prepared which display a critical miscibility transition near room temperature. We found that such vesicles exhibit concentration fluctuations whose size, composition, and timescales vary consistently with critical exponents for two-dimensional conserved order parameter systems. However, the critical miscibility transition is also observed in vesicles formed directly from the membranes of living cells, despite their more complex composition and the presence of membrane proteins. I will describe our critical fluctuation measurements and also review a variety of more recent work by other researchers. Proximity to a critical point alters the spatial distribution and aggregation tendencies of proteins, and makes lipid mixtures more susceptible to domain formation by protein-mediated interactions, such as adhesion zones. Recent work suggests that critical temperature depression may also be relevant to the mechanism of anaesthetic action.

Stochastic fluctuation induced the competition between extinction and recurrence in a model of tumor growth

International Nuclear Information System (INIS)

Li, Dongxi; Xu, Wei; Sun, Chunyan; Wang, Liang

2012-01-01

We investigate the phenomenon that stochastic fluctuation induced the competition between tumor extinction and recurrence in the model of tumor growth derived from the catalytic Michaelis–Menten reaction. We analyze the probability transitions between the extinction state and the state of the stable tumor by the Mean First Extinction Time (MFET) and Mean First Return Time (MFRT). It is found that the positional fluctuations hinder the transition, but the environmental fluctuations, to a certain level, facilitate the tumor extinction. The observed behavior could be used as prior information for the treatment of cancer. -- Highlights: ► Stochastic fluctuation induced the competition between extinction and recurrence. ► The probability transitions are investigated. ► The positional fluctuations hinder the transition. ► The environmental fluctuations, to a certain level, facilitate the tumor extinction. ► The observed behavior can be used as prior information for the treatment of cancer.

Fractional Stochastic Differential Equations Satisfying Fluctuation-Dissipation Theorem

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Li, Lei; Liu, Jian-Guo; Lu, Jianfeng

2017-10-01

We propose in this work a fractional stochastic differential equation (FSDE) model consistent with the over-damped limit of the generalized Langevin equation model. As a result of the `fluctuation-dissipation theorem', the differential equations driven by fractional Brownian noise to model memory effects should be paired with Caputo derivatives, and this FSDE model should be understood in an integral form. We establish the existence of strong solutions for such equations and discuss the ergodicity and convergence to Gibbs measure. In the linear forcing regime, we show rigorously the algebraic convergence to Gibbs measure when the `fluctuation-dissipation theorem' is satisfied, and this verifies that satisfying `fluctuation-dissipation theorem' indeed leads to the correct physical behavior. We further discuss possible approaches to analyze the ergodicity and convergence to Gibbs measure in the nonlinear forcing regime, while leave the rigorous analysis for future works. The FSDE model proposed is suitable for systems in contact with heat bath with power-law kernel and subdiffusion behaviors.

Entrainment of Breast Cell Lines Results in Rhythmic Fluctuations of MicroRNAs

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Rafael Chacolla-Huaringa

2017-07-01

Full Text Available Circadian rhythms are essential for temporal (~24 h regulation of molecular processes in diverse species. Dysregulation of circadian gene expression has been implicated in the pathogenesis of various disorders, including hypertension, diabetes, depression, and cancer. Recently, microRNAs (miRNAs have been identified as critical modulators of gene expression post-transcriptionally, and perhaps involved in circadian clock architecture or their output functions. The aim of the present study is to explore the temporal expression of miRNAs among entrained breast cell lines. For this purpose, we evaluated the temporal (28 h expression of 2006 miRNAs in MCF-10A, MCF-7, and MDA-MB-231 cells using microarrays after serum shock entrainment. We noted hundreds of miRNAs that exhibit rhythmic fluctuations in each breast cell line, and some of them across two or three cell lines. Afterwards, we validated the rhythmic profiles exhibited by miR-141-5p, miR-1225-5p, miR-17-5p, miR-222-5p, miR-769-3p, and miR-548ay-3p in the above cell lines, as well as in ZR-7530 and HCC-1954 using RT-qPCR. Our results show that serum shock entrainment in breast cells lines induces rhythmic fluctuations of distinct sets of miRNAs, which have the potential to be related to endogenous circadian clock, but extensive investigation is required to elucidate that connection.

Coarse-graining and hybrid methods for efficient simulation of stochastic multi-scale models of tumour growth

International Nuclear Information System (INIS)

Cruz, Roberto de la; Guerrero, Pilar; Calvo, Juan; Alarcón, Tomás

2017-01-01

The development of hybrid methodologies is of current interest in both multi-scale modelling and stochastic reaction–diffusion systems regarding their applications to biology. We formulate a hybrid method for stochastic multi-scale models of cells populations that extends the remit of existing hybrid methods for reaction–diffusion systems. Such method is developed for a stochastic multi-scale model of tumour growth, i.e. population-dynamical models which account for the effects of intrinsic noise affecting both the number of cells and the intracellular dynamics. In order to formulate this method, we develop a coarse-grained approximation for both the full stochastic model and its mean-field limit. Such approximation involves averaging out the age-structure (which accounts for the multi-scale nature of the model) by assuming that the age distribution of the population settles onto equilibrium very fast. We then couple the coarse-grained mean-field model to the full stochastic multi-scale model. By doing so, within the mean-field region, we are neglecting noise in both cell numbers (population) and their birth rates (structure). This implies that, in addition to the issues that arise in stochastic-reaction diffusion systems, we need to account for the age-structure of the population when attempting to couple both descriptions. We exploit our coarse-graining model so that, within the mean-field region, the age-distribution is in equilibrium and we know its explicit form. This allows us to couple both domains consistently, as upon transference of cells from the mean-field to the stochastic region, we sample the equilibrium age distribution. Furthermore, our method allows us to investigate the effects of intracellular noise, i.e. fluctuations of the birth rate, on collective properties such as travelling wave velocity. We show that the combination of population and birth-rate noise gives rise to large fluctuations of the birth rate in the region at the leading edge

Determinants of cell-to-cell variability in protein kinase signaling.

Directory of Open Access Journals (Sweden)

Matthias Jeschke

Full Text Available Cells reliably sense environmental changes despite internal and external fluctuations, but the mechanisms underlying robustness remain unclear. We analyzed how fluctuations in signaling protein concentrations give rise to cell-to-cell variability in protein kinase signaling using analytical theory and numerical simulations. We characterized the dose-response behavior of signaling cascades by calculating the stimulus level at which a pathway responds ('pathway sensitivity' and the maximal activation level upon strong stimulation. Minimal kinase cascades with gradual dose-response behavior show strong variability, because the pathway sensitivity and the maximal activation level cannot be simultaneously invariant. Negative feedback regulation resolves this trade-off and coordinately reduces fluctuations in the pathway sensitivity and maximal activation. Feedbacks acting at different levels in the cascade control different aspects of the dose-response curve, thereby synergistically reducing the variability. We also investigated more complex, ultrasensitive signaling cascades capable of switch-like decision making, and found that these can be inherently robust to protein concentration fluctuations. We describe how the cell-to-cell variability of ultrasensitive signaling systems can be actively regulated, e.g., by altering the expression of phosphatase(s or by feedback/feedforward loops. Our calculations reveal that slow transcriptional negative feedback loops allow for variability suppression while maintaining switch-like decision making. Taken together, we describe design principles of signaling cascades that promote robustness. Our results may explain why certain signaling cascades like the yeast pheromone pathway show switch-like decision making with little cell-to-cell variability.

Stochastic fluctuations and distributed control of gene expression impact cellular memory.

Directory of Open Access Journals (Sweden)

Guillaume Corre

Full Text Available Despite the stochastic noise that characterizes all cellular processes the cells are able to maintain and transmit to their daughter cells the stable level of gene expression. In order to better understand this phenomenon, we investigated the temporal dynamics of gene expression variation using a double reporter gene model. We compared cell clones with transgenes coding for highly stable mRNA and fluorescent proteins with clones expressing destabilized mRNA-s and proteins. Both types of clones displayed strong heterogeneity of reporter gene expression levels. However, cells expressing stable gene products produced daughter cells with similar level of reporter proteins, while in cell clones with short mRNA and protein half-lives the epigenetic memory of the gene expression level was completely suppressed. Computer simulations also confirmed the role of mRNA and protein stability in the conservation of constant gene expression levels over several cell generations. These data indicate that the conservation of a stable phenotype in a cellular lineage may largely depend on the slow turnover of mRNA-s and proteins.

Coarse-graining and hybrid methods for efficient simulation of stochastic multi-scale models of tumour growth

Science.gov (United States)

de la Cruz, Roberto; Guerrero, Pilar; Calvo, Juan; Alarcón, Tomás

2017-12-01

The development of hybrid methodologies is of current interest in both multi-scale modelling and stochastic reaction-diffusion systems regarding their applications to biology. We formulate a hybrid method for stochastic multi-scale models of cells populations that extends the remit of existing hybrid methods for reaction-diffusion systems. Such method is developed for a stochastic multi-scale model of tumour growth, i.e. population-dynamical models which account for the effects of intrinsic noise affecting both the number of cells and the intracellular dynamics. In order to formulate this method, we develop a coarse-grained approximation for both the full stochastic model and its mean-field limit. Such approximation involves averaging out the age-structure (which accounts for the multi-scale nature of the model) by assuming that the age distribution of the population settles onto equilibrium very fast. We then couple the coarse-grained mean-field model to the full stochastic multi-scale model. By doing so, within the mean-field region, we are neglecting noise in both cell numbers (population) and their birth rates (structure). This implies that, in addition to the issues that arise in stochastic-reaction diffusion systems, we need to account for the age-structure of the population when attempting to couple both descriptions. We exploit our coarse-graining model so that, within the mean-field region, the age-distribution is in equilibrium and we know its explicit form. This allows us to couple both domains consistently, as upon transference of cells from the mean-field to the stochastic region, we sample the equilibrium age distribution. Furthermore, our method allows us to investigate the effects of intracellular noise, i.e. fluctuations of the birth rate, on collective properties such as travelling wave velocity. We show that the combination of population and birth-rate noise gives rise to large fluctuations of the birth rate in the region at the leading edge of

Non-genetic heterogeneity, criticality and cell differentiation.

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Pal, Mainak; Ghosh, Sayantari; Bose, Indrani

2014-11-27

The different cell types in a living organism acquire their identity through the process of cell differentiation in which multipotent progenitor cells differentiate into distinct cell types. Experimental evidence and analysis of large-scale microarray data establish the key role played by a two-gene motif in cell differentiation in a number of cell systems. The two genes express transcription factors which repress each other's expression and autoactivate their own production. A number of theoretical models have recently been proposed based on the two-gene motif to provide a physical understanding of how cell differentiation occurs. In this paper, we study a simple model of cell differentiation which assumes no cooperativity in the regulation of gene expression by the transcription factors. The latter repress each other's activity directly through DNA binding and indirectly through the formation of heterodimers. We specifically investigate how deterministic processes combined with stochasticity contribute in bringing about cell differentiation. The deterministic dynamics of our model give rise to a supercritical pitchfork bifurcation from an undifferentiated stable steady state to two differentiated stable steady states. The stochastic dynamics of our model are studied using the approaches based on the Langevin equations and the linear noise approximation. The simulation results provide a new physical understanding of recent experimental observations. We further propose experimental measurements of quantities like the variance and the lag-1 autocorrelation function in protein fluctuations as the early signatures of an approaching bifurcation point in the cell differentiation process.

A stochastic model of cell replicative senescence based on telomere shortening, oxidative stress, and somatic mutations in nuclear and mitochondrial DNA.

Science.gov (United States)

Sozou, P D; Kirkwood, T B

2001-12-21

Human diploid fibroblast cells can divide for only a limited number of times in vitro, a phenomenon known as replicative senescence or the Hayflick limit. Variability in doubling potential is observed within a clone of cells, and between two sister cells arising from a single mitotic division. This strongly suggests that the process by which cells become senescent is intrinsically stochastic. Among the various biochemical mechanisms that have been proposed to explain replicative senescence, particular interest has been focussed on the role of telomere reduction. In the absence of telomerase--an enzyme switched off in normal diploid fibro-blasts-cells lose telomeric DNA at each cell division. According to the telomere hypothesis of cell senescence, cells eventually reach a critically short telomere length and cell cycle arrest follows. In support of this concept, forced expression of telomerase in normal fibroblasts appears to prevent cell senescence. Nevertheless, the telomere hypothesis in its basic form has some difficulty in explaining the marked stochastic variations seen in the replicative lifespans of individual cells within a culture, and there is strong empirical and theoretical support for the concept that other kinds of damage may contribute to cellular ageing. We describe a stochastic network model of cell senescence in which a primary role is played by telomere reduction but in which other mechanisms (oxidative stress linked particularly to mitochondrial damage, and nuclear somatic mutations) also contribute. The model gives simulation results that are in good agreement with published data on intra-clonal variability in cell doubling potential and permits an analysis of how the various elements of the stochastic network interact. Such integrative models may aid in developing new experimental approaches aimed at unravelling the intrinsic complexity of the mechanisms contributing to human cell ageing. Copyright 2001 Academic Press.

Comparison between periodic and stochastic parabolic light trapping structures for thin-film microcrystalline Silicon solar cells.

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Peters, M; Battaglia, C; Forberich, K; Bläsi, B; Sahraei, N; Aberle, A G

2012-12-31

Light trapping is of very high importance for silicon photovoltaics (PV) and especially for thin-film silicon solar cells. In this paper we investigate and compare theoretically the light trapping properties of periodic and stochastic structures having similar geometrical features. The theoretical investigations are based on the actual surface geometry of a scattering structure, characterized by an atomic force microscope. This structure is used for light trapping in thin-film microcrystalline silicon solar cells. Very good agreement is found in a first comparison between simulation and experimental results. The geometrical parameters of the stochastic structure are varied and it is found that the light trapping mainly depends on the aspect ratio (length/height). Furthermore, the maximum possible light trapping with this kind of stochastic structure geometry is investigated. In a second step, the stochastic structure is analysed and typical geometrical features are extracted, which are then arranged in a periodic structure. Investigating the light trapping properties of the periodic structure, we find that it performs very similar to the stochastic structure, in agreement with reports in literature. From the obtained results we conclude that a potential advantage of periodic structures for PV applications will very likely not be found in the absorption enhancement in the solar cell material. However, uniformity and higher definition in production of these structures can lead to potential improvements concerning electrical characteristics and parasitic absorption, e.g. in a back reflector.

Designing a stochastic genetic switch by coupling chaos and bistability.

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Zhao, Xiang; Ouyang, Qi; Wang, Hongli

2015-11-01

In stem cell differentiation, a pluripotent stem cell becomes progressively specialized and generates specific cell types through a series of epigenetic processes. How cells can precisely determine their fate in a fluctuating environment is a currently unsolved problem. In this paper, we suggest an abstract gene regulatory network to describe mathematically the differentiation phenomenon featuring stochasticity, divergent cell fates, and robustness. The network consists of three functional motifs: an upstream chaotic motif, a buffering motif of incoherent feed forward loop capable of generating a pulse, and a downstream motif which is bistable. The dynamic behavior is typically a transient chaos with fractal basin boundaries. The trajectories take transiently chaotic journeys before divergently settling down to the bistable states. The ratio of the probability that the high state is achieved to the probability that the low state is reached can maintain a constant in a population of cells with varied molecular fluctuations. The ratio can be turned up or down when proper parameters are adjusted. The model suggests a possible mechanism for the robustness against fluctuations that is prominently featured in pluripotent cell differentiations and developmental phenomena.

Designing a stochastic genetic switch by coupling chaos and bistability

Energy Technology Data Exchange (ETDEWEB)

Zhao, Xiang [State Key Laboratory for Mesoscopic Physics and School of Physics, Peking University, Beijing 100871 (China); Ouyang, Qi [State Key Laboratory for Mesoscopic Physics and School of Physics, Peking University, Beijing 100871 (China); Center for Quantitative Biology, Peking University, Beijing 100871 (China); The Peking-Tsinghua Center for Life Sciences, Beijing 100871 (China); Wang, Hongli, E-mail: hlwang@pku.edu.cn [State Key Laboratory for Mesoscopic Physics and School of Physics, Peking University, Beijing 100871 (China); Center for Quantitative Biology, Peking University, Beijing 100871 (China)

2015-11-15

In stem cell differentiation, a pluripotent stem cell becomes progressively specialized and generates specific cell types through a series of epigenetic processes. How cells can precisely determine their fate in a fluctuating environment is a currently unsolved problem. In this paper, we suggest an abstract gene regulatory network to describe mathematically the differentiation phenomenon featuring stochasticity, divergent cell fates, and robustness. The network consists of three functional motifs: an upstream chaotic motif, a buffering motif of incoherent feed forward loop capable of generating a pulse, and a downstream motif which is bistable. The dynamic behavior is typically a transient chaos with fractal basin boundaries. The trajectories take transiently chaotic journeys before divergently settling down to the bistable states. The ratio of the probability that the high state is achieved to the probability that the low state is reached can maintain a constant in a population of cells with varied molecular fluctuations. The ratio can be turned up or down when proper parameters are adjusted. The model suggests a possible mechanism for the robustness against fluctuations that is prominently featured in pluripotent cell differentiations and developmental phenomena.

Gaussian fluctuation of the diffusion exponent of virus capsid in a living cell nucleus

Science.gov (United States)

Itto, Yuichi

2018-05-01

In their work [4], Bosse et al. experimentally showed that virus capsid exhibits not only normal diffusion but also anomalous diffusion in nucleus of a living cell. There, it was found that the distribution of fluctuations of the diffusion exponent characterizing them takes the Gaussian form, which is, quite remarkably, the same form for two different types of the virus. This suggests high robustness of such fluctuations. Here, the statistical property of local fluctuations of the diffusion exponent of the virus capsid in the nucleus is studied. A maximum-entropy-principle approach (originally proposed for a different virus in a different cell) is applied for obtaining the fluctuation distribution of the exponent. Largeness of the number of blocks identified with local areas of interchromatin corrals is also examined based on the experimental data. It is shown that the Gaussian distribution of the local fluctuations can be derived, in accordance with the above form. In addition, it is quantified how the fluctuation distribution on a long time scale is different from the Gaussian distribution.

Visualization of phosphatidic acid fluctuations in the plasma membrane of living cells.

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José P Ferraz-Nogueira

Full Text Available We developed genetically-encoded fluorescent sensors based on Förster Resonance Energy Transfer to monitor phosphatidic acid (PA fluctuations in the plasma membrane using Spo20 as PA-binding motif. Basal PA levels and phospholipase D activity varied in different cell types. In addition, stimuli that activate PA phosphatases, leading to lower PA levels, increased lamellipodia and filopodia formation. Lower PA levels were observed in the leading edge than in the trailing edge of migrating HeLa cells. In MSC80 and OLN93 cells, which are stable cell lines derived from Schwann cells and oligodendrocytes, respectively, a higher ratio of diacylglycerol to PA levels was demonstrated in the membrane processes involved in myelination, compared to the cell body. We propose that the PA sensors reported here are valuable tools to unveil the role of PA in a variety of intracellular signaling pathways.

Interpretation of Cellular Imaging and AQP4 Quantification Data in a Single Cell Simulator

Directory of Open Access Journals (Sweden)

Seon B. Kim

2014-03-01

Full Text Available The goal of the present study is to integrate different datasets in cell biology to derive additional quantitative information about a gene or protein of interest within a single cell using computational simulations. We propose a novel prototype cell simulator as a quantitative tool to integrate datasets including dynamic information about transcript and protein levels and the spatial information on protein trafficking in a complex cellular geometry. In order to represent the stochastic nature of transcription and gene expression, our cell simulator uses event-based stochastic simulations to capture transcription, translation, and dynamic trafficking events. In a reconstructed cellular geometry, a realistic microtubule structure is generated with a novel growth algorithm for simulating vesicular transport and trafficking events. In a case study, we investigate the change in quantitative expression levels of a water channel-aquaporin 4-in a single astrocyte cell, upon pharmacological treatment. Gillespie based discrete time approximation method results in stochastic fluctuation of mRNA and protein levels. In addition, we compute the dynamic trafficking of aquaporin-4 on microtubules in this reconstructed astrocyte. Computational predictions are validated with experimental data. The demonstrated cell simulator facilitates the analysis and prediction of protein expression dynamics.

Non-linear Membrane Properties in Entorhinal Cortical Stellate Cells Reduce Modulation of Input-Output Responses by Voltage Fluctuations

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Fernandez, Fernando R.; Malerba, Paola; White, John A.

2015-01-01

The presence of voltage fluctuations arising from synaptic activity is a critical component in models of gain control, neuronal output gating, and spike rate coding. The degree to which individual neuronal input-output functions are modulated by voltage fluctuations, however, is not well established across different cortical areas. Additionally, the extent and mechanisms of input-output modulation through fluctuations have been explored largely in simplified models of spike generation, and with limited consideration for the role of non-linear and voltage-dependent membrane properties. To address these issues, we studied fluctuation-based modulation of input-output responses in medial entorhinal cortical (MEC) stellate cells of rats, which express strong sub-threshold non-linear membrane properties. Using in vitro recordings, dynamic clamp and modeling, we show that the modulation of input-output responses by random voltage fluctuations in stellate cells is significantly limited. In stellate cells, a voltage-dependent increase in membrane resistance at sub-threshold voltages mediated by Na+ conductance activation limits the ability of fluctuations to elicit spikes. Similarly, in exponential leaky integrate-and-fire models using a shallow voltage-dependence for the exponential term that matches stellate cell membrane properties, a low degree of fluctuation-based modulation of input-output responses can be attained. These results demonstrate that fluctuation-based modulation of input-output responses is not a universal feature of neurons and can be significantly limited by subthreshold voltage-gated conductances. PMID:25909971

Stochastic model explains formation of cell arrays on H/O-diamond patterns

Czech Academy of Sciences Publication Activity Database

Ukraintsev, Egor; Brož, A.; Hubálek Kalbáčová, M.; Kromka, Alexander; Rezek, Bohuslav

2015-01-01

Roč. 10, č. 4 (2015), "041006-1"-"041006-9" ISSN 1934-8630 R&D Projects: GA ČR GA15-01687S Institutional support: RVO:68378271 Keywords : cell migration * biofilm array * diamond surface * stochastic simulations Subject RIV: BO - Biophysics Impact factor: 2.105, year: 2015

Magnetic Nano- and Micro- Particles in Living Cells: Kinetics and Fluctuations

Science.gov (United States)

Pease, C.; Chiang, N.; Pierce, C.; Muthusamy, N.; Sooryakumar, R.

2015-03-01

Functional nano and micro materials have recently been used not only as diagnostic tools for extracellular studies but also as intracellular drug delivery vehicles and as internal probes of the cell. To realize proper cellular applications, it is important not only to achieve efficient delivery of these materials to targeted cells, but also to control their movement and activity within the confines of the cell. In this presentation, superparamagnetic nano and micro particles are utilized as probes, with their responses to weak external magnetic fields enabling them to be maneuvered within a cell. In order to generate the required local magnetic fields needed for manipulation, the fields emanating from microscopic domain walls stabilized on patterned surface profiles are used in conjunction with weak external magnetic fields to create mobile traps that can localize and transport the internalized particle. Preliminary findings on creating the mobile traps suitable for applications to probe the interior of cells, and the responses, both Brownian fluctuations and directed motion, of particles ranging in size from 200 nm to 1 micron within HS-5 cells will be presented. Future applications to probe cellular behavior within the framework of emerging biomaterials will be discussed.

Fluctuating chemohydrodynamics and the stochastic motion of self-diffusiophoretic particles

Science.gov (United States)

Gaspard, Pierre; Kapral, Raymond

2018-04-01

The propulsion of active particles by self-diffusiophoresis is driven by asymmetric catalytic reactions on the particle surface that generate a mechanochemical coupling between the fluid velocity and the concentration fields of fuel and product in the surrounding solution. Because of thermal and molecular fluctuations in the solution, the motion of micrometric or submicrometric active particles is stochastic. Coupled Langevin equations describing the translation, rotation, and reaction of such active particles are deduced from fluctuating chemohydrodynamics and fluctuating boundary conditions at the interface between the fluid and the particle. These equations are consistent with microreversibility and the Onsager-Casimir reciprocal relations between affinities and currents and provide a thermodynamically consistent basis for the investigation of the dynamics of active particles propelled by diffusiophoretic mechanisms.

Stochastic approach and fluctuation theorem for charge transport in diodes

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Gu, Jiayin; Gaspard, Pierre

2018-05-01

A stochastic approach for charge transport in diodes is developed in consistency with the laws of electricity, thermodynamics, and microreversibility. In this approach, the electron and hole densities are ruled by diffusion-reaction stochastic partial differential equations and the electric field generated by the charges is determined with the Poisson equation. These equations are discretized in space for the numerical simulations of the mean density profiles, the mean electric potential, and the current-voltage characteristics. Moreover, the full counting statistics of the carrier current and the measured total current including the contribution of the displacement current are investigated. On the basis of local detailed balance, the fluctuation theorem is shown to hold for both currents.

Stochastic interpretation of the advection-diffusion equation and its relevance to bed load transport

Science.gov (United States)

Ancey, C.; Bohorquez, P.; Heyman, J.

2015-12-01

The advection-diffusion equation is one of the most widespread equations in physics. It arises quite often in the context of sediment transport, e.g., for describing time and space variations in the particle activity (the solid volume of particles in motion per unit streambed area). Phenomenological laws are usually sufficient to derive this equation and interpret its terms. Stochastic models can also be used to derive it, with the significant advantage that they provide information on the statistical properties of particle activity. These models are quite useful when sediment transport exhibits large fluctuations (typically at low transport rates), making the measurement of mean values difficult. Among these stochastic models, the most common approach consists of random walk models. For instance, they have been used to model the random displacement of tracers in rivers. Here we explore an alternative approach, which involves monitoring the evolution of the number of particles moving within an array of cells of finite length. Birth-death Markov processes are well suited to this objective. While the topic has been explored in detail for diffusion-reaction systems, the treatment of advection has received no attention. We therefore look into the possibility of deriving the advection-diffusion equation (with a source term) within the framework of birth-death Markov processes. We show that in the continuum limit (when the cell size becomes vanishingly small), we can derive an advection-diffusion equation for particle activity. Yet while this derivation is formally valid in the continuum limit, it runs into difficulty in practical applications involving cells or meshes of finite length. Indeed, within our stochastic framework, particle advection produces nonlocal effects, which are more or less significant depending on the cell size and particle velocity. Albeit nonlocal, these effects look like (local) diffusion and add to the intrinsic particle diffusion (dispersal due

Nonlinear dynamics of mushy layers induced by external stochastic fluctuations.

Science.gov (United States)

Alexandrov, Dmitri V; Bashkirtseva, Irina A; Ryashko, Lev B

2018-02-28

The time-dependent process of directional crystallization in the presence of a mushy layer is considered with allowance for arbitrary fluctuations in the atmospheric temperature and friction velocity. A nonlinear set of mushy layer equations and boundary conditions is solved analytically when the heat and mass fluxes at the boundary between the mushy layer and liquid phase are induced by turbulent motion in the liquid and, as a result, have the corresponding convective form. Namely, the 'solid phase-mushy layer' and 'mushy layer-liquid phase' phase transition boundaries as well as the solid fraction, temperature and concentration (salinity) distributions are found. If the atmospheric temperature and friction velocity are constant, the analytical solution takes a parametric form. In the more common case when they represent arbitrary functions of time, the analytical solution is given by means of the standard Cauchy problem. The deterministic and stochastic behaviour of the phase transition process is analysed on the basis of the obtained analytical solutions. In the case of stochastic fluctuations in the atmospheric temperature and friction velocity, the phase transition interfaces (mushy layer boundaries) move faster than in the deterministic case. A cumulative effect of these noise contributions is revealed as well. In other words, when the atmospheric temperature and friction velocity fluctuate simultaneously due to the influence of different external processes and phenomena, the phase transition boundaries move even faster. This article is part of the theme issue 'From atomistic interfaces to dendritic patterns'.This article is part of the theme issue 'From atomistic interfaces to dendritic patterns'. © 2018 The Author(s).

Kac limit and thermodynamic characterization of stochastic dynamics driven by Poisson-Kac fluctuations

Science.gov (United States)

Giona, Massimiliano; Brasiello, Antonio; Crescitelli, Silvestro

2017-07-01

We analyze the thermodynamic properties of stochastic differential equations driven by smooth Poisson-Kac fluctuations, and their convergence, in the Kac limit, towards Wiener-driven Langevin equations. Using a Markovian embedding of the stochastic work variable, it is proved that the Kac-limit convergence implies a Stratonovich formulation of the limit Langevin equations, in accordance with the Wong-Zakai theorem. Exact moment analysis applied to the case of a purely frictional system shows the occurrence of different regimes and crossover phenomena in the parameter space.

Frontiers in Fluctuation Spectroscopy: Measuring protein dynamics and protein spatio-temporal connectivity

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Digman, Michelle

Fluorescence fluctuation spectroscopy has evolved from single point detection of molecular diffusion to a family of microscopy imaging correlation tools (i.e. ICS, RICS, STICS, and kICS) useful in deriving spatial-temporal dynamics of proteins in living cells The advantage of the imaging techniques is the simultaneous measurement of all points in an image with a frame rate that is increasingly becoming faster with better sensitivity cameras and new microscopy modalities such as the sheet illumination technique. A new frontier in this area is now emerging towards a high level of mapping diffusion rates and protein dynamics in the 2 and 3 dimensions. In this talk, I will discuss the evolution of fluctuation analysis from the single point source to mapping diffusion in whole cells and the technology behind this technique. In particular, new methods of analysis exploit correlation of molecular fluctuations originating from measurement of fluctuation correlations at distant points (pair correlation analysis) and methods that exploit spatial averaging of fluctuations in small regions (iMSD). For example the pair correlation fluctuation (pCF) analyses done between adjacent pixels in all possible radial directions provide a window into anisotropic molecular diffusion. Similar to the connectivity atlas of neuronal connections from the MRI diffusion tensor imaging these new tools will be used to map the connectome of protein diffusion in living cells. For biological reaction-diffusion systems, live single cell spatial-temporal analysis of protein dynamics provides a mean to observe stochastic biochemical signaling in the context of the intracellular environment which may lead to better understanding of cancer cell invasion, stem cell differentiation and other fundamental biological processes. National Institutes of Health Grant P41-RRO3155.

Neutral dynamics and cell renewal of colonic crypts in homeostatic regime

Science.gov (United States)

Fendrik, A. J.; Romanelli, L.; Rotondo, E.

2018-05-01

The self renewal process in colonic crypts is the object of several studies. We present here a new compartment model with the following characteristics: (a) we distinguish different classes of cells: stem cells, six generations of transit amplifying cells and the differentiated cells; (b) in order to take into account the monoclonal character of crypts in homeostatic regimes we include symmetric divisions of the stem cells. We first consider the dynamic differential equations that describe the evolution of the mean values of the populations, but the small observed value of the total number of cells involved plus the huge dispersion of experimental data found in the literature leads us to study the stochastic discrete process. This analysis allows us to study fluctuations, the neutral drift that leads to monoclonality, and the effects of the fixation of mutant clones.

Single cell Hi-C reveals cell-to-cell variability in chromosome structure

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Schoenfelder, Stefan; Yaffe, Eitan; Dean, Wendy; Laue, Ernest D.; Tanay, Amos; Fraser, Peter

2013-01-01

Large-scale chromosome structure and spatial nuclear arrangement have been linked to control of gene expression and DNA replication and repair. Genomic techniques based on chromosome conformation capture assess contacts for millions of loci simultaneously, but do so by averaging chromosome conformations from millions of nuclei. Here we introduce single cell Hi-C, combined with genome-wide statistical analysis and structural modeling of single copy X chromosomes, to show that individual chromosomes maintain domain organisation at the megabase scale, but show variable cell-to-cell chromosome territory structures at larger scales. Despite this structural stochasticity, localisation of active gene domains to boundaries of territories is a hallmark of chromosomal conformation. Single cell Hi-C data bridge current gaps between genomics and microscopy studies of chromosomes, demonstrating how modular organisation underlies dynamic chromosome structure, and how this structure is probabilistically linked with genome activity patterns. PMID:24067610

Cell Cycle Related Differentiation of Bone Marrow Cells into Lung Cells

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Dooner, Mark; Aliotta, Jason M.; Pimental, Jeffrey; Dooner, Gerri J.; Abedi, Mehrdad; Colvin, Gerald; Liu, Qin; Weier, Heinz-Ulli; Dooner, Mark S.; Quesenberry, Peter J.

2007-12-31

Green-fluorescent protein (GFP) labeled marrow cells transplanted into lethally irradiated mice can be detected in the lungs of transplanted mice and have been shown to express lung specific proteins while lacking the expression of hematopoietic markers. We have studied marrow cells induced to transit cell cycle by exposure to IL-3, IL-6, IL-11 and steel factor at different times of culture corresponding to different phases of cell cycle. We have found that marrow cells at the G1/S interface have a 3-fold increase in cells which assume a lung phenotype and that this increase is no longer seen in late S/G2. These cells have been characterized as GFP{sup +} CD45{sup -} and GFP{sup +} cytokeratin{sup +}. Thus marrow cells with the capacity to convert into cells with a lung phenotype after transplantation show a reversible increase with cytokine induced cell cycle transit. Previous studies have shown the phenotype of bone marrow stem cells fluctuates reversibly as these cells traverse cell cycle, leading to a continuum model of stem cell regulation. The present studies indicate that marrow stem cell production of nonhematopoietic cells also fluctuates on a continuum.

Relation Between the Cell Volume and the Cell Cycle Dynamics in Mammalian cell

International Nuclear Information System (INIS)

Magno, A.C.G.; Oliveira, I.L.; Hauck, J.V.S.

2016-01-01

The main goal of this work is to add and analyze an equation that represents the volume in a dynamical model of the mammalian cell cycle proposed by Gérard and Goldbeter (2011) [1]. The cell division occurs when the cyclinB/Cdkl complex is totally degraded (Tyson and Novak, 2011)[2] and it reaches a minimum value. At this point, the cell is divided into two newborn daughter cells and each one will contain the half of the cytoplasmic content of the mother cell. The equations of our base model are only valid if the cell volume, where the reactions occur, is constant. Whether the cell volume is not constant, that is, the rate of change of its volume with respect to time is explicitly taken into account in the mathematical model, then the equations of the original model are no longer valid. Therefore, every equations were modified from the mass conservation principle for considering a volume that changes with time. Through this approach, the cell volume affects all model variables. Two different dynamic simulation methods were accomplished: deterministic and stochastic. In the stochastic simulation, the volume affects every model's parameters which have molar unit, whereas in the deterministic one, it is incorporated into the differential equations. In deterministic simulation, the biochemical species may be in concentration units, while in stochastic simulation such species must be converted to number of molecules which are directly proportional to the cell volume. In an effort to understand the influence of the new equation a stability analysis was performed. This elucidates how the growth factor impacts the stability of the model's limit cycles. In conclusion, a more precise model, in comparison to the base model, was created for the cell cycle as it now takes into consideration the cell volume variation (paper)

Stochastic modeling of cell growth with symmetric or asymmetric division

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Marantan, Andrew; Amir, Ariel

2016-07-01

We consider a class of biologically motivated stochastic processes in which a unicellular organism divides its resources (volume or damaged proteins, in particular) symmetrically or asymmetrically between its progeny. Assuming the final amount of the resource is controlled by a growth policy and subject to additive and multiplicative noise, we derive the recursive integral equation describing the evolution of the resource distribution over subsequent generations and use it to study the properties of stable resource distributions. We find conditions under which a unique stable resource distribution exists and calculate its moments for the class of affine linear growth policies. Moreover, we apply an asymptotic analysis to elucidate the conditions under which the stable distribution (when it exists) has a power-law tail. Finally, we use the results of this asymptotic analysis along with the moment equations to draw a stability phase diagram for the system that reveals the counterintuitive result that asymmetry serves to increase stability while at the same time widening the stable distribution. We also briefly discuss how cells can divide damaged proteins asymmetrically between their progeny as a form of damage control. In the appendixes, motivated by the asymmetric division of cell volume in Saccharomyces cerevisiae, we extend our results to the case wherein mother and daughter cells follow different growth policies.

Nonequilibrium Gyrokinetic Fluctuation Theory and Sampling Noise in Gyrokinetic Particle-in-cell Simulations

International Nuclear Information System (INIS)

Krommes, John A.

2007-01-01

The present state of the theory of fluctuations in gyrokinetic (GK) plasmas and especially its application to sampling noise in GK particle-in-cell (PIC) simulations is reviewed. Topics addressed include the Δf method, the fluctuation-dissipation theorem for both classical and GK many-body plasmas, the Klimontovich formalism, sampling noise in PIC simulations, statistical closure for partial differential equations, the theoretical foundations of spectral balance in the presence of arbitrary noise sources, and the derivation of Kadomtsev-type equations from the general formalism

Nonequilibrium Gyrokinetic Fluctuation Theory and Sampling Noise in Gyrokinetic Particle-in-cell Simulations

Energy Technology Data Exchange (ETDEWEB)

John A. Krommes

2007-10-09

The present state of the theory of fluctuations in gyrokinetic GK plasmas and especially its application to sampling noise in GK particle-in-cell PIC simulations is reviewed. Topics addressed include the Δf method, the fluctuation-dissipation theorem for both classical and GK many-body plasmas, the Klimontovich formalism, sampling noise in PIC simulations, statistical closure for partial differential equations, the theoretical foundations of spectral balance in the presence of arbitrary noise sources, and the derivation of Kadomtsev-type equations from the general formalism.

