Stochastic Gene Expression: Modeling, Analysis, and Identification

• Published in: The Control Handbook pp. 545 - 566
• Format: Book Chapter
• Language: English
• Published: United Kingdom CRC Press 2011; Taylor & Francis Group
• Subjects: Electronics engineering; ENVIRONMENTAL ENGINEERING & TECHNOLOGY; SCIENCE: GENERAL ISSUES; Reaction Rate Equations; Stochastic Description; SWE; Identify Model Parameters; Propensity Function; Moment Closure; Cumulative Distribution Function; Stochastic Gene Expression; Stochastic Simulations; Linear SDE; Forward Kolmogorov Equation; Random Time Change; MGF; Gene Networks; 21-1; Deterministic Description; Kinetic Monte Carlo Simulations; Mass Action Kinetics; Higher Order Moments; Gillespie Algorithm; IFAC; 4LacI W2; National Academy; Lac Operon
• ISBN: 9781420073607; 1420073605
• DOI: 10.1201/b10382-31

In living cells, many key events such as gene expression and protein-protein interactions follow from elementary reactions between the cellular consituents at the molecular level (e.g., genes, RNAs, proteins). There is considerable inherent randomness in the order and timing of these reactions. This randomness can be attributed to the random collisions among cellular constituents whose motion is induced by thermal energy and follows specific statistical distributions. The result is fluctuations in the molecular copy numbers of reaction products both among similar cells and within a single cell over time. These fluctuations (commonly referred to as cellular noise) can propagate downstream, impacting events and processes in accordance to the dynamics of the network interconnection.