Ankit Gupta: Katalogdaten im Frühjahrssemester 2018

NameHerr Dr. Ankit Gupta
Dep. Biosysteme
ETH Zürich, BSS J 13.1
Klingelbergstrasse 48
4056 Basel

636-0016-00LComputational Systems Biology: Stochastic Approaches Information 4 KP3GM. H. Khammash, A. Gupta
KurzbeschreibungThis course is concerned with the development of computational methods for modeling, simulation, and analysis of stochasticity in living cells. Using these tools, the course explores the richness of stochastic phenomena, how it arises from the interactions of dynamics and noise, and its biological implications.
LernzielTo understand the origins and implications of stochastic noise in living cells, and to learn the computational tools for the modeling, simulation, analysis, and identification of stochastic biochemical reaction networks.
InhaltThe cellular environment is abuzz with noise. A key source of this noise is the randomness that characterizes the motion of cellular constituents at the molecular level. Cellular noise not only results in random fluctuations (over time) within individual cells, but it is also a main source of phenotypic variability among clonal cell populations.

Review of basic probability and stochastic processes; Introduction to stochastic gene expression; deterministic vs. stochastic models; the stochastic chemical kinetics framework; a rigorous derivation of the chemical master equation; moment computations; linear vs. nonlinear propensities; linear noise approximations; Monte Carlo simulations; Gillespie's Stochastic Simulation Algorithm (SSA) and variants; direct methods for the solution of the Chemical Master Equation; moment closure methods; intrinsic and extrinsic noise in gene expression; parameter identification from noise; propagation of noise in cell networks; noise suppression in cells; the role of feedback; exploiting noise; bimodality and stochastic switches.
LiteraturLiterature will be distributed during the course as needed.
Voraussetzungen / BesonderesStudents are expected to have completed the course `Mathematical modeling for systems biology (BSc Biotechnology) or `Computational systems biology (MSc Computational biology and bioinformatics). Concurrent enrollment in `Computational Systems Biology: Deterministic Approaches is recommended.