<\/p>\n
The first session will be devoted to a didactical introduction to Boolean dynamical modelling of regulatory\/signaling networks. Through simple examples, we will emphasise key differences between the most used updating methods and discuss their limitations. These examples will further enable us to contrast the behaviours positive and negative circuits regulatory circuits, from both dynamical and biological point of views. We will also review several extensions of the Boolean formalism, which were specifically developped to model more precisely the behaviour of biological networks. Finally, we will stress some of the computational challenges arising with large\/complex regulatory networks, and discuss some of the current approaches to cope with these challenges.<\/p>\n
The second session will be devoted to a tutorial on the use of the<\/span> software GINsim (http:\/\/ginsim.org<\/a>). <\/span>Using this software, participants will be trained to<\/span> build and analy<\/span>s<\/span>e a <\/span>small but sophisticate<\/span> multi-level <\/span>logical model, <\/span>thereby<\/span>illustrating the concepts introduced during the course. <\/span><\/p>\n Students need to have GINsim installed on their laptop, with a JAVA version 1.7 or higher. Files with dependencies should be downloaded from: Further reading<\/b><\/span>:<\/span><\/p>\n A review to logical modelling: An article specifically introducting the GINsim tutorial: We will step-by-step build quantitative ODE models of a metabolic network integrating regulation on both the metabolic and gene expression level, and investigate the effect of these layers of regulation on the networks dynamics. The course will be structured around the case of carbon catabolite repression in bacteria, using simple kinetic models. All simulations will be carried out by means of Matlab (no toolboxes needed). The hand-out<\/a> and the models and simulation code<\/a> are available from the instructor.<\/p>\n Kremling A, Geiselmann J, Ropers D, de Jong H (2015). Understanding carbon catabolite repression in Escherichia coli<\/em> using quantitative models. Trends Microbiol.<\/em>, 23:99-109<\/p>\n<\/div>\n After a brief recall on constraint-based modeling, we will study the resource balance analysis (RBA) framework. We will illustrate the capability of prediction of RBA on a core-model of Bacillus subtilis<\/em>: ribosome abundance, hierarchy of carbon and nitrogen source, cellular configurations maximizing the production of a compound of interest, etc. All simulations will be carried out by means of Matlab (release 2016 or later) and having either Cplex version 12.4.0.0 (preferred) or the optimization toolbox for solving linear programming optimization problems.<\/p>\n The RBA code using Cplex or the Matlab optimization toolbox can be found here<\/a>. Please test it before the course by following these instructions<\/a>. During embryonic development, cells divide and successively differentiate to acquire specific identities. This differentiation process is controlled by gene regulatory networks that typically display bi- or multistability. We will see how to build and to analyze such multistable systems based on ODE and bifurcation theory. We will then describe a particular model developed to account for cell fate specification during early embryonic development in mammals. These models will be simulated with Matlab\/Octave and\/or XPP-AUTO ( http:\/\/homepages.ulb.ac.be\/~dgonze\/AUSSOIS2021\/<\/a>\u00a0 ; \u00a0http:\/\/www.math.pitt.edu\/~bard\/xpp\/xpp.html<\/a>).<\/p>\n<\/div>\n Enzyme economy in metabolic models (Wolfram Liebermeister, INRAE Jouy en Josas)<\/p>\n In this blackboard course, you will learn about an economic aspect of microbial metabolism: the protein cost associated with metabolic fluxes. We discuss how this cost can be approximated based on a principle of minimal enzyme investments, how enzyme efficiencies can be used to predict metabolic fluxes, and how simplified cost functions for Flux Balance Analysis can be derived. By considering a partitioning of protein resources between ribosomes and metabolic enzymes, predictions about enzyme cost can be translated into cell growth rates. Then we have a brief look at large-scale constraint-based cell models that partition the protein budget into individual enzymes. Such models predict metabolic strategies and protein investments from metabolic network structure, from physical and physiological constraints (such as limited cell space, protein composition, and enzyme catalytic rates), and from an assumed drive for fast or efficient growth.