Stochastic Predator-Prey Dynamics of Transposons in the Human Genome

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Xue, Chi; Goldenfeld, Nigel

2016-11-01

Transposable elements, or transposons, are DNA sequences that can jump from site to site in the genome during the life cycle of a cell, usually encoding the very enzymes which perform their excision. However, some transposons are parasitic, relying on the enzymes produced by the regular transposons. In this case, we show that a stochastic model, which takes into account the small copy numbers of the active transposons in a cell, predicts noise-induced predator-prey oscillations with a characteristic time scale that is much longer than the cell replication time, indicating that the state of the predator-prey oscillator is stored in the genome and transmitted to successive generations. Our work demonstrates the important role of the number fluctuations in the expression of mobile genetic elements, and shows explicitly how ecological concepts can be applied to the dynamics and fluctuations of living genomes.

Estimating radiative feedbacks from stochastic fluctuations in surface temperature and energy imbalance

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Proistosescu, C.; Donohoe, A.; Armour, K.; Roe, G.; Stuecker, M. F.; Bitz, C. M.

2017-12-01

Joint observations of global surface temperature and energy imbalance provide for a unique opportunity to empirically constrain radiative feedbacks. However, the satellite record of Earth's radiative imbalance is relatively short and dominated by stochastic fluctuations. Estimates of radiative feedbacks obtained by regressing energy imbalance against surface temperature depend strongly on sampling choices and on assumptions about whether the stochastic fluctuations are primarily forced by atmospheric or oceanic variability (e.g. Murphy and Forster 2010, Dessler 2011, Spencer and Braswell 2011, Forster 2016). We develop a framework around a stochastic energy balance model that allows us to parse the different contributions of atmospheric and oceanic forcing based on their differing impacts on the covariance structure - or lagged regression - of temperature and radiative imbalance. We validate the framework in a hierarchy of general circulation models: the impact of atmospheric forcing is examined in unforced control simulations of fixed sea-surface temperature and slab ocean model versions; the impact of oceanic forcing is examined in coupled simulations with prescribed ENSO variability. With the impact of atmospheric and oceanic forcing constrained, we are able to predict the relationship between temperature and radiative imbalance in a fully coupled control simulation, finding that both forcing sources are needed to explain the structure of the lagged-regression. We further model the dependence of feedback estimates on sampling interval by considering the effects of a finite equilibration time for the atmosphere, and issues of smoothing and aliasing. Finally, we develop a method to fit the stochastic model to the short timeseries of temperature and radiative imbalance by performing a Bayesian inference based on a modified version of the spectral Whittle likelihood. We are thus able to place realistic joint uncertainty estimates on both stochastic forcing and

Modeling the response of a standard accretion disc to stochastic viscous fluctuations

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Ahmad, Naveel; Misra, Ranjeev; Iqbal, Naseer; Maqbool, Bari; Hamid, Mubashir

2018-01-01

The observed variability of X-ray binaries over a wide range of time-scales can be understood in the framework of a stochastic propagation model, where viscous fluctuations at different radii induce accretion rate variability that propagate inwards to the X-ray producing region. The scenario successfully explains the power spectra, the linear rms-flux relation as well as the time-lag between different energy photons. The predictions of this model have been obtained using approximate analytical solutions or empirically motivated models which take into account the effect of these propagating variability on the radiative process of complex accretion flows. Here, we study the variation of the accretion rate due to such viscous fluctuations using a hydro-dynamical code for the standard geometrically thin, gas pressure dominated α-disc with a zero torque boundary condition. Our results confirm earlier findings that the time-lag between a perturbation and the resultant inner accretion rate variation depends on the frequency (or time-period) of the perturbation. Here we have quantified that the time-lag tlag ∝f-0.54 , for time-periods less than the viscous time-scale of the perturbation radius and is nearly constant otherwise. This, coupled with radiative process would produce the observed frequency dependent time-lag between different energy bands. We also confirm that if there are random Gaussian fluctuations of the α-parameter at different radii, the resultant inner accretion rate has a power spectrum which is a power-law.

An Îto stochastic differential equations model for the dynamics of the MCF-7 breast cancer cell line treated by radiotherapy.

Science.gov (United States)

Oroji, Amin; Omar, Mohd; Yarahmadian, Shantia

2016-10-21

In this paper, a new mathematical model is proposed for studying the population dynamics of breast cancer cells treated by radiotherapy by using a system of stochastic differential equations. The novelty of the model is essentially in capturing the concept of the cell cycle in the modeling to be able to evaluate the tumor lifespan. According to the cell cycle, each cell belongs to one of three subpopulations G, S, or M, representing gap, synthesis and mitosis subpopulations. Cells in the M subpopulation are highly radio-sensitive, whereas cells in the S subpopulation are highly radio-resistant. Therefore, in the process of radiotherapy, cell death rates of different subpopulations are not equal. In addition, since flow cytometry is unable to detect apoptotic cells accurately, the small changes in cell death rate in each subpopulation during treatment are considered. Subsequently, the proposed model is calibrated using experimental data from previous experiments involving the MCF-7 breast cancer cell line. Consequently, the proposed model is able to predict tumor lifespan based on the number of initial carcinoma cells. The results show the effectiveness of the radiation under the condition of stability, which describes the decreasing trend of the tumor cells population. Copyright © 2016 Elsevier Ltd. All rights reserved.

Scheduling of Power System Cells Integrating Stochastic Power Generation

International Nuclear Information System (INIS)

Costa, L.M.

2008-12-01

Energy supply and climate change are nowadays two of the most outstanding problems which societies have to cope with under a context of increasing energy needs. Public awareness of these problems is driving political willingness to take actions for tackling them in a swift and efficient manner. Such actions mainly focus in increasing energy efficiency, in decreasing dependence on fossil fuels, and in reducing greenhouse gas emissions. In this context, power systems are undergoing important changes in the way they are planned and managed. On the one hand, vertically integrated structures are being replaced by market structures in which power systems are un-bundled. On the other, power systems that once relied on large power generation facilities are witnessing the end of these facilities' life-cycle and, consequently, their decommissioning. The role of distributed energy resources such as wind and solar power generators is becoming increasingly important in this context. However, the large-scale integration of such type of generation presents many challenges due, for instance, to the uncertainty associated to the variability of their production. Nevertheless, advanced forecasting tools may be combined with more controllable elements such as energy storage devices, gas turbines, and controllable loads to form systems that aim to reduce the impacts that may be caused by these uncertainties. This thesis addresses the management under market conditions of these types of systems that act like independent societies and which are herewith named power system cells. From the available literature, a unified view of power system scheduling problems is also proposed as a first step for managing sets of power system cells in a multi-cell management framework. Then, methodologies for performing the optimal day-ahead scheduling of single power system cells are proposed, discussed and evaluated under both a deterministic and a stochastic framework that directly integrates the

Visualizing cell state transition using Raman spectroscopy.

Directory of Open Access Journals (Sweden)

Taro Ichimura

Full Text Available System level understanding of the cell requires detailed description of the cell state, which is often characterized by the expression levels of proteins. However, understanding the cell state requires comprehensive information of the cell, which is usually obtained from a large number of cells and their disruption. In this study, we used Raman spectroscopy, which can report changes in the cell state without introducing any label, as a non-invasive method with single cell capability. Significant differences in Raman spectra were observed at the levels of both the cytosol and nucleus in different cell-lines from mouse, indicating that Raman spectra reflect differences in the cell state. Difference in cell state was observed before and after the induction of differentiation in neuroblastoma and adipocytes, showing that Raman spectra can detect subtle changes in the cell state. Cell state transitions during embryonic stem cell (ESC differentiation were visualized when Raman spectroscopy was coupled with principal component analysis (PCA, which showed gradual transition in the cell states during differentiation. Detailed analysis showed that the diversity between cells are large in undifferentiated ESC and in mesenchymal stem cells compared with terminally differentiated cells, implying that the cell state in stem cells stochastically fluctuates during the self-renewal process. The present study strongly indicates that Raman spectral morphology, in combination with PCA, can be used to establish cells' fingerprints, which can be useful for distinguishing and identifying different cellular states.

The Effect of Thermal Fluctuation on the Receptor-Mediated Adhesion of a Cell Membrane to an Elastic Substrate

Directory of Open Access Journals (Sweden)

Bahador Marzban

2017-04-01

Full Text Available Mechanics of the bilayer membrane play an important role in many biological and bioengineering problems such as cell–substrate and cell–nanomaterial interactions. In this work, we study the effect of thermal fluctuation and the substrate elasticity on the cell membrane–substrate adhesion. We model the adhesion of a fluctuating membrane on an elastic substrate as a two-step reaction comprised of the out-of-plane membrane fluctuation and the receptor–ligand binding. The equilibrium closed bond ratio as a function of substrate rigidity was computed by developing a coupled Fourier space Brownian dynamics and Monte Carlo method. The simulation results show that there exists a crossover value of the substrate rigidity at which the closed bond ratio is maximal.

Derivation of stochastic differential equations for scrape-off layer plasma fluctuations from experimentally measured statistics

Energy Technology Data Exchange (ETDEWEB)

Mekkaoui, Abdessamad [IEK-4 Forschungszentrum Juelich 52428 (Germany)

2013-07-01

A method to derive stochastic differential equations for intermittent plasma density dynamics in magnetic fusion edge plasma is presented. It uses a measured first four moments (mean, variance, Skewness and Kurtosis) and the correlation time of turbulence to write a Pearson equation for the probability distribution function of fluctuations. The Fokker-Planck equation is then used to derive a Langevin equation for the plasma density fluctuations. A theoretical expectations are used as a constraints to fix the nonlinearity structure of the stochastic differential equation. In particular when the quadratically nonlinear dynamics is assumed, then it is shown that the plasma density is driven by a multiplicative Wiener process and evolves on the turbulence correlation time scale, while the linear growth is quadratically damped by the fluctuation level. Strong criteria for statistical discrimination of experimental time series are proposed as an alternative to the Kurtosis-Skewness scaling. This scaling is broadly used in contemporary literature to characterize edge turbulence, but it is inappropriate because a large family of distributions could share this scaling. Strong criteria allow us to focus on the relevant candidate distribution and approach a nonlinear structure of edge turbulence model.

A deterministic model predicts the properties of stochastic calcium oscillations in airway smooth muscle cells.

Science.gov (United States)

Cao, Pengxing; Tan, Xiahui; Donovan, Graham; Sanderson, Michael J; Sneyd, James

2014-08-01

The inositol trisphosphate receptor ([Formula: see text]) is one of the most important cellular components responsible for oscillations in the cytoplasmic calcium concentration. Over the past decade, two major questions about the [Formula: see text] have arisen. Firstly, how best should the [Formula: see text] be modeled? In other words, what fundamental properties of the [Formula: see text] allow it to perform its function, and what are their quantitative properties? Secondly, although calcium oscillations are caused by the stochastic opening and closing of small numbers of [Formula: see text], is it possible for a deterministic model to be a reliable predictor of calcium behavior? Here, we answer these two questions, using airway smooth muscle cells (ASMC) as a specific example. Firstly, we show that periodic calcium waves in ASMC, as well as the statistics of calcium puffs in other cell types, can be quantitatively reproduced by a two-state model of the [Formula: see text], and thus the behavior of the [Formula: see text] is essentially determined by its modal structure. The structure within each mode is irrelevant for function. Secondly, we show that, although calcium waves in ASMC are generated by a stochastic mechanism, [Formula: see text] stochasticity is not essential for a qualitative prediction of how oscillation frequency depends on model parameters, and thus deterministic [Formula: see text] models demonstrate the same level of predictive capability as do stochastic models. We conclude that, firstly, calcium dynamics can be accurately modeled using simplified [Formula: see text] models, and, secondly, to obtain qualitative predictions of how oscillation frequency depends on parameters it is sufficient to use a deterministic model.

Separating intrinsic from extrinsic fluctuations in dynamic biological systems.

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Hilfinger, Andreas; Paulsson, Johan

2011-07-19

From molecules in cells to organisms in ecosystems, biological populations fluctuate due to the intrinsic randomness of individual events and the extrinsic influence of changing environments. The combined effect is often too complex for effective analysis, and many studies therefore make simplifying assumptions, for example ignoring either intrinsic or extrinsic effects to reduce the number of model assumptions. Here we mathematically demonstrate how two identical and independent reporters embedded in a shared fluctuating environment can be used to identify intrinsic and extrinsic noise terms, but also how these contributions are qualitatively and quantitatively different from what has been previously reported. Furthermore, we show for which classes of biological systems the noise contributions identified by dual-reporter methods correspond to the noise contributions predicted by correct stochastic models of either intrinsic or extrinsic mechanisms. We find that for broad classes of systems, the extrinsic noise from the dual-reporter method can be rigorously analyzed using models that ignore intrinsic stochasticity. In contrast, the intrinsic noise can be rigorously analyzed using models that ignore extrinsic stochasticity only under very special conditions that rarely hold in biology. Testing whether the conditions are met is rarely possible and the dual-reporter method may thus produce flawed conclusions about the properties of the system, particularly about the intrinsic noise. Our results contribute toward establishing a rigorous framework to analyze dynamically fluctuating biological systems.

Stochastic motion of a particle in a model fluctuating medium

International Nuclear Information System (INIS)

Moreau, M.; Gaveau, B.; Perera, A.; Frankowicz, M.

1993-01-01

We present several models of time fluctuating media with finite memory, consisting in one and two-dimensional lattices, the Modes of which fluctuate between two internal states according to a Poisson process. A particle moves on the lattice, the diffusion by the Modes depending on their internal state. Such models can be used for the microscopic theory of reaction constants in a dense phase, or for the study of diffusion or reactivity in a complex medium. In a number of cases, the transmission probability of the medium is computed exactly; it is shown that stochastic resonances can occur, an optimal transmission being obtained for a convenient choice of parameters. In more general situations, approximate solutions are given in the case of short and moderate memory of the obstacles. The diffusion in an infinite two-dimensional lattice is studied, and the memory is shown to affect the distribution of the particles rather than the diffusion law. (author). 25 refs, 5 figs

Output power fluctuations due to different weights of macro particles used in particle-in-cell simulations of Cerenkov devices

International Nuclear Information System (INIS)

Bao, Rong; Li, Yongdong; Liu, Chunliang; Wang, Hongguang

2016-01-01

The output power fluctuations caused by weights of macro particles used in particle-in-cell (PIC) simulations of a backward wave oscillator and a travelling wave tube are statistically analyzed. It is found that the velocities of electrons passed a specific slow-wave structure form a specific electron velocity distribution. The electron velocity distribution obtained in PIC simulation with a relative small weight of macro particles is considered as an initial distribution. By analyzing this initial distribution with a statistical method, the estimations of the output power fluctuations caused by different weights of macro particles are obtained. The statistical method is verified by comparing the estimations with the simulation results. The fluctuations become stronger with increasing weight of macro particles, which can also be determined reversely from estimations of the output power fluctuations. With the weights of macro particles optimized by the statistical method, the output power fluctuations in PIC simulations are relatively small and acceptable.

Modulating cell-to-cell variability and sensitivity to death ligands by co-drugging

International Nuclear Information System (INIS)

Flusberg, Deborah A; Sorger, Peter K

2013-01-01

TRAIL (tumor necrosis factor-related apoptosis-inducing ligand) holds promise as an anti-cancer therapeutic but efficiently induces apoptosis in only a subset of tumor cell lines. Moreover, even in clonal populations of responsive lines, only a fraction of cells dies in response to TRAIL and individual cells exhibit cell-to-cell variability in the timing of cell death. Fractional killing in these cell populations appears to arise not from genetic differences among cells but rather from differences in gene expression states, fluctuations in protein levels and the extent to which TRAIL-induced death or survival pathways become activated. In this study, we ask how cell-to-cell variability manifests in cell types with different sensitivities to TRAIL, as well as how it changes when cells are exposed to combinations of drugs. We show that individual cells that survive treatment with TRAIL can regenerate the sensitivity and death-time distribution of the parental population, demonstrating that fractional killing is a stable property of cell populations. We also show that cell-to-cell variability in the timing and probability of apoptosis in response to treatment can be tuned using combinations of drugs that together increase apoptotic sensitivity compared to treatment with one drug alone. In the case of TRAIL, modulation of cell-to-cell variability by co-drugging appears to involve a reduction in the threshold for mitochondrial outer membrane permeabilization. (paper)

Robust synchronization control scheme of a population of nonlinear stochastic synthetic genetic oscillators under intrinsic and extrinsic molecular noise via quorum sensing.

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Chen, Bor-Sen; Hsu, Chih-Yuan

2012-10-26

Collective rhythms of gene regulatory networks have been a subject of considerable interest for biologists and theoreticians, in particular the synchronization of dynamic cells mediated by intercellular communication. Synchronization of a population of synthetic genetic oscillators is an important design in practical applications, because such a population distributed over different host cells needs to exploit molecular phenomena simultaneously in order to emerge a biological phenomenon. However, this synchronization may be corrupted by intrinsic kinetic parameter fluctuations and extrinsic environmental molecular noise. Therefore, robust synchronization is an important design topic in nonlinear stochastic coupled synthetic genetic oscillators with intrinsic kinetic parameter fluctuations and extrinsic molecular noise. Initially, the condition for robust synchronization of synthetic genetic oscillators was derived based on Hamilton Jacobi inequality (HJI). We found that if the synchronization robustness can confer enough intrinsic robustness to tolerate intrinsic parameter fluctuation and extrinsic robustness to filter the environmental noise, then robust synchronization of coupled synthetic genetic oscillators is guaranteed. If the synchronization robustness of a population of nonlinear stochastic coupled synthetic genetic oscillators distributed over different host cells could not be maintained, then robust synchronization could be enhanced by external control input through quorum sensing molecules. In order to simplify the analysis and design of robust synchronization of nonlinear stochastic synthetic genetic oscillators, the fuzzy interpolation method was employed to interpolate several local linear stochastic coupled systems to approximate the nonlinear stochastic coupled system so that the HJI-based synchronization design problem could be replaced by a simple linear matrix inequality (LMI)-based design problem, which could be solved with the help of LMI

Stochastic models to study the impact of mixing on a fed-batch culture of Saccharomyces cerevisiae.

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Delvigne, F; Lejeune, A; Destain, J; Thonart, P

2006-01-01

The mechanisms of interaction between microorganisms and their environment in a stirred bioreactor can be modeled by a stochastic approach. The procedure comprises two submodels: a classical stochastic model for the microbial cell circulation and a Markov chain model for the concentration gradient calculus. The advantage lies in the fact that the core of each submodel, i.e., the transition matrix (which contains the probabilities to shift from a perfectly mixed compartment to another in the bioreactor representation), is identical for the two cases. That means that both the particle circulation and fluid mixing process can be analyzed by use of the same modeling basis. This assumption has been validated by performing inert tracer (NaCl) and stained yeast cells dispersion experiments that have shown good agreement with simulation results. The stochastic model has been used to define a characteristic concentration profile experienced by the microorganisms during a fermentation test performed in a scale-down reactor. The concentration profiles obtained in this way can explain the scale-down effect in the case of a Saccharomyces cerevisiae fed-batch process. The simulation results are analyzed in order to give some explanations about the effect of the substrate fluctuation dynamics on S. cerevisiae.

Universality in stochastic exponential growth.

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Iyer-Biswas, Srividya; Crooks, Gavin E; Scherer, Norbert F; Dinner, Aaron R

2014-07-11

Recent imaging data for single bacterial cells reveal that their mean sizes grow exponentially in time and that their size distributions collapse to a single curve when rescaled by their means. An analogous result holds for the division-time distributions. A model is needed to delineate the minimal requirements for these scaling behaviors. We formulate a microscopic theory of stochastic exponential growth as a Master Equation that accounts for these observations, in contrast to existing quantitative models of stochastic exponential growth (e.g., the Black-Scholes equation or geometric Brownian motion). Our model, the stochastic Hinshelwood cycle (SHC), is an autocatalytic reaction cycle in which each molecular species catalyzes the production of the next. By finding exact analytical solutions to the SHC and the corresponding first passage time problem, we uncover universal signatures of fluctuations in exponential growth and division. The model makes minimal assumptions, and we describe how more complex reaction networks can reduce to such a cycle. We thus expect similar scalings to be discovered in stochastic processes resulting in exponential growth that appear in diverse contexts such as cosmology, finance, technology, and population growth.

Fluctuations and synchrony of RNA synthesis in nucleoli.

Science.gov (United States)

Pliss, Artem; Kuzmin, Andrey N; Kachynski, Aliaksandr V; Baev, Alexander; Berezney, Ronald; Prasad, Paras N

2015-06-01

Ribosomal RNA (rRNA) sequences are synthesized at exceptionally high rates and, together with ribosomal proteins (r-proteins), are utilized as building blocks for the assembly of pre-ribosomal particles. Although it is widely acknowledged that tight regulation and coordination of rRNA and r-protein production are fundamentally important for the maintenance of cellular homeostasis, still little is known about the real-time kinetics of the ribosome component synthesis in individual cells. In this communication we introduce a label-free MicroRaman spectrometric approach for monitoring rRNA synthesis in live cultured cells. Remarkably high and rapid fluctuations of rRNA production rates were revealed by this technique. Strikingly, the changes in the rRNA output were synchronous for ribosomal genes located in separate nucleoli of the same cell. Our findings call for the development of new concepts to elucidate the coordination of ribosomal components production. In this regard, numerical modeling further demonstrated that the production of rRNA and r-proteins can be coordinated, regardless of the fluctuations in rRNA synthesis. Overall, our quantitative data reveal a spectacular interplay of inherently stochastic rates of RNA synthesis and the coordination of gene expression.

Phenotypic switching of populations of cells in a stochastic environment

Science.gov (United States)

Hufton, Peter G.; Lin, Yen Ting; Galla, Tobias

2018-02-01

In biology phenotypic switching is a common bet-hedging strategy in the face of uncertain environmental conditions. Existing mathematical models often focus on periodically changing environments to determine the optimal phenotypic response. We focus on the case in which the environment switches randomly between discrete states. Starting from an individual-based model we derive stochastic differential equations to describe the dynamics, and obtain analytical expressions for the mean instantaneous growth rates based on the theory of piecewise-deterministic Markov processes. We show that optimal phenotypic responses are non-trivial for slow and intermediate environmental processes, and systematically compare the cases of periodic and random environments. The best response to random switching is more likely to be heterogeneity than in the case of deterministic periodic environments, net growth rates tend to be higher under stochastic environmental dynamics. The combined system of environment and population of cells can be interpreted as host-pathogen interaction, in which the host tries to choose environmental switching so as to minimise growth of the pathogen, and in which the pathogen employs a phenotypic switching optimised to increase its growth rate. We discuss the existence of Nash-like mutual best-response scenarios for such host-pathogen games.

Stochasticity in Ca2+ increase in spines enables robust and sensitive information coding.

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Takuya Koumura

Full Text Available A dendritic spine is a very small structure (∼0.1 µm3 of a neuron that processes input timing information. Why are spines so small? Here, we provide functional reasons; the size of spines is optimal for information coding. Spines code input timing information by the probability of Ca2+ increases, which makes robust and sensitive information coding possible. We created a stochastic simulation model of input timing-dependent Ca2+ increases in a cerebellar Purkinje cell's spine. Spines used probability coding of Ca2+ increases rather than amplitude coding for input timing detection via stochastic facilitation by utilizing the small number of molecules in a spine volume, where information per volume appeared optimal. Probability coding of Ca2+ increases in a spine volume was more robust against input fluctuation and more sensitive to input numbers than amplitude coding of Ca2+ increases in a cell volume. Thus, stochasticity is a strategy by which neurons robustly and sensitively code information.

Exact protein distributions for stochastic models of gene expression using partitioning of Poisson processes.

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Pendar, Hodjat; Platini, Thierry; Kulkarni, Rahul V

2013-04-01

Stochasticity in gene expression gives rise to fluctuations in protein levels across a population of genetically identical cells. Such fluctuations can lead to phenotypic variation in clonal populations; hence, there is considerable interest in quantifying noise in gene expression using stochastic models. However, obtaining exact analytical results for protein distributions has been an intractable task for all but the simplest models. Here, we invoke the partitioning property of Poisson processes to develop a mapping that significantly simplifies the analysis of stochastic models of gene expression. The mapping leads to exact protein distributions using results for mRNA distributions in models with promoter-based regulation. Using this approach, we derive exact analytical results for steady-state and time-dependent distributions for the basic two-stage model of gene expression. Furthermore, we show how the mapping leads to exact protein distributions for extensions of the basic model that include the effects of posttranscriptional and posttranslational regulation. The approach developed in this work is widely applicable and can contribute to a quantitative understanding of stochasticity in gene expression and its regulation.

Exact protein distributions for stochastic models of gene expression using partitioning of Poisson processes

Science.gov (United States)

Pendar, Hodjat; Platini, Thierry; Kulkarni, Rahul V.

2013-04-01

Stochasticity in gene expression gives rise to fluctuations in protein levels across a population of genetically identical cells. Such fluctuations can lead to phenotypic variation in clonal populations; hence, there is considerable interest in quantifying noise in gene expression using stochastic models. However, obtaining exact analytical results for protein distributions has been an intractable task for all but the simplest models. Here, we invoke the partitioning property of Poisson processes to develop a mapping that significantly simplifies the analysis of stochastic models of gene expression. The mapping leads to exact protein distributions using results for mRNA distributions in models with promoter-based regulation. Using this approach, we derive exact analytical results for steady-state and time-dependent distributions for the basic two-stage model of gene expression. Furthermore, we show how the mapping leads to exact protein distributions for extensions of the basic model that include the effects of posttranscriptional and posttranslational regulation. The approach developed in this work is widely applicable and can contribute to a quantitative understanding of stochasticity in gene expression and its regulation.

Changes in atomic populations due to edge plasma fluctuations

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Hammami, R., E-mail: ramzi.hammami@univ-provence.fr [PIIM, Aix-Marseille Université and CNRS, centre Saint Jérôme, Marseille 13397 (France); Capes, H. [PIIM, Aix-Marseille Université and CNRS, centre Saint Jérôme, Marseille 13397 (France); Catoire, F. [CELIA, Université de Bordeaux 1 and CNRS, Domaine du Haut Carré, Talence 33405 (France); Godbert-Mouret, L.; Koubiti, M.; Marandet, Y.; Mekkaoui, A.; Rosato, J.; Stamm, R. [PIIM, Aix-Marseille Université and CNRS, centre Saint Jérôme, Marseille 13397 (France)

2013-07-15

The population balance of atoms or ions in an edge plasma is calculated in the presence of fluctuating density or temperature. We have used a stochastic model taking advantage of the knowledge of the plasma parameter statistical properties, and assuming a stepwise constant stochastic process for the fluctuating variable. The model is applied to simplified atomic systems such as three level hydrogen atoms or the ionization balance of carbon affected by electronic temperature or density fluctuations obeying a gamma PDF, and an exponential waiting time distribution.

Direct Correlation between Motile Behavior and Protein Abundance in Single Cells.

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Yann S Dufour

2016-09-01

Full Text Available Understanding how stochastic molecular fluctuations affect cell behavior requires the quantification of both behavior and protein numbers in the same cells. Here, we combine automated microscopy with in situ hydrogel polymerization to measure single-cell protein expression after tracking swimming behavior. We characterized the distribution of non-genetic phenotypic diversity in Escherichia coli motility, which affects single-cell exploration. By expressing fluorescently tagged chemotaxis proteins (CheR and CheB at different levels, we quantitatively mapped motile phenotype (tumble bias to protein numbers using thousands of single-cell measurements. Our results disagreed with established models until we incorporated the role of CheB in receptor deamidation and the slow fluctuations in receptor methylation. Beyond refining models, our central finding is that changes in numbers of CheR and CheB affect the population mean tumble bias and its variance independently. Therefore, it is possible to adjust the degree of phenotypic diversity of a population by adjusting the global level of expression of CheR and CheB while keeping their ratio constant, which, as shown in previous studies, confers functional robustness to the system. Since genetic control of protein expression is heritable, our results suggest that non-genetic diversity in motile behavior is selectable, supporting earlier hypotheses that such diversity confers a selective advantage.

Origins of Cell-to-Cell Bioprocessing Diversity and Implications of the Extracellular Environment Revealed at the Single-Cell Level.

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Vasdekis, A E; Silverman, A M; Stephanopoulos, G

2015-12-14

Bioprocess limitations imposed by microbial cell-to-cell phenotypic diversity remain poorly understood. To address this, we investigated the origins of such culture diversity during lipid production and assessed the impact of the fermentation microenvironment. We measured the single-cell lipid production dynamics in a time-invariant microfluidic environment and discovered that production is not monotonic, but rather sporadic with time. To characterize this, we introduce bioprocessing noise and identify its epigenetic origins. We linked such intracellular production fluctuations with cell-to-cell productivity diversity in culture. This unmasked the phenotypic diversity amplification by the culture microenvironment, a critical parameter in strain engineering as well as metabolic disease treatment.

Effect of Stochastic Charge Fluctuations on Dust Dynamics

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Matthews, Lorin; Shotorban, Babak; Hyde, Truell

2017-10-01

The charging of particles in a plasma environment occurs through the collection of electrons and ions on the particle surface. Depending on the particle size and the plasma density, the standard deviation of the number of collected elementary charges, which fluctuates due to the randomness in times of collisions with electrons or ions, may be a significant fraction of the equilibrium charge. We use a discrete stochastic charging model to simulate the variations in charge across the dust surface as well as in time. The resultant asymmetric particle potentials, even for spherical grains, has a significant impact on the particle coagulation rate as well as the structure of the resulting aggregates. We compare the effects on particle collisions and growth in typical laboratory and astrophysical plasma environments. This work was supported by the National Science Foundation under Grant PHY-1414523.

An intramolecular charge transfer process based fluorescent probe for monitoring subtle pH fluctuation in living cells.

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Sun, Mingtai; Du, Libo; Yu, Huan; Zhang, Kui; Liu, Yang; Wang, Suhua

2017-01-01

It is crucial to monitor intracellular pH values and their fluctuation since the organelles of cells have different pH distribution. Herein we construct a new small molecule fluorescent probe HBT-O for monitoring the subtle pH values within the scope of neutral to acid in living cells. The probe exhibited good water solubility, a marked turquoise to olivine emission color change in response to pH, and tremendous fluorescence hypochromatic shift of ∼50nm (1718cm -1 ) as well as the increased fluorescence intensity when the pH value changed from neutral to acid. Thus, the probe HBT-O can distinguish the subtle changes in the range of normal pH values from neutral to acid with significant fluorescence changes. These properties can be attributed to the intramolecular charge transfer (ICT) process of the probe upon protonation in buffer solutions at varied pH values. Moreover, the probe was reversible and nearly non-toxic for living cells. Then the probe was successfully used to detect pH fluctuation in living cells by exhibiting different fluorescence colors and intensity. These findings demonstrate that the probe will find useful applications in biology and biomedical research. Copyright © 2016 Elsevier B.V. All rights reserved.

Stochastic response and bifurcation of periodically driven nonlinear oscillators by the generalized cell mapping method

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Han, Qun; Xu, Wei; Sun, Jian-Qiao

2016-09-01

The stochastic response of nonlinear oscillators under periodic and Gaussian white noise excitations is studied with the generalized cell mapping based on short-time Gaussian approximation (GCM/STGA) method. The solutions of the transition probability density functions over a small fraction of the period are constructed by the STGA scheme in order to construct the GCM over one complete period. Both the transient and steady-state probability density functions (PDFs) of a smooth and discontinuous (SD) oscillator are computed to illustrate the application of the method. The accuracy of the results is verified by direct Monte Carlo simulations. The transient responses show the evolution of the PDFs from being Gaussian to non-Gaussian. The effect of a chaotic saddle on the stochastic response is also studied. The stochastic P-bifurcation in terms of the steady-state PDFs occurs with the decrease of the smoothness parameter, which corresponds to the deterministic pitchfork bifurcation.

Pervasive and stochastic changes in the TCR repertoire of regulatory T-cell-deficient mice.

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Zheng, Lingjie; Sharma, Rahul; Kung, John T; Deshmukh, Umesh S; Jarjour, Wael N; Fu, Shu Man; Ju, Shyr-Te

2008-04-01

We hypothesize that regulatory T-cell (Treg)-deficient strains have an altered TCR repertoire in part due to the expansion of autoimmune repertoire by self-antigen. We compared the Vbeta family expression profile between B6 and Treg-lacking B6.Cg-Foxp3(sf)(/Y) (B6.sf) mice using fluorescent anti-Vbeta mAbs and observed no changes. However, while the spectratypes of 20 Vbeta families among B6 mice were highly similar, the Vbeta family spectratypes of B6.sf mice were remarkably different from B6 mice and from each other. Significant spectratype changes in many Vbeta families were also observed in Treg-deficient IL-2 knockout (KO) and IL-2Ralpha KO mice. Such changes were not observed with anti-CD3 mAb-treated B6 mice or B6 CD4+CD25- T cells. TCR transgenic (OT-II.sf) mice displayed dramatic reduction of clonotypic TCR with concomitant increase in T cells bearing non-transgenic Vbeta and Valpha families, including T cells with dual receptors expressing reduced levels of transgenic Valpha and endogenous Valpha. Collectively, the data demonstrate that Treg deficiency allows polyclonal expansion of T cells in a stochastic manner, resulting in widespread changes in the TCR repertoire.

Stochastic and information-thermodynamic structures of population dynamics in a fluctuating environment

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Kobayashi, Tetsuya J.; Sughiyama, Yuki

2017-07-01

Adaptation in a fluctuating environment is a process of fueling environmental information to gain fitness. Living systems have gradually developed strategies for adaptation from random and passive diversification of the phenotype to more proactive decision making, in which environmental information is sensed and exploited more actively and effectively. Understanding the fundamental relation between fitness and information is therefore crucial to clarify the limits and universal properties of adaptation. In this work, we elucidate the underlying stochastic and information-thermodynamic structure in this process, by deriving causal fluctuation relations (FRs) of fitness and information. Combined with a duality between phenotypic and environmental dynamics, the FRs reveal the limit of fitness gain, the relation of time reversibility with the achievability of the limit, and the possibility and condition for gaining excess fitness due to environmental fluctuation. The loss of fitness due to causal constraints and the limited capacity of real organisms is shown to be the difference between time-forward and time-backward path probabilities of phenotypic and environmental dynamics. Furthermore, the FRs generalize the concept of the evolutionary stable state (ESS) for fluctuating environment by giving the probability that the optimal strategy on average can be invaded by a suboptimal one owing to rare environmental fluctuation. These results clarify the information-thermodynamic structures in adaptation and evolution.

Stochastic pumping of non-equilibrium steady-states: how molecules adapt to a fluctuating environment.

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Astumian, R D

2018-01-11

In the absence of input energy, a chemical reaction in a closed system ineluctably relaxes toward an equilibrium state governed by a Boltzmann distribution. The addition of a catalyst to the system provides a way for more rapid equilibration toward this distribution, but the catalyst can never, in and of itself, drive the system away from equilibrium. In the presence of external fluctuations, however, a macromolecular catalyst (e.g., an enzyme) can absorb energy and drive the formation of a steady state between reactant and product that is not determined solely by their relative energies. Due to the ubiquity of non-equilibrium steady states in living systems, the development of a theory for the effects of external fluctuations on chemical systems has been a longstanding focus of non-equilibrium thermodynamics. The theory of stochastic pumping has provided insight into how a non-equilibrium steady-state can be formed and maintained in the presence of dissipation and kinetic asymmetry. This effort has been greatly enhanced by a confluence of experimental and theoretical work on synthetic molecular machines designed explicitly to harness external energy to drive non-equilibrium transport and self-assembly.

Observation of fluctuations responsible for stochastic ion heating in a turbulent plasma

International Nuclear Information System (INIS)

Amagishi, Y.; Iguchi, H.; Ito, Y.; Kawabe, T.

1977-10-01

Experiments are described in which the correlation time and fluctuation level of ion acoustic waves are measured under the condition of turbulent heating using twin capacitive probes. At the anomalously resistive time, the correlation time becomes shorter, typically several periods of ion waves, and the energy density of the waves is of the order of 10 -2 n sub(e)T sub(e). The ion heating rate previously reported is well explained by these results to be due to stochastic mechanism. (auth.)

Cell membrane softening in human breast and cervical cancer cells

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Händel, Chris; Schmidt, B. U. Sebastian; Schiller, Jürgen; Dietrich, Undine; Möhn, Till; Kießling, Tobias R.; Pawlizak, Steve; Fritsch, Anatol W.; Horn, Lars-Christian; Briest, Susanne; Höckel, Michael; Zink, Mareike; Käs, Josef A.

2015-08-01

Biomechanical properties are key to many cellular functions such as cell division and cell motility and thus are crucial in the development and understanding of several diseases, for instance cancer. The mechanics of the cellular cytoskeleton have been extensively characterized in cells and artificial systems. The rigidity of the plasma membrane, with the exception of red blood cells, is unknown and membrane rigidity measurements only exist for vesicles composed of a few synthetic lipids. In this study, thermal fluctuations of giant plasma membrane vesicles (GPMVs) directly derived from the plasma membranes of primary breast and cervical cells, as well as breast cell lines, are analyzed. Cell blebs or GPMVs were studied via thermal membrane fluctuations and mass spectrometry. It will be shown that cancer cell membranes are significantly softer than their non-malignant counterparts. This can be attributed to a loss of fluid raft forming lipids in malignant cells. These results indicate that the reduction of membrane rigidity promotes aggressive blebbing motion in invasive cancer cells.

Fluctuating dynamics of nematic liquid crystals using the stochastic method of lines

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Bhattacharjee, A. K.; Menon, Gautam I.; Adhikari, R.

