Biochemical Programming (24h, 3 ECTS)

Coordinator: François Fages.

Teachers in 2023-2024

• Jérôme Féret,

• François Fages.

Aims

Over the past two decades, 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.

Evaluation

The evaluation is composed of one written examination (and sometimes of one modelling/programming project). Previous exams are available on teachers' pages (see the course handouts section below). For the written examination, any non-electronic documents are allowed.

Course outline

1. Protein interaction calculus (Jérôme Féret, 12h)

• Introduction to biological models and rule-based modeling in the Kappa-calculus

• Scalable stochastic simulation algorithm, introduction to the KaSim tool.

• Theoretical computer science techniques applied to bio modeling: causality analysis, static analysis.

• Model reduction.

2. Chemical reaction networks (CRN) as a programming language (François Fages, 12h)

• Introduction to the Biochemical Abstract Machine tool Biocham-4

• Hierarchy of CRN semantics: differential (ODE), stochastic (CTMC), discrete (Petri net), Boolean

• Turing completeness of continuous CRNs: chemical compilation of mixed analog/digital programs

• Specifying behaviours in (quantitative) temporal logic: model-checking and model synthesis

Course handouts

Course handouts can be found here:

• F. Fages: http://lifeware.inria.fr/wiki/Fages/BiochemicalProgramming
• J. Féret: https://www.di.ens.fr/~feret/teaching/2022-2023/MPRI.2.19/
• (J. Krivine: http://www.pps.jussieu.fr/~jkrivine/homepage/Teaching.html teacher until 2020-2021)

French and English

The lectures will be given in English upon request. All slides, documents and the examination subjects will be in English.

Related courses

2.11.1 Approximation Algorithms & molecular programming 2.06.1 Abstract interpretation 2.29.1 Graph algorithms 2.35.1 Constraint programming 2.03.1 Concurrency

Prerequisites

Knowledge in formal methods in computer science and in differential calculus are useful but not a prerequisite.

There is no prerequisite in Biology, the basics of cell biology will be introduced as needed through examples all along the course.

Pedagogic team

Tentative calendar

On Mondays 12.45-15.45 room 1004

Bibliography

• Rule Based Modelling of Cellular Signalling. V. Danos, J. Feret, W. Fontana, R. Harmer and J. Krivine. Proceedings of CONCUR’07 : 18th International Conference on concurrency theory. Springer-Verlag, LNCS 4703, 2007.

• Artificial Intelligence in Biological Modeling. F. Fages. In A Guided Tour of Artificial Intelligence Research. Springer-Verlag, to appear. ( pdf)

• Cells as Machines: towards Deciphering Biochemical Programs in the Cell. F. Fages and S. Soliman. In Proc. 10th International Conference on Distributed Computing and Internet Technology ICDCIT'14, pages 50–67, volume 8337 of Lecture Notes in Computer Science. Springer-Verlag, 2014. ( pdf)

• Formal cell biology in BIOCHAM. F. Fages and S. Soliman, 8th International School on Formal Methods for the Design of Computer, Communication and Software Systems: Computational Systems Biology, Springer-Verlag, LNCS 5016, 2008. ( pdf)

• Synthetic biology: New engineering rules for an emerging discipline. E. Andrianantoandro, S. Basu, D. Karig, and R. Weiss, Molecular Systems Biology, 2006.

Previous years

Some former students of this course who continued for a PhD Thesis or a Post-Doc in Computational Systems Biology:

• 2004-2005: Jean Krivine, CNRS Paris-Diderot
• 2004-2005: Colas Le Guernic, CNRS Grenoble, New York University
• 2005-2006: Sylvain Pradalier, Inria Rocquencourt, Dassault-Systèmes
• 2005-2006: Fabien Tharissan, MdC UPMC
• 2006-2007: Aurélien Rizk, Inria Rocquencourt, Paul Sherrer Institute Zurich, Co-Founder&CTO InterAx Biotech
• 2008-2009: Steven Gay, Inria Rocquencourt, Google Paris
• 2008-2009: Laurent Bulteau, Univ. Nantes
• 2011-2012: Guillaume Madelaine, Univ. Lille
• 2013-2014: Andrea Beica, ENS Paris
• 2014-2015: Virgile Andreani, Inria Saclay, Pasteur Institute
• 2015-2016: Guillaume Le Guludec, course project followed by internship at Inria Saclay then best paper award CMSB 2017, Prix La Recherche magazine 2019
• 2016-2017: Arthur Carcano, Inria Paris, Pasteur Institute
• 2016-2017: Theotime Grohens, Inria Lyon
• 2018-2019: Martin Larralde, EMBL Heidelberg