<\/p>\n
We will start with the basics of the logical formalism to model and analyze regulatory\/signaling networks. We will discuss a variety of computational methods that enable the analysis of rather large networks. The software GINsim (http:\/\/ginsim.org<\/a>) will be used to build and analyze a simple logical model, illustrating the concepts introduced during the course. We will then introduce model composition and\u00a0a cellular automata framework\u00a0for the logical modeling of\u00a0multi-cellular systems. In particular, we will focus on pattern formation in hexagonal grids, defining and simulating logical models using the software EpiLog (http:\/\/epilog-tool.org\/<\/a>).<\/p>\n Students need to have GINsim<\/span> and EpiLog<\/span> installed on their laptop, with a JAVA version 1.7 or higher. Files with dependencies should be downloaded from: 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 the simple models described in Kremling et al. (2015). All simulations will be carried out by means of Matlab<\/span> (no toolboxes needed), using code provided by 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<\/span> (release 2016 or later) and having either Cplex<\/span> 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>. The cellular environment is\u00a0abuzz with noise. A key source of this noise is the randomness that\u00a0characterizes the motion of cellular constituents at the molecular level. Cellular\u00a0noise not only results in random fluctuations (over time)\u00a0within individual\u00a0cells, but it is also a main source of phenotypic variability among clonal cell\u00a0populations. This course is concerned with the computational\u00a0methods for modeling, simulation, and analysis of stochasticity in\u00a0living\u00a0cells.<\/p>\n The course topics include: introduction to stochastic gene expression; deterministic\u00a0vs<\/em>. stochastic models; the stochastic chemical kinetics framework; the chemical master equation; moment computations; the linear noise approximation; Monte Carlo simulations; Gillespie’s\u00a0Stochastic Simulation Algorithm (SSA) and its variants. Exercises will require\u00a0Matlab<\/span> or R<\/span>.<\/p>\n<\/div>\n In this blackboard course, I will address an economic aspect of microbial metabolism: the protein cost associated with metabolic fluxes. I show how this cost can be approximated based on enzyme kinetics and on the assumption of minimal enzyme investments, and we will discuss how enzyme costs per unit flux, once they are known, 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 cellular growth rates. At the end of the course, we study constraint-based models that consider a fine-grained partitioning of the protein budget into individual enzymes. Such models predict metabolic strategies and protein investments solely from metabolic network structure, from physical and physiological constraints (such as limited cell space, protein composition, and presumable catalytic rates of enzymes), and from an assumed drive for fast growth.<\/p>\n Prerequisites: very 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<\/a>. PLoS Comput. Biol.<\/em> 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 <\/span> software to be installed from https:\/\/processing.org\/download\/<\/a><\/p>\n<\/div>\n
\nhttp:\/\/ginsim.org\/sites\/default\/files\/GINsim-3.0.0b-with-deps.jar<\/a>
\nhttp:\/\/epilog-tool.org\/sites\/default\/files\/EpiLog-v1.1.1.jar<\/a><\/p>\n<\/div>\n<\/div>\n<\/div>\n
\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
Resource Balance Analysis<\/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>\nStochastic modeling, simulation and analysis
\n<\/strong><\/h5>\nMustafa Khammash<\/a>
\n<\/strong>ETHZ, Basel
\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<\/a>. PLoS Comput. Biol.<\/em> 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