[{"command":"insert","method":null,"selector":".course-modal-ajax-placeholder-968992","data":"\u003Cdiv class=\u0022container content-rte\u0022\u003E\n  \u003Cdiv class=\u0022row\u0022\u003E\n    \u003Cdiv class=\u0022box col-md-6 bg-primary text-white\u0022\u003E\n      \u003Ch2 class=\u0022h1 text-left\u0022\u003E  Computational Systems Biology and Network Medicine\n\u003C\/h2\u003E\n                  \u003Cdiv class=\u0022field-spacing\u0022\u003E\n        \u003Cdiv class=\u0022field__label h3 dash\u0022\u003EECTS :\n          2.5\n        \u003C\/div\u003E\n      \u003C\/div\u003E\n                  \u003Cdiv class=\u0022field-spacing\u0022\u003E\n        \u003Cdiv class=\u0022field__label h3 dash\u0022\u003ESemester:\n          Semestre 1\n        \u003C\/div\u003E\n      \u003C\/div\u003E\n            \u003Cdiv class=\u0022field-spacing\u0022\u003E\n      \u003Cdiv class=\u0022field__label h3 dash\u0022\u003ED\u00e9tail du volume horaire\u003C\/div\u003E\n                        \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n                          \u003Cspan class=\u0022field__label h4 mt-5 \u0022\u003ELecture:\u003C\/span\u003E\n                                      \u003Cspan\u003E12\u003C\/span\u003E\n                      \u003C\/div\u003E\n                                              \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n                          \u003Cspan class=\u0022field__label h4 mt-5 \u0022\u003EDirected study:\u003C\/span\u003E\n                                      \u003Cspan\u003E12\u003C\/span\u003E\n                      \u003C\/div\u003E\n                                                            \u003C\/div\u003E\n        \u003Cdiv class=\u0022field-spacing\u0022\u003E\n      \u003Cdiv class=\u0022field__label h3 dash\u0022\u003ELangue d\u0026#039;enseignement\u003C\/div\u003E\n                        \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n                                      \u003Cspan\u003EAnglais\u003C\/span\u003E\n                      \u003C\/div\u003E\n                  \u003C\/div\u003E\n        \u003Cdiv class=\u0022field-spacing\u0022\u003E\n      \u003Cdiv class=\u0022field__label h3 dash\u0022\u003EEnseignement \u00e0 distance\u003C\/div\u003E\n                        \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n                                      \u003Cspan\u003Enon\u003C\/span\u003E\n                      \u003C\/div\u003E\n                  \u003C\/div\u003E\n      \u003C\/div\u003E\n    \u003Cdiv class=\u0022box col-md-6\u0022\u003E\n            \u003Cdiv class=\u0022field-spacing\u0022\u003E\n      \u003Cdiv class=\u0022field__label h3 dash\u0022\u003EPr\u00e9requis\u003C\/div\u003E\n                        \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n                                      \u003Cspan\u003E\u003Cp\u003EThe prerequisites for the course are a solid background in molecular and cellular biology, and a confirmed grasp of computer programming. The students are also assumed to have basic knowledge of discrete mathematics.\u003C\/p\u003E\u003C\/span\u003E\n                      \u003C\/div\u003E\n                  \u003C\/div\u003E\n        \u003Cdiv class=\u0022field-spacing\u0022\u003E\n      \u003Cdiv class=\u0022field__label h3 dash\u0022\u003EProgramme \/ plan \/ contenus\u003C\/div\u003E\n                        \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n                                      \u003Cspan\u003E\u003Cp\u003E1. Introduction to Systems Biology and Network Medicine\u003C\/p\u003E\n\u003Cp\u003E2. Foundations\u003C\/p\u003E\n\u003Cp\u003E\u0026nbsp; \u0026nbsp;a. Topological analysis\u003C\/p\u003E\n\u003Cp\u003E\u0026nbsp; \u0026nbsp; \u0026nbsp; i. Graphs: definition and fundamental algorithms\u003C\/p\u003E\n\u003Cp\u003E\u0026nbsp; \u0026nbsp; \u0026nbsp; ii. Graph measures\u003C\/p\u003E\n\u003Cp\u003E\u0026nbsp; \u0026nbsp; \u0026nbsp;iii. Case studies\u003C\/p\u003E\n\u003Cp\u003E\u0026nbsp; \u0026nbsp; \u0026nbsp; iv. Random networks study\u003C\/p\u003E\n\u003Cp\u003E\u0026nbsp; \u0026nbsp;b. Discrete