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In this course, we will develop and calibrate models using the most commonly encountered formalism: ordinary-differential equations. By means of an example, we will see how to go from data to a fully calibrated models. More specifically, we will draft a model of the system, find realistic estimates for parameter values, propose experiments to be performed on this system to characterize its behavior, fit the model to the (simulated) data and analyze its dynamics.<\/p>\n
As a running example, we will use the toggle switch, a synthetic genetic network built in E. coli<\/em>. It is one of the most extensively-studied system. Yet interesting quantitative questions are still open. The target audience of this course is mostly biologists that are searching for a practical introduction to modeling. Researchers willing to get more familiar with the use of global optimization tools might be interested as well.<\/p>\n Important note<\/span>:<\/b> We will use Matlab<\/span> for all computations. Therefore, it is expected that participants will come with the installed program. No toolboxes are needed. Student versions are available at 35 \u20ac.<\/p>\n <\/p>\n<\/div>\n <\/p>\n Microorganisms often encounter dynamic conditions such as varying levels and types of stresses and nutrients. <\/span>Evolutionarily-successful microorganisms accumulate most offspring across those varying environmental states. <\/span>Different adaptation and survival strategies can maximise fitness of microorganisms in dynamic conditions. <\/span>The net fitness value of a strategy is dependent on the exact environmental dynamics, the fitness costs and benefits of the used molecular circuitry, and the fitness costs and benefits of phenotypic stochasticity.<\/span><\/p>\n I will introduce a framework that can address and compare the fitness of adaptation and survival strategies in dynamic environments. I will illustrate its usage by comparing different adaptation strategies, which have been experimentally shown to be used by microorganisms. I will introduce and analyze various experimentally-observed phenomena, including bet-hedging, stochastic phenotype switching, phenotypic diversification, responsive adaptation and stochastic adaptation, using this theory. My blackboard teaching will therefore introduce you to a theoretical framework for the evaluation, interpretation and comparison of microbial adaptation and survival strategies on the basis of their evolutionarily potential, i.e. fitness.<\/span><\/p>\n <\/p>\n <\/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 <\/b>(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 consider model composition to account for multi-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: <\/p>\n<\/div>\n <\/p>\n<\/div>\n<\/div>\n This hands-on session is an introduction to the modeling of the dynamics of stochastic gene expression. As a toy model, the linear \u00ab central dogma \u00bb model will be considered (one gene->one mRNA->one protein). After a brief overview of the theoretical analysis of this system (mass action laws, methods of moments), we will simulate the stochastic dynamics of the system using Gillespie\u2019s algorithm, that accounts for stochasticity due to low copy numbers and compare the simulation results with analytical solutions. Extensions of this analysis will be considered depending on time.<\/p>\n Students need to have Matlab <\/span>installed on their laptop.<\/p>\n <\/p>\n <\/p>\n<\/div>\n <\/p>\n 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\u00a0 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(2):99-109<\/p>\n <\/p>\n <\/p>\n Signal transduction networks are challenging targets for modeling, especially at larger scale. This is due to the internal states carried by the signaling components, which encode and transmit the information through the network. Dealing with these states leads to scalability problems at two distinct level: First in model formulation, and second in model execution.<\/p>\n In this session, we will look at scalable methods to define and execute models of signal transduction networks. I will start with discussing different methods for modeling signal transduction, and discuss their potential and limitations. Thereafter, I will introduce rxncon, the reaction-contingency language. We will cover how to use rxncon to formalize signal transduction, and how to convert the rxncon network into executable models. Finally, we will have a hands-on session, trying out model creation and analysis from a small rxncon network.<\/p>\n Students need rxncon<\/span>, cytoscape<\/span>, BoolNet<\/span> and NFsim installed on their laptop<\/span><\/span>. Please see the complete installation guide<\/strong><\/a>, including dependencies (python<\/span>, R<\/span>, perl<\/span>).<\/p>\n <\/p>\n <\/p>","protected":false},"excerpt":{"rendered":" Blackboard teaching and Computer practicals Basics in modeling and model calibration: the toggle switch example Gregory BATT INRIA Saclay – Ile-de-France and Pasteur Institute, Paris In this course, we will develop and calibrate models using the most commonly encountered formalism: ordinary-differential equations. By means of an example, we will\u2026<\/p>\n
\nBacterial fitness maximisation and phenotypic diversification strategies<\/span><\/strong><\/h5>\n
Frank BRUGGEMAN<\/a>
\n<\/strong>Free University, Amsterdam<\/em><\/h5>\n
\nQualitative dynamical modelling of (multi-) cellular networks<\/strong><\/h5>\n
Claudine CHAOUIYA<\/a>
\n<\/strong>Gulbenkian Institute, Lisbon<\/em><\/h5>\n
\nhttp:\/\/ginsim.org\/sites\/default\/files\/GINsim-2.9.4-with-deps.jar<\/a>
\nhttp:\/\/epilog-tool.org\/sites\/default\/files\/Epilog-0.4-SNAPSHOT-jar-with-dependencies.jar<\/a><\/p>\n
\n<\/div>\nModeling of stochastic gene expression<\/strong><\/h5>\n
Eugenio CINQUEMANI<\/a><\/strong>
\nINRIA, Grenoble<\/em><\/h5>\nDynamic models integrating metabolism and gene expression<\/strong><\/h5>\n
Hidde DE JONG<\/a>
\n<\/strong>INRIA, Grenoble<\/em><\/h5>\n
\n<\/div>\nScalable mechanistic modeling of cellular signal transduction<\/strong><\/h5>\n
Markus KRANTZ<\/a>
\n<\/strong>Humboldt University, Berlin
\n<\/em><\/h5>\n
\n<\/div>\n