Life as an emergent property of networks of chemical reactions involving proteins and nucleic acids. Mathematical theories of metabolism, gene regulation, signal transduction, chemotaxis, excitability, motility, mitosis, development, and immunity. Applications to directed molecular evolution, DNA computing, and metabolic and genetic engineering.

Course Meeting Times

Lectures: 2 sessions / week, 1.5 hours / session


What is the relationship between genotype and phenotype? Classical geneticists studied genes with easily observable phenotypic effects, but often single genes do not affect phenotype in a simple, obvious fashion. This complexity is not surprising when one considers genes and proteins not as isolated molecules, but rather as nodes of an intricate network of chemical reactions. It is well-known that networks with nonlinearity and feedback loops can possess nonintuitive properties, and metabolic and genetic networks are no exception. This course is devoted to mathematical tools for analyzing and engineering the dynamical behaviors of such biochemical networks. Algorithms for statistical analysis of biological sequences and other data are not covered, as they are taught elsewhere in 10.90 Bioinformatics and 18.417 Introduction to Computational Molecular Biology.


  • rudimentary knowledge of biology, chemistry, and physics
  • familiarity with linear algebra, multivariate calculus, and probability theory
  • knowledge of a programming language (preferably MATLAB?)

Course Requirements

  • problem sets
  • class presentations
  • final project or exam


  • Directed molecular evolution
  • DNA computing and self-assembly
  • Metabolic networks (enzyme kinetics, flux analysis, engineering)
  • Procaryotic gene regulation (lac operon, lambda phage, circadian rhythms)
  • Membrane excitability
  • Signal transduction
  • LTP and autophosphorylation
  • Chemotaxis
  • Cell signaling in slime molds
  • Motility
  • Mitosis
  • Development (Drosophila)
  • Immune system