dynamics\u003C\/p\u003E\n\u003Cp\u003E\u0026nbsp; \u0026nbsp; \u0026nbsp; i. Boolean networks\u003C\/p\u003E\n\u003Cp\u003E\u0026nbsp; \u0026nbsp; \u0026nbsp; ii. Petri nets\u003C\/p\u003E\n\u003Cp\u003E3. Applications to systems biology \u0026amp; tools\u003C\/p\u003E\n\u003Cp\u003E\u0026nbsp; \u0026nbsp;a. Introduction\u003C\/p\u003E\n\u003Cp\u003E\u0026nbsp; \u0026nbsp;b. Types of biological networks\u003C\/p\u003E\n\u003Cp\u003E\u0026nbsp; \u0026nbsp; \u0026nbsp;i. Types and functional roles\u003C\/p\u003E\n\u003Cp\u003E\u0026nbsp; \u0026nbsp; \u0026nbsp;ii. Databases\u003C\/p\u003E\n\u003Cp\u003E\u0026nbsp; \u0026nbsp; \u0026nbsp;iii. Types and data acquisition\u003C\/p\u003E\n\u003Cp\u003E\u0026nbsp; \u0026nbsp;c. Integration of data into networks\u003C\/p\u003E\n\u003Cp\u003E\u0026nbsp; \u0026nbsp; \u0026nbsp;i. Network inference\u003C\/p\u003E\n\u003Cp\u003E\u0026nbsp; \u0026nbsp; \u0026nbsp;ii. Functional integration\u003C\/p\u003E\n\u003Cp\u003E\u0026nbsp; \u0026nbsp;d. Modelling of network dynamics\u003C\/p\u003E\n\u003Cp\u003E\u0026nbsp; \u0026nbsp; \u0026nbsp;i. Modelling principles\u003C\/p\u003E\n\u003Cp\u003E\u0026nbsp; \u0026nbsp; \u0026nbsp;ii. Case studies and software tools\u003C\/p\u003E\u003C\/span\u003E\n                      \u003C\/div\u003E\n                  \u003C\/div\u003E\n        \u003Cdiv class=\u0022field-spacing\u0022\u003E\n      \u003Cdiv class=\u0022field__label h3 dash\u0022\u003EObjectifs d\u0026#039;apprentissage\u003C\/div\u003E\n                        \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n                                      \u003Cspan\u003E\u003Cp\u003EUnderstand the fundamental principles of network modelling and analysis applied to biological systems, in particular to molecular biology.\u003C\/p\u003E\u003C\/span\u003E\n                      \u003C\/div\u003E\n                  \u003C\/div\u003E\n        \u003Cdiv class=\u0022field-spacing\u0022\u003E\n      \u003Cdiv class=\u0022field__label h3 dash\u0022\u003EOrganisation g\u00e9n\u00e9rale et modalit\u00e9s p\u00e9dagogiques\u003C\/div\u003E\n                        \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n                                      \u003Cspan\u003E\u003Cp\u003EThe objective of this course is to provide the fundamental background combining theoretical, computational and biological knowledge for this challenge. The course introduces the theoretical frameworks and the computational methods applied to Systems Biology, with a specific focus on networks. The content of the course is divided in three major parts:\u003Cbr\u003E\nThe first part will be dedicated to the fundamental knowledge of networks, and in-depth topological analysis of complex networks. Different types of networks, case studies and applications in biology will be discussed.\u003Cbr\u003E\nIn the second part, emphasis will be on dynamical networks and on how discrete based mathematical formalisms can be used to describe their dynamics and delineate their properties. Two modelling approaches will be presented, Boolean networks and Petri nets, with concrete examples in biology.\u003Cbr\u003E\nIn the third part, the students will be introduced to the standards of network and model description commonly used in Systems Biology. Then, different types of intracellular networks will be analyzed in detail, along with a thorough presentation of available resources such as model repositories and databases. Bottom up and top down approaches for network construction and inference will be discussed and different data types will be presented. Lastly, the students will learn about functional data integration and limitations.\u003C\/p\u003E\u003C\/span\u003E\n                      \u003C\/div\u003E\n                  \u003C\/div\u003E\n                \u003Cdiv class=\u0022field-spacing\u0022\u003E\n      \u003Cdiv class=\u0022field__label h3 dash\u0022\u003EComp\u00e9tences\u003C\/div\u003E\n                        \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n                                      \u003Cspan\u003E\u003Cp\u003E\u003Cstrong\u003EComp\u00e9tence 1.4\u003C\/strong\u003E\u003Cbr\u003E\n\u003Cstrong\u003EProduire et valider des donn\u00e9es scientifiques.