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Systems Biology: nonlinear mechanistic models

PhD course, November 2015 - Fall 2016

Objective:

The aim of the course is to provide an introduction to the dynamical mechanistic models used in systems biology. The emphasis is on nonlinear models, system analysis, and identification.

Course leaders and examinators:

Organization

~12-15 lectures, starting in November 2015 and finishing in Fall 2016

  • lecture 1 [CA]: Wednesday November 4th, 9:00 - 11:00 in Algoritmen (B-huset, A-corridor)
  • lecture 2 [CA]: Tuesday November 10th, 15:15 - 17:00 in Algoritmen (B-huset, A-corridor)
  • lecture 3 [CA]: Thursday November 19th, 15:15 - 17:00 in Algoritmen (B-huset, A-corridor)
  • lecture 4 [CA]: Wednesday November 25th, 15:15 - 17:00 in Algoritmen (B-huset, A-corridor)
  • lecture 5 [CA]: Tuesday December 1st, 15:15 - 17:00 in Algoritmen (B-huset, A-corridor)
  • lecture 6 [GC]: Tuesday December 15th, 8:15 - 10:00 in Gaddan, IMT, floor 11 (Hospital Campus; a map is here. If lost contact Gunnar Cedersund at 0702-512323)
  • lecture 7 [MG]: Wednesday February 17th, 10:15 - 12:00 in Algoritmen (B-huset, A-corridor)
  • lecture 8 [GC]: November 14th, 8:30 - 12:00 in IMT1, IMT, floor 13 (Hospital Campus)
  • lecture 9 [GC]: December 7th, 8:30 - 12:00 in IMT2, IMT, floor 13 (Hospital Campus)

Course content:

  • Introduction to nonlinear dynamical systems: single and two-species dynamics, equilibria and (multi)stability, phase plane, oscillations, bifurcations. [Lecturer: Altafini; November 2015]
  • Small-scale examples: autoregulatory gene circuits, epidemic models (HIV dynamics), single neuron model (Hodgkin-Huxley and Fitzhugh-Nagumo), adaptation (bacterial chemotaxis, sensory responses). [Lecturer: Altafini, November 2015]
  • Modeling approaches for large-scale networks of biological reactions (gene networks, metabolic networks). Stoichiometric network analysis, Chemical reaction network theory. [Lecturer: Altafini, November 2015]
  • Continuation methods and higher order bifurcations [Lecturer: Gunnar Cedersund, December 2015]
  • Large-scale network inference (reverse engineering methods, LASSO, LASSIM, etc). [Lecturer: Mika Gustafsson (IFM); January-February 2016]
  • Practical identifiability analysis (basics and shortcomings of structural identifiability tools, covariance-based local methods, and sensitivity analysis; profile-likelihood; MCMC and Bayesian parameter estimation). [Lecturer: Cedersund; March 2016]
  • Practical observability analysis (the challenge of unique predictions in unidentifiable systems, core-predictions, MCMC, prediction profile likelihood, Cluster Newton, neutral parameters, ensemble modeling). [Lecturer: Cedersund; March 2016]
  • Model discrimination tools (basics and shortcomings of AIC/BIC, F-test, and likelihood ratio; bootstrap-based alternatives). [Lecturer: Cedersund; April 2016]
  • Model reduction (balanced truncation, lumping, sensitivity-analysis based methods, time-scale based methods). [Lecturer: Cedersund; April 2016]

Course material:

Exam:

Hand-in exercises during the course. Credits: 6 p + optional final project (3 p extra)


Page responsible: Claudio Altafini & Gunnar Cedersund
Last updated: 2016-10-20