1st SEMESTER (September to January)
During the first semester, from September to end of January, students must attend 5 core compulsory modules and 5 elective modules (chosen among 10), for a total of 30 European Credits (ECTS).
Refresher courses in either biology or mathematics and computer science are also proposed. Additionally, a course on Innovation and Entrepreneurship in Biotechnology is proposed. Two of these courses offer the possibility of an optional evaluation, allowing students to earn up to 6 supplementary European Credits (ECTS) beyond the total of 60 ECTS required for the diploma.
The courses proposed in each module are described bellow.
Refresher and Supplementary Courses (0 or up to 6 ECTS)
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Core Modules (17.5 ECTS)
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Elective Modules (12.5 ECTS)
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Seminars:
This program is enriched by a seminar series given by international speakers from the academy and from the industry. Students may be involved in shaping this series.
1st SEMESTER (September to January) course description
UE3.12 Cell-Free Systems
Course Objectives:
Syllabus:
Course Prerequisites:
Course Organization:
Bibliography:
| UE1.1 Introduction to Biology Course Objectives: To facilitate research at common borders, the advanced introduction course aims at fostering a better understanding of the expectations, constraints, approaches and mode of thinking of a scientific partner across disciplines.Within two week, this course brings participants belonging to a non-biological community (mathematics, engineering, chemistry, physical or computer sciences) to understand the research frontiers in biological sciences. To reach this goal, key objects and concepts explaining the current research questions and methods will be presented and all the important and recent subdisciplines of biology will be covered. Course Prerequisites: None Course Organization: This 40h course is organized in: – 32h of Integrated Plenary Course – Tutorials that cover: Molecular Biology, Genomics, Systems Biology, Synthetic Biology, Structural Biology, Evolution, Cell Biology, Bioinformatics, Developmental Biology – 8h of Practical course: Presentation of the laboratory and of the Good Laboratory Practice Discover the key Molecular Biology and Biochemistry tools and techniques used daily in biology laboratories. Bibliography: – Johnson AD, Alberts B, Morgan D, Lewis J, Roberts K, Raff M, Walter P. Molecular Biology of the Cell, Sixth Edition. W. W. Norton & Company, Inc. 2014. |
| UE1.2 Introduction to Mathematics and Computer Science for Biology Course Objectives: The course will present basic computational and mathematical concepts needed for genomic and data analysis. At the end of the course, the student should be able to: – perform basic statistical analysis on datasets – understand formal concepts used for describing biological problems – use R for basic data analysis – be autonomous in a Unix/Linux environment Syllabus: – Probability-Statistics: Descriptive statistics, Probability, Random variables, Distributions, Central Limit Theorem, Hypothesis testing, Linear regression, Probabilistic modeling, Tests – Data Analysis & Simulation: Introduction to R language, Introduction to Linux Course Prerequisites: The prerequisites for the course are a basic knowledge of discrete mathematics and an eagerness to engage in computational analysis. Students are assumed to have a solid background in biology. Course Organization: The course comprises theoretical lectures and integrated theoretical and practical sessions (hands-on). More analytically the course is organised as follows: – Theoretical lectures on statistics and data analysis: 9h – Introduction to Unix and command line data manipulation: 7.5h (computer room) – Introduction to R and statistical analysis: 6h (computer room) |
| UE1.3 Advanced Introduction to Mathematics and Computer Science for Biology Course Objectives: The course will present basic computational and mathematical concepts needed for systems biology approaches and data analysis and simulation. The course should enable students to understand more sophisticated mathematical and computational notions encountered in other modules. At the end of the course, the student should be able to: – understand formal concepts used for describing biological problems – describe basic notions of machine learning – understand formal concepts used for describing biological problems – use Python for basic data analysis Syllabus: – Data Analysis & Simulation – Machine Learning: Supervised learning, Unsupervised learning, Evolutionary algorithms – Modeling (regular languages): Regular expressions, Finite automata, Formal grammar – Introduction to discrete modelling and the use of formal mathematical structures to represent problems. – Data Analysis & Simulation: Introduction to Python Course Prerequisites: The prerequisites for the course are a basic knowledge of discrete mathematics and an eagerness to engage in computational analysis. Students are assumed to have a solid background in biology. |
