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相关论文: Stochastic Model of Yeast Cell Cycle Network

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The processes, resulting in the transcription of RNA, are intrinsically noisy. It was observed experimentally that the synthesis of mRNA molecules is driven by short, burst-like, events. An accurate prediction of the protein level often…

种群与进化 · 定量生物学 2009-10-14 Vlad Elgart

Gene expression is inherently noisy as many steps in the read-out of the genetic information are stochastic. To disentangle the effect of different sources of stochasticity in such systems, we consider various models that describe some…

分子网络 · 定量生物学 2015-06-05 Rahul Marathe , David Gomez , Stefan Klumpp

The seemingly stochastic transient dynamics of neocortical circuits observed in vivo have been hypothesized to represent a signature of ongoing stochastic inference. In vitro neurons, on the other hand, exhibit a highly deterministic…

神经元与认知 · 定量生物学 2017-03-14 Mihai A. Petrovici , Johannes Bill , Ilja Bytschok , Johannes Schemmel , Karlheinz Meier

Deterministic and stochastic Boolean network models are build for the dynamics of negative feedback loops of the p53 pathways. It is shown that the main function of the negative feedback in the p53 pathways is to keep p53 at a low steady…

分子网络 · 定量生物学 2009-04-16 Hao Ge , Min Qian

Stochastic reaction networks are mathematical models with a wide range of applications in biochemistry, ecology, and epidemiology, and are often complex to analyze. Except for some special cases, it is generally difficult to predict how the…

概率论 · 数学 2026-04-02 Daniele Cappelletti , Giulio Cuniberti , Paola Siri

From the response to external stimuli to cell division and death, the dynamics of living cells is based on the expression of specific genes at specific times. The decision when to express a gene is implemented by the binding and unbinding…

分子网络 · 定量生物学 2009-11-13 Johannes Berg

Biological systems are characterized by the ubiquitous roles of weak, that is, non-covalent molecular interactions, small, often very small, numbers of specific molecules per cell, and Brownian motion. These combine to produce stochastic…

细胞行为 · 定量生物学 2023-04-27 Michael W. Klymkowsky

The yeast galactose network has provided many insights into how eukaryotic gene circuits regulate metabolic function. However, there is currently no consensus model of the network that incorporates protein dilution due to cellular growth.…

分子网络 · 定量生物学 2016-01-14 Michele Monti , Marta R A Matos , Jeong-Mo Choi , Michael S Ferry , Bartlomiej Borek

Multipotent stem or progenitor cells undergo a sequential series of binary fate decisions, which ultimately generate the diversity of differentiated cells. Efforts to understand cell fate control have focused on simple gene regulatory…

分子网络 · 定量生物学 2009-09-15 David V. Foster , Jacob G. Foster , Sui Huang , Stuart A. Kauffman

After collecting data from observations or experiments, the next step is to build an appropriate mathematical or stochastic model to describe the data so that further studies can be done with the help of the models. In this article, the…

数据分析、统计与概率 · 物理学 2023-07-19 A. M. Mathai , H. J. Haubold

Delays and stochasticity have both served as crucially valuable ingredients in mathematical descriptions of control, physical, and biological systems. In this work, we investigate how explicitly dynamical stochasticity in delays modulates…

分子网络 · 定量生物学 2023-07-10 Bhargav R. Karamched , Christopher E. Miles

Cell polarity refers to the spatial asymmetry of molecules on the cell membrane. Altschuler, Angenent, Wang and Wu have proposed a stochastic model for studying the emergence of polarity in the presence of feedback between molecules. We…

概率论 · 数学 2012-06-19 Ankit Gupta

Genes are connected in complex networks of interactions where often the product of one gene is a transcription factor that alters the expression of another. Many of these networks are based on a few fundamental motifs leading to switches…

分子网络 · 定量生物学 2026-03-05 Zitao Yang , Rebecca J. Rousseau , Sara D. Mahdavi , Hernan G. Garcia , Rob Phillips

Adaptive-network models are typically studied using deterministic differential equations which approximately describe their dynamics. In simulations, however, the discrete nature of the network gives rise to intrinsic noise which can…

统计力学 · 物理学 2012-09-04 Tim Rogers , William Clifford-Brown , Catherine Mills , Tobias Galla

Biochemical reaction networks in living cells usually involve reversible covalent modification of signaling molecules, such as protein phosphorylation. Under conditions of small molecule numbers, as is frequently the case in living cells,…

分子网络 · 定量生物学 2013-05-29 C. Metzner , M. Sajitz-Hermstein , M. Schmidberger , B. Fabry

The stochastic simulation of large-scale biochemical reaction networks is of great importance for systems biology since it enables the study of inherently stochastic biological mechanisms at the whole cell scale. Stochastic Simulation…

分子网络 · 定量生物学 2017-10-30 Elias S. Manolakos , Elias Kouskoumvekakis

Methods of modeling cellular regulatory networks as diverse as differential equations and Boolean networks co-exist, however, without any closer correspondence to each other. With the example system of the fission yeast cell cycle control…

分子网络 · 定量生物学 2015-03-13 Maria Davidich , Stefan Bornholdt

Cell phenotype dynamic homeostasis contrasts with the inherent randomness of intracellular reactions. Although feedback control of master regulatory genes (MRG) is a key strategy for maintaining gene network expression ranges limited,…

Evolving biomolecular networks have to combine the stability against perturbations with flexibility allowing their constituents to assume new roles in the cell. Gene duplication followed by functional divergence of associated proteins is a…

分子网络 · 定量生物学 2007-05-23 Sergei Maslov , Kim Sneppen , Kasper Astrup Eriksen

Understanding design principles of molecular interaction networks is an important goal of molecular systems biology. Some insights have been gained into features of their network topology through the discovery of graph theoretic patterns…

分子网络 · 定量生物学 2013-01-18 David Murrugarra , Reinhard Laubenbacher
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