2010-07-01

We construct Langevin equations describing the fluctuations of the tensor order parameter Qαβ in nematic liquid crystals by adding noise terms to time-dependent variational equations that follow from the Ginzburg-Landau-de Gennes free energy. The noise is required to preserve the symmetry and tracelessness of the tensor order parameter and must satisfy a fluctuation-dissipation relation at thermal equilibrium. We construct a noise with these properties in a basis of symmetric traceless matrices and show that the Langevin equations can be solved numerically in this basis using a stochastic version of the method of lines. The numerical method is validated by comparing equilibrium probability distributions, structure factors, and dynamic correlations obtained from these numerical solutions with analytic predictions. We demonstrate excellent agreement between numerics and theory. This methodology can be applied to the study of phenomena where fluctuations in both the magnitude and direction of nematic order are important, as for instance, in the nematic swarms which produce enhanced opalescence near the isotropic-nematic transition or the problem of nucleation of the nematic from the isotropic phase.

Robustness of MEK-ERK Dynamics and Origins of Cell-to-Cell Variability in MAPK Signaling

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Sarah Filippi

2016-06-01

Full Text Available Cellular signaling processes can exhibit pronounced cell-to-cell variability in genetically identical cells. This affects how individual cells respond differentially to the same environmental stimulus. However, the origins of cell-to-cell variability in cellular signaling systems remain poorly understood. Here, we measure the dynamics of phosphorylated MEK and ERK across cell populations and quantify the levels of population heterogeneity over time using high-throughput image cytometry. We use a statistical modeling framework to show that extrinsic noise, particularly that from upstream MEK, is the dominant factor causing cell-to-cell variability in ERK phosphorylation, rather than stochasticity in the phosphorylation/dephosphorylation of ERK. We furthermore show that without extrinsic noise in the core module, variable (including noisy signals would be faithfully reproduced downstream, but the within-module extrinsic variability distorts these signals and leads to a drastic reduction in the mutual information between incoming signal and ERK activity.

Decoherence in yeast cell populations and its implications for genome-wide expression noise.

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Briones, M R S; Bosco, F

2009-01-20

Gene expression "noise" is commonly defined as the stochastic variation of gene expression levels in different cells of the same population under identical growth conditions. Here, we tested whether this "noise" is amplified with time, as a consequence of decoherence in global gene expression profiles (genome-wide microarrays) of synchronized cells. The stochastic component of transcription causes fluctuations that tend to be amplified as time progresses, leading to a decay of correlations of expression profiles, in perfect analogy with elementary relaxation processes. Measuring decoherence, defined here as a decay in the auto-correlation function of yeast genome-wide expression profiles, we found a slowdown in the decay of correlations, opposite to what would be expected if, as in mixing systems, correlations decay exponentially as the equilibrium state is reached. Our results indicate that the populational variation in gene expression (noise) is a consequence of temporal decoherence, in which the slow decay of correlations is a signature of strong interdependence of the transcription dynamics of different genes.

Viability in holder of irradiated cells: distinguish between repair and cell multiplication

International Nuclear Information System (INIS)

Araujo, A.C. de.

1980-01-01

In experiments in which liquid holding recovery (LHR) was measured, the majority of cellular population is formed by non-viable cells and cell multiplication may be important for LHR expression. In order to distinguish between recuperation of viability (true LHR) and cell multiplication, it was necessary to employ improved plating techniques and a fluctuation test based on Poisson distribution. Our results are an indication that this fluctuation test, used together with the traditional method, is a good tool to distinguish repair from cell multiplication. (author)

Computing molecular fluctuations in biochemical reaction systems based on a mechanistic, statistical theory of irreversible processes.

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Kulasiri, Don

2011-01-01

We discuss the quantification of molecular fluctuations in the biochemical reaction systems within the context of intracellular processes associated with gene expression. We take the molecular reactions pertaining to circadian rhythms to develop models of molecular fluctuations in this chapter. There are a significant number of studies on stochastic fluctuations in intracellular genetic regulatory networks based on single cell-level experiments. In order to understand the fluctuations associated with the gene expression in circadian rhythm networks, it is important to model the interactions of transcriptional factors with the E-boxes in the promoter regions of some of the genes. The pertinent aspects of a near-equilibrium theory that would integrate the thermodynamical and particle dynamic characteristics of intracellular molecular fluctuations would be discussed, and the theory is extended by using the theory of stochastic differential equations. We then model the fluctuations associated with the promoter regions using general mathematical settings. We implemented ubiquitous Gillespie's algorithms, which are used to simulate stochasticity in biochemical networks, for each of the motifs. Both the theory and the Gillespie's algorithms gave the same results in terms of the time evolution of means and variances of molecular numbers. As biochemical reactions occur far away from equilibrium-hence the use of the Gillespie algorithm-these results suggest that the near-equilibrium theory should be a good approximation for some of the biochemical reactions. © 2011 Elsevier Inc. All rights reserved.

Simulated bacterial infection disrupts the circadian fluctuation of immune cells in wrinkle-lipped bats (Chaerephon plicatus

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Philipp Weise

2017-08-01

Full Text Available Background Leukocyte concentrations follow a circadian pattern in mammals, with elevated values at times of potential contact with pathogens and parasites. We hypothesized that this pattern is disturbed after an immune challenge. Methods In Thailand, we captured wrinkle-lipped bats (Chaerephon plicatus, when they returned to their colony at dawn. We challenged half of the animals (experimental group with bacterial lipopolysaccharides and treated the others only with the carrier liquid (control group. We then compared body mass changes and differences in circulating immune cell counts at 8 h post-treatment. Results In experimental animals, we observed an increase in total leukocyte and neutrophil numbers of 17% and 95%, respectively. In control animals, concentrations of leukocytes decreased by 44% and those of neutrophils remained constant. Experimental treatment had no effect on lymphocytes, yet changes in eosinophil numbers were explained by sex. Eosinophils decreased by 66% in females and by 62% in males. Basophils and monocytes were rarest among all observed cell types and analysis was either impossible because of low numbers or yielded no significant effects, respectively. Discussion Our findings show that a simulated bacterial infection triggered a neutrophil-associated immune response in wrinkle-lipped bats, indicating a disruption of the diurnal fluctuation of immune cells. Our study suggests that bats exhibit circadian rhythms in immune cell counts. The magnitude of these fluctuations may vary across species according to specific-specific infection risks associated with colony sizes or specific roosting habits.

Deterministic direct reprogramming of somatic cells to pluripotency.

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Rais, Yoach; Zviran, Asaf; Geula, Shay; Gafni, Ohad; Chomsky, Elad; Viukov, Sergey; Mansour, Abed AlFatah; Caspi, Inbal; Krupalnik, Vladislav; Zerbib, Mirie; Maza, Itay; Mor, Nofar; Baran, Dror; Weinberger, Leehee; Jaitin, Diego A; Lara-Astiaso, David; Blecher-Gonen, Ronnie; Shipony, Zohar; Mukamel, Zohar; Hagai, Tzachi; Gilad, Shlomit; Amann-Zalcenstein, Daniela; Tanay, Amos; Amit, Ido; Novershtern, Noa; Hanna, Jacob H

2013-10-03

Somatic cells can be inefficiently and stochastically reprogrammed into induced pluripotent stem (iPS) cells by exogenous expression of Oct4 (also called Pou5f1), Sox2, Klf4 and Myc (hereafter referred to as OSKM). The nature of the predominant rate-limiting barrier(s) preventing the majority of cells to successfully and synchronously reprogram remains to be defined. Here we show that depleting Mbd3, a core member of the Mbd3/NuRD (nucleosome remodelling and deacetylation) repressor complex, together with OSKM transduction and reprogramming in naive pluripotency promoting conditions, result in deterministic and synchronized iPS cell reprogramming (near 100% efficiency within seven days from mouse and human cells). Our findings uncover a dichotomous molecular function for the reprogramming factors, serving to reactivate endogenous pluripotency networks while simultaneously directly recruiting the Mbd3/NuRD repressor complex that potently restrains the reactivation of OSKM downstream target genes. Subsequently, the latter interactions, which are largely depleted during early pre-implantation development in vivo, lead to a stochastic and protracted reprogramming trajectory towards pluripotency in vitro. The deterministic reprogramming approach devised here offers a novel platform for the dissection of molecular dynamics leading to establishing pluripotency at unprecedented flexibility and resolution.

Making sense of snapshot data: ergodic principle for clonal cell populations.

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Thomas, Philipp

2017-11-01

Population growth is often ignored when quantifying gene expression levels across clonal cell populations. We develop a framework for obtaining the molecule number distributions in an exponentially growing cell population taking into account its age structure. In the presence of generation time variability, the average acquired across a population snapshot does not obey the average of a dividing cell over time, apparently contradicting ergodicity between single cells and the population. Instead, we show that the variation observed across snapshots with known cell age is captured by cell histories, a single-cell measure obtained from tracking an arbitrary cell of the population back to the ancestor from which it originated. The correspondence between cells of known age in a population with their histories represents an ergodic principle that provides a new interpretation of population snapshot data. We illustrate the principle using analytical solutions of stochastic gene expression models in cell populations with arbitrary generation time distributions. We further elucidate that the principle breaks down for biochemical reactions that are under selection, such as the expression of genes conveying antibiotic resistance, which gives rise to an experimental criterion with which to probe selection on gene expression fluctuations. © 2017 The Author(s).

Simulations of DSB Yields and Radiation-induced Chromosomal Aberrations in Human Cells Based on the Stochastic Track Structure Induced by HZE Particles

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Ponomarev, Artem; Plante, Ianik; George, Kerry; Wu, Honglu

2014-01-01

The formation of double-strand breaks (DSBs) and chromosomal aberrations (CAs) is of great importance in radiation research and, specifically, in space applications. We are presenting a new particle track and DNA damage model, in which the particle stochastic track structure is combined with the random walk (RW) structure of chromosomes in a cell nucleus. The motivation for this effort stems from the fact that the model with the RW chromosomes, NASARTI (NASA radiation track image) previously relied on amorphous track structure, while the stochastic track structure model RITRACKS (Relativistic Ion Tracks) was focused on more microscopic targets than the entire genome. We have combined chromosomes simulated by RWs with stochastic track structure, which uses nanoscopic dose calculations performed with the Monte-Carlo simulation by RITRACKS in a voxelized space. The new simulations produce the number of DSBs as function of dose and particle fluence for high-energy particles, including iron, carbon and protons, using voxels of 20 nm dimension. The combined model also calculates yields of radiation-induced CAs and unrejoined chromosome breaks in normal and repair deficient cells. The joined computational model is calibrated using the relative frequencies and distributions of chromosomal aberrations reported in the literature. The model considers fractionated deposition of energy to approximate dose rates of the space flight environment. The joined model also predicts of the yields and sizes of translocations, dicentrics, rings, and more complex-type aberrations formed in the G0/G1 cell cycle phase during the first cell division after irradiation. We found that the main advantage of the joined model is our ability to simulate small doses: 0.05-0.5 Gy. At such low doses, the stochastic track structure proved to be indispensable, as the action of individual delta-rays becomes more important.

NIR Ratiometric Luminescence Detection of pH Fluctuation in Living Cells with Hemicyanine Derivative-Assembled Upconversion Nanophosphors.

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Li, Haixia; Dong, Hao; Yu, Mingming; Liu, Chunxia; Li, Zhanxian; Wei, Liuhe; Sun, Ling-Dong; Zhang, Hongyan

2017-09-05

It is crucial for cell physiology to keep the homeostasis of pH, and it is highly demanded yet challenging to develop luminescence resonance energy transfer (LRET)-based near-infrared (NIR) ratiometric luminescent sensor for the detection of pH fluctuation with NIR excitation. As promising energy donors for LRET, upconversion nanoparticles (UCNPs) have been widely used to fabricate nanosensors, but the relatively low LRET efficiency limits their application in bioassay. To improve the LRET efficiency, core/shell/shell structured β-NaGdF 4 @NaYF 4 :Yb,Tm@NaYF 4 UCNPs were prepared and decorated with hemicyanine dyes as an LRET-based NIR ratiometric luminescent pH fluctuation-nanosensor for the first time. The as-developed nanosensor not only exhibits good antidisturbance ability, but it also can reversibly sense pH and linearly sense pH in a range of 6.0-9.0 and 6.8-9.0 from absorption and upconversion emission spectra, respectively. In addition, the nanosensor displays low dark toxicity under physiological temperature, indicating good biocompatibility. Furthermore, live cell imaging results revealed that the sensor can selectively monitor pH fluctuation via ratiometric upconversion luminescence behavior.

Multi-cellular logistics of collective cell migration.

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Masataka Yamao

Full Text Available During development, the formation of biological networks (such as organs and neuronal networks is controlled by multicellular transportation phenomena based on cell migration. In multi-cellular systems, cellular locomotion is restricted by physical interactions with other cells in a crowded space, similar to passengers pushing others out of their way on a packed train. The motion of individual cells is intrinsically stochastic and may be viewed as a type of random walk. However, this walk takes place in a noisy environment because the cell interacts with its randomly moving neighbors. Despite this randomness and complexity, development is highly orchestrated and precisely regulated, following genetic (and even epigenetic blueprints. Although individual cell migration has long been studied, the manner in which stochasticity affects multi-cellular transportation within the precisely controlled process of development remains largely unknown. To explore the general principles underlying multicellular migration, we focus on the migration of neural crest cells, which migrate collectively and form streams. We introduce a mechanical model of multi-cellular migration. Simulations based on the model show that the migration mode depends on the relative strengths of the noise from migratory and non-migratory cells. Strong noise from migratory cells and weak noise from surrounding cells causes "collective migration," whereas strong noise from non-migratory cells causes "dispersive migration." Moreover, our theoretical analyses reveal that migratory cells attract each other over long distances, even without direct mechanical contacts. This effective interaction depends on the stochasticity of the migratory and non-migratory cells. On the basis of these findings, we propose that stochastic behavior at the single-cell level works effectively and precisely to achieve collective migration in multi-cellular systems.

Stochastic amplification of fluctuations in cortical up-states.

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Jorge Hidalgo

Full Text Available Cortical neurons are bistable; as a consequence their local field potentials can fluctuate between quiescent and active states, generating slow 0.5 2 Hz oscillations which are widely known as transitions between Up and Down States. Despite a large number of studies on Up-Down transitions, deciphering its nature, mechanisms and function are still today challenging tasks. In this paper we focus on recent experimental evidence, showing that a class of spontaneous oscillations can emerge within the Up states. In particular, a non-trivial peak around 20 Hz appears in their associated power-spectra, what produces an enhancement of the activity power for higher frequencies (in the 30-90 Hz band. Moreover, this rhythm within Ups seems to be an emergent or collective phenomenon given that individual neurons do not lock to it as they remain mostly unsynchronized. Remarkably, similar oscillations (and the concomitant peak in the spectrum do not appear in the Down states. Here we shed light on these findings by using different computational models for the dynamics of cortical networks in presence of different levels of physiological complexity. Our conclusion, supported by both theory and simulations, is that the collective phenomenon of "stochastic amplification of fluctuations"--previously described in other contexts such as Ecology and Epidemiology--explains in an elegant and parsimonious manner, beyond model-dependent details, this extra-rhythm emerging only in the Up states but not in the Downs.

Stochastic Predictions of Cell Kill During Stereotactic Ablative Radiation Therapy: Do Hypoxia and Reoxygenation Really Matter?

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Harriss-Phillips, Wendy M., E-mail: wharrphil@gmail.com [Department of Medical Physics, Royal Adelaide Hospital, Adelaide, South Australia (Australia); School of Chemistry and Physics, University of Adelaide, Adelaide, South Australia (Australia); Bezak, Eva [School of Chemistry and Physics, University of Adelaide, Adelaide, South Australia (Australia); International Centre for Allied Health Evidence, University of South Australia, Adelaide, South Australia (Australia); Sansom Institute for Health Research, University of South Australia, Adelaide, South Australia (Australia); Potter, Andrew [Department of Radiation Oncology, Royal Adelaide Hospital, Adelaide, South Australia (Australia); Adelaide Radiotherapy Centre, Genesis CancerCare, Adelaide, South Australia (Australia)

2016-07-15

Purpose: To simulate stereotactic ablative radiation therapy on hypoxic and well-oxygenated in silico tumors, incorporating probabilistic parameter distributions and linear-quadratic versus linear-quadratic-cubic methodology and the evaluation of optimal fractionation schemes using biological effective dose (BED{sub α/β=10} {sub or} {sub 3}) comparisons. Methods and Materials: A temporal tumor growth and radiation therapy algorithm simulated high-dose external beam radiation therapy using stochastic methods. Realistic biological proliferative cellular hierarchy and pO{sub 2} histograms were incorporated into the 10{sup 8}-cell tumor model, with randomized radiation therapy applied during continual cell proliferation and volume-based gradual tumor reoxygenation. Dose fractions ranged from 6-35 Gy, with predictive outcomes presented in terms of the total doses (converted to BED) required to eliminate all cells that could potentially regenerate the tumor. Results: Well-oxygenated tumor control BED{sub 10} outcomes were not significantly different for high-dose versus conventional radiation therapy (BED{sub 10}: 79-84 Gy; Equivalent Dose in 2 Gy fractions with α/β of 10: 66-70 Gy); however, total treatment times decreased from 7 down to 1-3 weeks. For hypoxic tumors, an additional 28 Gy (51 Gy BED{sub 10}) was required, with BED{sub 10} increasing with dose per fraction due to wasted dose in the final fraction. Fractions of 9 Gy compromised well for total treatment time and BED, with BED{sub 10}:BED{sub 3} of 84:176 Gy for oxic and 132:278 Gy for non-reoxygenating hypoxic tumors. Initial doses of 12 Gy followed by 6 Gy further increased the therapeutic ratio. When delivering ≥9 Gy per fraction, applying reoxygenation and/or linear-quadratic-cubic cell survival both affected tumor control doses by a significant 1-2 fractions. Conclusions: The complex temporal dynamics of tumor oxygenation combined with probabilistic cell kinetics in the modeling of

Noise Enhances Action Potential Generation in Mouse Sensory Neurons via Stochastic Resonance.

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Onorato, Irene; D'Alessandro, Giuseppina; Di Castro, Maria Amalia; Renzi, Massimiliano; Dobrowolny, Gabriella; Musarò, Antonio; Salvetti, Marco; Limatola, Cristina; Crisanti, Andrea; Grassi, Francesca

2016-01-01

Noise can enhance perception of tactile and proprioceptive stimuli by stochastic resonance processes. However, the mechanisms underlying this general phenomenon remain to be characterized. Here we studied how externally applied noise influences action potential firing in mouse primary sensory neurons of dorsal root ganglia, modelling a basic process in sensory perception. Since noisy mechanical stimuli may cause stochastic fluctuations in receptor potential, we examined the effects of sub-threshold depolarizing current steps with superimposed random fluctuations. We performed whole cell patch clamp recordings in cultured neurons of mouse dorsal root ganglia. Noise was added either before and during the step, or during the depolarizing step only, to focus onto the specific effects of external noise on action potential generation. In both cases, step + noise stimuli triggered significantly more action potentials than steps alone. The normalized power norm had a clear peak at intermediate noise levels, demonstrating that the phenomenon is driven by stochastic resonance. Spikes evoked in step + noise trials occur earlier and show faster rise time as compared to the occasional ones elicited by steps alone. These data suggest that external noise enhances, via stochastic resonance, the recruitment of transient voltage-gated Na channels, responsible for action potential firing in response to rapid step-wise depolarizing currents.

A millifluidic study of cell-to-cell heterogeneity in growth-rate and cell-division capability in populations of isogenic cells of Chlamydomonas reinhardtii.

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Shima P Damodaran

Full Text Available To address possible cell-to-cell heterogeneity in growth dynamics of isogenic cell populations of Chlamydomonas reinhardtii, we developed a millifluidic drop-based device that not only allows the analysis of populations grown from single cells over periods of a week, but is also able to sort and collect drops of interest, containing viable and healthy cells, which can be used for further experimentation. In this study, we used isogenic algal cells that were first synchronized in mixotrophic growth conditions. We show that these synchronized cells, when placed in droplets and kept in mixotrophic growth conditions, exhibit mostly homogeneous growth statistics, but with two distinct subpopulations: a major population with a short doubling-time (fast-growers and a significant subpopulation of slowly dividing cells (slow-growers. These observations suggest that algal cells from an isogenic population may be present in either of two states, a state of restricted division and a state of active division. When isogenic cells were allowed to propagate for about 1000 generations on solid agar plates, they displayed an increased heterogeneity in their growth dynamics. Although we could still identify the original populations of slow- and fast-growers, drops inoculated with a single progenitor cell now displayed a wider diversity of doubling-times. Moreover, populations dividing with the same growth-rate often reached different cell numbers in stationary phase, suggesting that the progenitor cells differed in the number of cell divisions they could undertake. We discuss possible explanations for these cell-to-cell heterogeneities in growth dynamics, such as mutations, differential aging or stochastic variations in metabolites and macromolecules yielding molecular switches, in the light of single-cell heterogeneities that have been reported among isogenic populations of other eu- and prokaryotes.

What is a stem cell?

Science.gov (United States)

Slack, Jonathan M W

2018-05-15

The historical roots of the stem cell concept are traced with respect to its usage in embryology and in hematology. The modern consensus definition of stem cells, comprising both pluripotent stem cells in culture and tissue-specific stem cells in vivo, is explained and explored. Methods for identifying stem cells are discussed with respect to cell surface markers, telomerase, label retention and transplantability, and properties of the stem cell niche are explored. The CreER method for identifying stem cells in vivo is explained, as is evidence in favor of a stochastic rather than an obligate asymmetric form of cell division. In conclusion, it is found that stem cells do not possess any unique and specific molecular markers; and stem cell behavior depends on the environment of the cell as well as the stem cell's intrinsic qualities. Furthermore, the stochastic mode of division implies that stem cell behavior is a property of a cell population not of an individual cell. In this sense, stem cells do not exist in isolation but only as a part of multicellular system. This article is categorized under: Adult Stem Cells, Tissue Renewal, and Regeneration > Tissue Stem Cells and Niches Adult Stem Cells, Tissue Renewal, and Regeneration > Methods and Principles Adult Stem Cells, Tissue Renewal, and Regeneration > Environmental Control of Stem Cells. © 2018 Wiley Periodicals, Inc.

Extinction time of a stochastic predator-prey model by the generalized cell mapping method

Science.gov (United States)

Han, Qun; Xu, Wei; Hu, Bing; Huang, Dongmei; Sun, Jian-Qiao

2018-03-01

The stochastic response and extinction time of a predator-prey model with Gaussian white noise excitations are studied by the generalized cell mapping (GCM) method based on the short-time Gaussian approximation (STGA). The methods for stochastic response probability density functions (PDFs) and extinction time statistics are developed. The Taylor expansion is used to deal with non-polynomial nonlinear terms of the model for deriving the moment equations with Gaussian closure, which are needed for the STGA in order to compute the one-step transition probabilities. The work is validated with direct Monte Carlo simulations. We have presented the transient responses showing the evolution from a Gaussian initial distribution to a non-Gaussian steady-state one. The effects of the model parameter and noise intensities on the steady-state PDFs are discussed. It is also found that the effects of noise intensities on the extinction time statistics are opposite to the effects on the limit probability distributions of the survival species.

Simulations of DSB Yields and Radiation-induced Chromosomal Aberrations in Human Cells Based on the Stochastic Track Structure iIduced by HZE Particles

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Ponomarev, Artem; Plante, Ianik; George, Kerry; Wu, Honglu

2014-01-01

The formation of double-strand breaks (DSBs) and chromosomal aberrations (CAs) is of great importance in radiation research and, specifically, in space applications. We are presenting a new particle track and DNA damage model, in which the particle stochastic track structure is combined with the random walk (RW) structure of chromosomes in a cell nucleus. The motivation for this effort stems from the fact that the model with the RW chromosomes, NASARTI (NASA radiation track image) previously relied on amorphous track structure, while the stochastic track structure model RITRACKS (Relativistic Ion Tracks) was focused on more microscopic targets than the entire genome. We have combined chromosomes simulated by RWs with stochastic track structure, which uses nanoscopic dose calculations performed with the Monte-Carlo simulation by RITRACKS in a voxelized space. The new simulations produce the number of DSBs as function of dose and particle fluence for high-energy particles, including iron, carbon and protons, using voxels of 20 nm dimension. The combined model also calculates yields of radiation-induced CAs and unrejoined chromosome breaks in normal and repair deficient cells. The joined computational model is calibrated using the relative frequencies and distributions of chromosomal aberrations reported in the literature. The model considers fractionated deposition of energy to approximate dose rates of the space flight environment. The joined model also predicts of the yields and sizes of translocations, dicentrics, rings, and more complex-type aberrations formed in the G0/G1 cell cycle phase during the first cell division after irradiation. We found that the main advantage of the joined model is our ability to simulate small doses: 0.05-0.5 Gy. At such low doses, the stochastic track structure proved to be indispensable, as the action of individual delta-rays becomes more important.

The average number of alpha-particle hits to the cell nucleus required to eradicate a tumour cell population

International Nuclear Information System (INIS)

Roeske, John C; Stinchcomb, Thomas G

2006-01-01

Alpha-particle emitters are currently being considered for the treatment of micrometastatic disease. Based on in vitro studies, it has been speculated that only a few alpha-particle hits to the cell nucleus are considered lethal. However, such estimates do not consider the stochastic variations in the number of alpha-particle hits, energy deposited, or in the cell survival process itself. Using a tumour control probability (TCP) model for alpha-particle emitters, we derive an estimate of the average number of hits to the cell nucleus required to provide a high probability of eradicating a tumour cell population. In simulation studies, our results demonstrate that the average number of hits required to achieve a 90% TCP for 10 4 clonogenic cells ranges from 18 to 108. Those cells that have large cell nuclei, high radiosensitivities and alpha-particle emissions occurring primarily in the nuclei tended to require more hits. As the clinical implementation of alpha-particle emitters is considered, this type of analysis may be useful in interpreting clinical results and in designing treatment strategies to achieve a favourable therapeutic outcome. (note)

Mechanosensation Dynamically Coordinates Polar Growth and Cell Wall Assembly to Promote Cell Survival.

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Davì, Valeria; Tanimoto, Hirokazu; Ershov, Dmitry; Haupt, Armin; De Belly, Henry; Le Borgne, Rémi; Couturier, Etienne; Boudaoud, Arezki; Minc, Nicolas

2018-04-23

How growing cells cope with size expansion while ensuring mechanical integrity is not known. In walled cells, such as those of microbes and plants, growth and viability are both supported by a thin and rigid encasing cell wall (CW). We deciphered the dynamic mechanisms controlling wall surface assembly during cell growth, using a sub-resolution microscopy approach to monitor CW thickness in live rod-shaped fission yeast cells. We found that polar cell growth yielded wall thinning and that thickness negatively influenced growth. Thickness at growing tips exhibited a fluctuating behavior with thickening phases followed by thinning phases, indicative of a delayed feedback promoting thickness homeostasis. This feedback was mediated by mechanosensing through the CW integrity pathway, which probes strain in the wall to adjust synthase localization and activity to surface growth. Mutants defective in thickness homeostasis lysed by rupturing the wall, demonstrating its pivotal role for walled cell survival. Copyright © 2018 Elsevier Inc. All rights reserved.

Stochastic thermodynamics, fluctuation theorems and molecular machines

International Nuclear Information System (INIS)

Seifert, Udo

2012-01-01

Stochastic thermodynamics as reviewed here systematically provides a framework for extending the notions of classical thermodynamics such as work, heat and entropy production to the level of individual trajectories of well-defined non-equilibrium ensembles. It applies whenever a non-equilibrium process is still coupled to one (or several) heat bath(s) of constant temperature. Paradigmatic systems are single colloidal particles in time-dependent laser traps, polymers in external flow, enzymes and molecular motors in single molecule assays, small biochemical networks and thermoelectric devices involving single electron transport. For such systems, a first-law like energy balance can be identified along fluctuating trajectories. For a basic Markovian dynamics implemented either on the continuum level with Langevin equations or on a discrete set of states as a master equation, thermodynamic consistency imposes a local-detailed balance constraint on noise and rates, respectively. Various integral and detailed fluctuation theorems, which are derived here in a unifying approach from one master theorem, constrain the probability distributions for work, heat and entropy production depending on the nature of the system and the choice of non-equilibrium conditions. For non-equilibrium steady states, particularly strong results hold like a generalized fluctuation–dissipation theorem involving entropy production. Ramifications and applications of these concepts include optimal driving between specified states in finite time, the role of measurement-based feedback processes and the relation between dissipation and irreversibility. Efficiency and, in particular, efficiency at maximum power can be discussed systematically beyond the linear response regime for two classes of molecular machines, isothermal ones such as molecular motors, and heat engines such as thermoelectric devices, using a common framework based on a cycle decomposition of entropy production. (review article)

Stochastic TDHF and the Boltzman-Langevin equation

International Nuclear Information System (INIS)

Suraud, E.; Reinhard, P.G.

1991-01-01

Outgoing from a time-dependent theory of correlations, we present a stochastic differential equation for the propagation of ensembles of Slater determinants, called Stochastic Time-Dependent Hartree-Fock (Stochastic TDHF). These ensembles are allowed to develop large fluctuations in the Hartree-Fock mean fields. An alternative stochastic differential equation, the Boltzmann-Langevin equation, can be derived from Stochastic TDHF by averaging over subensembles with small fluctuations

Cell-to-Cell Communication Circuits: Quantitative Analysis of Synthetic Logic Gates

Science.gov (United States)

Hoffman-Sommer, Marta; Supady, Adriana; Klipp, Edda

2012-01-01

One of the goals in the field of synthetic biology is the construction of cellular computation devices that could function in a manner similar to electronic circuits. To this end, attempts are made to create biological systems that function as logic gates. In this work we present a theoretical quantitative analysis of a synthetic cellular logic-gates system, which has been implemented in cells of the yeast Saccharomyces cerevisiae (Regot et al., 2011). It exploits endogenous MAP kinase signaling pathways. The novelty of the system lies in the compartmentalization of the circuit where all basic logic gates are implemented in independent single cells that can then be cultured together to perform complex logic functions. We have constructed kinetic models of the multicellular IDENTITY, NOT, OR, and IMPLIES logic gates, using both deterministic and stochastic frameworks. All necessary model parameters are taken from literature or estimated based on published kinetic data, in such a way that the resulting models correctly capture important dynamic features of the included mitogen-activated protein kinase pathways. We analyze the models in terms of parameter sensitivity and we discuss possible ways of optimizing the system, e.g., by tuning the culture density. We apply a stochastic modeling approach, which simulates the behavior of whole populations of cells and allows us to investigate the noise generated in the system; we find that the gene expression units are the major sources of noise. Finally, the model is used for the design of system modifications: we show how the current system could be transformed to operate on three discrete values. PMID:22934039

Optimal stochastic coordinated scheduling of proton exchange membrane fuel cell-combined heat and power, wind and photovoltaic units in micro grids considering hydrogen storage

International Nuclear Information System (INIS)

Bornapour, Mosayeb; Hooshmand, Rahmat-Allah; Khodabakhshian, Amin; Parastegari, Moein

2017-01-01

Highlights: •Stochastic model is proposed for coordinated scheduling of renewable energy sources. •The effect of combined heat and power is considered. •Hydrogen storage is considered for fuel cells. •Maximizing profits of micro grid is considered as objective function. •Considering the uncertainties of problem lead to profit increasing. -- Abstract: Nowadays, renewable energy sources and combined heat and power units are extremely used in micro grids, so it is necessary to schedule these units to improve the performance of the system. In this regard, a stochastic model is proposed in this paper to schedule proton exchange membrane fuel cell-combined heat and power, wind turbines, and photovoltaic units coordinately in a micro grid while considering hydrogen storage. Hydrogen storage strategy is considered for the operation of proton exchange membrane fuel cell-combined heat and power units. To consider stochastic generation of renewable energy source units in this paper, a scenario-based method is used. In this method, the uncertainties of electrical market price, the wind speed, and solar irradiance are considered. This stochastic scheduling problem is a mixed integer- nonlinear programming which considers the proposed objective function and variables of coordinated scheduling of PEMFC-CHP, wind turbines and photovoltaic units. It also considers hydrogen storage strategy and converts it to a mixed integer nonlinear problem. In this study a modified firefly algorithm is used to solve the problem. This method is examined on modified 33-bus distributed network as a MG for its performance.

Meaningful interpretation of subdiffusive measurements in living cells (crowded environment) by fluorescence fluctuation microscopy.

Science.gov (United States)

Baumann, Gerd; Place, Robert F; Földes-Papp, Zeno

2010-08-01

In living cell or its nucleus, the motions of molecules are complicated due to the large crowding and expected heterogeneity of the intracellular environment. Randomness in cellular systems can be either spatial (anomalous) or temporal (heterogeneous). In order to separate both processes, we introduce anomalous random walks on fractals that represented crowded environments. We report the use of numerical simulation and experimental data of single-molecule detection by fluorescence fluctuation microscopy for detecting resolution limits of different mobile fractions in crowded environment of living cells. We simulate the time scale behavior of diffusion times tau(D)(tau) for one component, e.g. the fast mobile fraction, and a second component, e.g. the slow mobile fraction. The less the anomalous exponent alpha the higher the geometric crowding of the underlying structure of motion that is quantified by the ratio of the Hausdorff dimension and the walk exponent d(f)/d(w) and specific for the type of crowding generator used. The simulated diffusion time decreases for smaller values of alpha # 1 but increases for a larger time scale tau at a given value of alpha # 1. The effect of translational anomalous motion is substantially greater if alpha differs much from 1. An alpha value close to 1 contributes little to the time dependence of subdiffusive motions. Thus, quantitative determination of molecular weights from measured diffusion times and apparent diffusion coefficients, respectively, in temporal auto- and crosscorrelation analyses and from time-dependent fluorescence imaging data are difficult to interpret and biased in crowded environments of living cells and their cellular compartments; anomalous dynamics on different time scales tau must be coupled with the quantitative analysis of how experimental parameters change with predictions from simulated subdiffusive dynamics of molecular motions and mechanistic models. We first demonstrate that the crowding exponent

The effects of intrinsic noise on the behaviour of bistable cell regulatory systems under quasi-steady state conditions.

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de la Cruz, Roberto; Guerrero, Pilar; Spill, Fabian; Alarcón, Tomás

2015-08-21

We analyse the effect of intrinsic fluctuations on the properties of bistable stochastic systems with time scale separation operating under quasi-steady state conditions. We first formulate a stochastic generalisation of the quasi-steady state approximation based on the semi-classical approximation of the partial differential equation for the generating function associated with the chemical master equation. Such approximation proceeds by optimising an action functional whose associated set of Euler-Lagrange (Hamilton) equations provides the most likely fluctuation path. We show that, under appropriate conditions granting time scale separation, the Hamiltonian can be re-scaled so that the set of Hamilton equations splits up into slow and fast variables, whereby the quasi-steady state approximation can be applied. We analyse two particular examples of systems whose mean-field limit has been shown to exhibit bi-stability: an enzyme-catalysed system of two mutually inhibitory proteins and a gene regulatory circuit with self-activation. Our theory establishes that the number of molecules of the conserved species is order parameters whose variation regulates bistable behaviour in the associated systems beyond the predictions of the mean-field theory. This prediction is fully confirmed by direct numerical simulations using the stochastic simulation algorithm. This result allows us to propose strategies whereby, by varying the number of molecules of the three conserved chemical species, cell properties associated to bistable behaviour (phenotype, cell-cycle status, etc.) can be controlled.

The effects of intrinsic noise on the behaviour of bistable cell regulatory systems under quasi-steady state conditions

Energy Technology Data Exchange (ETDEWEB)

Cruz, Roberto; Alarcón, Tomás de la [Centre de Recerca Matemàtica. Edifici C, Campus de Bellaterra, 08193 Bellaterra (Barcelona) (Spain); Departament de Matemàtiques, Universitat Autònoma de Barcelona, 08193 Bellaterra (Barcelona) (Spain); Guerrero, Pilar [Department of Mathematics, University College London, Gower Street, London WC1E 6BT (United Kingdom); Spill, Fabian [Department of Biomedical Engineering, Boston University, 44 Cummington Street, Boston, Massachusetts 02215 (United States); Department of Mechanical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139 (United States)

2015-08-21

We analyse the effect of intrinsic fluctuations on the properties of bistable stochastic systems with time scale separation operating under quasi-steady state conditions. We first formulate a stochastic generalisation of the quasi-steady state approximation based on the semi-classical approximation of the partial differential equation for the generating function associated with the chemical master equation. Such approximation proceeds by optimising an action functional whose associated set of Euler-Lagrange (Hamilton) equations provides the most likely fluctuation path. We show that, under appropriate conditions granting time scale separation, the Hamiltonian can be re-scaled so that the set of Hamilton equations splits up into slow and fast variables, whereby the quasi-steady state approximation can be applied. We analyse two particular examples of systems whose mean-field limit has been shown to exhibit bi-stability: an enzyme-catalysed system of two mutually inhibitory proteins and a gene regulatory circuit with self-activation. Our theory establishes that the number of molecules of the conserved species is order parameters whose variation regulates bistable behaviour in the associated systems beyond the predictions of the mean-field theory. This prediction is fully confirmed by direct numerical simulations using the stochastic simulation algorithm. This result allows us to propose strategies whereby, by varying the number of molecules of the three conserved chemical species, cell properties associated to bistable behaviour (phenotype, cell-cycle status, etc.) can be controlled.