| UE1.4 Innovation and Entrepreneurship in Biotechnology Course Objectives: Give an overview of steps and procedures for entrepreneurship, with a focus on biotechnologies. Syllabus: – Interactive session about entrepreneurship. Thoughts and discussions about entrepreneurship and its scope, leading to the identification of modules needed for business start-up – Presentation of support organizations. Global presentation and Focus on Genopole: its ecosystem, Biotechnology entrepreneurship specificity and the early-stage support program Shaker. – Testimonials of actors: Entrepreneurs / Incubator / Funders – Presentation and meeting with the Paris Saclay Cancer Cluster stakeholders, followed by an on-site visit. – Student’s case study : Preparation of a pitch for a biotech start-up project session of projects presentations and guidelines of this pitch exercise personal and team students work restitution with an oral presentation Course Organization: 20 hours of face-to-face teaching spread over 2 weeks Supervised project to be prepared Oral presentation |
| UE2.1 Biological Parts and Devices Course Objectives: This module will equip the student with broad knowledge of synthetic biology, and the engineering of biological parts and devices. Students will identify and define key concepts in Synthetic Biology / Engineering Biology: history, philosophy, objectives, positioning between biology and engineering, standardisation, chasses, and International Genetically Engineered Machine (iGEM). They will be able to describe the roles of different genetic parts: promoters, ribosome binding sites, terminators, genes, reporters, plasmids, small RNAs, and ribo-regulators. They will be able to retrieve those parts from the common parts registries, and identify and annotate them in a given DNA sequence. The students will be able to interpret genetic designs for expression of single and multi-gene circuits with different types of behaviour: constitutive, inducible, dynamic oscillatory, spatially patterned. They will be able to construct simple circuits using RNA, protein, and nucleoprotein parts; while taking into account expression levels (transcription and translation), codon usage, RNA stability, protein half-life, noise, and burden of the circuits. The students will survey and compare prokaryotic and eukaryotic genetic circuits from literature, identifying differences between them. They will be able to troubleshoot malfunctioning biological circuits and build computational models of simple circuits. Finally, they will be able to critique biological circuit designs, propose improvements to the designs, and justify and defend those improvements. Course Prerequisites: None Course Organization: The course module is organised as 11h of lectures and 9h of tutorials to introduce knowledge and methodological tools. Bibliography: A prescribed textbook is not required in this course. Powerpoint slides with primary literature citations will be provided. However, students are encouraged to do background reading using recommended literature below: – Brophy JA, Voigt CA. Principles of genetic circuit design. Nat Methods. 2014, 11: 508-520. -Way JC, Collins JJ, Keasling JD, Silver PA. Integrating biological redesign: where synthetic biology came from and where it needs to go. Cell 2014, 157: 151-161. – Lienert F, Lohmueller JJ, Garg A, Silver PA. Synthetic biology in mammalian cells: next generation research tools and therapeutics. Nat Rev Mol Cell Biol 2014, 15:95-107. – Yeh BJ, Lim WA. Synthetic biology: lessons from the history of synthetic organic chemistry. Nature Chemical Biology, 2007, 3: 521–525. – Meng F, Ellis T. The second decade of synthetic biology: 2010–2020. Nature Communications, 2020, 11: 1–4. – Voigt CA. Synthetic biology 2020–2030: six commercially-available products that are changing our world. Nature Communications, 2020, 11: 10–15. – Grozinger L, Amos M, Gorochowski TE, Carbonell P, Oyarzún DA, Stoof R, Fellermann H, Zuliani P, Tas H, Goñi-Moreno A. Pathways to cellular supremacy in biocomputing. Nature Communications, 2019, 10: 1–11. |
