Master 2 course C2-19 Biochemical Programming

Master Parisien de Recherche en Informatique University Paris Diderot, UPMC, Paris-Saclay, X, ENS (pedagogical server)

Over the past decade, formal methods from Theoretical Computer Science have been successfully applied in Life Sciences to decipher biological processes, mostly at the molecular and cellular levels.

This course aims at presenting these methods and research issues in computational systems biology and synthetic biology. It is based on the vision of cells as machines - biochemical reaction systems as programs and on the use of concepts and tools from Computer Science to master the complexity of cell processes.

Unlike most programs, biochemical computation involves state transitions that are stochastic rather than deterministic, continuous-time rather than discrete-time, poorly localized in compartments instead of well-structured in modules, and created by evolution instead of by rational design.

The course addresses fundamental research issues in Computer Science about the interplay between structure and dynamics in large interaction networks, and on the mixed continuous (analog) and discrete (digital) computation model of biochemical networks.

• the slides will be available below at https://lifeware.inria.fr/wiki/Fages/BiochemicalProgramming
• the TP are not graded, they are just proposed to you as a mean to practice the taught concepts
• bibliography:
  • chapitre de livre facile à lire La cellule, un calculateur analogique chimique 2020
  • (older lecture notes 2017)

25 jan 2023 16.15-19.15 room 1004

• Introduction to Chemical Reaction Networks (CRNs) pdf
  • TP Lotka-Volterra CRN (online Jupyter notebook of BIOCHAM)
• Abstract interpretation of CRNs: hierarchy of semantics, typings pdf

1er février

• Quasi-steady state approximation pdf
  • TP Michaelis-Menten reduction
• Turing completeness of continuous CRNs pdf
  • TP synthetic oscillators
  • TP chemical arithmetic

8 février

• Moment closures pdf
• Quantitative temporal logic, CRN robustness and parameter optimization pdf
  • TP robustness optimization in MAPK signaling network
  • TP doctor in the cell

15 février

• Computation Tree Logic, CRN model-checking pdf
  • TP MAPK model-checking
• Probably Approximately Correct Learning pdf
  • TP PAC learning

22 février: break

1er mars: break

8 mars: written examination of both parts of the course (50%-50% grade)

• Previous written examinations with solutions:
  •  March 2023, March 2022, (March 2021 programming/modeling project), March 2020, March 2019, (March 2018 programming/modeling project), Feb 2017, Nov 2015 (plus March 2016 programming/modeling project), Feb 2015, Mar. 2014, Mar. 2013, Mar. 2012, Feb. 2011, Nov. 2009, Nov. 2008, Nov. 2007, Nov. 2006.