Plant adaptation to fluctuating environment and biomass production are strongly dependent on guard cell potassium channels

Science.gov (United States)

Lebaudy, Anne; Vavasseur, Alain; Hosy, Eric; Dreyer, Ingo; Leonhardt, Nathalie; Thibaud, Jean-Baptiste; Véry, Anne-Aliénor; Simonneau, Thierry; Sentenac, Hervé

2008-01-01

At least four genes encoding plasma membrane inward K+ channels (Kin channels) are expressed in Arabidopsis guard cells. A double mutant plant was engineered by disruption of a major Kin channel gene and expression of a dominant negative channel construct. Using the patch-clamp technique revealed that this mutant was totally deprived of guard cell Kin channel (GCKin) activity, providing a model to investigate the roles of this activity in the plant. GCKin activity was found to be an essential effector of stomatal opening triggered by membrane hyperpolarization and thereby of blue light-induced stomatal opening at dawn. It improved stomatal reactivity to external or internal signals (light, CO2 availability, and evaporative demand). It protected stomatal function against detrimental effects of Na+ when plants were grown in the presence of physiological concentrations of this cation, probably by enabling guard cells to selectively and rapidly take up K+ instead of Na+ during stomatal opening, thereby preventing deleterious effects of Na+ on stomatal closure. It was also shown to be a key component of the mechanisms that underlie the circadian rhythm of stomatal opening, which is known to gate stomatal responses to extracellular and intracellular signals. Finally, in a meteorological scenario with higher light intensity during the first hours of the photophase, GCKin activity was found to allow a strong increase (35%) in plant biomass production. Thus, a large diversity of approaches indicates that GCKin activity plays pleiotropic roles that crucially contribute to plant adaptation to fluctuating and stressing natural environments. PMID:18367672

Stochastic switching in biology: from genotype to phenotype

International Nuclear Information System (INIS)

Bressloff, Paul C

2017-01-01

There has been a resurgence of interest in non-equilibrium stochastic processes in recent years, driven in part by the observation that the number of molecules (genes, mRNA, proteins) involved in gene expression are often of order 1–1000. This means that deterministic mass-action kinetics tends to break down, and one needs to take into account the discrete, stochastic nature of biochemical reactions. One of the major consequences of molecular noise is the occurrence of stochastic biological switching at both the genotypic and phenotypic levels. For example, individual gene regulatory networks can switch between graded and binary responses, exhibit translational/transcriptional bursting, and support metastability (noise-induced switching between states that are stable in the deterministic limit). If random switching persists at the phenotypic level then this can confer certain advantages to cell populations growing in a changing environment, as exemplified by bacterial persistence in response to antibiotics. Gene expression at the single-cell level can also be regulated by changes in cell density at the population level, a process known as quorum sensing. In contrast to noise-driven phenotypic switching, the switching mechanism in quorum sensing is stimulus-driven and thus noise tends to have a detrimental effect. A common approach to modeling stochastic gene expression is to assume a large but finite system and to approximate the discrete processes by continuous processes using a system-size expansion. However, there is a growing need to have some familiarity with the theory of stochastic processes that goes beyond the standard topics of chemical master equations, the system-size expansion, Langevin equations and the Fokker–Planck equation. Examples include stochastic hybrid systems (piecewise deterministic Markov processes), large deviations and the Wentzel–Kramers–Brillouin (WKB) method, adiabatic reductions, and queuing/renewal theory. The major aim of

Single-cell Analysis of Lambda Immunity Regulation

DEFF Research Database (Denmark)

Bæk, Kristoffer Torbjørn; Svenningsen, Sine Lo; Eisen, Harvey

2003-01-01

We have examined expression of the ¿cI operon in single cells via a rexgfp substitution. Although average fluorescence agreed with expectations for expression of ¿-repressor, fluorescence fluctuated greatly from cell-to-cell. Fluctuations in repressor concentration are not predicted by previous m...

Stochastic approach to microphysics

Energy Technology Data Exchange (ETDEWEB)

Aron, J.C.

1987-01-01

The presently widespread idea of ''vacuum population'', together with the quantum concept of vacuum fluctuations leads to assume a random level below that of matter. This stochastic approach starts by a reminder of the author's previous work, first on the relation of diffusion laws with the foundations of microphysics, and then on hadron spectrum. Following the latter, a random quark model is advanced; it gives to quark pairs properties similar to those of a harmonic oscillator or an elastic string, imagined as an explanation to their asymptotic freedom and their confinement. The stochastic study of such interactions as electron-nucleon, jets in e/sup +/e/sup -/ collisions, or pp -> ..pi../sup 0/ + X, gives form factors closely consistent with experiment. The conclusion is an epistemological comment (complementarity between stochastic and quantum domains, E.P.R. paradox, etc...).

Stochastic plasma heating by electrostatic waves: a comparison between a particle-in-cell simulation and a laboratory experiment

International Nuclear Information System (INIS)

Fivaz, M.; Fasoli, A.; Appert, K.; Trans, T.M.; Tran, M.Q.; Skiff, F.

1993-08-01

Dynamical chaos is produced by the interaction between plasma particles and two electrostatic waves. Experiments performed in a linear magnetized plasma and a 1D particle-in-cell simulation agree qualitatively: above a threshold wave amplitude, ion stochastic diffusion and heating occur on a fast time scale. Self-consistency appears to limit the extent of the heating process. (author) 5 figs., 18 refs

The roles of shear and cross-correlations on the fluctuation levels in simple stochastic models. Revision

International Nuclear Information System (INIS)

Krommes, J.A.

1999-01-01

Highly simplified models of random flows interacting with background microturbulence are analyzed. In the limit of very rapid velocity fluctuations, it is shown rigorously that the fluctuation level of a passively advected scalar is not controlled by the rms shear. In a model with random velocities dependent only on time, the level of cross-correlations between the flows and the background turbulence regulates the saturation level. This effect is illustrated by considering a simple stochastic-oscillator model, both exactly and with analysis and numerical solutions of the direct-interaction approximation. Implications for the understanding of self-consistent turbulence are discussed briefly

Switch-like reprogramming of gene expression after fusion of multinucleate plasmodial cells of two Physarum polycephalum sporulation mutants

Energy Technology Data Exchange (ETDEWEB)

Walter, Pauline; Hoffmann, Xenia-Katharina; Ebeling, Britta; Haas, Markus; Marwan, Wolfgang, E-mail: wolfgang.marwan@ovgu.de

2013-05-24

Highlights: •We investigate reprogramming of gene expression in multinucleate single cells. •Cells of two differentiation control mutants are fused. •Fused cells proceed to alternative gene expression patterns. •The population of nuclei damps stochastic fluctuations in gene expression. •Dynamic processes of cellular reprogramming can be observed by repeated sampling of a cell. -- Abstract: Nonlinear dynamic processes involving the differential regulation of transcription factors are considered to impact the reprogramming of stem cells, germ cells, and somatic cells. Here, we fused two multinucleate plasmodial cells of Physarum polycephalum mutants defective in different sporulation control genes while being in different physiological states. The resulting heterokaryons established one of two significantly different expression patterns of marker genes while the plasmodial halves that were fused to each other synchronized spontaneously. Spontaneous synchronization suggests that switch-like control mechanisms spread over and finally control the entire plasmodium as a result of cytoplasmic mixing. Regulatory molecules due to the large volume of the vigorously streaming cytoplasm will define concentrations in acting on the population of nuclei and in the global setting of switches. Mixing of a large cytoplasmic volume is expected to damp stochasticity when individual nuclei deliver certain RNAs at low copy number into the cytoplasm. We conclude that spontaneous synchronization, the damping of molecular noise in gene expression by the large cytoplasmic volume, and the option to take multiple macroscopic samples from the same plasmodium provide unique options for studying the dynamics of cellular reprogramming at the single cell level.

Switch-like reprogramming of gene expression after fusion of multinucleate plasmodial cells of two Physarum polycephalum sporulation mutants

International Nuclear Information System (INIS)

Walter, Pauline; Hoffmann, Xenia-Katharina; Ebeling, Britta; Haas, Markus; Marwan, Wolfgang

2013-01-01

Highlights: •We investigate reprogramming of gene expression in multinucleate single cells. •Cells of two differentiation control mutants are fused. •Fused cells proceed to alternative gene expression patterns. •The population of nuclei damps stochastic fluctuations in gene expression. •Dynamic processes of cellular reprogramming can be observed by repeated sampling of a cell. -- Abstract: Nonlinear dynamic processes involving the differential regulation of transcription factors are considered to impact the reprogramming of stem cells, germ cells, and somatic cells. Here, we fused two multinucleate plasmodial cells of Physarum polycephalum mutants defective in different sporulation control genes while being in different physiological states. The resulting heterokaryons established one of two significantly different expression patterns of marker genes while the plasmodial halves that were fused to each other synchronized spontaneously. Spontaneous synchronization suggests that switch-like control mechanisms spread over and finally control the entire plasmodium as a result of cytoplasmic mixing. Regulatory molecules due to the large volume of the vigorously streaming cytoplasm will define concentrations in acting on the population of nuclei and in the global setting of switches. Mixing of a large cytoplasmic volume is expected to damp stochasticity when individual nuclei deliver certain RNAs at low copy number into the cytoplasm. We conclude that spontaneous synchronization, the damping of molecular noise in gene expression by the large cytoplasmic volume, and the option to take multiple macroscopic samples from the same plasmodium provide unique options for studying the dynamics of cellular reprogramming at the single cell level

A model for cell migration in non-isotropic fibrin networks with an application to pancreatic tumor islets.

Science.gov (United States)

Chen, Jiao; Weihs, Daphne; Vermolen, Fred J

2018-04-01

Cell migration, known as an orchestrated movement of cells, is crucially important for wound healing, tumor growth, immune response as well as other biomedical processes. This paper presents a cell-based model to describe cell migration in non-isotropic fibrin networks around pancreatic tumor islets. This migration is determined by the mechanical strain energy density as well as cytokines-driven chemotaxis. Cell displacement is modeled by solving a large system of ordinary stochastic differential equations where the stochastic parts result from random walk. The stochastic differential equations are solved by the use of the classical Euler-Maruyama method. In this paper, the influence of anisotropic stromal extracellular matrix in pancreatic tumor islets on T-lymphocytes migration in different immune systems is investigated. As a result, tumor peripheral stromal extracellular matrix impedes the immune response of T-lymphocytes through changing direction of their migration.

Stochasticity in materials structure, properties, and processing—A review

Science.gov (United States)

Hull, Robert; Keblinski, Pawel; Lewis, Dan; Maniatty, Antoinette; Meunier, Vincent; Oberai, Assad A.; Picu, Catalin R.; Samuel, Johnson; Shephard, Mark S.; Tomozawa, Minoru; Vashishth, Deepak; Zhang, Shengbai

2018-03-01

We review the concept of stochasticity—i.e., unpredictable or uncontrolled fluctuations in structure, chemistry, or kinetic processes—in materials. We first define six broad classes of stochasticity: equilibrium (thermodynamic) fluctuations; structural/compositional fluctuations; kinetic fluctuations; frustration and degeneracy; imprecision in measurements; and stochasticity in modeling and simulation. In this review, we focus on the first four classes that are inherent to materials phenomena. We next develop a mathematical framework for describing materials stochasticity and then show how it can be broadly applied to these four materials-related stochastic classes. In subsequent sections, we describe structural and compositional fluctuations at small length scales that modify material properties and behavior at larger length scales; systems with engineered fluctuations, concentrating primarily on composite materials; systems in which stochasticity is developed through nucleation and kinetic phenomena; and configurations in which constraints in a given system prevent it from attaining its ground state and cause it to attain several, equally likely (degenerate) states. We next describe how stochasticity in these processes results in variations in physical properties and how these variations are then accentuated by—or amplify—stochasticity in processing and manufacturing procedures. In summary, the origins of materials stochasticity, the degree to which it can be predicted and/or controlled, and the possibility of using stochastic descriptions of materials structure, properties, and processing as a new degree of freedom in materials design are described.

Assessing the Nano-Dynamics of the Cell Surface

Energy Technology Data Exchange (ETDEWEB)

Bae, Chil Man [Dept. of Physiology and Biophysics, State University of New York, Buffalo (United States); Park, Ik Keun [Mechanical Engineering, Seoul National University of Technology, Seoul (Korea, Republic of); Bulter, Peter J. [Dept. of Bioengineering, The Pennsylvania State University, University Park (United States)

2012-06-15

It is important to know the mechanism of cell membrane fluctuation because it can be readout for the nanomechanical interaction between cytoskeleton and plasma membrane. Traditional techniques, however, have drawbacks such as probe contact with the cell surface, complicate analysis, and limit spatial and temporal resolution. In this study, we developed a new system for non-contact measurement of nano-scale localized-cell surface dynamics using modified-scanning ion-conductance microscopy. With 2 nm resolution, we determined that endothelial cells have local membrane fluctuations of -20 nm, actin depolymerization causes increase in fluctuation amplitude, and ATP depletion abolishes all membrane fluctuations.

A stochastic approach to multi-gene expression dynamics

International Nuclear Information System (INIS)

Ochiai, T.; Nacher, J.C.; Akutsu, T.

2005-01-01

In the last years, tens of thousands gene expression profiles for cells of several organisms have been monitored. Gene expression is a complex transcriptional process where mRNA molecules are translated into proteins, which control most of the cell functions. In this process, the correlation among genes is crucial to determine the specific functions of genes. Here, we propose a novel multi-dimensional stochastic approach to deal with the gene correlation phenomena. Interestingly, our stochastic framework suggests that the study of the gene correlation requires only one theoretical assumption-Markov property-and the experimental transition probability, which characterizes the gene correlation system. Finally, a gene expression experiment is proposed for future applications of the model

Appearance of deterministic mixing behavior from ensembles of fluctuating hydrodynamics simulations of the Richtmyer-Meshkov instability

Science.gov (United States)

Narayanan, Kiran; Samtaney, Ravi

2018-04-01

We obtain numerical solutions of the two-fluid fluctuating compressible Navier-Stokes (FCNS) equations, which consistently account for thermal fluctuations from meso- to macroscales, in order to study the effect of such fluctuations on the mixing behavior in the Richtmyer-Meshkov instability (RMI). The numerical method used was successfully verified in two stages: for the deterministic fluxes by comparison against air-SF6 RMI experiment, and for the stochastic terms by comparison against the direct simulation Monte Carlo results for He-Ar RMI. We present results from fluctuating hydrodynamic RMI simulations for three He-Ar systems having length scales with decreasing order of magnitude that span from macroscopic to mesoscopic, with different levels of thermal fluctuations characterized by a nondimensional Boltzmann number (Bo). For a multidimensional FCNS system on a regular Cartesian grid, when using a discretization of a space-time stochastic flux Z (x ,t ) of the form Z (x ,t ) →1 /√{h ▵ t }N (i h ,n Δ t ) for spatial interval h , time interval Δ t , h , and Gaussian noise N should be greater than h0, with h0 corresponding to a cell volume that contains a sufficient number of molecules of the fluid such that the fluctuations are physically meaningful and produce the right equilibrium spectrum. For the mesoscale RMI systems simulated, it was desirable to use a cell size smaller than this limit in order to resolve the viscous shock. This was achieved by using a modified regularization of the noise term via Z (h3,h03)>x ,t →1 /√ ▵ t max(i h ,n Δ t ) , with h0=ξ h ∀h mixing behavior emerges as the ensemble-averaged behavior of several fluctuating instances, whereas when Bo≈1 , a deviation from deterministic behavior is observed. For all cases, the FCNS solution provides bounds on the growth rate of the amplitude of the mixing layer.

Appearance of deterministic mixing behavior from ensembles of fluctuating hydrodynamics simulations of the Richtmyer-Meshkov instability

KAUST Repository

Narayanan, Kiran

2018-04-19

We obtain numerical solutions of the two-fluid fluctuating compressible Navier-Stokes (FCNS) equations, which consistently account for thermal fluctuations from meso- to macroscales, in order to study the effect of such fluctuations on the mixing behavior in the Richtmyer-Meshkov instability (RMI). The numerical method used was successfully verified in two stages: for the deterministic fluxes by comparison against air-SF6 RMI experiment, and for the stochastic terms by comparison against the direct simulation Monte Carlo results for He-Ar RMI. We present results from fluctuating hydrodynamic RMI simulations for three He-Ar systems having length scales with decreasing order of magnitude that span from macroscopic to mesoscopic, with different levels of thermal fluctuations characterized by a nondimensional Boltzmann number (Bo). For a multidimensional FCNS system on a regular Cartesian grid, when using a discretization of a space-time stochastic flux Z(x,t) of the form Z(x,t)→1/-tN(ih,nΔt) for spatial interval h, time interval Δt, h, and Gaussian noise N should be greater than h0, with h0 corresponding to a cell volume that contains a sufficient number of molecules of the fluid such that the fluctuations are physically meaningful and produce the right equilibrium spectrum. For the mesoscale RMI systems simulated, it was desirable to use a cell size smaller than this limit in order to resolve the viscous shock. This was achieved by using a modified regularization of the noise term via Zx,t→1/-tmaxh3,h03Nih,nΔt, with h0=ξhfluctuating instances, whereas when Bo≈1, a deviation from deterministic behavior is observed. For all cases, the FCNS solution provides bounds on the growth rate of the amplitude of the mixing layer.

Appearance of deterministic mixing behavior from ensembles of fluctuating hydrodynamics simulations of the Richtmyer-Meshkov instability

KAUST Repository

Narayanan, Kiran; Samtaney, Ravi

2018-01-01

We obtain numerical solutions of the two-fluid fluctuating compressible Navier-Stokes (FCNS) equations, which consistently account for thermal fluctuations from meso- to macroscales, in order to study the effect of such fluctuations on the mixing behavior in the Richtmyer-Meshkov instability (RMI). The numerical method used was successfully verified in two stages: for the deterministic fluxes by comparison against air-SF6 RMI experiment, and for the stochastic terms by comparison against the direct simulation Monte Carlo results for He-Ar RMI. We present results from fluctuating hydrodynamic RMI simulations for three He-Ar systems having length scales with decreasing order of magnitude that span from macroscopic to mesoscopic, with different levels of thermal fluctuations characterized by a nondimensional Boltzmann number (Bo). For a multidimensional FCNS system on a regular Cartesian grid, when using a discretization of a space-time stochastic flux Z(x,t) of the form Z(x,t)→1/-tN(ih,nΔt) for spatial interval h, time interval Δt, h, and Gaussian noise N should be greater than h0, with h0 corresponding to a cell volume that contains a sufficient number of molecules of the fluid such that the fluctuations are physically meaningful and produce the right equilibrium spectrum. For the mesoscale RMI systems simulated, it was desirable to use a cell size smaller than this limit in order to resolve the viscous shock. This was achieved by using a modified regularization of the noise term via Zx,t→1/-tmaxh3,h03Nih,nΔt, with h0=ξhfluctuating instances, whereas when Bo≈1, a deviation from deterministic behavior is observed. For all cases, the FCNS solution provides bounds on the growth rate of the amplitude of the mixing layer.

Cell membranes in radiation injury

International Nuclear Information System (INIS)

Koeteles, G.J.

1986-01-01

Cell membrane-related phenomena caused by low linear energy transfer radiation with doses lower than those producing cell killing are outlined. Micromorphological alterations as well as functional activities appearing with the receptors and in binding sites render it possible to reveal early and temporary changes. The cell injuries are suggested to transfer damaging conditions to surviving cells and to contribute to further development of non-stochastic effects in tissues

Modelling non-homogeneous stochastic reaction-diffusion systems: the case study of gemcitabine-treated non-small cell lung cancer growth.

Science.gov (United States)

Lecca, Paola; Morpurgo, Daniele

2012-01-01

Reaction-diffusion based models have been widely used in the literature for modeling the growth of solid tumors. Many of the current models treat both diffusion/consumption of nutrients and cell proliferation. The majority of these models use classical transport/mass conservation equations for describing the distribution of molecular species in tumor spheroids, and the Fick's law for describing the flux of uncharged molecules (i.e oxygen, glucose). Commonly, the equations for the cell movement and proliferation are first order differential equations describing the rate of change of the velocity of the cells with respect to the spatial coordinates as a function of the nutrient's gradient. Several modifications of these equations have been developed in the last decade to explicitly indicate that the tumor includes cells, interstitial fluids and extracellular matrix: these variants provided a model of tumor as a multiphase material with these as the different phases. Most of the current reaction-diffusion tumor models are deterministic and do not model the diffusion as a local state-dependent process in a non-homogeneous medium at the micro- and meso-scale of the intra- and inter-cellular processes, respectively. Furthermore, a stochastic reaction-diffusion model in which diffusive transport of the molecular species of nutrients and chemotherapy drugs as well as the interactions of the tumor cells with these species is a novel approach. The application of this approach to he scase of non-small cell lung cancer treated with gemcitabine is also novel. We present a stochastic reaction-diffusion model of non-small cell lung cancer growth in the specification formalism of the tool Redi, we recently developed for simulating reaction-diffusion systems. We also describe how a spatial gradient of nutrients and oncological drugs affects the tumor progression. Our model is based on a generalization of the Fick's first diffusion law that allows to model diffusive transport in non

A stochastic step model of replicative senescence explains ROS production rate in ageing cell populations.

Directory of Open Access Journals (Sweden)

Conor Lawless

Full Text Available Increases in cellular Reactive Oxygen Species (ROS concentration with age have been observed repeatedly in mammalian tissues. Concomitant increases in the proportion of replicatively senescent cells in ageing mammalian tissues have also been observed. Populations of mitotic human fibroblasts cultured in vitro, undergoing transition from proliferation competence to replicative senescence are useful models of ageing human tissues. Similar exponential increases in ROS with age have been observed in this model system. Tracking individual cells in dividing populations is difficult, and so the vast majority of observations have been cross-sectional, at the population level, rather than longitudinal observations of individual cells.One possible explanation for these observations is an exponential increase in ROS in individual fibroblasts with time (e.g. resulting from a vicious cycle between cellular ROS and damage. However, we demonstrate an alternative, simple hypothesis, equally consistent with these observations which does not depend on any gradual increase in ROS concentration: the Stochastic Step Model of Replicative Senescence (SSMRS. We also demonstrate that, consistent with the SSMRS, neither proliferation-competent human fibroblasts of any age, nor populations of hTERT overexpressing human fibroblasts passaged beyond the Hayflick limit, display high ROS concentrations. We conclude that longitudinal studies of single cells and their lineages are now required for testing hypotheses about roles and mechanisms of ROS increase during replicative senescence.

A stochastic step model of replicative senescence explains ROS production rate in ageing cell populations.

Science.gov (United States)

Lawless, Conor; Jurk, Diana; Gillespie, Colin S; Shanley, Daryl; Saretzki, Gabriele; von Zglinicki, Thomas; Passos, João F

2012-01-01

Increases in cellular Reactive Oxygen Species (ROS) concentration with age have been observed repeatedly in mammalian tissues. Concomitant increases in the proportion of replicatively senescent cells in ageing mammalian tissues have also been observed. Populations of mitotic human fibroblasts cultured in vitro, undergoing transition from proliferation competence to replicative senescence are useful models of ageing human tissues. Similar exponential increases in ROS with age have been observed in this model system. Tracking individual cells in dividing populations is difficult, and so the vast majority of observations have been cross-sectional, at the population level, rather than longitudinal observations of individual cells.One possible explanation for these observations is an exponential increase in ROS in individual fibroblasts with time (e.g. resulting from a vicious cycle between cellular ROS and damage). However, we demonstrate an alternative, simple hypothesis, equally consistent with these observations which does not depend on any gradual increase in ROS concentration: the Stochastic Step Model of Replicative Senescence (SSMRS). We also demonstrate that, consistent with the SSMRS, neither proliferation-competent human fibroblasts of any age, nor populations of hTERT overexpressing human fibroblasts passaged beyond the Hayflick limit, display high ROS concentrations. We conclude that longitudinal studies of single cells and their lineages are now required for testing hypotheses about roles and mechanisms of ROS increase during replicative senescence.

A simple stochastic model for dipole moment fluctuations in numerical dynamo simulations

Directory of Open Access Journals (Sweden)

Domenico G. eMeduri

2016-04-01

Full Text Available Earth's axial dipole field changes in a complex fashion on many differenttime scales ranging from less than a year to tens of million years.Documenting, analysing, and replicating this intricate signalis a challenge for data acquisition, theoretical interpretation,and dynamo modelling alike. Here we explore whether axial dipole variationscan be described by the superposition of a slow deterministic driftand fast stochastic fluctuations, i.e. by a Langevin-type system.The drift term describes the time averaged behaviour of the axial dipole variations,whereas the stochastic part mimics complex flow interactions over convective time scales.The statistical behaviour of the system is described by a Fokker-Planck equation whichallows useful predictions, including the average rates of dipole reversals and excursions.We analyse several numerical dynamo simulations, most of which havebeen integrated particularly long in time, and also the palaeomagneticmodel PADM2M which covers the past 2 Myr.The results show that the Langevin description provides a viable statistical modelof the axial dipole variations on time scales longer than about 1 kyr.For example, the axial dipole probability distribution and the average reversalrate are successfully predicted.The exception is PADM2M where the stochastic model reversal rate seems too low.The dependence of the drift on the axial dipolemoment reveals the nonlinear interactions that establish thedynamo balance. A separate analysis of inductive and diffusive magnetic effectsin three dynamo simulations suggests that the classical quadraticquenching of induction predicted by mean-field theory seems at work.

Cells from the adult corneal stroma can be reprogrammed to a neuron-like cell using exogenous growth factors

Energy Technology Data Exchange (ETDEWEB)

Greene, Carol Ann, E-mail: carol.greene@auckland.ac.nz; Chang, Chuan-Yuan; Fraser, Cameron J.; Nelidova, Dasha E.; Chen, Jing A.; Lim, Angela; Brebner, Alex; McGhee, Jennifer; Sherwin, Trevor; Green, Colin R.

2014-03-10

Cells thought to be stem cells isolated from the cornea of the eye have been shown to exhibit neurogenic potential. We set out to uncover the identity and location of these cells within the cornea and to elucidate their neuronal protein and gene expression profile during the process of switching to a neuron-like cell. Here we report that every cell of the adult human and rat corneal stroma is capable of differentiating into a neuron-like cell when treated with neurogenic differentiation specifying growth factors. Furthermore, the expression of genes regulating neurogenesis and mature neuronal structure and function was increased. The switch from a corneal stromal cell to a neuron-like cell was also shown to occur in vivo in intact corneas of living rats. Our results clearly indicate that lineage specifying growth factors can affect changes in the protein and gene expression profiles of adult cells, suggesting that possibly many adult cell populations can be made to switch into another type of mature cell by simply modifying the growth factor environment. - Highlights: • Adult corneal stromal cells can differentiated into neuron-like cells. • Neuronal specification of the adult stromal cell population is stochastic. • Neuronal specification in an adult cell population can be brought about by growth factors.

Cells from the adult corneal stroma can be reprogrammed to a neuron-like cell using exogenous growth factors

International Nuclear Information System (INIS)

Greene, Carol Ann; Chang, Chuan-Yuan; Fraser, Cameron J.; Nelidova, Dasha E.; Chen, Jing A.; Lim, Angela; Brebner, Alex; McGhee, Jennifer; Sherwin, Trevor; Green, Colin R.

2014-01-01

Cells thought to be stem cells isolated from the cornea of the eye have been shown to exhibit neurogenic potential. We set out to uncover the identity and location of these cells within the cornea and to elucidate their neuronal protein and gene expression profile during the process of switching to a neuron-like cell. Here we report that every cell of the adult human and rat corneal stroma is capable of differentiating into a neuron-like cell when treated with neurogenic differentiation specifying growth factors. Furthermore, the expression of genes regulating neurogenesis and mature neuronal structure and function was increased. The switch from a corneal stromal cell to a neuron-like cell was also shown to occur in vivo in intact corneas of living rats. Our results clearly indicate that lineage specifying growth factors can affect changes in the protein and gene expression profiles of adult cells, suggesting that possibly many adult cell populations can be made to switch into another type of mature cell by simply modifying the growth factor environment. - Highlights: • Adult corneal stromal cells can differentiated into neuron-like cells. • Neuronal specification of the adult stromal cell population is stochastic. • Neuronal specification in an adult cell population can be brought about by growth factors

Asymmetry and aging of mycobacterial cells leads to variable growth and antibiotic susceptibility

OpenAIRE

Aldridge, Bree B.; Fernandez-Suarez, Marta; Heller, Danielle; Ambravaneswaran, Vijay; Irimia, Daniel; Toner, Mehmet; Fortune, Sarah

2011-01-01

Cells use both deterministic and stochastic mechanisms to generate cell–to-cell heterogeneity, which enables the population to better withstand environmental stress. Here we show that, within a clonal population of mycobacteria, there is significant deterministic heterogeneity in elongation rate that arises because mycobacteria grow in an unusual, unipolar fashion. Division of the asymmetrically growing mother cell gives rise to daughter cells that differ in elongation rate and size. Because ...

The genetic network controlling plasma cell differentiation.

Science.gov (United States)

Nutt, Stephen L; Taubenheim, Nadine; Hasbold, Jhagvaral; Corcoran, Lynn M; Hodgkin, Philip D

2011-10-01

Upon activation by antigen, mature B cells undergo immunoglobulin class switch recombination and differentiate into antibody-secreting plasma cells, the endpoint of the B cell developmental lineage. Careful quantitation of these processes, which are stochastic, independent and strongly linked to the division history of the cell, has revealed that populations of B cells behave in a highly predictable manner. Considerable progress has also been made in the last few years in understanding the gene regulatory network that controls the B cell to plasma cell transition. The mutually exclusive transcriptomes of B cells and plasma cells are maintained by the antagonistic influences of two groups of transcription factors, those that maintain the B cell program, including Pax5, Bach2 and Bcl6, and those that promote and facilitate plasma cell differentiation, notably Irf4, Blimp1 and Xbp1. In this review, we discuss progress in the definition of both the transcriptional and cellular events occurring during late B cell differentiation, as integrating these two approaches is crucial to defining a regulatory network that faithfully reflects the stochastic features and complexity of the humoral immune response. 2011 Elsevier Ltd. All rights reserved.

Stochastic biological response to radiation. Comprehensive analysis of gene expression

International Nuclear Information System (INIS)

Inoue, Tohru; Hirabayashi, Yoko

2012-01-01

Authors explain that the radiation effect on biological system is stochastic along the law of physics, differing from chemical effect, using instances of Cs-137 gamma-ray (GR) and benzene (BZ) exposures to mice and of resultant comprehensive analyses of gene expression. Single GR irradiation is done with Gamma Cell 40 (CSR) to C57BL/6 or C3H/He mouse at 0, 0.6 and 3 Gy. BE is given orally at 150 mg/kg/day for 5 days x 2 weeks. Bone marrow cells are sampled 1 month after the exposure. Comprehensive gene expression is analyzed by Gene Chip Mouse Genome 430 2.0 Array (Affymetrix) and data are processed by programs like case normalization, statistics, network generation, functional analysis etc. GR irradiation brings about changes of gene expression, which are classifiable in common genes variable commonly on the dose change and stochastic genes variable stochastically within each dose: e.g., with Welch-t-test, significant differences are between 0/3 Gy (dose-specific difference, 455 pbs (probe set), in stochastic 2113 pbs), 0/0.6 Gy (267 in 1284 pbs) and 0.6/3 Gy (532 pbs); and with one-way analysis of variation (ANOVA) and hierarchial/dendrographic analyses, 520 pbs are shown to involve the dose-dependent 226 and dose-specific 294 pbs. It is also shown that at 3 Gy, expression of common genes are rather suppressed, including those related to the proliferation/apoptosis of B/T cells, and of stochastic genes, related to cell division/signaling. Ven diagram of the common genes of above 520 pbs, stochastic 2113 pbs at 3 Gy and 1284 pbs at 0.6 Gy shows the overlapping genes 29, 2 and 4, respectively, indicating only 35 pbs are overlapping in total. Network analysis of changes by GR shows the rather high expression of genes around hub of cAMP response element binding protein (CREB) at 0.6 Gy, and rather variable expression around CREB hub/suppressed expression of kinesin hub at 3 Gy; in the network by BZ exposure, unchanged or low expression around p53 hub and suppression

Effect of dedifferentiation on time to mutation acquisition in stem cell-driven cancers.

Directory of Open Access Journals (Sweden)

Alexandra Jilkine

2014-03-01

Full Text Available Accumulating evidence suggests that many tumors have a hierarchical organization, with the bulk of the tumor composed of relatively differentiated short-lived progenitor cells that are maintained by a small population of undifferentiated long-lived cancer stem cells. It is unclear, however, whether cancer stem cells originate from normal stem cells or from dedifferentiated progenitor cells. To address this, we mathematically modeled the effect of dedifferentiation on carcinogenesis. We considered a hybrid stochastic-deterministic model of mutation accumulation in both stem cells and progenitors, including dedifferentiation of progenitor cells to a stem cell-like state. We performed exact computer simulations of the emergence of tumor subpopulations with two mutations, and we derived semi-analytical estimates for the waiting time distribution to fixation. Our results suggest that dedifferentiation may play an important role in carcinogenesis, depending on how stem cell homeostasis is maintained. If the stem cell population size is held strictly constant (due to all divisions being asymmetric, we found that dedifferentiation acts like a positive selective force in the stem cell population and thus speeds carcinogenesis. If the stem cell population size is allowed to vary stochastically with density-dependent reproduction rates (allowing both symmetric and asymmetric divisions, we found that dedifferentiation beyond a critical threshold leads to exponential growth of the stem cell population. Thus, dedifferentiation may play a crucial role, the common modeling assumption of constant stem cell population size may not be adequate, and further progress in understanding carcinogenesis demands a more detailed mechanistic understanding of stem cell homeostasis.

Effect of Dedifferentiation on Time to Mutation Acquisition in Stem Cell-Driven Cancers

Science.gov (United States)

Jilkine, Alexandra; Gutenkunst, Ryan N.

2014-01-01

Accumulating evidence suggests that many tumors have a hierarchical organization, with the bulk of the tumor composed of relatively differentiated short-lived progenitor cells that are maintained by a small population of undifferentiated long-lived cancer stem cells. It is unclear, however, whether cancer stem cells originate from normal stem cells or from dedifferentiated progenitor cells. To address this, we mathematically modeled the effect of dedifferentiation on carcinogenesis. We considered a hybrid stochastic-deterministic model of mutation accumulation in both stem cells and progenitors, including dedifferentiation of progenitor cells to a stem cell-like state. We performed exact computer simulations of the emergence of tumor subpopulations with two mutations, and we derived semi-analytical estimates for the waiting time distribution to fixation. Our results suggest that dedifferentiation may play an important role in carcinogenesis, depending on how stem cell homeostasis is maintained. If the stem cell population size is held strictly constant (due to all divisions being asymmetric), we found that dedifferentiation acts like a positive selective force in the stem cell population and thus speeds carcinogenesis. If the stem cell population size is allowed to vary stochastically with density-dependent reproduction rates (allowing both symmetric and asymmetric divisions), we found that dedifferentiation beyond a critical threshold leads to exponential growth of the stem cell population. Thus, dedifferentiation may play a crucial role, the common modeling assumption of constant stem cell population size may not be adequate, and further progress in understanding carcinogenesis demands a more detailed mechanistic understanding of stem cell homeostasis. PMID:24603301

The quantitative description of radiation-unduced cell inactivation. 9. Some remarks on the relative biological effect of ionizing radiation upon reproductive death of diploid and polyploid cells

International Nuclear Information System (INIS)

Barsukov, V.S.; Malinovskij, O.V.

1978-01-01

A model of repproductive death of cells is suggested and tested on the basis of data on relative biological efficiency (RBE) with provision for microdosimetry approaches. The model considers irradiation of cells with charged particles under conditions of linear energy transfers (LET) to the cell nucleus by a particle. This energy is subject to great fluctuations, and irradiation of a cell should be characterized not by the absorbed dose but rather by its stochastic analogue - specific energy. It appears then that RBE should be determined from the specific energy-response relationshjp rather than from dose-response relationship. The followjng main results were obtained: experimental relationship between the yield of sublesions, one-track and two-track lethals and LET correspond to that expected from the literature data; under the conditions considered, RBE can be determined from absorbed dose-pesponse dependency; RBE for sublesions, one- and two-track lethals taken separatly does not depend on the radiation dose and the relationshjp between the resulting RBE and the response is governed by redistribution of contributions of one- and two-track lethals at different doses. The plausibility of the model suggested is thus confirmed

Single Cell Dynamics Causes Pareto-Like Effect in Stimulated T Cell Populations.

Science.gov (United States)

Cosette, Jérémie; Moussy, Alice; Onodi, Fanny; Auffret-Cariou, Adrien; Neildez-Nguyen, Thi My Anh; Paldi, Andras; Stockholm, Daniel

2015-12-09

Cell fate choice during the process of differentiation may obey to deterministic or stochastic rules. In order to discriminate between these two strategies we used time-lapse microscopy of individual murine CD4 + T cells that allows investigating the dynamics of proliferation and fate commitment. We observed highly heterogeneous division and death rates between individual clones resulting in a Pareto-like dominance of a few clones at the end of the experiment. Commitment to the Treg fate was monitored using the expression of a GFP reporter gene under the control of the endogenous Foxp3 promoter. All possible combinations of proliferation and differentiation were observed and resulted in exclusively GFP-, GFP+ or mixed phenotype clones of very different population sizes. We simulated the process of proliferation and differentiation using a simple mathematical model of stochastic decision-making based on the experimentally observed parameters. The simulations show that a stochastic scenario is fully compatible with the observed Pareto-like imbalance in the final population.

Stochastic inflation and nonlinear gravity

International Nuclear Information System (INIS)

Salopek, D.S.; Bond, J.R.