| UE2.2 Genome Engineering Course Objectives: Genome engineering technologies are revolutionising Life Sciences as they enable the rational design of synthetic biological systems. Recent advances in genome engineering have dramatically expanded our ability to engineer cells in a directed and combinatorial manner. At the end of the course, students will be able to: – Explain the principles of several genome engineering techniques – Adapt genome engineering techniques to a given scientific issue – Conceive orthogonal systems and explain their advantages and limitations – Evaluate on-going research in the development of flexibly programmable chassis Syllabus: This course will cover up-to-date techniques for DNA assembly (eg Gibson, Golden Gate, LIC, SLIC…), for site-directed DNA recombination (eg Cre-Lox, integrase, etc.), multiplex gene and genome editing (eg PACE, CRISPR, TALEN…), genome-scale engineering (eg genome reduction, genome synthesis, genome transplantation, MAGE, CAGE…) and explore the implications of continued advances toward the development of flexibly programmable “chassis”, novel biochemistries (eg non-canonical amino acids, XNA…), and safer engineering (eg genetic confinement, synthetic consortia…). Course Prerequisites: None Course Organization: The course module is organised in 14h of lectures and 6h of practical courses to introduce knowledge and methodological tools. Bibliography: Textbook is not required in this course. We will use primary literature materials and power point slides. But, students are welcome to do some background reading using recommended reviews below: – Esvelt KM, Wang HH. Genome-scale engineering for systems and synthetic biology. Mol Syst Biol 2013, 9:641. – Annaluru N, et al. Total synthesis of a functional designer eukaryotic chromosome. Science 2014, 344:55-58. – Kosuri S, Church GM. Large-scale de novo DNA synthesis: technologies and applications. Nat Methods 2014, 11:499-507. |
| UE2.3 Metabolic Engineering Course Objectives: Metabolic engineering is becoming a mature field of research with many industrial applications. It is a process of optimizing native metabolic pathways and regulatory networks or assembling heterologous metabolic pathways for production of targeted molecules using molecular, genetic and combinatorial approaches. At the end of the course, students will be able to: – Explain the various steps and methods necessary to perform a metabolic engineering project – Conceive a workflow from design to test in order to produce a given molecule in a given chassis strain – Evaluate various methods in order to optimise the production yields of metabolic pathways – Adapt metabolic engineering to build biosensor and biocomputing devices Syllabus: The purpose of the metabolic engineering is to generate a cell factory that produces cost-effective molecules at industrial scale. This course aims to provide the basic concepts and tools that have been developed the past 20 years when designing, building and testing metabolic pathways for bioproduction purposes. Practical applications and successes will be surveyed by an industrial (Abolis Biotech). A particular emphasis will be given to two research forefronts of metabolic engineering (1) how can we learn to improve pathway performances using flux analysis and genetic engineering and (2) how pathway engineering can be used to build biosensor and biocomputation devices. Course Prerequisites: Understanding metabolic pathways and enzymatic reactions is crucial. A background in molecular biology, gene expression, regulation, and recombinant DNA technology is typically required, as well as knowledge of chemical or biochemical engineering principles. A solid understanding of central metabolism and major metabolic pathways is also essential. In addition, the use of computational bioinformatic tools is required in this course. Course Organization: The course module consists of 12 hours of lectures and 8 hours of tutorials introducing the main concepts and workflows of metabolic engineering. Throughout the course, students will apply their knowledge of metabolic engineering, systems biology, and synthetic biology using the Galaxy-SynBioCAD platform (https://galaxy-synbiocad.org/) to assess the feasibility of producing a heterologous compound in a chassis strain such as Escherichia coli or Saccharomyces cerevisiae. At the end of the course, students will be asked to design a metabolic pathway in a given chassis strain, propose an experimental plan to construct and test the designed pathway, and evaluate the associated costs, including manpower and consumables. The assessment will be based on two components: An oral presentation (15 minutes followed by 5 minutes of questions). A short bibliographic report (maximum 1,000 words). Bibliography: A textbook is not required in this course. We will use primary literature materials and power point slides. But, students are welcome to do some background reading using recommended reviews below: – Stephanopoulos GN, Aristidou AA, Nielsen J. Metabolic Engineering: Principles and Methodologies. San Diego: Academic Press 1998. – Palsson BO. Systems Biology. Cambridge University Press, New York, NY, 2006. – Smolke CD. The Metabolic Pathway Engineering Handbook, CRC Press (two volumes edited book), 2010. – Halper R. Systems Metabolic Engineering, Methods in Molecular Biology, Vol. 985, Springer, 2013. – https://doi.org/10.1039/D0CS00155D – https://www.sciencedirect.com/journal/metabolic-engineering – https://doi.org/10.1016/j.ymben.2020.10.005 – https://doi.org/10.1016/j.ymben.2020.08.015 |
