Teachers: Gregory Batt, Vincent Danos, O. Michel, Vincent Schachter, and A. Spicher

Computational Biology 1 - Basic formal modeling

Basics of numerical simulation and limitations: computational aspects of numerical simulation for Ordinary Differential Equations (ODEs), conversion from stochastic to differential rates (including transport), stochastics DEs, Gillespie algorithm. Numerical errors, computational cost of numerical simulation, stiffness, dimensionality, parameter uncertainties and the question of biological significance. Spatially heterogeneous systems, crowding, diffusion limited kinetics.

Boolean and qualitative models: modeling and basic analysis of a simple gene network, dynamics of boolean and qualitative models, Thomas' rules, detailed analysis of a complex gene network, search for steady states, formal verification, network inference, connection with experimental data.

Model building and analysis: notions of sensitivity (local, solution-based and global), sensitivities computation; notions of robustness and computational aspects; optimizing genetic circuits by global sensitivity analysis; theory of biological robustness; bioinformatics papers; parameter estimation (identifiability, various optimization-based search methods); benchmarks for identification of ordinary differential equations from time series data.

Metabolic networks: steady state assumption, admissible flows, decomposition into extreme modes, biomass production rate predictions, analysis, reconstruction.

Computational Biology 2 - Advanced formal modeling

Rules and reactions: soft introduction to the kappa basics, relation to ordinary reaction networks, refinement of rule sets (qualitative), stochastic vs. deterministic dynamics, reminder on stochastic and deterministic rate constants (units, typical magnitudes, conversion), basics of simulations, refinement of rule sets (quantitative), trivial circuits to illustrate the basic definitions, small circuits, kinase cascades, large scale ppi network dynamics (and percolation), compositional drift, simple allosteric systems, large networks, percolation experiments, causality and stories (formal notion of pathway), model reduction, dna processing models, thermodynamic model of complex assembly, energy functions, positional entropy model, Wegsheider conditions for reactions and rules, advanced allosteric models, kinetic traps, rule-based molecular dynamics (a la Peter Dittrich, le Novere), pde and patterns (a la Meinhardt, Kholodenko, Stelling).

Membrane interactions/bigraphs: motivations, from rules to bigraphs and bigraphical molecular systems representing transport, dynamic compartmentalisation
MGS and morphogenesis.