[{"command":"insert","method":null,"selector":".course-modal-ajax-placeholder-959346","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  Machine Learning in Synthetic Biology and Metabolic Engineering\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\u003ESemestre calendaire :\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\u003ECours magistraux :\u003C\/span\u003E\n                                      \u003Cspan\u003E20\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\u003EBasic knowledge of Python programming and the use of Python libraries.\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\u003EAt the end of this course, the student will be able to:\u003C\/p\u003E\n\u003Cp\u003E- Understand traditional learning methods such as multiple linear regression, support vector machines, random forests, and neural networks (dense, convolutional and recurrent) as well as active and reinforcement learning methods.\u003C\/p\u003E\n\u003Cp\u003E- Understand cross-validation methods for classification and regression\u003C\/p\u003E\n\u003Cp\u003E- Define the needs for experimental design and learning for classical problems in the construction of metabolic pathways and synthetic circuits developed in synthetic biology\u003C\/p\u003E\n\u003Cp\u003E- Design a synthetic circuit to be implanted in a biological system (cellular or acellular) capable of performing a basic learning operation (weighted sum, activation function)\u003C\/p\u003E\n\u003Cp\u003E- Use Python libraries (such as pyDoE, Keras, Scikit-Learn) to create an experimental design and simple learning on a biological data set.\u003C\/p\u003E\n\u003Cp\u003E- To be familiar with the use of learning methods in the fields of human health and image recognition.\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\u003ETeaching in this EU will include lectures in experimental design, machine learning, and their applications in synthetic biology and human health. Some courses will be given by external invited speakers. Simple exercises (TD) will be given during the courses on specific points. At the end of the EU, students will be required to carry out a design of experiment and\/or machine learning project on a problem and a data set resulting from synthetic biology. The project will require the use of Python libraries.\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\u003EGricourt G, Meyer P, Duigou T, Faulon JL. Artificial Intelligence Methods and Models for Retro-Biosynthesis. ACS Synth Biol. 2024;13(8):2276-2294.\u003Cbr\u003E\nFaulon JL, Ahavi P, Hoang A, Reservoir Computing with Bacteria, bioRxiv 2024 DOI: 10.1101\/2024.09.12.612674\u003Cbr\u003E\nFaure L, Mollet B, Liebermeister W et al. A neural-mechanistic hybrid approach improving the predictive power of genome-scale metabolic models. Nat Commun., 2023, 14, 4669\u003Cbr\u003E\nBorkowski O, Koch M, Zettor A, Pandi A, Cardoso Batista A, Soudier P, Faulon JL. Large scale active-learning-guided exploration to maximize cell-free production. Nat Commun. 11(1): 1872, 2020.\u003Cbr\u003E\nPandi A, Koch M, Voyvodic PL, Soudier P, Bonnet J, Kushwaha M, Faulon JL. Metabolic perceptrons for neural computing in biological systems. Nat Commun. 2019, 10:3880.\u003Cbr\u003E\nCarbonell P, Jervis AJ, Robinson CJ, Yan C, Dunstan M, Swainston N, Vinaixa M, Hollywood KA, Currin A, Rattray NJW, et al. An automated Design-Build-Test-Learn pipeline for enhanced microbial production of fine chemicals. Commun Biol. 2018, 1:66.\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}]