\u003C\/strong\u003E\u003C\/p\u003E\n\u003Cp\u003E\u003Cstrong\u003EComp\u00e9tence 2.1\u003C\/strong\u003E\u003Cbr\u003E\n\u003Cstrong\u003EActualiser ses connaissances th\u00e9oriques et technologiques par une veille dans son domaine en relation avec l\u2019\u00e9tat de la recherche\u003C\/strong\u003E\u003C\/p\u003E\n\u003Cp\u003E\u003Cstrong\u003EComp\u00e9tence 2.2\u003C\/strong\u003E\u003Cbr\u003E\n\u003Cstrong\u003ESynth\u00e9tiser et structurer un \u00e9tat de l\u2019art sur une probl\u00e9matique d\u00e9finie, en autonomie, dans un contexte li\u00e9 \u00e0 la recherche scientifique et\/ou recherche \u0026amp; d\u00e9veloppement\u003C\/strong\u003E\u003C\/p\u003E\n\u003Cp\u003E\u003Cstrong\u003EComp\u00e9tence 2.3\u003C\/strong\u003E\u003Cbr\u003E\n\u003Cstrong\u003ESe former \u00e0 de nouveaux outils technologiques.\u003C\/strong\u003E\u003C\/p\u003E\n\u003Cp\u003E\u003Cstrong\u003EComp\u00e9tence 3.1\u003C\/strong\u003E\u003Cbr\u003E\n\u003Cstrong\u003EStructurer et pr\u00e9senter des donn\u00e9es et concepts scientifiques \u00e0 des sp\u00e9cialistes ou \u00e0 des n\u00e9ophytes.\u003C\/strong\u003E\u003C\/p\u003E\n\u003Cp\u003E\u003Cstrong\u003EComp\u00e9tence 3.2\u003C\/strong\u003E\u003Cbr\u003E\n\u003Cstrong\u003EArgumenter les choix m\u00e9thodologiques et les interpr\u00e9tations d\u2019une \u00e9tude.\u003C\/strong\u003E\u003C\/p\u003E\n\u003Cp\u003E\u003Cstrong\u003EComp\u00e9tence 3.3\u003C\/strong\u003E\u003Cbr\u003E\n\u003Cstrong\u003EExtraire et questionner des informations pertinentes d\u2019un document scientifique ou de conf\u00e9rences en anglais.\u003C\/strong\u003E\u003C\/p\u003E\n\u003Cp\u003E\u003Cstrong\u003EComp\u00e9tence 4.1\u003C\/strong\u003E\u003Cbr\u003E\n\u003Cstrong\u003ETravailler en autonomie et collaborer avec les membres d\u2019une \u00e9quipe afin d\u2019atteindre des objectifs communs.\u003C\/strong\u003E\u003C\/p\u003E\u003C\/span\u003E\n                      \u003C\/div\u003E\n                  \u003C\/div\u003E\n        \u003Cdiv class=\u0022field-spacing\u0022\u003E\n      \u003Cdiv class=\u0022field__label h3 dash\u0022\u003EBibliographie\u003C\/div\u003E\n                        \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n                                      \u003Cspan\u003E\u003Cp\u003E1.\u0026nbsp;Barab\u00e1si, A. L., Gulbahce, N., \u0026amp; Loscalzo, J. (2011).\u0026nbsp;\u003Cem\u003ENetwork medicine: a network-based approach to human disease\u003C\/em\u003E. Nature Reviews Genetics, 12(1), 56\u201368.\u003C\/p\u003E\n\u003Cp\u003E2.\u0026nbsp;Jorge G.T. Za\u00f1udo, Steven N. Steinway, R\u00e9ka Albert, \u003Cem\u003EDiscrete dynamic network modeling of oncogenic signaling: Mechanistic insights for personalized treatment of cancer,\u003C\/em\u003E Current Opinion in Systems Biology, Volume 9, 2018, Pages 1-10.\u003C\/p\u003E\n\u003Cp\u003E3. Monika Heiner, David R. Gilbert, Robin Donaldson: \u003Cem\u003EPetri Nets for Systems and Synthetic Biology\u003C\/em\u003E. SFM 2008: 215-264\u003C\/p\u003E\n\u003Cp\u003E4. C\u00e9lia Biane, Franck Delaplace: \u003Cem\u003ECausal Reasoning on Boolean Control Networks Based on Abduction: Theory and Application to Cancer Drug Discovery.\u003C\/em\u003E\u0026nbsp;IEEE ACM Trans. Comput. Biol. Bioinform. 16(5): 1574-1585 (2019)\u003C\/p\u003E\u003C\/span\u003E\n                      \u003C\/div\u003E\n                  \u003C\/div\u003E\n        \u003Cdiv class=\u0022field-spacing\u0022\u003E\n      \u003Cdiv class=\u0022field__label h3 dash\u0022\u003ENature de l\u0026#039;\u00e9valuation\u003C\/div\u003E\n                        \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n                                      \u003Cspan\u003EEvaluation Continue non Int\u00e9grale\u003C\/span\u003E\n                      \u003C\/div\u003E\n                  \u003C\/div\u003E\n      \u003C\/div\u003E\n  \u003C\/div\u003E\n\u003C\/div\u003E\n","settings":null}]