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Learning is based on synaptic plasticity, which affects and is driven by neural activity. Because pre- and postsynaptic spiking activity is shaped by randomness, the synaptic weights follow a stochastic process, requiring a probabilistic…

神经元与认知 · 定量生物学 2026-01-14 Jakob Stubenrauch , Naomi Auer , Richard Kempter , Benjamin Lindner

General discrete one-dimensional stochastic models to describe the transport of single molecules along coupled parallel lattices with period $N$ are developed. Theoretical analysis that allows to calculate explicitly the steady-state…

软凝聚态物质 · 物理学 2009-11-11 Evgeny B. Stukalin , Anatoly B. Kolomeisky

The formation and regulation of macromolecular complexes provides the backbone of most cellular processes, including gene regulation and signal transduction. The inherent complexity of assembling macromolecular structures makes current…

分子网络 · 定量生物学 2007-05-23 Leonor Saiz , Jose M. G. Vilar

A protein undergoes conformational dynamics with multiple time scales, which results in fluctuating enzyme activities. Recent studies in single molecule enzymology have observe this "age-old" dynamic disorder phenomenon directly. However,…

生物大分子 · 定量生物学 2009-11-10 Zhanghan Wu , Vlad Elgart , Hong Qian , Jianhua Xing

In this work we study, at the single molecular level, the thermodynamic and dynamic characteristics of an enzymatic reaction comprising a rate limiting step. We investigate how the stability of the enzyme-state stationary probability…

亚细胞过程 · 定量生物学 2015-05-20 Moisés Santillán

In the past one hundred years, deterministic rate equations have been successfully used to infer enzyme-catalysed reaction mechanisms and to estimate rate constants from reaction kinetics experiments conducted in vitro. In recent years,…

亚细胞过程 · 定量生物学 2013-12-02 Ramon Grima , Nils Walter , Santiago Schnell

The propagation of signalling molecules within cellular networks is affected by network topology, but also by the spatial arrangement of cells in the networks. Understanding the collective reaction--diffusion behaviour in space of signals…

无序系统与神经网络 · 物理学 2025-01-09 Adel Mehrpooya , Vivien J. Challis , Pascal R. Buenzli

Continuum models for the spatial dynamics of growing cell populations have been widely used to investigate the mechanisms underpinning tissue development and tumour invasion. These models consist of nonlinear partial differential equations…

组织与器官 · 定量生物学 2019-07-15 Mark AJ Chaplain , Tommaso Lorenzi , Fiona R Macfarlane

Motor proteins are active enzymatic molecules that drive a variety of biological processes, including transfer of genetic information, cellular transport, cell motility and muscles contraction. It is known that these biological molecular…

软凝聚态物质 · 物理学 2018-01-17 Luiza V. F. Gomes , Anatoly B. Kolomeisky

Metabolic heterogeneity is widely recognised as the next challenge in our understanding of non-genetic variation. A growing body of evidence suggests that metabolic heterogeneity may result from the inherent stochasticity of intracellular…

分子网络 · 定量生物学 2020-10-08 Mona K Tonn , Philipp Thomas , Mauricio Barahona , Diego A Oyarzún

Protein dynamics is a fundamental element to comprehend their biological functions. However, a theoretical picture providing microscopic-detail explanation of its relevant features is still missing. One of the outmost relevant properties…

软凝聚态物质 · 物理学 2023-06-14 Luca Maggi

Biochemical reaction networks are subjected to large fluctuations attributable to small molecule numbers, yet underlie reliable biological functions. Most theoretical approaches describe them as purely deterministic or stochastic dynamical…

分子网络 · 定量生物学 2013-06-11 Jingkui Wang , Marc Lefranc , Quentin Thommen

We study existence of solutions in the variational sense for a class of stochastic phase-field models describing moving boundary problems. The models consist of stochastic reaction-diffusion equations with singular diffusion forced by a…

概率论 · 数学 2026-01-12 Amjad Saef , Wilhelm Stannat

Gene expression is significantly stochastic making modeling of genetic networks challenging. We present an approximation that allows the calculation of not only the mean and variance but also the distribution of protein numbers. We assume…

分子网络 · 定量生物学 2008-12-18 Vahid Shahrezaei , Peter S. Swain

Phenotypic variation is a hallmark of cellular physiology. Metabolic heterogeneity, in particular, underpins single-cell phenomena such as microbial drug tolerance and growth variability. Much research has focussed on transcriptomic and…

分子网络 · 定量生物学 2019-01-31 Mona K. Tonn , Philipp Thomas , Mauricio Barahona , Diego A Oyarzún

We present a simple Markov model of spiking neural dynamics that can be analytically solved to characterize the stochastic dynamics of a finite-size spiking neural network. We give closed-form estimates for the equilibrium distribution,…

神经元与认知 · 定量生物学 2007-05-23 H. Soula , C. C. Chow

We introduce a simulation strategy to consistently couple continuum biomembrane dynamics to the motion of discrete biological macromolecules residing within or on the membrane. The methodology is used to study the diffusion of integral…

软凝聚态物质 · 物理学 2009-05-26 Ali Naji , Paul J. Atzberger , Frank L. H. Brown

Single receptor-ligand bonds have finite lifetimes, so that biological systems can dynamically react to changes in their environment. In cell adhesion, adhesion bonds usually act cooperatively in adhesion clusters. Outside the cellular…

软凝聚态物质 · 物理学 2007-05-23 T. Erdmann , U. S. Schwarz

Proteins are polymerized by cyclic machines called ribosome 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…

生物物理 · 物理学 2009-11-13 Ashok Garai , Debashish Chowdhury , T. V. Ramakrishnan

We study the dynamics of protein folding via statistical energy-landscape theory. In particular, we concentrate on the local-connectivity case with the folding progress described by the fraction of native conformations. We obtain…

软凝聚态物质 · 物理学 2007-05-23 Chi-Lun Lee , Chien-Ting Lin , George Stell , Jin Wang