1991-01-01

We show how nonlinear effects of the metric and scalar fields may be included in stochastic inflation. Our formalism can be applied to non-Gaussian fluctuation models for galaxy formation. Fluctuations with wavelengths larger than the horizon length are governed by a network of Langevin equations for the physical fields. Stochastic noise terms arise from quantum fluctuations that are assumed to become classical at horizon crossing and that then contribute to the background. Using Hamilton-Jacobi methods, we solve the Arnowitt-Deser-Misner constraint equations which allows us to separate the growing modes from the decaying ones in the drift phase following each stochastic impulse. We argue that the most reasonable choice of time hypersurfaces for the Langevin system during inflation is T=ln(Ha), where H and a are the local values of the Hubble parameter and the scale factor, since T is the natural time for evolving the short-wavelength scalar field fluctuations in an inhomogeneous background

Colonic stem cell data are consistent with the immortal model of stem cell division under non-random strand segregation.

Science.gov (United States)

Walters, K

2009-06-01

Colonic stem cells are thought to reside towards the base of crypts of the colon, but their numbers and proliferation mechanisms are not well characterized. A defining property of stem cells is that they are able to divide asymmetrically, but it is not known whether they always divide asymmetrically (immortal model) or whether there are occasional symmetrical divisions (stochastic model). By measuring diversity of methylation patterns in colon crypt samples, a recent study found evidence in favour of the stochastic model, assuming random segregation of stem cell DNA strands during cell division. Here, the effect of preferential segregation of the template strand is considered to be consistent with the 'immortal strand hypothesis', and explore the effect on conclusions of previously published results. For a sample of crypts, it is shown how, under the immortal model, to calculate mean and variance of the number of unique methylation patterns allowing for non-random strand segregation and compare them with those observed. The calculated mean and variance are consistent with an immortal model that incorporates non-random strand segregation for a range of stem cell numbers and levels of preferential strand segregation. Allowing for preferential strand segregation considerably alters previously published conclusions relating to stem cell numbers and turnover mechanisms. Evidence in favour of the stochastic model may not be as strong as previously thought.

Modeling PSA Problems - II: A Cell-to-Cell Transport Theory Approach

International Nuclear Information System (INIS)

Labeau, P.E.; Izquierdo, J.M.

2005-01-01

In the first paper of this series, we presented an extension of the classical theory of dynamic reliability in which the actual occurrence of an event causing a change in the system dynamics is possibly delayed. The concept of stimulus activation, which triggers the realization of an event after a distributed time delay, was introduced. This gives a new understanding of competing events in the sequence delineation process.In the context of the level-2 probabilistic safety analysis (PSA), the information on stimulus activation mainly consists of regions of the process variables space where the activation can occur with a given probability. The evolution equations of the extended theory of probabilistic dynamics are therefore particularized to a transport process between discrete cells defined in phase-space on this basis. Doing so, an integrated and coherent approach to level-2 PSA problems is propounded. This amounts to including the stimulus concept and the associated stochastic delays discussed in the first paper in the frame of a cell-to-cell transport process.In addition, this discrete model provides a theoretical basis for the definition of appropriate numerical schemes for integrated level-2 PSA applications

Models for microtubule cargo transport coupling the Langevin equation to stochastic stepping motor dynamics: Caring about fluctuations.

Science.gov (United States)

Bouzat, Sebastián

2016-01-01

One-dimensional models coupling a Langevin equation for the cargo position to stochastic stepping dynamics for the motors constitute a relevant framework for analyzing multiple-motor microtubule transport. In this work we explore the consistence of these models focusing on the effects of the thermal noise. We study how to define consistent stepping and detachment rates for the motors as functions of the local forces acting on them in such a way that the cargo velocity and run-time match previously specified functions of the external load, which are set on the base of experimental results. We show that due to the influence of the thermal fluctuations this is not a trivial problem, even for the single-motor case. As a solution, we propose a motor stepping dynamics which considers memory on the motor force. This model leads to better results for single-motor transport than the approaches previously considered in the literature. Moreover, it gives a much better prediction for the stall force of the two-motor case, highly compatible with the experimental findings. We also analyze the fast fluctuations of the cargo position and the influence of the viscosity, comparing the proposed model to the standard one, and we show how the differences on the single-motor dynamics propagate to the multiple motor situations. Finally, we find that the one-dimensional character of the models impede an appropriate description of the fast fluctuations of the cargo position at small loads. We show how this problem can be solved by considering two-dimensional models.

Relatively slow stochastic gene-state switching in the presence of positive feedback significantly broadens the region of bimodality through stabilizing the uninduced phenotypic state.

Science.gov (United States)

Ge, Hao; Wu, Pingping; Qian, Hong; Xie, Xiaoliang Sunney

2018-03-01

Within an isogenic population, even in the same extracellular environment, individual cells can exhibit various phenotypic states. The exact role of stochastic gene-state switching regulating the transition among these phenotypic states in a single cell is not fully understood, especially in the presence of positive feedback. Recent high-precision single-cell measurements showed that, at least in bacteria, switching in gene states is slow relative to the typical rates of active transcription and translation. Hence using the lac operon as an archetype, in such a region of operon-state switching, we present a fluctuating-rate model for this classical gene regulation module, incorporating the more realistic operon-state switching mechanism that was recently elucidated. We found that the positive feedback mechanism induces bistability (referred to as deterministic bistability), and that the parameter range for its occurrence is significantly broadened by stochastic operon-state switching. We further show that in the absence of positive feedback, operon-state switching must be extremely slow to trigger bistability by itself. However, in the presence of positive feedback, which stabilizes the induced state, the relatively slow operon-state switching kinetics within the physiological region are sufficient to stabilize the uninduced state, together generating a broadened parameter region of bistability (referred to as stochastic bistability). We illustrate the opposite phenotype-transition rate dependence upon the operon-state switching rates in the two types of bistability, with the aid of a recently proposed rate formula for fluctuating-rate models. The rate formula also predicts a maximal transition rate in the intermediate region of operon-state switching, which is validated by numerical simulations in our model. Overall, our findings suggest a biological function of transcriptional "variations" among genetically identical cells, for the emergence of bistability and

A deterministic and stochastic model for the system dynamics of tumor-immune responses to chemotherapy

Science.gov (United States)

Liu, Xiangdong; Li, Qingze; Pan, Jianxin

2018-06-01

Modern medical studies show that chemotherapy can help most cancer patients, especially for those diagnosed early, to stabilize their disease conditions from months to years, which means the population of tumor cells remained nearly unchanged in quite a long time after fighting against immune system and drugs. In order to better understand the dynamics of tumor-immune responses under chemotherapy, deterministic and stochastic differential equation models are constructed to characterize the dynamical change of tumor cells and immune cells in this paper. The basic dynamical properties, such as boundedness, existence and stability of equilibrium points, are investigated in the deterministic model. Extended stochastic models include stochastic differential equations (SDEs) model and continuous-time Markov chain (CTMC) model, which accounts for the variability in cellular reproduction, growth and death, interspecific competitions, and immune response to chemotherapy. The CTMC model is harnessed to estimate the extinction probability of tumor cells. Numerical simulations are performed, which confirms the obtained theoretical results.

Stochastic modelling of intermittent fluctuations in the scrape-off layer: Correlations, distributions, level crossings, and moment estimation

Energy Technology Data Exchange (ETDEWEB)

Garcia, O. E., E-mail: odd.erik.garcia@uit.no; Kube, R.; Theodorsen, A. [Department of Physics and Technology, UiT The Arctic University of Norway, N-9037 Tromsø (Norway); Pécseli, H. L. [Physics Department, University of Oslo, PO Box 1048 Blindern, N-0316 Oslo (Norway)

2016-05-15

A stochastic model is presented for intermittent fluctuations in the scrape-off layer of magnetically confined plasmas. The fluctuations in the plasma density are modeled by a super-position of uncorrelated pulses with fixed shape and duration, describing radial motion of blob-like structures. In the case of an exponential pulse shape and exponentially distributed pulse amplitudes, predictions are given for the lowest order moments, probability density function, auto-correlation function, level crossings, and average times for periods spent above and below a given threshold level. Also, the mean squared errors on estimators of sample mean and variance for realizations of the process by finite time series are obtained. These results are discussed in the context of single-point measurements of fluctuations in the scrape-off layer, broad density profiles, and implications for plasma–wall interactions due to the transient transport events in fusion grade plasmas. The results may also have wide applications for modelling fluctuations in other magnetized plasmas such as basic laboratory experiments and ionospheric irregularities.

Kinetic theory of age-structured stochastic birth-death processes

Science.gov (United States)

Greenman, Chris D.; Chou, Tom

2016-01-01

Classical age-structured mass-action models such as the McKendrick-von Foerster equation have been extensively studied but are unable to describe stochastic fluctuations or population-size-dependent birth and death rates. Stochastic theories that treat semi-Markov age-dependent processes using, e.g., the Bellman-Harris equation do not resolve a population's age structure and are unable to quantify population-size dependencies. Conversely, current theories that include size-dependent population dynamics (e.g., mathematical models that include carrying capacity such as the logistic equation) cannot be easily extended to take into account age-dependent birth and death rates. In this paper, we present a systematic derivation of a new, fully stochastic kinetic theory for interacting age-structured populations. By defining multiparticle probability density functions, we derive a hierarchy of kinetic equations for the stochastic evolution of an aging population undergoing birth and death. We show that the fully stochastic age-dependent birth-death process precludes factorization of the corresponding probability densities, which then must be solved by using a Bogoliubov--Born--Green--Kirkwood--Yvon-like hierarchy. Explicit solutions are derived in three limits: no birth, no death, and steady state. These are then compared with their corresponding mean-field results. Our results generalize both deterministic models and existing master equation approaches by providing an intuitive and efficient way to simultaneously model age- and population-dependent stochastic dynamics applicable to the study of demography, stem cell dynamics, and disease evolution.

Stochastic pump effect and geometric phases in dissipative and stochastic systems

Energy Technology Data Exchange (ETDEWEB)

Sinitsyn, Nikolai [Los Alamos National Laboratory

2008-01-01

The success of Berry phases in quantum mechanics stimulated the study of similar phenomena in other areas of physics, including the theory of living cell locomotion and motion of patterns in nonlinear media. More recently, geometric phases have been applied to systems operating in a strongly stochastic environment, such as molecular motors. We discuss such geometric effects in purely classical dissipative stochastic systems and their role in the theory of the stochastic pump effect (SPE).

Finite state projection based bounds to compare chemical master equation models using single-cell data

Energy Technology Data Exchange (ETDEWEB)

Fox, Zachary [School of Biomedical Engineering, Colorado State University, Fort Collins, Colorado 80523 (United States); Neuert, Gregor [Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, Tennessee 37232 (United States); Department of Pharmacology, School of Medicine, Vanderbilt University, Nashville, Tennessee 37232 (United States); Department of Biomedical Engineering, Vanderbilt University School of Engineering, Nashville, Tennessee 37232 (United States); Munsky, Brian [School of Biomedical Engineering, Colorado State University, Fort Collins, Colorado 80523 (United States); Department of Chemical and Biological Engineering, Colorado State University, Fort Collins, Colorado 80523 (United States)

2016-08-21

Emerging techniques now allow for precise quantification of distributions of biological molecules in single cells. These rapidly advancing experimental methods have created a need for more rigorous and efficient modeling tools. Here, we derive new bounds on the likelihood that observations of single-cell, single-molecule responses come from a discrete stochastic model, posed in the form of the chemical master equation. These strict upper and lower bounds are based on a finite state projection approach, and they converge monotonically to the exact likelihood value. These bounds allow one to discriminate rigorously between models and with a minimum level of computational effort. In practice, these bounds can be incorporated into stochastic model identification and parameter inference routines, which improve the accuracy and efficiency of endeavors to analyze and predict single-cell behavior. We demonstrate the applicability of our approach using simulated data for three example models as well as for experimental measurements of a time-varying stochastic transcriptional response in yeast.

PhysiCell: An open source physics-based cell simulator for 3-D multicellular systems.

Science.gov (United States)

Ghaffarizadeh, Ahmadreza; Heiland, Randy; Friedman, Samuel H; Mumenthaler, Shannon M; Macklin, Paul

2018-02-01

Many multicellular systems problems can only be understood by studying how cells move, grow, divide, interact, and die. Tissue-scale dynamics emerge from systems of many interacting cells as they respond to and influence their microenvironment. The ideal "virtual laboratory" for such multicellular systems simulates both the biochemical microenvironment (the "stage") and many mechanically and biochemically interacting cells (the "players" upon the stage). PhysiCell-physics-based multicellular simulator-is an open source agent-based simulator that provides both the stage and the players for studying many interacting cells in dynamic tissue microenvironments. It builds upon a multi-substrate biotransport solver to link cell phenotype to multiple diffusing substrates and signaling factors. It includes biologically-driven sub-models for cell cycling, apoptosis, necrosis, solid and fluid volume changes, mechanics, and motility "out of the box." The C++ code has minimal dependencies, making it simple to maintain and deploy across platforms. PhysiCell has been parallelized with OpenMP, and its performance scales linearly with the number of cells. Simulations up to 105-106 cells are feasible on quad-core desktop workstations; larger simulations are attainable on single HPC compute nodes. We demonstrate PhysiCell by simulating the impact of necrotic core biomechanics, 3-D geometry, and stochasticity on the dynamics of hanging drop tumor spheroids and ductal carcinoma in situ (DCIS) of the breast. We demonstrate stochastic motility, chemical and contact-based interaction of multiple cell types, and the extensibility of PhysiCell with examples in synthetic multicellular systems (a "cellular cargo delivery" system, with application to anti-cancer treatments), cancer heterogeneity, and cancer immunology. PhysiCell is a powerful multicellular systems simulator that will be continually improved with new capabilities and performance improvements. It also represents a significant

Biochemical Network Stochastic Simulator (BioNetS: software for stochastic modeling of biochemical networks

Directory of Open Access Journals (Sweden)

Elston Timothy C

2004-03-01

Full Text Available Abstract Background Intrinsic fluctuations due to the stochastic nature of biochemical reactions can have large effects on the response of biochemical networks. This is particularly true for pathways that involve transcriptional regulation, where generally there are two copies of each gene and the number of messenger RNA (mRNA molecules can be small. Therefore, there is a need for computational tools for developing and investigating stochastic models of biochemical networks. Results We have developed the software package Biochemical Network Stochastic Simulator (BioNetS for efficientlyand accurately simulating stochastic models of biochemical networks. BioNetS has a graphical user interface that allows models to be entered in a straightforward manner, and allows the user to specify the type of random variable (discrete or continuous for each chemical species in the network. The discrete variables are simulated using an efficient implementation of the Gillespie algorithm. For the continuous random variables, BioNetS constructs and numerically solvesthe appropriate chemical Langevin equations. The software package has been developed to scale efficiently with network size, thereby allowing large systems to be studied. BioNetS runs as a BioSpice agent and can be downloaded from http://www.biospice.org. BioNetS also can be run as a stand alone package. All the required files are accessible from http://x.amath.unc.edu/BioNetS. Conclusions We have developed BioNetS to be a reliable tool for studying the stochastic dynamics of large biochemical networks. Important features of BioNetS are its ability to handle hybrid models that consist of both continuous and discrete random variables and its ability to model cell growth and division. We have verified the accuracy and efficiency of the numerical methods by considering several test systems.

Particle transport due to magnetic fluctuations

International Nuclear Information System (INIS)

Stoneking, M.R.; Hokin, S.A.; Prager, S.C.; Fiksel, G.; Ji, H.; Den Hartog, D.J.

1994-01-01

Electron current fluctuations are measured with an electrostatic energy analyzer at the edge of the MST reversed-field pinch plasma. The radial flux of fast electrons (E>T e ) due to parallel streaming along a fluctuating magnetic field is determined locally by measuring the correlated product e B r >. Particle transport is small just inside the last closed flux surface (Γ e,mag e,total ), but can account for all observed particle losses inside r/a=0.8. Electron diffusion is found to increase with parallel velocity, as expected for diffusion in a region of field stochasticity

Fuel Cells

DEFF Research Database (Denmark)

Smith, Anders; Pedersen, Allan Schrøder

2014-01-01

Fuel cells have been the subject of intense research and development efforts for the past decades. Even so, the technology has not had its commercial breakthrough yet. This entry gives an overview of the technological challenges and status of fuel cells and discusses the most promising applications...... of the different types of fuel cells. Finally, their role in a future energy supply with a large share of fluctuating sustainable power sources, e.g., solar or wind, is surveyed....

Single-cell analysis of transcription kinetics across the cell cycle

Science.gov (United States)

Skinner, Samuel O; Xu, Heng; Nagarkar-Jaiswal, Sonal; Freire, Pablo R; Zwaka, Thomas P; Golding, Ido

2016-01-01

Transcription is a highly stochastic process. To infer transcription kinetics for a gene-of-interest, researchers commonly compare the distribution of mRNA copy-number to the prediction of a theoretical model. However, the reliability of this procedure is limited because the measured mRNA numbers represent integration over the mRNA lifetime, contribution from multiple gene copies, and mixing of cells from different cell-cycle phases. We address these limitations by simultaneously quantifying nascent and mature mRNA in individual cells, and incorporating cell-cycle effects in the analysis of mRNA statistics. We demonstrate our approach on Oct4 and Nanog in mouse embryonic stem cells. Both genes follow similar two-state kinetics. However, Nanog exhibits slower ON/OFF switching, resulting in increased cell-to-cell variability in mRNA levels. Early in the cell cycle, the two copies of each gene exhibit independent activity. After gene replication, the probability of each gene copy to be active diminishes, resulting in dosage compensation. DOI: http://dx.doi.org/10.7554/eLife.12175.001 PMID:26824388

Fluorescence fluctuation spectroscopy (FFS)

CERN Document Server

Tetin, Sergey

2012-01-01

This new volume of Methods in Enzymology continues the legacy of this premier serial with quality chapters authored by leaders in the field. This volume covers fluorescence fluctuation spectroscopy and includes chapters on such topics as Förster resonance energy transfer (fret) with fluctuation algorithms, protein corona on nanoparticles by FCS, and FFS approaches to the study of receptors in live cells. Continues the legacy of this premier serial with quality chapters authored by leaders in the field Covers fluorescence fluctuation spectroscopy Contains chapters on such topics as Förster resonance energy transfer (fret) with fluctuation algorithms, protein corona on nanoparticles by FCS, and FFS approaches to the study of receptors in live cells.

PhysiCell: An open source physics-based cell simulator for 3-D multicellular systems

Science.gov (United States)

Ghaffarizadeh, Ahmadreza; Mumenthaler, Shannon M.

2018-01-01

Many multicellular systems problems can only be understood by studying how cells move, grow, divide, interact, and die. Tissue-scale dynamics emerge from systems of many interacting cells as they respond to and influence their microenvironment. The ideal “virtual laboratory” for such multicellular systems simulates both the biochemical microenvironment (the “stage”) and many mechanically and biochemically interacting cells (the “players” upon the stage). PhysiCell—physics-based multicellular simulator—is an open source agent-based simulator that provides both the stage and the players for studying many interacting cells in dynamic tissue microenvironments. It builds upon a multi-substrate biotransport solver to link cell phenotype to multiple diffusing substrates and signaling factors. It includes biologically-driven sub-models for cell cycling, apoptosis, necrosis, solid and fluid volume changes, mechanics, and motility “out of the box.” The C++ code has minimal dependencies, making it simple to maintain and deploy across platforms. PhysiCell has been parallelized with OpenMP, and its performance scales linearly with the number of cells. Simulations up to 105-106 cells are feasible on quad-core desktop workstations; larger simulations are attainable on single HPC compute nodes. We demonstrate PhysiCell by simulating the impact of necrotic core biomechanics, 3-D geometry, and stochasticity on the dynamics of hanging drop tumor spheroids and ductal carcinoma in situ (DCIS) of the breast. We demonstrate stochastic motility, chemical and contact-based interaction of multiple cell types, and the extensibility of PhysiCell with examples in synthetic multicellular systems (a “cellular cargo delivery” system, with application to anti-cancer treatments), cancer heterogeneity, and cancer immunology. PhysiCell is a powerful multicellular systems simulator that will be continually improved with new capabilities and performance improvements. It also

Optimal shifting of Photovoltaic and load fluctuations from fuel cell and electrolyzer to lead acid battery in a Photovoltaic/hydrogen standalone power system for improved performance and life time

Science.gov (United States)

Tesfahunegn, S. G.; Ulleberg, Ø.; Vie, P. J. S.; Undeland, T. M.

Cost reduction is very critical in the pursuit of realizing more competitive clean and sustainable energy systems. In line with this goal a control method that enables minimization of the cost associated with performance and life time degradation of fuel cell and electrolyzer, and cost of battery replacement in PV/hydrogen standalone power systems is developed. The method uses the advantage of existing peak shaving battery to suppress short-term PV and load fluctuations while reducing impact on the cycle life of the battery itself. This is realized by diverting short-term cyclic charge/discharge events induced by PV/load power fluctuations to the upper band of the battery state of charge regime while operating the fuel cell and electrolyzer systems along stable (smooth) power curves. Comparative studies of the developed method with two other reference cases demonstrate that the proposed method fares better with respect to defined performance indices as fluctuation suppression rate and mean state of charge. Modeling of power electronics and design of controllers used in the study are also briefly discussed in Appendix A.

Spatial Evolutionary Games of Interaction among Generic Cancer Cells

DEFF Research Database (Denmark)

Bach, Lars Arve; Sumpter, David J.T.; Alsner, Jan

2003-01-01

Evolutionary game models of cellular interactions have shown that heterogeneity in the cellular genotypic composition is maintained through evolution to stable coexistence of growth-promoting and non-promoting cell types. We generalise these mean-field models and relax the assumption of perfect...... mixing of cells by instead implementing an individual-based model that includes the stochastic and spatial effects likely to occur in tumours. The scope for coexistence of genotypic strategies changed with the inclusion of explicit space and stochasticity. The spatial models show some interesting...... deviations from their mean-field counterparts, for example the possibility of altruistic (paracrine) cell strategies to thrive. Such effects can however, be highly sensitive to model implementation and the more realistic models with semi-synchronous and stochastic updating do not show evolution of altruism...

Hybrid framework for the simulation of stochastic chemical kinetics

International Nuclear Information System (INIS)

Duncan, Andrew; Erban, Radek; Zygalakis, Konstantinos

2016-01-01

Stochasticity plays a fundamental role in various biochemical processes, such as cell regulatory networks and enzyme cascades. Isothermal, well-mixed systems can be modelled as Markov processes, typically simulated using the Gillespie Stochastic Simulation Algorithm (SSA) [25]. While easy to implement and exact, the computational cost of using the Gillespie SSA to simulate such systems can become prohibitive as the frequency of reaction events increases. This has motivated numerous coarse-grained schemes, where the “fast” reactions are approximated either using Langevin dynamics or deterministically. While such approaches provide a good approximation when all reactants are abundant, the approximation breaks down when one or more species exist only in small concentrations and the fluctuations arising from the discrete nature of the reactions become significant. This is particularly problematic when using such methods to compute statistics of extinction times for chemical species, as well as simulating non-equilibrium systems such as cell-cycle models in which a single species can cycle between abundance and scarcity. In this paper, a hybrid jump-diffusion model for simulating well-mixed stochastic kinetics is derived. It acts as a bridge between the Gillespie SSA and the chemical Langevin equation. For low reactant reactions the underlying behaviour is purely discrete, while purely diffusive when the concentrations of all species are large, with the two different behaviours coexisting in the intermediate region. A bound on the weak error in the classical large volume scaling limit is obtained, and three different numerical discretisations of the jump-diffusion model are described. The benefits of such a formalism are illustrated using computational examples.

Hybrid framework for the simulation of stochastic chemical kinetics

Science.gov (United States)

Duncan, Andrew; Erban, Radek; Zygalakis, Konstantinos

2016-12-01

Stochasticity plays a fundamental role in various biochemical processes, such as cell regulatory networks and enzyme cascades. Isothermal, well-mixed systems can be modelled as Markov processes, typically simulated using the Gillespie Stochastic Simulation Algorithm (SSA) [25]. While easy to implement and exact, the computational cost of using the Gillespie SSA to simulate such systems can become prohibitive as the frequency of reaction events increases. This has motivated numerous coarse-grained schemes, where the "fast" reactions are approximated either using Langevin dynamics or deterministically. While such approaches provide a good approximation when all reactants are abundant, the approximation breaks down when one or more species exist only in small concentrations and the fluctuations arising from the discrete nature of the reactions become significant. This is particularly problematic when using such methods to compute statistics of extinction times for chemical species, as well as simulating non-equilibrium systems such as cell-cycle models in which a single species can cycle between abundance and scarcity. In this paper, a hybrid jump-diffusion model for simulating well-mixed stochastic kinetics is derived. It acts as a bridge between the Gillespie SSA and the chemical Langevin equation. For low reactant reactions the underlying behaviour is purely discrete, while purely diffusive when the concentrations of all species are large, with the two different behaviours coexisting in the intermediate region. A bound on the weak error in the classical large volume scaling limit is obtained, and three different numerical discretisations of the jump-diffusion model are described. The benefits of such a formalism are illustrated using computational examples.

Hybrid framework for the simulation of stochastic chemical kinetics

Energy Technology Data Exchange (ETDEWEB)

Duncan, Andrew, E-mail: a.duncan@imperial.ac.uk [Department of Mathematics, Imperial College, South Kensington Campus, London, SW7 2AZ (United Kingdom); Erban, Radek, E-mail: erban@maths.ox.ac.uk [Mathematical Institute, University of Oxford, Radcliffe Observatory Quarter, Woodstock Road, Oxford, OX2 6GG (United Kingdom); Zygalakis, Konstantinos, E-mail: k.zygalakis@ed.ac.uk [School of Mathematics, University of Edinburgh, Peter Guthrie Tait Road, Edinburgh, EH9 3FD (United Kingdom)

2016-12-01

Stochasticity plays a fundamental role in various biochemical processes, such as cell regulatory networks and enzyme cascades. Isothermal, well-mixed systems can be modelled as Markov processes, typically simulated using the Gillespie Stochastic Simulation Algorithm (SSA) [25]. While easy to implement and exact, the computational cost of using the Gillespie SSA to simulate such systems can become prohibitive as the frequency of reaction events increases. This has motivated numerous coarse-grained schemes, where the “fast” reactions are approximated either using Langevin dynamics or deterministically. While such approaches provide a good approximation when all reactants are abundant, the approximation breaks down when one or more species exist only in small concentrations and the fluctuations arising from the discrete nature of the reactions become significant. This is particularly problematic when using such methods to compute statistics of extinction times for chemical species, as well as simulating non-equilibrium systems such as cell-cycle models in which a single species can cycle between abundance and scarcity. In this paper, a hybrid jump-diffusion model for simulating well-mixed stochastic kinetics is derived. It acts as a bridge between the Gillespie SSA and the chemical Langevin equation. For low reactant reactions the underlying behaviour is purely discrete, while purely diffusive when the concentrations of all species are large, with the two different behaviours coexisting in the intermediate region. A bound on the weak error in the classical large volume scaling limit is obtained, and three different numerical discretisations of the jump-diffusion model are described. The benefits of such a formalism are illustrated using computational examples.

Analysis of allelic expression patterns in clonal somatic cells by single-cell RNA-seq.

Science.gov (United States)

Reinius, Björn; Mold, Jeff E; Ramsköld, Daniel; Deng, Qiaolin; Johnsson, Per; Michaëlsson, Jakob; Frisén, Jonas; Sandberg, Rickard

2016-11-01

Cellular heterogeneity can emerge from the expression of only one parental allele. However, it has remained controversial whether, or to what degree, random monoallelic expression of autosomal genes (aRME) is mitotically inherited (clonal) or stochastic (dynamic) in somatic cells, particularly in vivo. Here we used allele-sensitive single-cell RNA-seq on clonal primary mouse fibroblasts and freshly isolated human CD8 + T cells to dissect clonal and dynamic monoallelic expression patterns. Dynamic aRME affected a considerable portion of the cells' transcriptomes, with levels dependent on the cells' transcriptional activity. Notably, clonal aRME was detected, but it was surprisingly scarce (aRME occurs transiently within individual cells, and patterns of aRME are thus primarily scattered throughout somatic cell populations rather than, as previously hypothesized, confined to patches of clonally related cells.

Stochastic lattice model of synaptic membrane protein domains.

Science.gov (United States)

Li, Yiwei; Kahraman, Osman; Haselwandter, Christoph A

2017-05-01

Neurotransmitter receptor molecules, concentrated in synaptic membrane domains along with scaffolds and other kinds of proteins, are crucial for signal transmission across chemical synapses. In common with other membrane protein domains, synaptic domains are characterized by low protein copy numbers and protein crowding, with rapid stochastic turnover of individual molecules. We study here in detail a stochastic lattice model of the receptor-scaffold reaction-diffusion dynamics at synaptic domains that was found previously to capture, at the mean-field level, the self-assembly, stability, and characteristic size of synaptic domains observed in experiments. We show that our stochastic lattice model yields quantitative agreement with mean-field models of nonlinear diffusion in crowded membranes. Through a combination of analytic and numerical solutions of the master equation governing the reaction dynamics at synaptic domains, together with kinetic Monte Carlo simulations, we find substantial discrepancies between mean-field and stochastic models for the reaction dynamics at synaptic domains. Based on the reaction and diffusion properties of synaptic receptors and scaffolds suggested by previous experiments and mean-field calculations, we show that the stochastic reaction-diffusion dynamics of synaptic receptors and scaffolds provide a simple physical mechanism for collective fluctuations in synaptic domains, the molecular turnover observed at synaptic domains, key features of the observed single-molecule trajectories, and spatial heterogeneity in the effective rates at which receptors and scaffolds are recycled at the cell membrane. Our work sheds light on the physical mechanisms and principles linking the collective properties of membrane protein domains to the stochastic dynamics that rule their molecular components.

Fluctuations in Blood Marginal Zone B-Cell Frequencies May Reflect Migratory Patterns Associated with HIV-1 Disease Progression Status.

Science.gov (United States)

Gauvin, Julie; Chagnon-Choquet, Josiane; Poudrier, Johanne; Roger, Michel

2016-01-01

We have previously shown that overexpression of BLyS/BAFF was associated with increased relative frequencies of innate "precursor" marginal zone (MZ)-like B-cells in the blood of HIV-1-infected rapid and classic progressors. However, along with relatively normal BLyS/BAFF expression levels, these cells remain unaltered in elite-controllers (EC), rather, percentages of more mature MZ-like B-cells are decreased in the blood of these individuals. Fluctuations in frequencies of blood MZ-like B-cell populations may reflect migratory patterns associated with disease progression status, suggesting an important role for these cells in HIV-1 pathogenesis. We have therefore longitudinally measured plasma levels of B-tropic chemokines by ELISA-based technology as well as their ligands by flow-cytometry on blood B-cell populations of HIV-1-infected individuals with different rates of disease progression and uninfected controls. Migration potential of B-cell populations from these individuals were determined by chemotaxis assays. We found important modulations of CXCL13-CXCR5, CXCL12-CXCR4/CXCR7, CCL20-CCR6 and CCL25-CCR9 chemokine-axes and increased cell migration patterns in HIV progressors. Interestingly, frequencies of CCR6 expressing cells were significantly elevated within the precursor MZ-like population, consistent with increased migration in response to CCL20. Although we found little modulation of chemokine-axes in EC, cell migration was greater than that observed for uninfected controls, especially for MZ-like B-cells. Overall the immune response against HIV-1 may involve recruitment of MZ-like B-cells to peripheral sites. Moreover, our findings suggest that "regulated" attraction of these cells in a preserved BLyS/BAFF non-inflammatory environment, such as encountered in EC could be beneficial to the battle and even control of HIV.

Molecular machines in living cells. Seibutsu no bunshi kikai to sono system

Energy Technology Data Exchange (ETDEWEB)

Osawa, F. (Aichi Inst. of Tech., Nagoya (Japan))

1992-12-20

At first, flagellar motors of bacteria are reviewed as a typical example of molecular machines in living cells. A rotational motor is embedded in the cell membrane at the root of the flagellum. The driving power of the rotation is the flow of hydrogen ion from the inside to the outside of the cell. In a normal state of a bacterium, potential difference of about 0.2 V is produced by the ion pump existing in the cell membrane. A molecular motor of sliding motion of muscle attracts the attention on the relation of the input and output of the molecular motor. The mechanism of the smooth motion without fluctuation in the fluctuated environment and the fluctuated input is unknown. Next, the motion of Paramecium is discussed as an example of a system composed of a number of molecular machines. Paramecium moves to a place of a suitable temperature when placed in a water tank with temperature gradient, however, it does not stop the motion at the place of the suitable temperature and increases a probability to change the direction when leaving, that is it takes a method of indirect probabilistic control. 12 refs., 8 figs.

A mathematical model of collective cell migration in a three-dimensional, heterogeneous environment.

Science.gov (United States)

Stonko, David P; Manning, Lathiena; Starz-Gaiano, Michelle; Peercy, Bradford E

2015-01-01

Cell migration is essential in animal development, homeostasis, and disease progression, but many questions remain unanswered about how this process is controlled. While many kinds of individual cell movements have been characterized, less effort has been directed towards understanding how clusters of cells migrate collectively through heterogeneous, cellular environments. To explore this, we have focused on the migration of the border cells during Drosophila egg development. In this case, a cluster of different cell types coalesce and traverse as a group between large cells, called nurse cells, in the center of the egg chamber. We have developed a new model for this collective cell migration based on the forces of adhesion, repulsion, migration and stochastic fluctuation to generate the movement of discrete cells. We implement the model using Identical Math Cells, or IMCs. IMCs can each represent one biological cell of the system, or can be aggregated using increased adhesion forces to model the dynamics of larger biological cells. The domain of interest is filled with IMCs, each assigned specific biophysical properties to mimic a diversity of cell types. Using this system, we have successfully simulated the migration of the border cell cluster through an environment filled with larger cells, which represent nurse cells. Interestingly, our simulations suggest that the forces utilized in this model are sufficient to produce behaviors of the cluster that are observed in vivo, such as rotation. Our framework was developed to capture a heterogeneous cell population, and our implementation strategy allows for diverse, but precise, initial position specification over a three- dimensional domain. Therefore, we believe that this model will be useful for not only examining aspects of Drosophila oogenesis, but also for modeling other two or three-dimensional systems that have multiple cell types and where investigating the forces between cells is of interest.

A mathematical model of collective cell migration in a three-dimensional, heterogeneous environment.

Directory of Open Access Journals (Sweden)

David P Stonko

Full Text Available Cell migration is essential in animal development, homeostasis, and disease progression, but many questions remain unanswered about how this process is controlled. While many kinds of individual cell movements have been characterized, less effort has been directed towards understanding how clusters of cells migrate collectively through heterogeneous, cellular environments. To explore this, we have focused on the migration of the border cells during Drosophila egg development. In this case, a cluster of different cell types coalesce and traverse as a group between large cells, called nurse cells, in the center of the egg chamber. We have developed a new model for this collective cell migration based on the forces of adhesion, repulsion, migration and stochastic fluctuation to generate the movement of discrete cells. We implement the model using Identical Math Cells, or IMCs. IMCs can each represent one biological cell of the system, or can be aggregated using increased adhesion forces to model the dynamics of larger biological cells. The domain of interest is filled with IMCs, each assigned specific biophysical properties to mimic a diversity of cell types. Using this system, we have successfully simulated the migration of the border cell cluster through an environment filled with larger cells, which represent nurse cells. Interestingly, our simulations suggest that the forces utilized in this model are sufficient to produce behaviors of the cluster that are observed in vivo, such as rotation. Our framework was developed to capture a heterogeneous cell population, and our implementation strategy allows for diverse, but precise, initial position specification over a three- dimensional domain. Therefore, we believe that this model will be useful for not only examining aspects of Drosophila oogenesis, but also for modeling other two or three-dimensional systems that have multiple cell types and where investigating the forces between cells is of

Stationary Size Distributions of Growing Cells with Binary and Multiple Cell Division

Science.gov (United States)

Rading, M. M.; Engel, T. A.; Lipowsky, R.; Valleriani, A.

2011-10-01

Populations of unicellular organisms that grow under constant environmental conditions are considered theoretically. The size distribution of these cells is calculated analytically, both for the usual process of binary division, in which one mother cell produces always two daughter cells, and for the more complex process of multiple division, in which one mother cell can produce 2 n daughter cells with n=1,2,3,… . The latter mode of division is inspired by the unicellular algae Chlamydomonas reinhardtii. The uniform response of the whole population to different environmental conditions is encoded in the individual rates of growth and division of the cells. The analytical treatment of the problem is based on size-dependent rules for cell growth and stochastic transition processes for cell division. The comparison between binary and multiple division shows that these different division processes lead to qualitatively different results for the size distribution and the population growth rates.

Nonstationary stochastic charge fluctuations of a dust particle in plasmas.

Science.gov (United States)

Shotorban, B

2011-06-01

Stochastic charge fluctuations of a dust particle that are due to discreteness of electrons and ions in plasmas can be described by a one-step process master equation [T. Matsoukas and M. Russell, J. Appl. Phys. 77, 4285 (1995)] with no exact solution. In the present work, using the system size expansion method of Van Kampen along with the linear noise approximation, a Fokker-Planck equation with an exact Gaussian solution is developed by expanding the master equation. The Gaussian solution has time-dependent mean and variance governed by two ordinary differential equations modeling the nonstationary process of dust particle charging. The model is tested via the comparison of its results to the results obtained by solving the master equation numerically. The electron and ion currents are calculated through the orbital motion limited theory. At various times of the nonstationary process of charging, the model results are in a very good agreement with the master equation results. The deviation is more significant when the standard deviation of the charge is comparable to the mean charge in magnitude.

