Biological Dynamics & Networks CHE 559 / PHY 559 / AMS 537

Spring 2021 Tu/Th 3:00 - 4:20 PM online

Tom MacCarthy, Course PI

Guest lecturers: Ken Dill, TBA

This course will provide a solid foundation in key theoretical concepts for the study of dynamics in biological systems and networks at different scales ranging from the molecular level to metabolic and gene regulatory networks.


Reference Books

  • Ken Dill, Molecular Driving Forces: Statistical Thermodynamics in Biology, Chemistry, Physics, and Nanoscience
  • Bernhard Palsson, Systems Biology: simulation of dynamic network states
  • Eberhard Voit, A first course in Systems Biology
  • Uri Alon, An introduction to Systems Biology
  • M.E.J. Newman, Networks: an introduction


Topics Speakers
Introduction to networks and statistical thermodynamics
  1. Brief introduction to networks in biology and beyond
  2. Physical kinetics
  3. Diffusion, Smoluchowski
  4. Random flights
MacCarthy, Dill
Statistical thermodynamics II
  1. Waiting times
  2. Brownian ratchets
  3. Chemical kinetics
  4. Transition states
Biochemical networks
  1. Rate laws and basic properties of reactions
  2. Reversible linear and bilinear reactions
  3. Connected reversible linear and bilinear reactions
  4. Autocatalysis and dynamical stability

Reference: Palsson, Chapters 2 and 4

Enzyme kinetics
  1. Background on enzyme catalysis
  2. Michaelis-Menten kinetics
  3. Hill kinetics for enzyme regulation
  4. Cooperative phenomena

Reference: Palsson, Chapter 5

Network measurements
  1. Networks as graphs
  2. Non-biological networks: technological, social and information networks
  3. Degree distribution
  4. Centrality measures

Reference: Newman, Chapters 6,7

Large-scale structure of networks
  1. The small-world effect
  2. Power laws and scale-free networks
  3. Clustering coefficients

Reference: Newman chapter 8

Network evolution models
  1. Properties of random graphs
  2. Preferential attachment models

Reference: Newman chapters 12,14

Metabolic networks I
  1. Background on metabolism
  2. Modeling large systems using stoichiometric networks
  3. Case study: glycolysis

Reference: Palsson, Chapters 7 and 10

Metabolic networks II
  1. Metabolomics
  2. Metabolic network reconstruction
  3. Flux analysis

Reference: Voit, Chapters 3 and 8

Gene regulatory networks I
  1. Background on gene regulation and transcription networks
  2. Network motifs
  3. Biological oscillators and autoregulation

Reference: Alon, Chapters 2-4

Signal transduction systems
  1. Background on signal transduction
  2. Two-component signaling systems
  3. Bistability and hysteresis

Reference: Voit, Chapter 9

  1. Overview of biological robustness
  2. Robustness in signalling networks: bacterial chemotaxis
  3. Robust patterning in development

Reference: Alon, Chapters 7,8

Algorithms for network analysis
  1. Modularity in biology
  2. Community detection

Reference: Newman, Chapter 11

Modeling noise
  1. Definitions of intrinsic and extrinsic noise
  2. Case study: M.B. Elowitz et al., 2002, Stochastic gene expression in a single cell, Science, 297, 1183-1186.
  3. Gillespie algorithm