<\/p>\n Prerequisites: basic understanding of cell biology, enzyme kinetics, and mathematical optimality problems.<\/p>\n Noor E., Flamholz A., Bar-Even A., Davidi D., Milo R., Liebermeister W. (2016). The protein cost of metabolic fluxes: prediction from enzymatic rate laws and cost minimization. PLoS Comput. Biol. 12: e1005167. In this practical session, we will introduce basic methods to simulate systems containing mechanics and chemical reactions. We will in particular discuss how to extend Gillespie’s method when some of the reactions are affected by force, which is the case for example for the unbinding of a molecular bond under force, or for a molecular motor stepping along a filament. We will also discuss Brownian motion and consider simple problems of mechanical equilibrium, in which for example the position of a bead is constrained by a molecular link. The objective of the practical will be for the students to implement these methods and to write a simulation in Java of a bead being pulled by a molecular motor.<\/p>\n We will use the Processing software to be installed from https:\/\/processing.org\/download\/<\/a><\/p>\n<\/div>\n We will start with the basic laws of thermodynamics, and see a few fundamental implications in enzyme catalysis and metabolism (including flux direction constraints, the Haldane relationship, and the flux-force relationship). We will also learn how to apply these laws to constraint-based metabolic models, and when selecting parameters for kinetic models. Finally, we will see how thermodynamics can also be used in optimization problems, such as the Max-min Driving Force (MDF) algorithm. Exercises will be carried out in Python (Jupyter notebook). A good MILP solver (CPLEX or GUROBI) is recommended but not necessary.<\/p>\n Noor, E., Flamholz, A., Liebermeister, W., Bar-Even, A., and Milo, R. (2013). A note on the kinetics of enzyme action: A decomposition that highlights thermodynamic effects. FEBS Letters<\/em> 587, 2772\u20132777. \u00a0<\/strong><\/p>\n
http:\/\/ginsim.org\/sites\/default\/files\/GINsim-3.0.0b-with-deps.jar<\/a>
<\/span><\/p>\n
https:\/\/www.frontiersin.org\/articles\/10.3389\/fgene.2016.00094\/full<\/a><\/span><\/p>\n
https:\/\/www.frontiersin.org\/articles\/10.3389\/fphys.2018.00646\/full<\/a><\/span><\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/h5>\n
Dynamic models integrating metabolism and gene expression<\/strong><\/h5>\n
Hidde de Jong<\/a>
\n<\/strong>INRIA Grenoble – Rh\u00f4ne-Alpes<\/em><\/h5>\n
\n<\/div>\n<\/h5>\n
Computing resource allocation of the cell<\/strong><\/h5>\n
Anne Goelzer<\/a>
\n<\/strong>INRA Jouy en Josas<\/em><\/h5>\n
\nhttps:\/\/www.ibm.com\/analytics\/cplex-optimizer<\/a><\/p>\n<\/div>\n
\n<\/div>\nMultistability, bifurcation, and differentiation
\n<\/strong><\/h5>\nDidier Gonze<\/a>
\n<\/strong>Universit\u00e9 Libre de Bruxelles
\n<\/em><\/h5>\n
\n<\/div>\n<\/h5>\n
Enzyme economy in metabolic models<\/strong><\/h5>\n
Wolfram Liebermeister<\/a>
\n<\/strong>INRA Jouy en Josas
\n<\/em><\/h5>\n
\nWortel M.T., Noor E., Ferris M., Bruggeman F.J., Liebermeister W. (2018). Metabolic enzyme cost explains variable trade-offs between microbial growth rate and yield. PLoS Comput. Biol. 14: e1006010.<\/p>\n<\/div>\n
\n<\/div>\n<\/h5>\n
Simulation of intracellular mechanics<\/strong><\/h5>\n
Fran\u00e7ois N\u00e9d\u00e9lec<\/a>
\n<\/strong>EMBL Heidelberg
\n<\/em><\/h5>\n
\n<\/div>\n<\/h5>\n
The laws of thermodynamics: applications in models of metabolism
\n<\/strong><\/h5>\nElad Noor<\/a>
\n<\/strong>Weizmann Institute of Science<\/em><\/h5>\n
\nNoor, E., Bar-Even, A., Flamholz, A., Reznik, E., Liebermeister, W., and Milo, R. (2014). Pathway thermodynamics highlights kinetic obstacles in central metabolism. PLoS Computational Biology<\/em> 10, e1003483.
\nNoor, E. (2018). Removing both internal and unrealistic energy-generating cycles in Flux Balance Analysis. ArXiv:1803.04999 [q-Bio].<\/p>\n