Stochastic thermodynamics

Science.gov (United States)

Eichhorn, Ralf; Aurell, Erik

2014-04-01

'Stochastic thermodynamics as a conceptual framework combines the stochastic energetics approach introduced a decade ago by Sekimoto [1] with the idea that entropy can consistently be assigned to a single fluctuating trajectory [2]'. This quote, taken from Udo Seifert's [3] 2008 review, nicely summarizes the basic ideas behind stochastic thermodynamics: for small systems, driven by external forces and in contact with a heat bath at a well-defined temperature, stochastic energetics [4] defines the exchanged work and heat along a single fluctuating trajectory and connects them to changes in the internal (system) energy by an energy balance analogous to the first law of thermodynamics. Additionally, providing a consistent definition of trajectory-wise entropy production gives rise to second-law-like relations and forms the basis for a 'stochastic thermodynamics' along individual fluctuating trajectories. In order to construct meaningful concepts of work, heat and entropy production for single trajectories, their definitions are based on the stochastic equations of motion modeling the physical system of interest. Because of this, they are valid even for systems that are prevented from equilibrating with the thermal environment by external driving forces (or other sources of non-equilibrium). In that way, the central notions of equilibrium thermodynamics, such as heat, work and entropy, are consistently extended to the non-equilibrium realm. In the (non-equilibrium) ensemble, the trajectory-wise quantities acquire distributions. General statements derived within stochastic thermodynamics typically refer to properties of these distributions, and are valid in the non-equilibrium regime even beyond the linear response. The extension of statistical mechanics and of exact thermodynamic statements to the non-equilibrium realm has been discussed from the early days of statistical mechanics more than 100 years ago. This debate culminated in the development of linear response

An efficient parallel stochastic simulation method for analysis of nonviral gene delivery systems

KAUST Repository

Kuwahara, Hiroyuki

2011-01-01

Gene therapy has a great potential to become an effective treatment for a wide variety of diseases. One of the main challenges to make gene therapy practical in clinical settings is the development of efficient and safe mechanisms to deliver foreign DNA molecules into the nucleus of target cells. Several computational and experimental studies have shown that the design process of synthetic gene transfer vectors can be greatly enhanced by computational modeling and simulation. This paper proposes a novel, effective parallelization of the stochastic simulation algorithm (SSA) for pharmacokinetic models that characterize the rate-limiting, multi-step processes of intracellular gene delivery. While efficient parallelizations of the SSA are still an open problem in a general setting, the proposed parallel simulation method is able to substantially accelerate the next reaction selection scheme and the reaction update scheme in the SSA by exploiting and decomposing the structures of stochastic gene delivery models. This, thus, makes computationally intensive analysis such as parameter optimizations and gene dosage control for specific cell types, gene vectors, and transgene expression stability substantially more practical than that could otherwise be with the standard SSA. Here, we translated the nonviral gene delivery model based on mass-action kinetics by Varga et al. [Molecular Therapy, 4(5), 2001] into a more realistic model that captures intracellular fluctuations based on stochastic chemical kinetics, and as a case study we applied our parallel simulation to this stochastic model. Our results show that our simulation method is able to increase the efficiency of statistical analysis by at least 50% in various settings. © 2011 ACM.

Central dogma at the single-molecule level in living cells.

Science.gov (United States)

Li, Gene-Wei; Xie, X Sunney

2011-07-20

Gene expression originates from individual DNA molecules within living cells. Like many single-molecule processes, gene expression and regulation are stochastic, that is, sporadic in time. This leads to heterogeneity in the messenger-RNA and protein copy numbers in a population of cells with identical genomes. With advanced single-cell fluorescence microscopy, it is now possible to quantify transcriptomes and proteomes with single-molecule sensitivity. Dynamic processes such as transcription-factor binding, transcription and translation can be monitored in real time, providing quantitative descriptions of the central dogma of molecular biology and the demonstration that a stochastic single-molecule event can determine the phenotype of a cell.

StochPy: A Comprehensive, User-Friendly Tool for Simulating Stochastic Biological Processes

NARCIS (Netherlands)

T.R. Maarleveld (Timo); B.G. Olivier (Brett); F.J. Bruggeman (Frank)

2013-01-01

htmlabstractSingle-cell and single-molecule measurements indicate the importance of stochastic phenomena in cell biology. Stochasticity creates spontaneous differences in the copy numbers of key macromolecules and the timing of reaction events between genetically-identical cells. Mathematical models

WKB theory of large deviations in stochastic populations

Science.gov (United States)

Assaf, Michael; Meerson, Baruch

2017-06-01

Stochasticity can play an important role in the dynamics of biologically relevant populations. These span a broad range of scales: from intra-cellular populations of molecules to population of cells and then to groups of plants, animals and people. Large deviations in stochastic population dynamics—such as those determining population extinction, fixation or switching between different states—are presently in a focus of attention of statistical physicists. We review recent progress in applying different variants of dissipative WKB approximation (after Wentzel, Kramers and Brillouin) to this class of problems. The WKB approximation allows one to evaluate the mean time and/or probability of population extinction, fixation and switches resulting from either intrinsic (demographic) noise, or a combination of the demographic noise and environmental variations, deterministic or random. We mostly cover well-mixed populations, single and multiple, but also briefly consider populations on heterogeneous networks and spatial populations. The spatial setting also allows one to study large fluctuations of the speed of biological invasions. Finally, we briefly discuss possible directions of future work.

WKB theory of large deviations in stochastic populations

International Nuclear Information System (INIS)

Assaf, Michael; Meerson, Baruch

2017-01-01

Stochasticity can play an important role in the dynamics of biologically relevant populations. These span a broad range of scales: from intra-cellular populations of molecules to population of cells and then to groups of plants, animals and people. Large deviations in stochastic population dynamics—such as those determining population extinction, fixation or switching between different states—are presently in a focus of attention of statistical physicists. We review recent progress in applying different variants of dissipative WKB approximation (after Wentzel, Kramers and Brillouin) to this class of problems. The WKB approximation allows one to evaluate the mean time and/or probability of population extinction, fixation and switches resulting from either intrinsic (demographic) noise, or a combination of the demographic noise and environmental variations, deterministic or random. We mostly cover well-mixed populations, single and multiple, but also briefly consider populations on heterogeneous networks and spatial populations. The spatial setting also allows one to study large fluctuations of the speed of biological invasions. Finally, we briefly discuss possible directions of future work. (topical review)

Cell reprogramming modelled as transitions in a hierarchy of cell cycles

International Nuclear Information System (INIS)

Hannam, Ryan; Annibale, Alessia; Kühn, Reimer

2017-01-01

We construct a model of cell reprogramming (the conversion of fully differentiated cells to a state of pluripotency, known as induced pluripotent stem cells, or iPSCs) which builds on key elements of cell biology viz. cell cycles and cell lineages. Although reprogramming has been demonstrated experimentally, much of the underlying processes governing cell fate decisions remain unknown. This work aims to bridge this gap by modelling cell types as a set of hierarchically related dynamical attractors representing cell cycles. Stages of the cell cycle are characterised by the configuration of gene expression levels, and reprogramming corresponds to triggering transitions between such configurations. Two mechanisms were found for reprogramming in a two level hierarchy: cycle specific perturbations and a noise induced switching. The former corresponds to a directed perturbation that induces a transition into a cycle-state of a different cell type in the potency hierarchy (mainly a stem cell) whilst the latter is a priori undirected and could be induced, e.g. by a (stochastic) change in the cellular environment. These reprogramming protocols were found to be effective in large regimes of the parameter space and make specific predictions concerning reprogramming dynamics which are broadly in line with experimental findings. (paper)

The phenotypic equilibrium of cancer cells: From average-level stability to path-wise convergence.

Science.gov (United States)

Niu, Yuanling; Wang, Yue; Zhou, Da

2015-12-07

The phenotypic equilibrium, i.e. heterogeneous population of cancer cells tending to a fixed equilibrium of phenotypic proportions, has received much attention in cancer biology very recently. In the previous literature, some theoretical models were used to predict the experimental phenomena of the phenotypic equilibrium, which were often explained by different concepts of stabilities of the models. Here we present a stochastic multi-phenotype branching model by integrating conventional cellular hierarchy with phenotypic plasticity mechanisms of cancer cells. Based on our model, it is shown that: (i) our model can serve as a framework to unify the previous models for the phenotypic equilibrium, and then harmonizes the different kinds of average-level stabilities proposed in these models; and (ii) path-wise convergence of our model provides a deeper understanding to the phenotypic equilibrium from stochastic point of view. That is, the emergence of the phenotypic equilibrium is rooted in the stochastic nature of (almost) every sample path, the average-level stability just follows from it by averaging stochastic samples. Copyright © 2015 Elsevier Ltd. All rights reserved.

On the stochastic stability of MHD equilibria

International Nuclear Information System (INIS)

Teichmann, J.

1979-07-01

The stochastic stability in the large of stationary equilibria of ideal and dissipative magnetohydrodynamics under the influence of stationary random fluctuations is studied using the direct Liapunov method. Sufficient and necessary conditions for stability of the linearized Euler-Lagrangian systems are given. The destabilizing effect of stochastic fluctuations is demonstrated. (orig.)

Planar half-cell shaped precursor body

DEFF Research Database (Denmark)

2015-01-01

The invention relates to a half-cell shaped precursor body of either anode type or cathode type, the half-cell shaped precursor body being prepared to be free sintered to form a sintered or pre-sintered half-cell being adapted to be stacked in a solid oxide fuel cell stack. The obtained half......-cell has an improved planar shape, which remains planar also after a sintering process and during temperature fluctuations....

Stochastic Methods in Biology

CERN Document Server

Kallianpur, Gopinath; Hida, Takeyuki

1987-01-01

The use of probabilistic methods in the biological sciences has been so well established by now that mathematical biology is regarded by many as a distinct dis­ cipline with its own repertoire of techniques. The purpose of the Workshop on sto­ chastic methods in biology held at Nagoya University during the week of July 8-12, 1985, was to enable biologists and probabilists from Japan and the U. S. to discuss the latest developments in their respective fields and to exchange ideas on the ap­ plicability of the more recent developments in stochastic process theory to problems in biology. Eighteen papers were presented at the Workshop and have been grouped under the following headings: I. Population genetics (five papers) II. Measure valued diffusion processes related to population genetics (three papers) III. Neurophysiology (two papers) IV. Fluctuation in living cells (two papers) V. Mathematical methods related to other problems in biology, epidemiology, population dynamics, etc. (six papers) An important f...

Stem cells and cancer of the stomach and intestine.

Science.gov (United States)

Vries, Robert G J; Huch, Meritxell; Clevers, Hans

2010-10-01

Cancer in the 21st century has become the number one cause of death in developed countries. Although much progress has been made in improving patient survival, tumour relapse is one of the important causes of cancer treatment failure. An early observation in the study of cancer was the heterogeneity of tumours. Traditionally, this was explained by a combination of genomic instability of tumours and micro environmental factors leading to diverse phenotypical characteristics. It was assumed that cells in a tumour have an equal capacity to propagate the cancer. This model is currently known as the stochastic model. Recently, the Cancer stem cell model has been proposed to explain the heterogeneity of a tumour and its progression. According to this model, the heterogeneity of tumours is the result of aberrant differentiation of tumour cells into the cells of the tissue the tumour originated from. Tumours were suggested to contain stem cell-like cells, the cancer stem cells or tumour-initiating cells, which are uniquely capable of propagating a tumour much like normal stem cells fuel proliferation and differentiation in normal tissue. In this review we discuss the normal stem cell biology of the stomach and intestine followed by both the stochastic and cancer stem cell models in light of recent findings in the gastric and intestinal systems. The molecular pathways underlying normal and tumourigenic growth have been well studied, and recently the stem cells of the stomach and intestine have been identified. Furthermore, intestinal stem cells were identified as the cells-of-origin of colon cancer upon loss of the tumour suppressor APC. Lastly, several studies have proposed the positive identification of a cancer stem cell of human colon cancer. At the end we compare the cancer stem cell model and the stochastic model. We conclude that clonal evolution of tumour cells resulting from genetic mutations underlies tumour initiation and progression in both cancer models. This

Realizations of highly heterogeneous collagen networks via stochastic reconstruction for micromechanical analysis of tumor cell invasion

Science.gov (United States)

Nan, Hanqing; Liang, Long; Chen, Guo; Liu, Liyu; Liu, Ruchuan; Jiao, Yang

2018-03-01

Three-dimensional (3D) collective cell migration in a collagen-based extracellular matrix (ECM) is among one of the most significant topics in developmental biology, cancer progression, tissue regeneration, and immune response. Recent studies have suggested that collagen-fiber mediated force transmission in cellularized ECM plays an important role in stress homeostasis and regulation of collective cellular behaviors. Motivated by the recent in vitro observation that oriented collagen can significantly enhance the penetration of migrating breast cancer cells into dense Matrigel which mimics the intravasation process in vivo [Han et al. Proc. Natl. Acad. Sci. USA 113, 11208 (2016), 10.1073/pnas.1610347113], we devise a procedure for generating realizations of highly heterogeneous 3D collagen networks with prescribed microstructural statistics via stochastic optimization. Specifically, a collagen network is represented via the graph (node-bond) model and the microstructural statistics considered include the cross-link (node) density, valence distribution, fiber (bond) length distribution, as well as fiber orientation distribution. An optimization problem is formulated in which the objective function is defined as the squared difference between a set of target microstructural statistics and the corresponding statistics for the simulated network. Simulated annealing is employed to solve the optimization problem by evolving an initial network via random perturbations to generate realizations of homogeneous networks with randomly oriented fibers, homogeneous networks with aligned fibers, heterogeneous networks with a continuous variation of fiber orientation along a prescribed direction, as well as a binary system containing a collagen region with aligned fibers and a dense Matrigel region with randomly oriented fibers. The generation and propagation of active forces in the simulated networks due to polarized contraction of an embedded ellipsoidal cell and a small group

Stochastic Model of the n-Stage Reversible First-Order Reaction: Relation between the Time of First Passage to the Most Probable Microstate and the Mean Equilibrium Fluctuations Lifetime

Czech Academy of Sciences Publication Activity Database

Šolc, Milan

2002-01-01

Roč. 216, 07 (2002), s. 869-893 ISSN 0942-9352 R&D Projects: GA AV ČR IAA4032101 Institutional research plan: CEZ:AV0Z4032918 Keywords : stochastic kinetics * fluctuation * first-passage time Subject RIV: CF - Physical ; Theoretical Chemistry Impact factor: 0.854, year: 2002

Stochastic heating of a single Brownian particle by charge fluctuations in a radio-frequency produced plasma sheath

Science.gov (United States)

Schmidt, Christian; Piel, Alexander

2015-10-01

The Brownian motion of a single particle in the plasma sheath is studied to separate the effect of stochastic heating by charge fluctuations from heating by collective effects. By measuring the particle velocities in the ballistic regime and by carefully determining the particle mass from the Epstein drag it is shown that for a pressure of 10 Pa, which is typical of many experiments, the proper kinetic temperature of the Brownian particle remains close to the gas temperature and rises only slightly with particle size. This weak effect is confirmed by a detailed model for charging and charge fluctuations in the sheath. A substantial temperature rise is found for decreasing pressure, which approximately shows the expected scaling with p-2. The system under study is an example for non-equilibrium Brownian motion under the influence of white noise without corresponding dissipation.

A computational model for telomere-dependent cell-replicative aging.

Science.gov (United States)

Portugal, R D; Land, M G P; Svaiter, B F

2008-01-01

Telomere shortening provides a molecular basis for the Hayflick limit. Recent data suggest that telomere shortening also influence mitotic rate. We propose a stochastic growth model of this phenomena, assuming that cell division in each time interval is a random process which probability decreases linearly with telomere shortening. Computer simulations of the proposed stochastic telomere-regulated model provides good approximation of the qualitative growth of cultured human mesenchymal stem cells.

Computer simulation in cell radiobiology

International Nuclear Information System (INIS)

Yakovlev, A.Y.; Zorin, A.V.

1988-01-01

This research monograph demonstrates the possible ways of using stochastic simulation for exploring cell kinetics, emphasizing the effects of cell radiobiology. In vitro kinetics of normal and irradiated cells is the main subject, but some approaches to the simulation of controlled cell systems are considered as well: the epithelium of the small intestine in mice taken as a case in point. Of particular interest is the evaluation of simulation modelling as a tool for gaining insight into biological processes and hence the new inferences from concrete experimental data, concerning regularities in cell population response to irradiation. The book is intended to stimulate interest among computer science specialists in developing new, more efficient means for the simulation of cell systems and to help radiobiologists in interpreting the experimental data

Kalman filter parameter estimation for a nonlinear diffusion model of epithelial cell migration using stochastic collocation and the Karhunen-Loeve expansion.

Science.gov (United States)

Barber, Jared; Tanase, Roxana; Yotov, Ivan

2016-06-01

Several Kalman filter algorithms are presented for data assimilation and parameter estimation for a nonlinear diffusion model of epithelial cell migration. These include the ensemble Kalman filter with Monte Carlo sampling and a stochastic collocation (SC) Kalman filter with structured sampling. Further, two types of noise are considered -uncorrelated noise resulting in one stochastic dimension for each element of the spatial grid and correlated noise parameterized by the Karhunen-Loeve (KL) expansion resulting in one stochastic dimension for each KL term. The efficiency and accuracy of the four methods are investigated for two cases with synthetic data with and without noise, as well as data from a laboratory experiment. While it is observed that all algorithms perform reasonably well in matching the target solution and estimating the diffusion coefficient and the growth rate, it is illustrated that the algorithms that employ SC and KL expansion are computationally more efficient, as they require fewer ensemble members for comparable accuracy. In the case of SC methods, this is due to improved approximation in stochastic space compared to Monte Carlo sampling. In the case of KL methods, the parameterization of the noise results in a stochastic space of smaller dimension. The most efficient method is the one combining SC and KL expansion. Copyright © 2016 Elsevier Inc. All rights reserved.

Approaching complexity by stochastic methods: From biological systems to turbulence

Energy Technology Data Exchange (ETDEWEB)

Friedrich, Rudolf [Institute for Theoretical Physics, University of Muenster, D-48149 Muenster (Germany); Peinke, Joachim [Institute of Physics, Carl von Ossietzky University, D-26111 Oldenburg (Germany); Sahimi, Muhammad [Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, CA 90089-1211 (United States); Reza Rahimi Tabar, M., E-mail: mohammed.r.rahimi.tabar@uni-oldenburg.de [Department of Physics, Sharif University of Technology, Tehran 11155-9161 (Iran, Islamic Republic of); Institute of Physics, Carl von Ossietzky University, D-26111 Oldenburg (Germany); Fachbereich Physik, Universitaet Osnabrueck, Barbarastrasse 7, 49076 Osnabrueck (Germany)

2011-09-15

This review addresses a central question in the field of complex systems: given a fluctuating (in time or space), sequentially measured set of experimental data, how should one analyze the data, assess their underlying trends, and discover the characteristics of the fluctuations that generate the experimental traces? In recent years, significant progress has been made in addressing this question for a class of stochastic processes that can be modeled by Langevin equations, including additive as well as multiplicative fluctuations or noise. Important results have emerged from the analysis of temporal data for such diverse fields as neuroscience, cardiology, finance, economy, surface science, turbulence, seismic time series and epileptic brain dynamics, to name but a few. Furthermore, it has been recognized that a similar approach can be applied to the data that depend on a length scale, such as velocity increments in fully developed turbulent flow, or height increments that characterize rough surfaces. A basic ingredient of the approach to the analysis of fluctuating data is the presence of a Markovian property, which can be detected in real systems above a certain time or length scale. This scale is referred to as the Markov-Einstein (ME) scale, and has turned out to be a useful characteristic of complex systems. We provide a review of the operational methods that have been developed for analyzing stochastic data in time and scale. We address in detail the following issues: (i) reconstruction of stochastic evolution equations from data in terms of the Langevin equations or the corresponding Fokker-Planck equations and (ii) intermittency, cascades, and multiscale correlation functions.

Approaching complexity by stochastic methods: From biological systems to turbulence

International Nuclear Information System (INIS)

Friedrich, Rudolf; Peinke, Joachim; Sahimi, Muhammad; Reza Rahimi Tabar, M.

2011-01-01

This review addresses a central question in the field of complex systems: given a fluctuating (in time or space), sequentially measured set of experimental data, how should one analyze the data, assess their underlying trends, and discover the characteristics of the fluctuations that generate the experimental traces? In recent years, significant progress has been made in addressing this question for a class of stochastic processes that can be modeled by Langevin equations, including additive as well as multiplicative fluctuations or noise. Important results have emerged from the analysis of temporal data for such diverse fields as neuroscience, cardiology, finance, economy, surface science, turbulence, seismic time series and epileptic brain dynamics, to name but a few. Furthermore, it has been recognized that a similar approach can be applied to the data that depend on a length scale, such as velocity increments in fully developed turbulent flow, or height increments that characterize rough surfaces. A basic ingredient of the approach to the analysis of fluctuating data is the presence of a Markovian property, which can be detected in real systems above a certain time or length scale. This scale is referred to as the Markov-Einstein (ME) scale, and has turned out to be a useful characteristic of complex systems. We provide a review of the operational methods that have been developed for analyzing stochastic data in time and scale. We address in detail the following issues: (i) reconstruction of stochastic evolution equations from data in terms of the Langevin equations or the corresponding Fokker-Planck equations and (ii) intermittency, cascades, and multiscale correlation functions.

Quantifying the contribution of chromatin dynamics to stochastic gene expression reveals long, locus-dependent periods between transcriptional bursts.

Science.gov (United States)

Viñuelas, José; Kaneko, Gaël; Coulon, Antoine; Vallin, Elodie; Morin, Valérie; Mejia-Pous, Camila; Kupiec, Jean-Jacques; Beslon, Guillaume; Gandrillon, Olivier

2013-02-25

A number of studies have established that stochasticity in gene expression may play an important role in many biological phenomena. This therefore calls for further investigations to identify the molecular mechanisms at stake, in order to understand and manipulate cell-to-cell variability. In this work, we explored the role played by chromatin dynamics in the regulation of stochastic gene expression in higher eukaryotic cells. For this purpose, we generated isogenic chicken-cell populations expressing a fluorescent reporter integrated in one copy per clone. Although the clones differed only in the genetic locus at which the reporter was inserted, they showed markedly different fluorescence distributions, revealing different levels of stochastic gene expression. Use of chromatin-modifying agents showed that direct manipulation of chromatin dynamics had a marked effect on the extent of stochastic gene expression. To better understand the molecular mechanism involved in these phenomena, we fitted these data to a two-state model describing the opening/closing process of the chromatin. We found that the differences between clones seemed to be due mainly to the duration of the closed state, and that the agents we used mainly seem to act on the opening probability. In this study, we report biological experiments combined with computational modeling, highlighting the importance of chromatin dynamics in stochastic gene expression. This work sheds a new light on the mechanisms of gene expression in higher eukaryotic cells, and argues in favor of relatively slow dynamics with long (hours to days) periods of quiet state.

Environmental Stochasticity and the Speed of Evolution

Science.gov (United States)

Danino, Matan; Kessler, David A.; Shnerb, Nadav M.

2018-03-01

Biological populations are subject to two types of noise: demographic stochasticity due to fluctuations in the reproductive success of individuals, and environmental variations that affect coherently the relative fitness of entire populations. The rate in which the average fitness of a community increases has been considered so far using models with pure demographic stochasticity; here we present some theoretical considerations and numerical results for the general case where environmental variations are taken into account. When the competition is pairwise, fitness fluctuations are shown to reduce the speed of evolution, while under global competition the speed increases due to environmental stochasticity.

Chemotherapy curable malignancies and cancer stem cells: a biological review and hypothesis.

Science.gov (United States)

Savage, Philip

2016-11-21

Cytotoxic chemotherapy brings routine cures to only a small select group of metastatic malignancies comprising gestational trophoblast tumours, germ cell tumours, acute leukemia, Hodgkin's disease, high grade lymphomas and some of the rare childhood malignancies. We have previously postulated that the extreme sensitivity to chemotherapy for these malignancies is linked to the on-going high levels of apoptotic sensitivity that is naturally linked with the unique genetic events of nuclear fusion, meiosis, VDJ recombination, somatic hypermutation, and gastrulation that have occurred within the cells of origin of these malignancies. In this review we will examine the cancer stem cell/cancer cell relationship of each of the chemotherapy curable malignancies and how this relationship impacts on the resultant biology and pro-apoptotic sensitivity of the varying cancer cell types. In contrast to the common epithelial cancers, in each of the chemotherapy curable malignancies there are no conventional hierarchical cancer stem cells. However cells with cancer stem like qualities can arise stochastically from within the general tumour cell population. These stochastic stem cells acquire a degree of resistance to DNA damaging agents but also retain much of the key characteristics of the cancer cells from which they develop. We would argue that the balance between the acquired resistance of the stochastic cancer stem cell and the inherent chemotherapy sensitivity of parent tumour cell determines the overall chemotherapy curability of each diagnosis. The cancer stem cells in the chemotherapy curable malignancies appear to have two key biological differences from those of the more common chemotherapy incurable malignancies. The first difference is that the conventional hierarchical pattern of cancer stem cells is absent in each of the chemotherapy curable malignancies. The other key difference, we suggest, is that the stochastic stem cells in the chemotherapy curable malignancies

Measuring shape fluctuations in biological membranes

International Nuclear Information System (INIS)

Monzel, C; Sengupta, K

2016-01-01

Shape fluctuations of lipid membranes have intrigued cell biologists and physicists alike. In the cellular context, their origin—thermal or active—and their physiological significance are open questions. These small incessant displacements, also called membrane undulations, have mostly been studied in model membranes and membranes of simple cells like erythrocytes. Thermal fluctuations of such membranes have been very well described both theoretically and experimentally; active fluctuations are a topic of current interest. Experimentally, membrane fluctuations are not easy to measure, the main challenge being to develop techniques which are capable of measuring very small displacements at very high speed, and preferably over a large area and long time. Scattering techniques have given access to fluctuations in membrane stacks and a variety of optical microscopy based techniques have been devised to study membrane fluctuations of unilamellar vesicles, erythrocytes and other cells. Among them are flicker spectroscopy, dynamic light scattering, diffraction phase microscopy and reflection interference contrast microscopy. Each of these techniques has its advantages and limitations. Here we review the basic principles of the major experimental techniques used to measure bending or shape fluctuations of biomembranes. We report seminal results obtained with each technique and highlight how these studies furthered our understanding of physical properties of membranes and their interactions. We also discuss suggested role of membrane fluctuations in different biological processes. (topical review)

Analysis of Noise Mechanisms in Cell-Size Control.

Science.gov (United States)

Modi, Saurabh; Vargas-Garcia, Cesar Augusto; Ghusinga, Khem Raj; Singh, Abhyudai

2017-06-06

At the single-cell level, noise arises from multiple sources, such as inherent stochasticity of biomolecular processes, random partitioning of resources at division, and fluctuations in cellular growth rates. How these diverse noise mechanisms combine to drive variations in cell size within an isoclonal population is not well understood. Here, we investigate the contributions of different noise sources in well-known paradigms of cell-size control, such as adder (division occurs after adding a fixed size from birth), sizer (division occurs after reaching a size threshold), and timer (division occurs after a fixed time from birth). Analysis reveals that variation in cell size is most sensitive to errors in partitioning of volume among daughter cells, and not surprisingly, this process is well regulated among microbes. Moreover, depending on the dominant noise mechanism, different size-control strategies (or a combination of them) provide efficient buffering of size variations. We further explore mixer models of size control, where a timer phase precedes/follows an adder, as has been proposed in Caulobacter crescentus. Although mixing a timer and an adder can sometimes attenuate size variations, it invariably leads to higher-order moments growing unboundedly over time. This results in a power-law distribution for the cell size, with an exponent that depends inversely on the noise in the timer phase. Consistent with theory, we find evidence of power-law statistics in the tail of C. crescentus cell-size distribution, although there is a discrepancy between the observed power-law exponent and that predicted from the noise parameters. The discrepancy, however, is removed after data reveal that the size added by individual newborns in the adder phase itself exhibits power-law statistics. Taken together, this study provides key insights into the role of noise mechanisms in size homeostasis, and suggests an inextricable link between timer-based models of size control and

Multiscale Hy3S: Hybrid stochastic simulation for supercomputers

Directory of Open Access Journals (Sweden)

Kaznessis Yiannis N

2006-02-01

Full Text Available Abstract Background Stochastic simulation has become a useful tool to both study natural biological systems and design new synthetic ones. By capturing the intrinsic molecular fluctuations of "small" systems, these simulations produce a more accurate picture of single cell dynamics, including interesting phenomena missed by deterministic methods, such as noise-induced oscillations and transitions between stable states. However, the computational cost of the original stochastic simulation algorithm can be high, motivating the use of hybrid stochastic methods. Hybrid stochastic methods partition the system into multiple subsets and describe each subset as a different representation, such as a jump Markov, Poisson, continuous Markov, or deterministic process. By applying valid approximations and self-consistently merging disparate descriptions, a method can be considerably faster, while retaining accuracy. In this paper, we describe Hy3S, a collection of multiscale simulation programs. Results Building on our previous work on developing novel hybrid stochastic algorithms, we have created the Hy3S software package to enable scientists and engineers to both study and design extremely large well-mixed biological systems with many thousands of reactions and chemical species. We have added adaptive stochastic numerical integrators to permit the robust simulation of dynamically stiff biological systems. In addition, Hy3S has many useful features, including embarrassingly parallelized simulations with MPI; special discrete events, such as transcriptional and translation elongation and cell division; mid-simulation perturbations in both the number of molecules of species and reaction kinetic parameters; combinatorial variation of both initial conditions and kinetic parameters to enable sensitivity analysis; use of NetCDF optimized binary format to quickly read and write large datasets; and a simple graphical user interface, written in Matlab, to help users

Turbulent response in a stochastic regime

International Nuclear Information System (INIS)

Molvig, K.; Freidberg, J.P.; Potok, R.; Hirshman, S.P.; Whitson, J.C.; Tajima, T.

1981-06-01

The theory for the non-linear, turbulent response in a system with intrinsic stochasticity is considered. It is argued that perturbative Eulerian theories, such as the Direct Interaction Approximation (DIA), are inherently unsuited to describe such a system. The exponentiation property that characterizes stochasticity appears in the Lagrangian picture and cannot even be defined in the Eulerian representation. An approximation for stochastic systems - the Normal Stochastic Approximation - is developed and states that the perturbed orbit functions (Lagrangian fluctuations) behave as normally distributed random variables. This is independent of the Eulerian statistics and, in fact, we treat the Eulerian fluctuations as fixed. A simple model problem (appropriate for the electron response in the drift wave) is subjected to a series of computer experiments. To within numerical noise the results are in agreement with the Normal Stochastic Approximation. The predictions of the DIA for this mode show substantial qualitative and quantitative departures from the observations

Self-Sorting of White Blood Cells in a Lattice

Science.gov (United States)

Carlson, Robert H.; Gabel, Christopher V.; Chan, Shirley S.; Austin, Robert H.; Brody, James P.; James, D. W. Winkelman M.

1997-09-01

When a drop of human blood containing red and white blood cells is forced to move via hydrodynamic forces in a lattice of channels designed to mimic the capillary channels, the white cells self-fractionate into the different types of white cells. The pattern of white cells that forms is due to a combination of stretch-activated adhesion of cells with the walls, stochastic sticking probabilities, and heteroavoidance between granulocytes and lymphocytes.

Stochastic cooling of bunched beams from fluctuation and kinetic theory

International Nuclear Information System (INIS)

Chattopadhyay, S.

1982-09-01

A theoretical formalism for stochastic phase-space cooling of bunched beams in storage rings is developed on the dual basis of classical fluctuation theory and kinetic theory of many-body systems in phase-space. The physics is that of a collection of three-dimensional oscillators coupled via retarded nonconservative interactions determined by an electronic feedback loop. At the heart of the formulation is the existence of several disparate time-scales characterizing the cooling process. Both theoretical approaches describe the cooling process in the form of a Fokker-Planck transport equation in phase-space valid up to second order in the strength and first order in the auto-correlation of the cooling signal. With neglect of the collective correlations induced by the feedback loop, identical expressions are obtained in both cases for the coherent damping and Schottky noise diffusion coefficients. These are expressed in terms of Fourier coefficients in a harmonic decomposition in angle of the generalized nonconservative cooling force written in canonical action-angle variables of the particles in six-dimensional phase-space. Comparison of analytic results to a numerical simulation study with 90 pseudo-particles in a model cooling system is presented

Forced Expression of ZNF143 Restrains Cancer Cell Growth

International Nuclear Information System (INIS)

Izumi, Hiroto; Yasuniwa, Yoshihiro; Akiyama, Masaki; Yamaguchi, Takahiro; Kuma, Akihiro; Kitamura, Noriaki; Kohno, Kimitoshi

2011-01-01

We previously reported that the transcription factor Zinc Finger Protein 143 (ZNF143) regulates the expression of genes associated with cell cycle and cell division, and that downregulation of ZNF143 induces cell cycle arrest at G2/M. To assess the function of ZNF143 expression in the cell cycle, we established two cells with forced expression of ZNF143 derived from PC3 prostate cancer cell lines. These cell lines overexpress genes associated with cell cycle and cell division, such as polo-like kinase 1 (PLK1), aurora kinase B (AURKB) and some minichromosome maintenance complex components (MCM). However, the doubling time of cells with forced expression of ZNF143 was approximately twice as long as its control counterpart cell line. Analysis following serum starvation and re-seeding showed that PC3 cells were synchronized at G1 in the cell cycle. Also, ZNF143 expression fluctuated, and was at its lowest level in G2/M. However, PC3 cells with forced expression of ZNF143 synchronized at G2/M, and showed lack of cell cycle-dependent fluctuation of nuclear expression of MCM proteins. Furthermore, G2/M population of both cisplatin-resistant PCDP6 cells over-expressing ZNF143 (derived from PC3 cells) and cells with forced expression of ZNF143 was significantly higher than that of each counterpart, and the doubling time of PCDP6 cells is about 2.5 times longer than that of PC3 cells. These data suggested that fluctuations in ZNF143 expression are required both for gene expression associated with cell cycle and for cell division

Forced Expression of ZNF143 Restrains Cancer Cell Growth

Energy Technology Data Exchange (ETDEWEB)

Izumi, Hiroto, E-mail: h-izumi@med.uoeh-u.ac.jp; Yasuniwa, Yoshihiro; Akiyama, Masaki; Yamaguchi, Takahiro; Kuma, Akihiro; Kitamura, Noriaki; Kohno, Kimitoshi [Department of Molecular Biology, School of Medicine, University of Occupational and Environmental Health, 1-1 Iseigaoka, Yahatanishi-ku, Kitakyushu 807-8555 (Japan)

2011-10-19

We previously reported that the transcription factor Zinc Finger Protein 143 (ZNF143) regulates the expression of genes associated with cell cycle and cell division, and that downregulation of ZNF143 induces cell cycle arrest at G2/M. To assess the function of ZNF143 expression in the cell cycle, we established two cells with forced expression of ZNF143 derived from PC3 prostate cancer cell lines. These cell lines overexpress genes associated with cell cycle and cell division, such as polo-like kinase 1 (PLK1), aurora kinase B (AURKB) and some minichromosome maintenance complex components (MCM). However, the doubling time of cells with forced expression of ZNF143 was approximately twice as long as its control counterpart cell line. Analysis following serum starvation and re-seeding showed that PC3 cells were synchronized at G1 in the cell cycle. Also, ZNF143 expression fluctuated, and was at its lowest level in G2/M. However, PC3 cells with forced expression of ZNF143 synchronized at G2/M, and showed lack of cell cycle-dependent fluctuation of nuclear expression of MCM proteins. Furthermore, G2/M population of both cisplatin-resistant PCDP6 cells over-expressing ZNF143 (derived from PC3 cells) and cells with forced expression of ZNF143 was significantly higher than that of each counterpart, and the doubling time of PCDP6 cells is about 2.5 times longer than that of PC3 cells. These data suggested that fluctuations in ZNF143 expression are required both for gene expression associated with cell cycle and for cell division.

Forced Expression of ZNF143 Restrains Cancer Cell Growth

Directory of Open Access Journals (Sweden)

Kimitoshi Kohno

2011-10-01

Full Text Available We previously reported that the transcription factor Zinc Finger Protein 143 (ZNF143 regulates the expression of genes associated with cell cycle and cell division, and that downregulation of ZNF143 induces cell cycle arrest at G2/M. To assess the function of ZNF143 expression in the cell cycle, we established two cells with forced expression of ZNF143 derived from PC3 prostate cancer cell lines. These cell lines overexpress genes associated with cell cycle and cell division, such as polo-like kinase 1 (PLK1, aurora kinase B (AURKB and some minichromosome maintenance complex components (MCM. However, the doubling time of cells with forced expression of ZNF143 was approximately twice as long as its control counterpart cell line. Analysis following serum starvation and re-seeding showed that PC3 cells were synchronized at G1 in the cell cycle. Also, ZNF143 expression fluctuated, and was at its lowest level in G2/M. However, PC3 cells with forced expression of ZNF143 synchronized at G2/M, and showed lack of cell cycle-dependent fluctuation of nuclear expression of MCM proteins. Furthermore, G2/M population of both cisplatin-resistant PCDP6 cells over-expressing ZNF143 (derived from PC3 cells and cells with forced expression of ZNF143 was significantly higher than that of each counterpart, and the doubling time of PCDP6 cells is about 2.5 times longer than that of PC3 cells. These data suggested that fluctuations in ZNF143 expression are required both for gene expression associated with cell cycle and for cell division.

