title: Chapter 23: Stochastic modeling methods in cell biology.
abstract: Stochastic methods have been a staple for understanding complex systems in chemistry and physics. In the biological context, they are useful for understanding phenomena ranging from molecular-level fluctuations to cellular movement. We review the basic formalism behind stochastic methods and outline how they can be implemented for quantifying gene expression, movement of molecular motors, and the dynamics of cytoplasmic components. We show that stochastic methods are quantitative checks for proposed molecular mechanisms and can pose new questions for experiments. Structural information of cellular components can be incorporated into stochastic models to reveal new biological insights.
authors.affiliation: Department of Mechanical Engineering and Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA.
authors.forename: Sean X
authors.initials: SX
authors.lastname: Sun
authors.forename: Ganhui
authors.initials: G
authors.lastname: Lan
authors.forename: Erdinc
authors.initials: E
authors.lastname: Atilgan
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D003599:Cytoskeleton / Q000378:metabolism; D008954:Models, Biological; D008957:Models, Genetic; D020409:Molecular Motor Proteins / Q000378:metabolism; D015398:Signal Transduction; D013269:Stochastic Processes
chemical_list: D020409:Molecular Motor Proteins