Stewart-Ornstein Jacob, El-Samad Hana
Department of Biochemistry and Biophysics, California Institute for Quantitative Biosciences, University of California, San Francisco, CA, USA.
Methods Cell Biol. 2012;110:111-37. doi: 10.1016/B978-0-12-388403-9.00005-9.
Noise and stochasticity are fundamental to biology because they derive from the nature of biochemical reactions. Thermal motions of molecules translate into randomness in the sequence and timing of reactions, which leads to cell-cell variability ("noise") in mRNA and protein levels even in clonal populations of genetically identical cells. This is a quantitative phenotype that has important functional repercussions, including persistence in bacterial subpopulations challenged with antibiotics, and variability in the response of cancer cells to drugs. In this chapter, we present the modeling of such stochastic cellular behaviors using the formalism of jump Markov processes, whose probability distributions evolve according to the chemical master equation (CME). We also discuss the techniques used to solve the CME. These include kinetic Monte Carlo simulations techniques such as the stochastic simulation algorithm (SSA) and method closure techniques such as the linear noise approximation (LNA).
噪声和随机性是生物学的基本特征，因为它们源于生化反应的本质。分子的热运动转化为反应序列和时间的随机性，这导致即使在基因相同的细胞克隆群体中，mRNA和蛋白质水平也存在细胞间变异性（“噪声”）。这是一种具有重要功能影响的定量表型，包括在受到抗生素挑战的细菌亚群中的持久性，以及癌细胞对药物反应的变异性。在本章中，我们使用跳跃马尔可夫过程的形式主义来对这种随机细胞行为进行建模，其概率分布根据化学主方程（CME）演化。我们还讨论了用于求解CME的技术。这些技术包括动力学蒙特卡罗模拟技术，如随机模拟算法（SSA），以及方法封闭技术，如线性噪声近似（LNA）。