A simplified model for dynamics of cell rolling and cell-surface adhesion

International Nuclear Information System (INIS)

Cimrák, Ivan

2015-01-01

We propose a three dimensional model for the adhesion and rolling of biological cells on surfaces. We study cells moving in shear flow above a wall to which they can adhere via specific receptor-ligand bonds based on receptors from selectin as well as integrin family. The computational fluid dynamics are governed by the lattice-Boltzmann method. The movement and the deformation of the cells is described by the immersed boundary method. Both methods are fully coupled by implementing a two-way fluid-structure interaction. The adhesion mechanism is modelled by adhesive bonds including stochastic rules for their creation and rupture. We explore a simplified model with dissociation rate independent of the length of the bonds. We demonstrate that this model is able to resemble the mesoscopic properties, such as velocity of rolling cells

Bistable forespore engulfment in Bacillus subtilis by a zipper mechanism in absence of the cell wall.

Directory of Open Access Journals (Sweden)

Nikola Ojkic

2014-10-01

Full Text Available To survive starvation, the bacterium Bacillus subtilis forms durable spores. The initial step of sporulation is asymmetric cell division, leading to a large mother-cell and a small forespore compartment. After division is completed and the dividing septum is thinned, the mother cell engulfs the forespore in a slow process based on cell-wall degradation and synthesis. However, recently a new cell-wall independent mechanism was shown to significantly contribute, which can even lead to fast engulfment in [Formula: see text] 60 [Formula: see text] of the cases when the cell wall is completely removed. In this backup mechanism, strong ligand-receptor binding between mother-cell protein SpoIIIAH and forespore-protein SpoIIQ leads to zipper-like engulfment, but quantitative understanding is missing. In our work, we combined fluorescence image analysis and stochastic Langevin simulations of the fluctuating membrane to investigate the origin of fast bistable engulfment in absence of the cell wall. Our cell morphologies compare favorably with experimental time-lapse microscopy, with engulfment sensitive to the number of SpoIIQ-SpoIIIAH bonds in a threshold-like manner. By systematic exploration of model parameters, we predict regions of osmotic pressure and membrane-surface tension that produce successful engulfment. Indeed, decreasing the medium osmolarity in experiments prevents engulfment in line with our predictions. Forespore engulfment may thus not only be an ideal model system to study decision-making in single cells, but its biophysical principles are likely applicable to engulfment in other cell types, e.g. during phagocytosis in eukaryotes.

Adhesion behavior of endothelial progenitor cells to endothelial cells in simple shear flow

Science.gov (United States)

Gong, Xiao-Bo; Li, Yu-Qing; Gao, Quan-Chao; Cheng, Bin-Bin; Shen, Bao-Rong; Yan, Zhi-Qiang; Jiang, Zong-Lai

2011-12-01

The adhesion of endothelial progenitor cells (EPCs) on endothelial cells (ECs) is one of the critical physiological processes for the regenesis of vascular vessels and the prevention of serious cardiovascular diseases. Here, the rolling and adhesion behavior of EPCs on ECs was studied numerically. A two-dimensional numerical model was developed based on the immersed boundary method for simulating the rolling and adhesion of cells in a channel flow. The binding force arising from the catch bond of a receptor and ligand pair was modeled with stochastic Monte Carlo method and Hookean spring model. The effect of tumor necrosis factor alpha (TNF- α) on the expression of the number of adhesion molecules in ECs was analyzed experimentally. A flow chamber system with CCD camera was set up to observe the top view of the rolling of EPCs on the substrate cultivated with ECs. Numerical results prove that the adhesion of EPC on ECs is closely related to membrane stiffness of the cell and shear rate of the flow. It also suggests that the adhesion force between EPC and EC by P-selectin glycoprotein ligand-1 only is not strong enough to bond the cell onto vessel walls unless contributions of other catch bond are considered. Experimental results demonstrate that TNF- α enhanced the expressions of VCAM, ICAM, P-selectin and E-selectin in ECs, which supports the numerical results that the rolling velocity of EPC on TNF- α treated EC substrate decreases obviously compared with its velocity on the untreated one. It is found that because the adhesion is affected by both the rolling velocity and the deformability of the cell, an optimal stiffness of EPC may exist at a given shear rate of flow for achieving maximum adhesion rates.

Modified diffusion with memory for cyclone track fluctuations

Energy Technology Data Exchange (ETDEWEB)

Bernido, Christopher C., E-mail: cbernido@mozcom.com [Research Center for Theoretical Physics, Central Visayan Institute Foundation, Jagna, Bohol 6308 (Philippines); Carpio-Bernido, M. Victoria [Research Center for Theoretical Physics, Central Visayan Institute Foundation, Jagna, Bohol 6308 (Philippines); Escobido, Matthew G.O. [W. Sycip Graduate School of Business, Asian Institute of Management, 123 Paseo de Roxas Ave., Makati City 1260 (Philippines)

2014-06-13

Fluctuations in a time series for tropical cyclone tracks are investigated based on an exponentially modified Brownian motion. The mean square displacement (MSD) is evaluated and compared to a recent work on cyclone tracks based on fractional Brownian motion (fBm). Unlike the work based on fBm, the present approach is found to capture the behavior of MSD versus time graphs for cyclones even for large values of time. - Highlights: • Cyclone track fluctuations are modeled as stochastic processes with memory. • Stochastic memory functions beyond fractional Brownian motion are introduced. • The model captures the behavior of cyclone track fluctuations for longer periods of time. • The approach can model time series for other fluctuating phenomena.

Stochastic simulation of enzyme-catalyzed reactions with disparate timescales.

Science.gov (United States)

Barik, Debashis; Paul, Mark R; Baumann, William T; Cao, Yang; Tyson, John J

2008-10-01

Many physiological characteristics of living cells are regulated by protein interaction networks. Because the total numbers of these protein species can be small, molecular noise can have significant effects on the dynamical properties of a regulatory network. Computing these stochastic effects is made difficult by the large timescale separations typical of protein interactions (e.g., complex formation may occur in fractions of a second, whereas catalytic conversions may take minutes). Exact stochastic simulation may be very inefficient under these circumstances, and methods for speeding up the simulation without sacrificing accuracy have been widely studied. We show that the "total quasi-steady-state approximation" for enzyme-catalyzed reactions provides a useful framework for efficient and accurate stochastic simulations. The method is applied to three examples: a simple enzyme-catalyzed reaction where enzyme and substrate have comparable abundances, a Goldbeter-Koshland switch, where a kinase and phosphatase regulate the phosphorylation state of a common substrate, and coupled Goldbeter-Koshland switches that exhibit bistability. Simulations based on the total quasi-steady-state approximation accurately capture the steady-state probability distributions of all components of these reaction networks. In many respects, the approximation also faithfully reproduces time-dependent aspects of the fluctuations. The method is accurate even under conditions of poor timescale separation.

[Prediction of the molecular response to pertubations from single cell measurements].

Science.gov (United States)

Remacle, Françoise; Levine, Raphael D

2014-12-01

The response of protein signalization networks to perturbations is analysed from single cell measurements. This experimental approach allows characterizing the fluctuations in protein expression levels from cell to cell. The analysis is based on an information theoretic approach grounded in thermodynamics leading to a quantitative version of Le Chatelier principle which allows to predict the molecular response. Two systems are investigated: human macrophages subjected to lipopolysaccharide challenge, analogous to the immune response against Gram-negative bacteria and the response of the proteins involved in the mTOR signalizing network of GBM cancer cells to changes in partial oxygen pressure. © 2014 médecine/sciences – Inserm.

mTOR-regulated senescence and autophagy during reprogramming of somatic cells to pluripotency: a roadmap from energy metabolism to stem cell renewal and aging.

Science.gov (United States)

Menendez, Javier A; Vellon, Luciano; Oliveras-Ferraros, Cristina; Cufí, Sílvia; Vazquez-Martin, Alejandro

2011-11-01

Molecular controllers of the number and function of tissue stem cells may share common regulatory pathways for the nuclear reprogramming of somatic cells to become induced Pluripotent Stem Cells (iPSCs). If this hypothesis is true, testing the ability of longevity-promoting chemicals to improve reprogramming efficiency may provide a proof-of-concept validation tool for pivotal housekeeping pathways that limit the numerical and/or functional decline of adult stem cells. Reprogramming is a slow, stochastic process due to the complex and apparently unrelated cellular processes that are involved. First, forced expression of the Yamanaka cocktail of stemness factors, OSKM, is a stressful process that activates apoptosis and cellular senescence, which are the two primary barriers to cancer development and somatic reprogramming. Second, the a priori energetic infrastructure of somatic cells appears to be a crucial stochastic feature for optimal successful routing to pluripotency. If longevity-promoting compounds can ablate the drivers and effectors of cellular senescence while concurrently enhancing a bioenergetic shift from somatic oxidative mitochondria toward an alternative ATP-generating glycolytic metabotype, they could maximize the efficiency of somatic reprogramming to pluripotency. Support for this hypothesis is evidenced by recent findings that well-characterized mTOR inhibitors and autophagy activators (e.g., PP242, rapamycin and resveratrol) notably improve the speed and efficiency of iPSC generation. This article reviews the existing research evidence that the most established mTOR inhibitors can notably decelerate the cellular senescence that is imposed by DNA damage-like responses, which are somewhat equivalent to the responses caused by reprogramming factors. These data suggest that fine-tuning mTOR signaling can impact mitochondrial dynamics to segregate mitochondria that are destined for clearance through autophagy, which results in the loss of

The ultra-sensitive Nodewalk technique identifies stochastic from virtual, population-based enhancer hubs regulating MYC in 3D: Implications for the fitness of cancer cells

KAUST Repository

Sumida, Noriyuki; Sifakis, Emmanouil; Scholz, Barbara A; Fernandez Woodbridge, Alejandro; Kiani, Narsis A.; Gomez-Cabrero, David; Svensson, J Peter; Tegner, Jesper; Gondor, Anita; Ohlsson, Rolf

2018-01-01

The relationship between stochastic transcriptional bursts and dynamic 3D chromatin states is not well understood due to poor sensitivity and/or resolution of current chromatin structure-based assays. Consequently, it is not well established if enhancers operate individually and/or in clusters to coordinate gene transcription. In the current study, we introduce Nodewalk, which uniquely combines high sensitivity with high resolution to enable the analysis of chromatin networks in minute input material. The >10,000-fold increase in sensitivity over other many-to-all competing methods uncovered that active chromatin hubs identified in large input material, corresponding to 10 000 cells, flanking the MYC locus are primarily virtual. Thus, the close agreement between chromatin interactomes generated from aliquots corresponding to less than 10 cells with randomly re-sampled interactomes, we find that numerous distal enhancers positioned within flanking topologically associating domains (TADs) converge on MYC in largely mutually exclusive manners. Moreover, when comparing with several enhancer baits, the assignment of the MYC locus as the node with the highest dynamic importance index, indicates that it is MYC targeting its enhancers, rather than vice versa. Dynamic changes in the configuration of the boundary between TADs flanking MYC underlie numerous stochastic encounters with a diverse set of enhancers to depict the plasticity of its transcriptional regulation. Such an arrangement might increase the fitness of the cancer cell by increasing the probability of MYC transcription in response to a wide range of environmental cues encountered by the cell during the neoplastic process.

The ultra-sensitive Nodewalk technique identifies stochastic from virtual, population-based enhancer hubs regulating MYC in 3D: Implications for the fitness of cancer cells

KAUST Repository

Sumida, Noriyuki

2018-03-27

The relationship between stochastic transcriptional bursts and dynamic 3D chromatin states is not well understood due to poor sensitivity and/or resolution of current chromatin structure-based assays. Consequently, it is not well established if enhancers operate individually and/or in clusters to coordinate gene transcription. In the current study, we introduce Nodewalk, which uniquely combines high sensitivity with high resolution to enable the analysis of chromatin networks in minute input material. The >10,000-fold increase in sensitivity over other many-to-all competing methods uncovered that active chromatin hubs identified in large input material, corresponding to 10 000 cells, flanking the MYC locus are primarily virtual. Thus, the close agreement between chromatin interactomes generated from aliquots corresponding to less than 10 cells with randomly re-sampled interactomes, we find that numerous distal enhancers positioned within flanking topologically associating domains (TADs) converge on MYC in largely mutually exclusive manners. Moreover, when comparing with several enhancer baits, the assignment of the MYC locus as the node with the highest dynamic importance index, indicates that it is MYC targeting its enhancers, rather than vice versa. Dynamic changes in the configuration of the boundary between TADs flanking MYC underlie numerous stochastic encounters with a diverse set of enhancers to depict the plasticity of its transcriptional regulation. Such an arrangement might increase the fitness of the cancer cell by increasing the probability of MYC transcription in response to a wide range of environmental cues encountered by the cell during the neoplastic process.

Real-time tracking of cell cycle progression during CD8+ effector and memory T-cell differentiation.

Science.gov (United States)

Kinjyo, Ichiko; Qin, Jim; Tan, Sioh-Yang; Wellard, Cameron J; Mrass, Paulus; Ritchie, William; Doi, Atsushi; Cavanagh, Lois L; Tomura, Michio; Sakaue-Sawano, Asako; Kanagawa, Osami; Miyawaki, Atsushi; Hodgkin, Philip D; Weninger, Wolfgang

2015-02-24

The precise pathways of memory T-cell differentiation are incompletely understood. Here we exploit transgenic mice expressing fluorescent cell cycle indicators to longitudinally track the division dynamics of individual CD8(+) T cells. During influenza virus infection in vivo, naive T cells enter a CD62L(intermediate) state of fast proliferation, which continues for at least nine generations. At the peak of the anti-viral immune response, a subpopulation of these cells markedly reduces their cycling speed and acquires a CD62L(hi) central memory cell phenotype. Construction of T-cell family division trees in vitro reveals two patterns of proliferation dynamics. While cells initially divide rapidly with moderate stochastic variations of cycling times after each generation, a slow-cycling subpopulation displaying a CD62L(hi) memory phenotype appears after eight divisions. Phenotype and cell cycle duration are inherited by the progeny of slow cyclers. We propose that memory precursors cell-intrinsically modulate their proliferative activity to diversify differentiation pathways.

High-Resolution Replication Profiles Define the Stochastic Nature of Genome Replication Initiation and Termination

Directory of Open Access Journals (Sweden)

Michelle Hawkins

2013-11-01

Full Text Available Eukaryotic genome replication is stochastic, and each cell uses a different cohort of replication origins. We demonstrate that interpreting high-resolution Saccharomyces cerevisiae genome replication data with a mathematical model allows quantification of the stochastic nature of genome replication, including the efficiency of each origin and the distribution of termination events. Single-cell measurements support the inferred values for stochastic origin activation time. A strain, in which three origins were inactivated, confirmed that the distribution of termination events is primarily dictated by the stochastic activation time of origins. Cell-to-cell variability in origin activity ensures that termination events are widely distributed across virtually the whole genome. We propose that the heterogeneity in origin usage contributes to genome stability by limiting potentially deleterious events from accumulating at particular loci.

Extinction models for cancer stem cell therapy

Science.gov (United States)

Sehl, Mary; Zhou, Hua; Sinsheimer, Janet S.; Lange, Kenneth L.

2012-01-01

Cells with stem cell-like properties are now viewed as initiating and sustaining many cancers. This suggests that cancer can be cured by driving these cancer stem cells to extinction. The problem with this strategy is that ordinary stem cells are apt to be killed in the process. This paper sets bounds on the killing differential (difference between death rates of cancer stem cells and normal stem cells) that must exist for the survival of an adequate number of normal stem cells. Our main tools are birth–death Markov chains in continuous time. In this framework, we investigate the extinction times of cancer stem cells and normal stem cells. Application of extreme value theory from mathematical statistics yields an accurate asymptotic distribution and corresponding moments for both extinction times. We compare these distributions for the two cell populations as a function of the killing rates. Perhaps a more telling comparison involves the number of normal stem cells NH at the extinction time of the cancer stem cells. Conditioning on the asymptotic time to extinction of the cancer stem cells allows us to calculate the asymptotic mean and variance of NH. The full distribution of NH can be retrieved by the finite Fourier transform and, in some parameter regimes, by an eigenfunction expansion. Finally, we discuss the impact of quiescence (the resting state) on stem cell dynamics. Quiescence can act as a sanctuary for cancer stem cells and imperils the proposed therapy. We approach the complication of quiescence via multitype branching process models and stochastic simulation. Improvements to the τ-leaping method of stochastic simulation make it a versatile tool in this context. We conclude that the proposed therapy must target quiescent cancer stem cells as well as actively dividing cancer stem cells. The current cancer models demonstrate the virtue of attacking the same quantitative questions from a variety of modeling, mathematical, and computational perspectives

Imaging and reconstruction of cell cortex structures near the cell surface

Science.gov (United States)

Jin, Luhong; Zhou, Xiaoxu; Xiu, Peng; Luo, Wei; Huang, Yujia; Yu, Feng; Kuang, Cuifang; Sun, Yonghong; Liu, Xu; Xu, Yingke

2017-11-01

Total internal reflection fluorescence microscopy (TIRFM) provides high optical sectioning capability and superb signal-to-noise ratio for imaging of cell cortex structures. The development of multi-angle (MA)-TIRFM permits high axial resolution imaging and reconstruction of cellular structures near the cell surface. Cytoskeleton is composed of a network of filaments, which are important for maintenance of cell function. The high-resolution imaging and quantitative analysis of filament organization would contribute to our understanding of cytoskeleton regulation in cell. Here, we used a custom-developed MA-TIRFM setup, together with stochastic photobleaching and single molecule localization method, to enhance the lateral resolution of TIRFM imaging to about 100 nm. In addition, we proposed novel methods to perform filament segmentation and 3D reconstruction from MA-TIRFM images. Furthermore, we applied these methods to study the 3D localization of cortical actin and microtubule structures in U373 cancer cells. Our results showed that cortical actins localize ∼ 27 nm closer to the plasma membrane when compared with microtubules. We found that treatment of cells with chemotherapy drugs nocodazole and cytochalasin B disassembles cytoskeletal network and induces the reorganization of filaments towards the cell periphery. In summary, this study provides feasible approaches for 3D imaging and analyzing cell surface distribution of cytoskeletal network. Our established microscopy platform and image analysis toolkits would facilitate the study of cytoskeletal network in cells.

The stem cell state in plant development and in response to stress

Directory of Open Access Journals (Sweden)

Gideon eGrafi

2011-10-01

Full Text Available Stem cells are commonly defined by their developmental capabilities, namely, self-renewal and multitype differentiation, yet the biology of stem cells and their inherent features both in plants and animals are only beginning to be elucidated. In this review article we highlight the stem cell state in plants (with reference to animals and the plastic nature of plant somatic cells (often referred to as totipotency as well as the essence of cellular dedifferentiation. Based on recent published data, we illustrate the picture of stem cells with emphasis on their open chromatin conformation. We discuss the process of dedifferentiation and highlight its transient nature, its distinction from reentry into the cell cycle and its activation following exposure to stress. We also discuss the potential hazard that can be brought about by stress-induced dedifferentiation and its major impact on the genome, which can undergo stochastic, abnormal reorganization leading to genetic variation by means of DNA transposition and/or DNA recombination.

Stochastic Gravity: Theory and Applications

Directory of Open Access Journals (Sweden)

Hu Bei Lok

2004-01-01

Full Text Available Whereas semiclassical gravity is based on the semiclassical Einstein equation with sources given by the expectation value of the stress-energy tensor of quantum fields, stochastic semiclassical gravity is based on the Einstein-Langevin equation, which has in addition sources due to the noise kernel. The noise kernel is the vacuum expectation value of the (operator-valued stress-energy bi-tensor which describes the fluctuations of quantum matter fields in curved spacetimes. In the first part, we describe the fundamentals of this new theory via two approaches: the axiomatic and the functional. The axiomatic approach is useful to see the structure of the theory from the framework of semiclassical gravity, showing the link from the mean value of the stress-energy tensor to their correlation functions. The functional approach uses the Feynman-Vernon influence functional and the Schwinger-Keldysh closed-time-path effective action methods which are convenient for computations. It also brings out the open systems concepts and the statistical and stochastic contents of the theory such as dissipation, fluctuations, noise, and decoherence. We then focus on the properties of the stress-energy bi-tensor. We obtain a general expression for the noise kernel of a quantum field defined at two distinct points in an arbitrary curved spacetime as products of covariant derivatives of the quantum field's Green function. In the second part, we describe three applications of stochastic gravity theory. First, we consider metric perturbations in a Minkowski spacetime. We offer an analytical solution of the Einstein-Langevin equation and compute the two-point correlation functions for the linearized Einstein tensor and for the metric perturbations. Second, we discuss structure formation from the stochastic gravity viewpoint, which can go beyond the standard treatment by incorporating the full quantum effect of the inflaton fluctuations. Third, we discuss the backreaction

Radiation-induced perturbation of cell-to-cell signalling and communication

International Nuclear Information System (INIS)

Mariotti, L.; Facoetti, A.; Bertolotti, A.; Ranza, E.; Alloni, D.; Ottolenghi, A.

2011-01-01

The investigation of the bystander phenomena (i.e. the induction of damage in cells not directly traversed by radiation) is strictly related to the study of the mechanisms of intercellular communication and of the perturbative effects of radiation. A new possible way to try to solve the bystander puzzle is through a 'systems radiation biology' approach with the total integration of experimental and theoretical activities. In particular, this contribution will focus on: (1) 'ad hoc' experiments designed to quantify key parameters involved in intercellular signalling (focusing, as a pilot study, on release, decay and internalization of interleukin-6 molecules, their modulation by radiation, and possible differences between in vivo/in vitro behaviour); (2) the implementation and the development of two different modelling approaches: a stochastic model (based on a Monte Carlo code) that takes account of the local mechanisms of release and internalization of signalling molecules (e.g. cytokines) and an analytical model where signal molecules are treated as a population and their temporal behaviour is described by differential equations. This approach provided instruments to investigate the complex phenomena of signal transmission and the role of cell communication to guarantee (maintain) the robustness of the in vitro experimental systems against the effects of perturbations. (authors)

Thermally driven convective cells and tokamak edge turbulence

International Nuclear Information System (INIS)

Thayer, D.R.; Diamond, P.H.

1987-07-01

A unified theory for the dynamics of thermally driven convective cell turbulence is presented. The cells are excited by the combined effects of radiative cooling and resistivity gradient drive. The model also includes impurity dynamics. Parallel thermal and impurity flows enhanced by turbulent radial duffusion regulate and saturate overlapping cells, even in regimes dominated by thermal instability. Transport coefficients and fluctuation levels characteristic of the saturated turbulence are calculated. It is found that the impurity radiation increases transport coefficients for high density plasmas, while the parallel conduction damping, elevated by radial diffusion, in turn quenches the thermal instability. The enhancement due to radiative cooling provides a resolution to the dilemma of explaining the experimental observation that potential fluctuations exceed density fluctuations in the edge plasma (e PHI/T/sub e/ > n/n 0 )

Stochastic modelling of Listeria monocytogenes single cell growth in cottage cheese with mesophilic lactic acid bacteria from aroma producing cultures

DEFF Research Database (Denmark)

Østergaard, Nina Bjerre; Christiansen, Lasse Engbo; Dalgaard, Paw

2015-01-01

. 2014. Modelling the effect of lactic acid bacteria from starter- and aroma culture on growth of Listeria monocytogenes in cottage cheese. International Journal of Food Microbiology. 188, 15-25]. Growth of L. monocytogenes single cells, using lag time distributions corresponding to three different......A stochastic model was developed for simultaneous growth of low numbers of Listeria monocytogenes and populations of lactic acid bacteria from the aroma producing cultures applied in cottage cheese. During more than two years, different batches of cottage cheese with aroma culture were analysed...

Cell Growth Rate Dictates the Onset of Glass to Fluidlike Transition and Long Time Superdiffusion in an Evolving Cell Colony

Science.gov (United States)

Malmi-Kakkada, Abdul N.; Li, Xin; Samanta, Himadri S.; Sinha, Sumit; Thirumalai, D.

2018-04-01

Collective migration dominates many phenomena, from cell movement in living systems to abiotic self-propelling particles. Focusing on the early stages of tumor evolution, we enunciate the principles involved in cell dynamics and highlight their implications in understanding similar behavior in seemingly unrelated soft glassy materials and possibly chemokine-induced migration of CD 8+T cells. We performed simulations of tumor invasion using a minimal three-dimensional model, accounting for cell elasticity and adhesive cell-cell interactions, as well as cell birth and death, to establish that cell-growth-rate-dependent tumor expansion results in the emergence of distinct topological niches. Cells at the periphery move with higher velocity perpendicular to the tumor boundary, while the motion of interior cells is slower and isotropic. The mean-square displacement Δ (t ) of cells exhibits glassy behavior at times comparable to the cell cycle time, while exhibiting superdiffusive behavior, Δ (t )≈tα (α >1 ), at longer times. We derive the value of α ≈1.33 using a field theoretic approach based on stochastic quantization. In the process, we establish the universality of superdiffusion in a class of seemingly unrelated nonequilibrium systems. Superdiffusion at long times arises only if there is an imbalance between cell birth and death rates. Our findings for the collective migration, which also suggest that tumor evolution occurs in a polarized manner, are in quantitative agreement with in vitro experiments. Although set in the context of tumor invasion, the findings should also hold in describing the collective motion in growing cells and in active systems, where creation and annihilation of particles play a role.

Phase space fluctuations and dynamics of fluctuations of collective variables

Energy Technology Data Exchange (ETDEWEB)

Benhassine, B.; Farine, M.; Idier, D.; Remaud, B.; Sebille, F. (Lab. de Physique Nucleaire, IN2P3/CNRS, 44 - Nantes (France) Nantes Univ., 44 (France)); Hernandez, E.S. (Dept. de Fisica, Ciudad Universitaria, Buenos Aires (Argentina))

1992-08-03

Within the framework of theoretical approaches based on stochastic transport equation of one-body distribution function, a numerical treatment of the fluctuations of collective observables is studied and checked in comparison with analytical results either at equilibrium or close to it. (orig.).

Phase space fluctuations and dynamics of fluctuations of collective variables

International Nuclear Information System (INIS)

Benhassine, B.; Farine, M.; Idier, D.; Remaud, B.; Sebille, F.; Hernandez, E.S.

1992-01-01

Within the framework of theoretical approaches based on stochastic transport equation of one-body distribution function, a numerical treatment of the fluctuations of collective observables is studied and checked in comparison with analytical results either at equilibrium or close to it. (orig.)

Repopulation dynamics of single haematopoietic stem cells in mouse transplantation experiments: Importance of stem cell composition in competitor cells.

Science.gov (United States)

Ema, Hideo; Uchinomiya, Kouki; Morita, Yohei; Suda, Toshio; Iwasa, Yoh

2016-04-07

The transplantation of blood tissues from bone marrow into a lethally irradiated animal is an experimental procedure that is used to study how the blood system is reconstituted by haematopoietic stem cells (HSC). In a competitive repopulation experiment, a lethally irradiated mouse was transplanted with a single HSC as a test cell together with a number of bone marrow cells as competitor cells, and the fraction of the test cell progeny (percentage of chimerism) was traced over time. In this paper, we studied the stem cell kinetics in this experimental procedure. The balance between symmetric self-renewal and differentiation divisions in HSC determined the number of cells which HSC produce and the length of time for which HSC live after transplantation. The percentage of chimerism depended on the type of test cell (long-, intermediate-, or short-term HSC), as well as the type and number of HSC included in competitor cells. We next examined two alternative HSC differentiation models, one-step and multi-step differentiation models. Although these models differed in blood cell production, the percentage of chimerism appeared very similar. We also estimated the numbers of different types of HSC in competitor cells. Based on these results, we concluded that the experimental results inevitably include stochasticity with regard to the number and the type of HSC in competitor cells, and that, in order to detect different types of HSC, an appropriate number of competitor cells needs to be used in transplantation experiments. Copyright © 2016. Published by Elsevier Ltd.

Cell to Cell Variability of Radiation-Induced Foci: Relation between Observed Damage and Energy Deposition.

Science.gov (United States)

Gruel, Gaëtan; Villagrasa, Carmen; Voisin, Pascale; Clairand, Isabelle; Benderitter, Marc; Bottollier-Depois, Jean-François; Barquinero, Joan Francesc

2016-01-01

Most studies that aim to understand the interactions between different types of photon radiation and cellular DNA assume homogeneous cell irradiation, with all cells receiving the same amount of energy. The level of DNA damage is therefore generally determined by averaging it over the entire population of exposed cells. However, evaluating the molecular consequences of a stochastic phenomenon such as energy deposition of ionizing radiation by measuring only an average effect may not be sufficient for understanding some aspects of the cellular response to this radiation. The variance among the cells associated with this average effect may also be important for the behaviour of irradiated tissue. In this study, we accurately estimated the distribution of the number of radiation-induced γH2AX foci (RIF) per cell nucleus in a large population of endothelial cells exposed to 3 macroscopic doses of gamma rays from 60Co. The number of RIF varied significantly and reproducibly from cell to cell, with its relative standard deviation ranging from 36% to 18% depending on the macroscopic dose delivered. Interestingly, this relative cell-to-cell variability increased as the dose decreased, contrary to the mean RIF count per cell. This result shows that the dose effect, in terms of the number of DNA lesions indicated by RIF is not as simple as a purely proportional relation in which relative SD is constant with dose. To analyse the origins of this observed variability, we calculated the spread of the specific energy distribution for the different target volumes and subvolumes in which RIF can be generated. Variances, standard deviations and relative standard deviations all changed similarly from dose to dose for biological and calculated microdosimetric values. This similarity is an important argument that supports the hypothesis of the conservation of the association between the number of RIF per nucleus and the specific energy per DNA molecule. This comparison allowed us to

Cell to Cell Variability of Radiation-Induced Foci: Relation between Observed Damage and Energy Deposition.

Directory of Open Access Journals (Sweden)

Gaëtan Gruel

Full Text Available Most studies that aim to understand the interactions between different types of photon radiation and cellular DNA assume homogeneous cell irradiation, with all cells receiving the same amount of energy. The level of DNA damage is therefore generally determined by averaging it over the entire population of exposed cells. However, evaluating the molecular consequences of a stochastic phenomenon such as energy deposition of ionizing radiation by measuring only an average effect may not be sufficient for understanding some aspects of the cellular response to this radiation. The variance among the cells associated with this average effect may also be important for the behaviour of irradiated tissue. In this study, we accurately estimated the distribution of the number of radiation-induced γH2AX foci (RIF per cell nucleus in a large population of endothelial cells exposed to 3 macroscopic doses of gamma rays from 60Co. The number of RIF varied significantly and reproducibly from cell to cell, with its relative standard deviation ranging from 36% to 18% depending on the macroscopic dose delivered. Interestingly, this relative cell-to-cell variability increased as the dose decreased, contrary to the mean RIF count per cell. This result shows that the dose effect, in terms of the number of DNA lesions indicated by RIF is not as simple as a purely proportional relation in which relative SD is constant with dose. To analyse the origins of this observed variability, we calculated the spread of the specific energy distribution for the different target volumes and subvolumes in which RIF can be generated. Variances, standard deviations and relative standard deviations all changed similarly from dose to dose for biological and calculated microdosimetric values. This similarity is an important argument that supports the hypothesis of the conservation of the association between the number of RIF per nucleus and the specific energy per DNA molecule. This

Transient fluctuation relations for time-dependent particle transport

Science.gov (United States)

Altland, Alexander; de Martino, Alessandro; Egger, Reinhold; Narozhny, Boris

2010-09-01

We consider particle transport under the influence of time-varying driving forces, where fluctuation relations connect the statistics of pairs of time-reversed evolutions of physical observables. In many “mesoscopic” transport processes, the effective many-particle dynamics is dominantly classical while the microscopic rates governing particle motion are of quantum-mechanical origin. We here employ the stochastic path-integral approach as an optimal tool to probe the fluctuation statistics in such applications. Describing the classical limit of the Keldysh quantum nonequilibrium field theory, the stochastic path integral encapsulates the quantum origin of microscopic particle exchange rates. Dynamically, it is equivalent to a transport master equation which is a formalism general enough to describe many applications of practical interest. We apply the stochastic path integral to derive general functional fluctuation relations for current flow induced by time-varying forces. We show that the successive measurement processes implied by this setup do not put the derivation of quantum fluctuation relations in jeopardy. While in many cases the fluctuation relation for a full time-dependent current profile may contain excessive information, we formulate a number of reduced relations, and demonstrate their application to mesoscopic transport. Examples include the distribution of transmitted charge, where we show that the derivation of a fluctuation relation requires the combined monitoring of the statistics of charge and work.

Redox regulation of cell proliferation: Bioinformatics and redox proteomics approaches to identify redox-sensitive cell cycle regulators.

Science.gov (United States)

Foyer, Christine H; Wilson, Michael H; Wright, Megan H

2018-03-29

Plant stem cells are the foundation of plant growth and development. The balance of quiescence and division is highly regulated, while ensuring that proliferating cells are protected from the adverse effects of environment fluctuations that may damage the genome. Redox regulation is important in both the activation of proliferation and arrest of the cell cycle upon perception of environmental stress. Within this context, reactive oxygen species serve as 'pro-life' signals with positive roles in the regulation of the cell cycle and survival. However, very little is known about the metabolic mechanisms and redox-sensitive proteins that influence cell cycle progression. We have identified cysteine residues on known cell cycle regulators in Arabidopsis that are potentially accessible, and could play a role in redox regulation, based on secondary structure and solvent accessibility likelihoods for each protein. We propose that redox regulation may function alongside other known posttranslational modifications to control the functions of core cell cycle regulators such as the retinoblastoma protein. Since our current understanding of how redox regulation is involved in cell cycle control is hindered by a lack of knowledge regarding both which residues are important and how modification of those residues alters protein function, we discuss how critical redox modifications can be mapped at the molecular level. Crown Copyright © 2018. Published by Elsevier Inc. All rights reserved.

Target cells in internal dosimetry

Energy Technology Data Exchange (ETDEWEB)

Goessner, W

2003-07-01

Data related to radium induced bone sarcomas in humans are used as a model for defining target cells on bone surfaces and in the bone marrow. The differential distribution of radiation induced bone sarcoma types with a high ratio of non-bone producing, mainly fibroblastic tumours, challenges the ICRP concept that the bone lining cells are target cells. Multipotential mesenchymal stem cells are located within the range of alpha particles, and are the most likely target cells for the fibroblastic type of bone sarcoma. The histogenesis of bone sarcomas after irradiation with alpha emitters shows that their final histopathology is not dependent on a single target cell. Each target cell has a microenvironment, which has to be regarded as a synergistic morpho-functional tissue unit. For this the concept of 'histion', a term used in general pathology, is proposed. Interactions between target cells that have been hit by alpha-particles, leading to lethal, mutational or transformation events with all components of a 'histion', will prove critical to understanding the pathogenesis of both deterministic and stochastic late effects. (author)

Target cells in internal dosimetry

International Nuclear Information System (INIS)

Goessner, W.

2003-01-01

Data related to radium induced bone sarcomas in humans are used as a model for defining target cells on bone surfaces and in the bone marrow. The differential distribution of radiation induced bone sarcoma types with a high ratio of non-bone producing, mainly fibroblastic tumours, challenges the ICRP concept that the bone lining cells are target cells. Multipotential mesenchymal stem cells are located within the range of alpha particles, and are the most likely target cells for the fibroblastic type of bone sarcoma. The histogenesis of bone sarcomas after irradiation with alpha emitters shows that their final histopathology is not dependent on a single target cell. Each target cell has a microenvironment, which has to be regarded as a synergistic morpho-functional tissue unit. For this the concept of 'histion', a term used in general pathology, is proposed. Interactions between target cells that have been hit by alpha-particles, leading to lethal, mutational or transformation events with all components of a 'histion', will prove critical to understanding the pathogenesis of both deterministic and stochastic late effects. (author)

Active Brownian particles with velocity-alignment and active fluctuations

International Nuclear Information System (INIS)

Großmann, R; Schimansky-Geier, L; Romanczuk, P

2012-01-01

We consider a model of active Brownian particles (ABPs) with velocity alignment in two spatial dimensions with passive and active fluctuations. Here, active fluctuations refers to purely non-equilibrium stochastic forces correlated with the heading of an individual active particle. In the simplest case studied here, they are assumed to be independent stochastic forces parallel (speed noise) and perpendicular (angular noise) to the velocity of the particle. On the other hand, passive fluctuations are defined by a noise vector independent of the direction of motion of a particle, and may account, for example, for thermal fluctuations. We derive a macroscopic description of the ABP gas with velocity-alignment interaction. Here, we start from the individual-based description in terms of stochastic differential equations (Langevin equations) and derive equations of motion for the coarse-grained kinetic variables (density, velocity and temperature) via a moment expansion of the corresponding probability density function. We focus here on the different impact of active and passive fluctuations on onset of collective motion and show how active fluctuations in the active Brownian dynamics can change the phase-transition behaviour of the system. In particular, we show that active angular fluctuations lead to an earlier breakdown of collective motion and to the emergence of a new bistable regime in the mean-field case. (paper)

Aerofoil broadband and tonal noise modelling using stochastic sound sources and incorporated large scale fluctuations

Science.gov (United States)

Proskurov, S.; Darbyshire, O. R.; Karabasov, S. A.

