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Computational
Biology

Teachers: Gregory Batt (gregory.batt@inria.fr) and Jacob Ruess (TA)

Schedule: 12 sessions of 3 hours. Weekly sessions are generally composed of 2-hour lectures and 1-hour lab sessions. A final oral presentation is planned in January.

Evaluation: The course grade will be based on assiduity and participation, three written reports on lab sessions and the written report and oral defence of the final exam. The final exam will be a joint exam with other courses.

 

Overview of the course:

Using novel experimental techniques, quantitative data can be obtained on the functioning of biological systems at the molecular level. The complete exploitation of this novel information on system dynamics requires a model-based approach: models are proposed, analyzed and compared with respect to experimental data. Using models, various assumptions on biological mechanisms can be corroborated or invalidated by the experimental data on a rational basis. Experimentally-validated models can then be used to make novel predictions or orient system design. The objective of this course is to introduce the model-based approach of biological systems analysis from a practical point of view. The emphasis will be given on the modelling work, and on simple but important analysis methods. Such methods include state space analysis, global optimization for parameter search, and sensitivity analysis for robustness assessment. In addition we will investigate how to model biological variability observed at the molecular and cellular levels.  

 

Course objectives:

«Instilling in students the feel for biological systems and for models that are used to explore them»

 

  1. Testing the consistency of quantitative data produced in labs and current understanding of the functioning of the observed process
  2. Basic understanding of modelling: how to represent reality using mathematical notions
  3. Basic skills of analysis: numerical simulation, robustness, parameter search
  1. Notions on how to model biological variability

 

Requirements:

This course is made for people *not* familiar with biomolecular process modelling, dynamical system analysis, or Matlab programming. The sole requirements are therefore elementary calculus and notions of molecular and cellular biology, as well as a strong motivation to learn.

 

Recommended readings:

– Systems Biology in Practice: Concepts, Implementation and Application, by E Klipp, R Herwig, A Kowald and C Wierling, Wiley, 2005

– Mathematical modeling: bridging the gap between concept and realization in synthetic biology, by Y. Zheng and G.0 Sriram, Journal of biomedicine & biotechnology, 2010

Teachers

Vincent Danos, Jérôme Feret, Jean Krivine 

Hours

7 Sessions of 4 hours each (2 hours of lectures, 2 hours of practical works) + 3 full days in January

Evaluation

Three small take home assignments on three subparts of the module. One question on this course may be included in the big take home exam of CompBio I, Synthetic Biology and Systems Biology.

Overview of Rule-based Modelling-part

In this class, you will learn a new way, called rule-based modelling, to perform mechanistic modelling which is very different from that of the more usual approach through ODEs.

This approach builds models by writing, in a simple formal language called Kappa, rules that describe directly the possible interactions between proteins; these models can be equipped with rate laws and then simulated and analyzed with open source software tools (that we will provide).The class is primarily practical where you will develop, think about and analyze a series of increasingly complicated systems using Kappa. There is also some pure lecturing time in order to explain the novel analysis tools offered by Kappa.

Course objectives

The main aim of the class is to help you to develop your intuitions about how signalling networks operate. The use of rule-based modelling makes it easy to try out variants of a model, which sometimes have very different behaviour, and so there is a strongly emphasis on exploratory  learning. You will also learn some specific technical material, notably about stochastic simulation, that complements what you have learned about ODE-based deterministic simulation in other classes.

 

Requirements

There are no absolute prerequisites for this class although a passing familiarity with programming might ease the initial learning curve of writing rules and executing models and some basic intuitions about probability theory will help for understanding how simulation works.

 

Suggested reading

– Vincent Danos, Jérôme Feret, Walter Fontana, Russell Harmer,& Jean Krivine. Rule-based modelling of cellular signalling. Invited in International Conference on Concurrency Theory (CONCUR 2007), number 4703 in Lecture Notes in Computer Science. 2007, © Springer.

 

The second part of the class is the Whole Cell Modelling part

Description
In this course students will learn about and implement a physiological model of a cell then propose their own improvements and additions inspired by their research interests.
A large amount of work has been devoted to the mathematical and computational modelling of specific cellular processes. As accurate as these models may be, their isolation from the physiological cellular context hampers the study of the role they can play in global cellular behaviours. A whole ­cell model is an aggregate of mathematical representations of cellular subprocesses (e.g. translation, protein maturation, etc.) [see an example of a whole cell model]. Of course, such submodels need to be validated against experimental data. Eventually, we expect the aggregate model to explain high level behaviours of a cell like the growth rate. During this hands-on workshop, such a model will be realized.

Format
Participants will be divided into several groups. In part 1 of the course, the groups will implement a base model of a cell then they will propose extensions to the model based on their research interests and expertise. This will be the occasion to ask questions, draw and test hypotheses about very exciting emergent phenomena at both the cellular and population scales, in a research-­like fashion. In part 2 of the course, one week later, students will complete their implementations and present their extensions to each other.


The models will be written and simulated in MATLAB.