Titlebook: Stochastic Processes, Multiscale Modeling, and Numerical Methods for Computational Cellular Biology; David Holcman Book 2017 Springer Inte

清澈

Modeling and Stochastic Analysis of the Single Photon Responseod geometry. The stochastic modeling is used to analyze electrophysiological recordings and to extract in vivo biochemical constants. Modeling phototransduction has evolved at the far front of cell transduction and system biology and thus the approach presented here can be applied to many transducti

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Oafishness

Book 2017he biochemical reactions are diffusion-limited or reaction-limited. In the former case, oneneeds to adopt the framework of stochastic reaction-diffusion models, while in the latter, one can describe the processes by adopting the framework of Markov jump processes and stochastic differential equation

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乐章

Stability and Strong Convergence for Spatial Stochastic Kineticsck terms, by small number of participating agents, and by an overall noisy environment, a consistent analysis framework is clearly required. The review summarizes the required mathematical framework and techniques used for obtaining a priori bounds and stability estimates.

Gobble

Recent Mathematical Models of Axonal Transported to help understand how cargoes are transported inside an axon and how impairment of axonal transport affects cargo distribution. In this chapter, we review recent mathematical models of axonal transport and discuss open problems in this area.

脊椎动物

Test Models for Statistical Inference: Two-Dimensional Reaction Systems Displaying Limit Cycle Bifurth an emphasis on limit cycles, limit cycle bifurcations, and multistability. The results are then used for construction of two reaction systems, which are at the deterministic level described by two-dimensional third-degree kinetic ODEs. The first system displays a homoclinic bifurcation, and a coe

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Importance Sampling for Metastable and Multiscale Dynamical Systemsstems. We focus on the issue of metastability and on the effect of multiple scales. We discuss why seemingly reasonable schemes that follow large deviations optimal paths may perform poorly in practice, even though they are asymptotically optimal. Pre-asymptotic optimality is important when one deal