| UE2.4 Synthetic Biology Practical Course Course Objectives: The objectives of this course are: – To practice classical molecular biology techniques at the bench for the purpose of synthetic biology, especially when students are not familiar with the wet-lab. – To get the know-how of experimental acquisition of data from complex biological systems. – To go through the process of a synthetic biology project, from its experimental design to data interpretation. Syllabus: This course will cover all steps of a synthetic biology project (production of metabolites by a host), either for fundamental purposes such as understanding the rules controlling biological systems or for engineering purposes such as production of molecules of interest by living systems. Emphasis will be put on the concept of process, with quality control checkpoints, discussions on the inherent and manageable variability of the input sources (the biological system – the human experimenter) and collective data analysis sessions. As a practical course, it will recall general safety rules and good practices in the laboratory. Course Prerequisites: None Course Organization: As a practical course, this module will be organized in several sessions scheduled over two weeks on the basis of experimental constraints. During a first part, some gene manipulation will be performed (DNA purification, PCR amplification, digestion of recombinant plasmids) followed by experiments that enable the use of the model bacterium E. coli as a host (transformation, selection and clone analysis). A third part (data acquisition, processing and analysis) will consist of the implementation of the synthetic biology process: metabolites production, extraction, quantitative analysis. Whenever possible, students will be associated in teams involving a biologist and a non-biologist, to favor exchanges and questions. Each team will have in charge some parts of the project (with some redundancy between the teams). Bibliography: http://igem.org/Main_Page |
| UE2.5 Biosafety and Epistemology Questions on Synthetic Biology Course Objectives: At the end of this course, students will be able to: – describe the diversity of definitions, institutions and actors in synthetic biology in France. – identify the dominant trend in the ethics of engineering of life. – distinguish the plurality of points of view that the actors of synthetic biology have on their practice. – analyse the unanswered questions that synthetic biology raises about biotechnologies – conceive a reflexive approach towards synthetic biology by taking into account their own relationship with the discipline (trajectory, choice of object, meaning it has given to it, critical questioning with regard to the research objectives pursued, etc.) – describe several orthogonal systems and analyse their advantages and limitations Syllabus: The course will be divided into four parts: I. Definitions and actors involved: synthetic biology “seen from the outside”. II. The majority position: from ethics to engineering of living matter III. The critical capacities of researchers: limits or impotence of ethics? IV. The unthinking of ethical posture: about the blind spots of biotechnological progress V. Chemical toolkit as genetic firewall and reengineering living organisms. Course Prerequisites: none Course Organization: The teaching will take place in three sessions of three hours each and one session of two hours. It will be provided on one hand by two speakers, specialists in the sociology of science and technology, who have carried out a sociological survey on the work of the actors in synthetic biology and on the other hand by a speaker whose research work is in the field of xenobiology. The teaching will be based both on a synthesis of the literature on this subject and on the main results obtained by the sociological survey. The teaching will alternate between the presentation of the course content by the two speakers, soliciting students to encourage interaction with teachers and stimulate debate; collective reading of short texts in class and/or longer texts to read for the next session. The main evaluation of the teaching will consist in the writing of an essay by the students in which they will demonstrate a reflective approach to synthetic biology by reflecting on their own relationship to the discipline. Bibliography: – Aguiton-Angeli S. La démocratie des chimères. Gaouverner la biologique synthétique, Lormont, Le bord de l’eau, 2018. – Bensaude-Vincent B, Benoit-Browaeys D. Fabriquer la vie. Ou va la biologie de synthèse ? Paris, Du Seuil, 2011. – Cameron DE, Bashor CJ, Collins JJ. A brief history of synthetic biology. Nat Rev Microbiol. 