2017-12-01

The present work discusses modifications to the stochastic Fast Random Particle Mesh (FRPM) method featuring both tonal and broadband noise sources. The technique relies on the combination of incorporated vortex-shedding resolved flow available from Unsteady Reynolds-Averaged Navier-Stokes (URANS) simulation with the fine-scale turbulence FRPM solution generated via the stochastic velocity fluctuations in the context of vortex sound theory. In contrast to the existing literature, our method encompasses a unified treatment for broadband and tonal acoustic noise sources at the source level, thus, accounting for linear source interference as well as possible non-linear source interaction effects. When sound sources are determined, for the sound propagation, Acoustic Perturbation Equations (APE-4) are solved in the time-domain. Results of the method's application for two aerofoil benchmark cases, with both sharp and blunt trailing edges are presented. In each case, the importance of individual linear and non-linear noise sources was investigated. Several new key features related to the unsteady implementation of the method were tested and brought into the equation. Encouraging results have been obtained for benchmark test cases using the new technique which is believed to be potentially applicable to other airframe noise problems where both tonal and broadband parts are important.

Microgrid Reliability Modeling and Battery Scheduling Using Stochastic Linear Programming

Energy Technology Data Exchange (ETDEWEB)

Cardoso, Goncalo; Stadler, Michael; Siddiqui, Afzal; Marnay, Chris; DeForest, Nicholas; Barbosa-Povoa, Ana; Ferrao, Paulo

2013-05-23

This paper describes the introduction of stochastic linear programming into Operations DER-CAM, a tool used to obtain optimal operating schedules for a given microgrid under local economic and environmental conditions. This application follows previous work on optimal scheduling of a lithium-iron-phosphate battery given the output uncertainty of a 1 MW molten carbonate fuel cell. Both are in the Santa Rita Jail microgrid, located in Dublin, California. This fuel cell has proven unreliable, partially justifying the consideration of storage options. Several stochastic DER-CAM runs are executed to compare different scenarios to values obtained by a deterministic approach. Results indicate that using a stochastic approach provides a conservative yet more lucrative battery schedule. Lower expected energy bills result, given fuel cell outages, in potential savings exceeding 6percent.

Facile method to stain the bacterial cell surface for super-resolution fluorescence microscopy

Energy Technology Data Exchange (ETDEWEB)

Gunsolus, Ian L.; Hu, Dehong; Mihai, Cosmin; Lohse, Samuel E.; Lee, Chang-Soo; Torelli, Marco; Hamers, Robert J.; Murphy, Catherine; Orr, Galya; Haynes, Christy L.

2014-01-01

A method to fluorescently stain the surfaces of both Gram-negative and Gram-positive bacterial cells compatible with super-resolution fluorescence microscopy is presented. This method utilizes a commercially-available fluorescent probe to label primary amines at the surface of the cell. We demonstrate efficient staining of two bacterial strains, the Gram-negative Shewanella oneidensis MR-1 and the Gram-positive Bacillus subtilis 168. Using structured illumination microscopy and stochastic optical reconstruction microscopy, which require high quantum yield or specialized dyes, we show that this staining method may be used to resolve the bacterial cell surface with sub-diffraction-limited resolution. We further use this method to identify localization patterns of nanomaterials, specifically cadmium selenide quantum dots, following interaction with bacterial cells.

Extension of Nelson's stochastic quantization to finite temperature using thermo field dynamics

International Nuclear Information System (INIS)

Kobayashi, K.; Yamanaka, Y.

2011-01-01

We present an extension of Nelson's stochastic quantum mechanics to finite temperature. Utilizing the formulation of Thermo Field Dynamics (TFD), we can show that Ito's stochastic equations for tilde and non-tilde particle positions reproduce the TFD-type Schroedinger equation which is equivalent to the Liouville-von Neumann equation. In our formalism, the drift terms in the Ito's stochastic equation have the temperature dependence and the thermal fluctuation is induced through the correlation of the non-tilde and tilde particles. We show that our formalism satisfies the position-momentum uncertainty relation at finite temperature. -- Highlights: → Utilizing TFD, we extend Nelson's stochastic method to finite temperature. → We introduce stochastic equations for tilde and non-tilde particles. → Our stochastic equations can reproduce the TFD-type Schroedinger equation. → Our formalism satisfies the uncertainly relation at finite temperature.

Agent-Based Computational Modeling of Cell Culture: Understanding Dosimetry In Vitro as Part of In Vitro to In Vivo Extrapolation

Science.gov (United States)

Quantitative characterization of cellular dose in vitro is needed for alignment of doses in vitro and in vivo. We used the agent-based software, CompuCell3D (CC3D), to provide a stochastic description of cell growth in culture. The model was configured so that isolated cells assu...

From stochastic phase space evolution to Brownian motion in collective space

International Nuclear Information System (INIS)

Benhassine, B.; Farine, M.; Hernandez, E.S.; Idier, D.; Remaud, B.; Sebille, F.

1993-01-01

Within the framework of stochastic transport equations in phase space, the dynamics of fluctuations on collective variables in homogeneous fermion systems is studied. The transport coefficients are formally deduced in the relaxation time approximation and a general method to compute dynamically the dispersions of collective observables is proposed as a set of coupled equations. Independently, the general covariance matrix of phase space fluctuations and the dispersion on collective variables at equilibrium are derived. Detailed numerical applications show that dynamics of fluctuations can be extracted from noisy numerical simulations and that the leading parameter for collective fluctuations is the excitation energy whatever is its degree of thermalization. (authors). 16 refs., 12 figs

Intermittent targeted therapies and stochastic evolution in patients affected by chronic myeloid leukemia

Science.gov (United States)

Pizzolato, N.; Persano Adorno, D.; Valenti, D.; Spagnolo, B.

2016-05-01

Front line therapy for the treatment of patients affected by chronic myeloid leukemia (CML) is based on the administration of tyrosine kinase inhibitors, namely imatinib or, more recently, axitinib. Although imatinib is highly effective and represents an example of a successful molecular targeted therapy, the appearance of resistance is observed in a proportion of patients, especially those in advanced stages. In this work, we investigate the appearance of resistance in patients affected by CML, by modeling the evolutionary dynamics of cancerous cell populations in a simulated patient treated by an intermittent targeted therapy. We simulate, with the Monte Carlo method, the stochastic evolution of initially healthy cells to leukemic clones, due to genetic mutations and changes in their reproductive behavior. We first present the model and its validation with experimental data by considering a continuous therapy. Then, we investigate how fluctuations in the number of leukemic cells affect patient response to the therapy when the drug is administered with an intermittent time scheduling. Here we show that an intermittent therapy (IT) represents a valid choice in patients with high risk of toxicity, despite an associated delay to the complete restoration of healthy cells. Moreover, a suitably tuned IT can reduce the probability of developing resistance.

Fluctuating fitness shapes the clone-size distribution of immune repertoires.

Science.gov (United States)

Desponds, Jonathan; Mora, Thierry; Walczak, Aleksandra M

2016-01-12

The adaptive immune system relies on the diversity of receptors expressed on the surface of B- and T cells to protect the organism from a vast amount of pathogenic threats. The proliferation and degradation dynamics of different cell types (B cells, T cells, naive, memory) is governed by a variety of antigenic and environmental signals, yet the observed clone sizes follow a universal power-law distribution. Guided by this reproducibility we propose effective models of somatic evolution where cell fate depends on an effective fitness. This fitness is determined by growth factors acting either on clones of cells with the same receptor responding to specific antigens, or directly on single cells with no regard for clones. We identify fluctuations in the fitness acting specifically on clones as the essential ingredient leading to the observed distributions. Combining our models with experiments, we characterize the scale of fluctuations in antigenic environments and we provide tools to identify the relevant growth signals in different tissues and organisms. Our results generalize to any evolving population in a fluctuating environment.

Estimating intrinsic and extrinsic noise from single-cell gene expression measurements

Science.gov (United States)

Fu, Audrey Qiuyan; Pachter, Lior

2017-01-01

Gene expression is stochastic and displays variation (“noise”) both within and between cells. Intracellular (intrinsic) variance can be distinguished from extracellular (extrinsic) variance by applying the law of total variance to data from two-reporter assays that probe expression of identically regulated gene pairs in single cells. We examine established formulas [Elowitz, M. B., A. J. Levine, E. D. Siggia and P. S. Swain (2002): “Stochastic gene expression in a single cell,” Science, 297, 1183–1186.] for the estimation of intrinsic and extrinsic noise and provide interpretations of them in terms of a hierarchical model. This allows us to derive alternative estimators that minimize bias or mean squared error. We provide a geometric interpretation of these results that clarifies the interpretation in [Elowitz, M. B., A. J. Levine, E. D. Siggia and P. S. Swain (2002): “Stochastic gene expression in a single cell,” Science, 297, 1183–1186.]. We also demonstrate through simulation and re-analysis of published data that the distribution assumptions underlying the hierarchical model have to be satisfied for the estimators to produce sensible results, which highlights the importance of normalization. PMID:27875323

Stochastic chemical kinetics theory and (mostly) systems biological applications

CERN Document Server

Érdi, Péter; Lente, Gabor

2014-01-01

This volume reviews the theory and simulation methods of stochastic kinetics by integrating historical and recent perspectives, presents applications, mostly in the context of systems biology and also in combustion theory. In recent years, due to the development in experimental techniques, such as optical imaging, single cell analysis, and fluorescence spectroscopy, biochemical kinetic data inside single living cells have increasingly been available. The emergence of systems biology brought renaissance in the application of stochastic kinetic methods.

Sensory optimization by stochastic tuning.

Science.gov (United States)

Jurica, Peter; Gepshtein, Sergei; Tyukin, Ivan; van Leeuwen, Cees

2013-10-01

Individually, visual neurons are each selective for several aspects of stimulation, such as stimulus location, frequency content, and speed. Collectively, the neurons implement the visual system's preferential sensitivity to some stimuli over others, manifested in behavioral sensitivity functions. We ask how the individual neurons are coordinated to optimize visual sensitivity. We model synaptic plasticity in a generic neural circuit and find that stochastic changes in strengths of synaptic connections entail fluctuations in parameters of neural receptive fields. The fluctuations correlate with uncertainty of sensory measurement in individual neurons: The higher the uncertainty the larger the amplitude of fluctuation. We show that this simple relationship is sufficient for the stochastic fluctuations to steer sensitivities of neurons toward a characteristic distribution, from which follows a sensitivity function observed in human psychophysics and which is predicted by a theory of optimal allocation of receptive fields. The optimal allocation arises in our simulations without supervision or feedback about system performance and independently of coupling between neurons, making the system highly adaptive and sensitive to prevailing stimulation. PsycINFO Database Record (c) 2013 APA, all rights reserved.

Analytical investigation on cell temperature control method of planar solid oxide fuel cell

Energy Technology Data Exchange (ETDEWEB)

Inui, Y.; Ito, N.; Nakajima, T.; Urata, A. [Department of Electrical and Electronic Engineering, Toyohashi University of Technology, Tempaku-cho, Toyohashi (Japan)

2006-09-15

The solid oxide fuel cell (SOFC) has a problem in durability of the ceramics used as its cell materials because its operating temperature is very high and the cell temperature fluctuation induces thermal stress in the ceramics. The cell temperature distribution in the SOFC, therefore, should be kept as constant as possible during variable load operation through control of the average current density in the cell. Considering this fact, the authors numerically optimize the operating parameters of air utilization and the inlet gas temperature of the planar SOFC by minimizing the cell temperature shift from its nominal value and propose a new cell temperature control method that adopts these optimum operating parameters for each average current density. The effectiveness of the proposed method is very high and the temperature variation is suppressed to a very low level without lowering the single cell voltage for both the co-flow and counter-flow type cells, indicating that the proposed cell temperature control method makes variable load operation of the planar SOFC possible. (author)

Conceptualizing a tool to optimize therapy based on dynamic heterogeneity

International Nuclear Information System (INIS)

Liao, David; Estévez-Salmerón, Luis; Tlsty, Thea D

2012-01-01

Complex biological systems often display a randomness paralleled in processes studied in fundamental physics. This simple stochasticity emerges owing to the complexity of the system and underlies a fundamental aspect of biology called phenotypic stochasticity. Ongoing stochastic fluctuations in phenotype at the single-unit level can contribute to two emergent population phenotypes. Phenotypic stochasticity not only generates heterogeneity within a cell population, but also allows reversible transitions back and forth between multiple states. This phenotypic interconversion tends to restore a population to a previous composition after that population has been depleted of specific members. We call this tendency homeostatic heterogeneity. These concepts of dynamic heterogeneity can be applied to populations composed of molecules, cells, individuals, etc. Here we discuss the concept that phenotypic stochasticity both underlies the generation of heterogeneity within a cell population and can be used to control population composition, contributing, in particular, to both the ongoing emergence of drug resistance and an opportunity for depleting drug-resistant cells. Using notions of both ‘large’ and ‘small’ numbers of biomolecular components, we rationalize our use of Markov processes to model the generation and eradication of drug-resistant cells. Using these insights, we have developed a graphical tool, called a metronomogram, that we propose will allow us to optimize dosing frequencies and total course durations for clinical benefit. (paper)

Far from Equilibrium Percolation, Stochastic and Shape Resonances in the Physics of Life

Directory of Open Access Journals (Sweden)

Antonio Bianconi

2011-10-01

Full Text Available Key physical concepts, relevant for the cross-fertilization between condensed matter physics and the physics of life seen as a collective phenomenon in a system out-of-equilibrium, are discussed. The onset of life can be driven by: (a the critical fluctuations at the protonic percolation threshold in membrane transport; (b the stochastic resonance in biological systems, a mechanism that can exploit external and self-generated noise in order to gain efficiency in signal processing; and (c the shape resonance (or Fano resonance or Feshbach resonance in the association and dissociation processes of bio-molecules (a quantum mechanism that could play a key role to establish a macroscopic quantum coherence in the cell.

Stochastic deformation of a thermodynamic symplectic structure

OpenAIRE

Kazinski, P. O.

2008-01-01

A stochastic deformation of a thermodynamic symplectic structure is studied. The stochastic deformation procedure is analogous to the deformation of an algebra of observables like deformation quantization, but for an imaginary deformation parameter (the Planck constant). Gauge symmetries of thermodynamics and corresponding stochastic mechanics, which describes fluctuations of a thermodynamic system, are revealed and gauge fields are introduced. A physical interpretation to the gauge transform...

Stochasticity and stereotypy in the Ciona notochord.

Science.gov (United States)

Carlson, Maia; Reeves, Wendy; Veeman, Michael

2015-01-15

Fate mapping with single cell resolution has typically been confined to embryos with completely stereotyped development. The lineages giving rise to the 40 cells of the Ciona notochord are invariant, but the intercalation of those cells into a single-file column is not. Here we use genetic labeling methods to fate map the Ciona notochord with both high resolution and large sample sizes. We find that the ordering of notochord cells into a single column is not random, but instead shows a distinctive signature characteristic of mediolaterally-biased intercalation. We find that patterns of cell intercalation in the notochord are somewhat stochastic but far more stereotyped than previously believed. Cell behaviors vary by lineage, with the secondary notochord lineage being much more constrained than the primary lineage. Within the primary lineage, patterns of intercalation reflect the geometry of the intercalating tissue. We identify the latest point at which notochord morphogenesis is largely stereotyped, which is shortly before the onset of mediolateral intercalation and immediately after the final cell divisions in the primary lineage. These divisions are consistently oriented along the AP axis. Our results indicate that the interplay between stereotyped and stochastic cell behaviors in morphogenesis can only be assessed by fate mapping experiments that have both cellular resolution and large sample sizes. Copyright © 2014 Elsevier Inc. All rights reserved.

Cell cycle delays induced by heavy ion irradiation of synchronous mammalian cells

International Nuclear Information System (INIS)

Scholz, M.; Kraft-Weyrather, W.; Ritter, S.; Kraft, G.

1994-01-01

Cell cycle delays in V79 Chinese hamster cells induced by heavy ion exposure have been investigated using flow cytometry. Synchronous cell populations in G 1 -, S- and late-S/G 2 M-phase were used. Cells were irradiated with particles from Z = 10 (neon) up to Z = 96 (uranium) in the energy range from 2.4 to 17.4 MeV/u and the LET range from 415 to 16225 keV/μm at the UNILAC at GSI, Darmstadt. For comparison, experiments with 250 kV X-rays were performed. For light particles like neon, cell cycle perturbations comparable to those after X-ray irradiation were found, and with increasing LET an increasing delay per particle traversal was observed. For the highest LET-values, extended delays in G 1 -, S- and G 2 M-phase were detected immediately after irradiation. A large fraction of the cells remained in S-phase or G 2 M-phase up to 48 h or longer after irradiation. No significant cell age dependence of cycle delays was detected for the very high LET values. In addition to cell cycle delays, two effects related to the DNA-content as determined by flow cytometry were found after irradiation with very high LET particles, which were attributed to cell fusion and to drastic morphological changes of the cells. Estimations based on the dose deposited by a single particle hit in the cell nucleus and the actual number of hits show, that the basic trend of the experimental results can be explained by the stochastic properties of particle radiation. (orig.)

Fluctuation correlation models for receptor immobilization

Science.gov (United States)

Fourcade, B.

2017-12-01

Nanoscale dynamics with cycles of receptor diffusion and immobilization by cell-external-or-internal factors is a key process in living cell adhesion phenomena at the origin of a plethora of signal transduction pathways. Motivated by modern correlation microscopy approaches, the receptor correlation functions in physical models based on diffusion-influenced reaction is studied. Using analytical and stochastic modeling, this paper focuses on the hybrid regime where diffusion and reaction are not truly separable. The time receptor autocorrelation functions are shown to be indexed by different time scales and their asymptotic expansions are given. Stochastic simulations show that this analysis can be extended to situations with a small number of molecules. It is also demonstrated that this analysis applies when receptor immobilization is coupled to environmental noise.

Brain mast cells link the immune system to anxiety-like behavior

Science.gov (United States)

Nautiyal, Katherine M.; Ribeiro, Ana C.; Pfaff, Donald W.; Silver, Rae

2008-01-01

Mast cells are resident in the brain and contain numerous mediators, including neurotransmitters, cytokines, and chemokines, that are released in response to a variety of natural and pharmacological triggers. The number of mast cells in the brain fluctuates with stress and various behavioral and endocrine states. These properties suggest that mast cells are poised to influence neural systems underlying behavior. Using genetic and pharmacological loss-of-function models we performed a behavioral screen for arousal responses including emotionality, locomotor, and sensory components. We found that mast cell deficient KitW−sh/W−sh (sash−/−) mice had a greater anxiety-like phenotype than WT and heterozygote littermate control animals in the open field arena and elevated plus maze. Second, we show that blockade of brain, but not peripheral, mast cell activation increased anxiety-like behavior. Taken together, the data implicate brain mast cells in the modulation of anxiety-like behavior and provide evidence for the behavioral importance of neuroimmune links. PMID:19004805

Approximation and inference methods for stochastic biochemical kinetics—a tutorial review

International Nuclear Information System (INIS)

Schnoerr, David; Grima, Ramon; Sanguinetti, Guido

2017-01-01

Stochastic fluctuations of molecule numbers are ubiquitous in biological systems. Important examples include gene expression and enzymatic processes in living cells. Such systems are typically modelled as chemical reaction networks whose dynamics are governed by the chemical master equation. Despite its simple structure, no analytic solutions to the chemical master equation are known for most systems. Moreover, stochastic simulations are computationally expensive, making systematic analysis and statistical inference a challenging task. Consequently, significant effort has been spent in recent decades on the development of efficient approximation and inference methods. This article gives an introduction to basic modelling concepts as well as an overview of state of the art methods. First, we motivate and introduce deterministic and stochastic methods for modelling chemical networks, and give an overview of simulation and exact solution methods. Next, we discuss several approximation methods, including the chemical Langevin equation, the system size expansion, moment closure approximations, time-scale separation approximations and hybrid methods. We discuss their various properties and review recent advances and remaining challenges for these methods. We present a comparison of several of these methods by means of a numerical case study and highlight some of their respective advantages and disadvantages. Finally, we discuss the problem of inference from experimental data in the Bayesian framework and review recent methods developed the literature. In summary, this review gives a self-contained introduction to modelling, approximations and inference methods for stochastic chemical kinetics. (topical review)

Enhancement of large fluctuations to extinction in adaptive networks

Science.gov (United States)

Hindes, Jason; Schwartz, Ira B.; Shaw, Leah B.

2018-01-01

During an epidemic, individual nodes in a network may adapt their connections to reduce the chance of infection. A common form of adaption is avoidance rewiring, where a noninfected node breaks a connection to an infected neighbor and forms a new connection to another noninfected node. Here we explore the effects of such adaptivity on stochastic fluctuations in the susceptible-infected-susceptible model, focusing on the largest fluctuations that result in extinction of infection. Using techniques from large-deviation theory, combined with a measurement of heterogeneity in the susceptible degree distribution at the endemic state, we are able to predict and analyze large fluctuations and extinction in adaptive networks. We find that in the limit of small rewiring there is a sharp exponential reduction in mean extinction times compared to the case of zero adaption. Furthermore, we find an exponential enhancement in the probability of large fluctuations with increased rewiring rate, even when holding the average number of infected nodes constant.

Stochastic transport processes in discrete biological systems

CERN Document Server

Frehland, Eckart

1982-01-01

These notes are in part based on a course for advanced students in the applications of stochastic processes held in 1978 at the University of Konstanz. These notes contain the results of re­ cent studies on the stochastic description of ion transport through biological membranes. In particular, they serve as an introduction to an unified theory of fluctuations in complex biological transport systems. We emphasize that the subject of this volume is not to introduce the mathematics of stochastic processes but to present a field of theoretical biophysics in which stochastic methods are important. In the last years the study of membrane noise has become an important method in biophysics. Valuable information on the ion transport mechanisms in membranes can be obtained from noise analysis. A number of different processes such as the opening and closing of ion channels have been shown to be sources of the measured current or voltage fluctuations. Bio­ logical 'transport systems can be complex. For example, the tr...

A contribution to the systematics of stochastic volatility models

Czech Academy of Sciences Publication Activity Database

Slanina, František

2010-01-01

Roč. 389, č. 16 (2010), s. 3230-3239 ISSN 0378-4371 R&D Projects: GA MŠk OC09078 Institutional research plan: CEZ:AV0Z10100520 Keywords : fluctuations * econophysics * stochastic differential equations Subject RIV: BM - Solid Matter Physics ; Magnetism Impact factor: 1.521, year: 2010

Perturbation theory for continuous stochastic equations

International Nuclear Information System (INIS)

Chechetkin, V.R.; Lutovinov, V.S.

1987-01-01

The various general perturbational schemes for continuous stochastic equations are considered. These schemes have many analogous features with the iterational solution of Schwinger equation for S-matrix. The following problems are discussed: continuous stochastic evolution equations for probability distribution functionals, evolution equations for equal time correlators, perturbation theory for Gaussian and Poissonian additive noise, perturbation theory for birth and death processes, stochastic properties of systems with multiplicative noise. The general results are illustrated by diffusion-controlled reactions, fluctuations in closed systems with chemical processes, propagation of waves in random media in parabolic equation approximation, and non-equilibrium phase transitions in systems with Poissonian breeding centers. The rate of irreversible reaction X + X → A (Smoluchowski process) is calculated with the use of general theory based on continuous stochastic equations for birth and death processes. The threshold criterion and range of fluctuational region for synergetic phase transition in system with Poissonian breeding centers are also considered. (author)

Studies to the stochastic theory of coupled reactorkinetic-thermohydraulic systems Pt. 2

International Nuclear Information System (INIS)

Mesko, L.

1983-06-01

The description is given of the noise phenomena taking place in a multivariable coupled system by a comprehensive model based on the theory of stochastic fluctuations. A comparison is made with models using transfer function formalism for systems characterized by deterministic open and closed loop signal transmission properties. The advantages of the stochastic model are illustrated by simple reactor dynamical examples having diagnostical importance. (author)

Evolution in fluctuating environments: decomposing selection into additive components of the Robertson-Price equation.

Science.gov (United States)

Engen, Steinar; Saether, Bernt-Erik

2014-03-01

We analyze the stochastic components of the Robertson-Price equation for the evolution of quantitative characters that enables decomposition of the selection differential into components due to demographic and environmental stochasticity. We show how these two types of stochasticity affect the evolution of multivariate quantitative characters by defining demographic and environmental variances as components of individual fitness. The exact covariance formula for selection is decomposed into three components, the deterministic mean value, as well as stochastic demographic and environmental components. We show that demographic and environmental stochasticity generate random genetic drift and fluctuating selection, respectively. This provides a common theoretical framework for linking ecological and evolutionary processes. Demographic stochasticity can cause random variation in selection differentials independent of fluctuating selection caused by environmental variation. We use this model of selection to illustrate that the effect on the expected selection differential of random variation in individual fitness is dependent on population size, and that the strength of fluctuating selection is affected by how environmental variation affects the covariance in Malthusian fitness between individuals with different phenotypes. Thus, our approach enables us to partition out the effects of fluctuating selection from the effects of selection due to random variation in individual fitness caused by demographic stochasticity. © 2013 The Author(s). Evolution © 2013 The Society for the Study of Evolution.

Certain amplified genomic-DNA fragments (AGFs) may be involved in cell cycle progression and chloroquine is found to induce the production of cell-cycle-associated AGFs (CAGFs) in Plasmodium falciparum

OpenAIRE

Li, Gao-De

2015-01-01

It is well known that cyclins are a family of proteins that control cell-cycle progression by activating cyclin-dependent kinase. Based on our experimental results, we propose here a novel hypothesis that certain amplified genomic-DNA fragments (AGFs) may also be required for the cell cycle progression of eukaryotic cells and thus can be named as cell-cycle-associated AGFs (CAGFs). Like fluctuation in cyclin levels during cell cycle progression, these CAGFs are amplified and degraded at diffe...

Stem cell self-renewal in intestinal crypt

International Nuclear Information System (INIS)

Simons, Benjamin D.; Clevers, Hans

2011-01-01

As a rapidly cycling tissue capable of fast repair and regeneration, the intestinal epithelium has emerged as a favored model system to explore the principles of adult stem cell biology. However, until recently, the identity and characteristics of the stem cell population in both the small intestine and colon has remained the subject of debate. Recent studies based on targeted lineage tracing strategies, combined with the development of an organotypic culture system, have identified the crypt base columnar cell as the intestinal stem cell, and have unveiled the strategy by which the balance between proliferation and differentiation is maintained. These results show that intestinal stem cells operate in a dynamic environment in which frequent and stochastic stem cell loss is compensated by the proliferation of neighboring stem cells. We review the basis of these experimental findings and the insights they offer into the mechanisms of homeostatic stem cell regulation.

A Model of Yeast Cell-Cycle Regulation Based on a Standard Component Modeling Strategy for Protein Regulatory Networks.

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Teeraphan Laomettachit

Full Text Available To understand the molecular mechanisms that regulate cell cycle progression in eukaryotes, a variety of mathematical modeling approaches have been employed, ranging from Boolean networks and differential equations to stochastic simulations. Each approach has its own characteristic strengths and weaknesses. In this paper, we propose a "standard component" modeling strategy that combines advantageous features of Boolean networks, differential equations and stochastic simulations in a framework that acknowledges the typical sorts of reactions found in protein regulatory networks. Applying this strategy to a comprehensive mechanism of the budding yeast cell cycle, we illustrate the potential value of standard component modeling. The deterministic version of our model reproduces the phenotypic properties of wild-type cells and of 125 mutant strains. The stochastic version of our model reproduces the cell-to-cell variability of wild-type cells and the partial viability of the CLB2-dbΔ clb5Δ mutant strain. Our simulations show that mathematical modeling with "standard components" can capture in quantitative detail many essential properties of cell cycle control in budding yeast.

A stochastic simulation model for reliable PV system sizing providing for solar radiation fluctuations

International Nuclear Information System (INIS)

Kaplani, E.; Kaplanis, S.

2012-01-01

Highlights: ► Solar radiation data for European cities follow the Extreme Value or Weibull distribution. ► Simulation model for the sizing of SAPV systems based on energy balance and stochastic analysis. ► Simulation of PV Generator-Loads-Battery Storage System performance for all months. ► Minimum peak power and battery capacity required for reliable SAPV sizing for various European cities. ► Peak power and battery capacity reduced by more than 30% for operation 95% success rate. -- Abstract: The large fluctuations observed in the daily solar radiation profiles affect highly the reliability of the PV system sizing. Increasing the reliability of the PV system requires higher installed peak power (P m ) and larger battery storage capacity (C L ). This leads to increased costs, and makes PV technology less competitive. This research paper presents a new stochastic simulation model for stand-alone PV systems, developed to determine the minimum installed P m and C L for the PV system to be energy independent. The stochastic simulation model developed, makes use of knowledge acquired from an in-depth statistical analysis of the solar radiation data for the site, and simulates the energy delivered, the excess energy burnt, the load profiles and the state of charge of the battery system for the month the sizing is applied, and the PV system performance for the entire year. The simulation model provides the user with values for the autonomy factor d, simulating PV performance in order to determine the minimum P m and C L depending on the requirements of the application, i.e. operation with critical or non-critical loads. The model makes use of NASA’s Surface meteorology and Solar Energy database for the years 1990–2004 for various cities in Europe with a different climate. The results obtained with this new methodology indicate a substantial reduction in installed peak power and battery capacity, both for critical and non-critical operation, when compared to

Fluctuations in a system depending on several random parameters. Application to reactors (1962); Fluctuations d'un systeme dependant de plusieurs parametres aleatoires. Application aux reacteurs nucleaires (1962)

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Blaquiere, A [Faculte des Sciences de Paris, 75 (France); Pachowska, R [Universite Technique de Varsovie (Poland)

1962-07-01

We have previously developed a method for studying neutronic fluctuations in nuclear reactors using the analogy between the behaviour of a reactor and that of certain common radioelectric circuits. The fluctuations may then be calculated by introducing into the circuit a suitable noise source. By this method we have been able to consider the overall fluctuations in a particularly simple form and we have provided a physical significance for certain results obtained more laboriously by other methods. The object of the present report is to generalise this method and in particular to extend it to the case of a reactor having a cellular structure and to apply it to fluctuations within a cell. It is thus shown that the fluctuations in a cell are the resultant of two terms: - a rapidly evolving Poissonian noise, not related to the overall fluctuations; - a slowly evolving noise, when the reactor is not too far from criticality, which is related to the overall fluctuations. The first term arises from a rapid 'ordering' of the system, during which time the cells come mutually into equilibrium. The second term is due to the coordinated evolution of all the cells, after the end of the first transitory phase. The conclusions reached show that it would be useful to complete the study with an analysis of non-linear phenomena which can considerably influence the transitory behaviour of the cells during the initial pre-equilibrium phase. This report also Stresses the relationship of the new method to the old methods. It tends also to place pile fluctuation theory in a more general framework, that of the fluctuations of a system depending on several random parameters; from this point of view, the method could easily be transposed and adapted to the study of other physical problems of this type. (authors) [French] Nous avons precedemment developpe une methode d'etude des fluctuations neutroniques des reacteurs nucleaires mettant a profit l'analogie entre le comportement d

Cell banking for regulatory T cell-based therapy: strategies to overcome the impact of cryopreservation on the Treg viability and phenotype.

Science.gov (United States)

Gołąb, Karolina; Grose, Randall; Placencia, Veronica; Wickrema, Amittha; Solomina, Julia; Tibudan, Martin; Konsur, Evelyn; Ciepły, Kamil; Marek-Trzonkowska, Natalia; Trzonkowski, Piotr; Millis, J Michael; Fung, John; Witkowski, Piotr

2018-02-09

The first clinical trials with adoptive Treg therapy have shown safety and potential efficacy. Feasibility of such therapy could be improved if cells are cryopreserved and stored until optimal timing for infusion. Herein, we report the evaluation of two cell-banking strategies for Treg therapy: 1) cryopreservation of CD4 + cells for subsequent Treg isolation/expansion and 2) cryopreservation of ex-vivo expanded Tregs (CD4 + CD25 hi CD127 lo/- cells). First, we checked how cryopreservation affects cell viability and Treg markers expression. Then, we performed Treg isolation/expansion with the final products release testing. We observed substantial decrease in cell number recovery after thawing and overnight culture. This observation might be explained by the high percentage of necrotic and apoptotic cells found just after thawing. Furthermore, we noticed fluctuations in percentage of CD4 + CD25 hi CD127 - and CD4 + FoxP3 + cells obtained from cryopreserved CD4 + as well as Treg cells. However, after re-stimulation Tregs expanded well, presented a stable phenotype and fulfilled the release criteria at the end of expansions. Cryopreservation of CD4 + cells for subsequent Treg isolation/expansion and cryopreservation of expanded Tregs with re-stimulation and expansion after thawing, are promising solutions to overcome detrimental effects of cryopreservation. Both of these cell-banking strategies for Treg therapy can be applied when designing new clinical trials.

NKT Cell Responses to B Cell Lymphoma.

Science.gov (United States)

Li, Junxin; Sun, Wenji; Subrahmanyam, Priyanka B; Page, Carly; Younger, Kenisha M; Tiper, Irina V; Frieman, Matthew; Kimball, Amy S; Webb, Tonya J

2014-06-01

Natural killer T (NKT) cells are a unique subset of CD1d-restricted T lymphocytes that express characteristics of both T cells and natural killer cells. NKT cells mediate tumor immune-surveillance; however, NKT cells are numerically reduced and functionally impaired in lymphoma patients. Many hematologic malignancies express CD1d molecules and co-stimulatory proteins needed to induce anti-tumor immunity by NKT cells, yet most tumors are poorly immunogenic. In this study, we sought to investigate NKT cell responses to B cell lymphoma. In the presence of exogenous antigen, both mouse and human NKT cell lines produce cytokines following stimulation by B cell lymphoma lines. NKT cell populations were examined ex vivo in mouse models of spontaneous B cell lymphoma, and it was found that during early stages, NKT cell responses were enhanced in lymphoma-bearing animals compared to disease-free animals. In contrast, in lymphoma-bearing animals with splenomegaly and lymphadenopathy, NKT cells were functionally impaired. In a mouse model of blastoid variant mantle cell lymphoma, treatment of tumor-bearing mice with a potent NKT cell agonist, α-galactosylceramide (α-GalCer), resulted in a significant decrease in disease pathology. Ex vivo studies demonstrated that NKT cells from α-GalCer treated mice produced IFN-γ following α-GalCer restimulation, unlike NKT cells from vehicle-control treated mice. These data demonstrate an important role for NKT cells in the immune response to an aggressive hematologic malignancy like mantle cell lymphoma.

A space-jump derivation for non-local models of cell-cell adhesion and non-local chemotaxis.

Science.gov (United States)

Buttenschön, Andreas; Hillen, Thomas; Gerisch, Alf; Painter, Kevin J

2018-01-01

Cellular adhesion provides one of the fundamental forms of biological interaction between cells and their surroundings, yet the continuum modelling of cellular adhesion has remained mathematically challenging. In 2006, Armstrong et al. proposed a mathematical model in the form of an integro-partial differential equation. Although successful in applications, a derivation from an underlying stochastic random walk has remained elusive. In this work we develop a framework by which non-local models can be derived from a space-jump process. We show how the notions of motility and a cell polarization vector can be naturally included. With this derivation we are able to include microscopic biological properties into the model. We show that particular choices yield the original Armstrong model, while others lead to more general models, including a doubly non-local adhesion model and non-local chemotaxis models. Finally, we use random walk simulations to confirm that the corresponding continuum model represents the mean field behaviour of the stochastic random walk.

Quality control system response to stochastic growth of amyloid fibrils

DEFF Research Database (Denmark)

Pigolotti, S.; Lizana, L.; Sneppen, K.

2013-01-01

We introduce a stochastic model describing aggregation of misfolded proteins and degradation by the protein quality control system in a single cell. Aggregate growth is contrasted by the cell quality control system, that attacks them at different stages of the growth process, with an efficiency...... that decreases with their size. Model parameters are estimated from experimental data. Two qualitatively different behaviors emerge: a homeostatic state, where the quality control system is stable and aggregates of large sizes are not formed, and an oscillatory state, where the quality control system...

Fluctuations in protein synthesis from a single RNA template: stochastic kinetics of ribosomes.

Science.gov (United States)

Garai, Ashok; Chowdhury, Debashish; Ramakrishnan, T V

2009-01-01

Proteins are polymerized by cyclic machines called ribosomes, which use their messenger RNA (mRNA) track also as the corresponding template, and the process is called translation. We explore, in depth and detail, the stochastic nature of the translation. We compute various distributions associated with the translation process; one of them--namely, the dwell time distribution--has been measured in recent single-ribosome experiments. The form of the distribution, which fits best with our simulation data, is consistent with that extracted from the experimental data. For our computations, we use a model that captures both the mechanochemistry of each individual ribosome and their steric interactions. We also demonstrate the effects of the sequence inhomogeneities of real genes on the fluctuations and noise in translation. Finally, inspired by recent advances in the experimental techniques of manipulating single ribosomes, we make theoretical predictions on the force-velocity relation for individual ribosomes. In principle, all our predictions can be tested by carrying out in vitro experiments.

Threshold for extinction and survival in stochastic tumor immune system

Science.gov (United States)

Li, Dongxi; Cheng, Fangjuan

2017-10-01

This paper mainly investigates the stochastic character of tumor growth and extinction in the presence of immune response of a host organism. Firstly, the mathematical model describing the interaction and competition between the tumor cells and immune system is established based on the Michaelis-Menten enzyme kinetics. Then, the threshold conditions for extinction, weak persistence and stochastic persistence of tumor cells are derived by the rigorous theoretical proofs. Finally, stochastic simulation are taken to substantiate and illustrate the conclusion we have derived. The modeling results will be beneficial to understand to concept of immunoediting, and develop the cancer immunotherapy. Besides, our simple theoretical model can help to obtain new insight into the complexity of tumor growth.