2014, 12:381-390. – Flocco G, Guyonvarch M. À quoi rêve la biologie de synthèse ? Légitimations et critiques de l’« amélioration du vivant ». Socio, 2019, 12:49-72. – Flocco G, Guyonvarch M. Points de vue éthiques sur la biologie de synthèse. La “marche du progrès” en question. in “Les nouveaux territoires de la bioéthique. Traité de bioéthique IV”, Emmanuel and François Hirsch (eds), Érès, 2018, pp. 307-317. – Raimbault B, Cointet JP, Joly PB. Mapping the Emergence of Synthetic Biology. PLoS One. 2016, 11:e0161522. – Schmidt M. Xenobiology: a new form of life as the ultimate biosafety tool. Bioessays. 2010, 32:322-331. |
| UE3.1 Cell Factory Design Course Objectives: Metabolic engineering and cell factory design are disciplines that sprung up at the interface of chemical engineering, biotechnology, biochemistry, classical genetics and modelling. In particular, the design of a cell factory involves global analysis of the production organism (genomics, transcriptomics, proteomics, metabolomics) coupled to the development of a dedicated, mathematical model of the whole cell in order to define in silico the optimization strategy and the required modification of the strain to be implemented through the means of genetic manipulations. At the end of the course, students will be able to: – Explore models enabling to handle in details entire cellular networks – Evaluate different strategies for in silico cell factory design – Choose the most suitable approach to conceive a bacterial cell factory for a given target metabolite Syllabus: In this course, we will seek to practically explore the “pipeline” of chassis design. The methods for the analysis of flux distribution, from constraint-based modelling (Flux Balance Analysis, Resource Balance Analysis, etc.) to dynamical modelling (Ordinary Differential Equations, etc.) are at the core of this course. Course Prerequisites: Several optional modules from Master 1 BMP and BA are highly recommended: Systems Biology I Systems Biology II Cellular Economics Course Organization: The course module is organised in 11h of lectures, 3h of tutorials and 6h of practical courses to introduce knowledge and methodological tools. Bibliography: Recent and old scientific articles, to explain the fundamentals and scientific advances in systems and synthetic biology. Two general references on these approaches: – Klipp E, Liebermeister W, Wierling C, Kowald A, Lehrach H, Herwig R. Systems Biology: A Textbook. Wiley-Blackwell, 2011. – Kholodenko BN, Westerhoff HV. Metabolic engineering in the post genomic era. Horizon Bioscience, 2004. |
| UE3.2 Chips for Molecular Evolution Course Objectives: The main pedagogical intention behind this training is to: – introduce students to the use of the microfluidic tool for biology research, to highlight its specificities and potentialities. – allow students to build/use on their own a specific microfluidic chip which allows monitoring growth, gene expression or mutation accumulation in single cells of rod-shaped bacteria on long timescales (i.e. few hundreds of generations) At the end of the training, students will be able to: – state the differences (advantages and disadvantages) between macrofluidic and microfluidic systems for biology research – define and explain the main steps in the construction of a microfluidic chip – build a microfluidic chip in PDMS (from a reusable master mold) – perform image and data analysis on images previously generated using this type of chip – design, plan a master mold (circuit) modification to facilitate its use in the lab or to address a specific biological question defined by the student Syllabus: – Collective debriefing on the purpose of the practical course and the protocol – Manipulations: preparing E. coli, pouring, degassing, baking, then peeling off the PDMS chip from the master mold, creating inlet/outlet, sealing the chip to a coverslip by using plasma treatment, injecting/spinning cells into chip’s growth channels, preparing tubing and growth medium, flowing the growth medium into the device by using a syringe pump, performing image and data analysis on images previously generated using this type of chip Course Prerequisites: None Bibliography: – Taheri-Araghi S, Jun S. In Hydrocarbon and Lipid Microbiology Protocols: Single-Cell and Single-Molecule Methods. Springer. 2015, pp 5–16 – Robert L, Ollion J, Robert J, Song X, Matic I, Elez M. Mutation dynamics and fitness effects followed in single cells. Science. 2018, 359:1283-1286. – Robert L, Ollion J, Elez M. Real-time visualization of mutations and their fitness effects in single bacteria. Nature Protocols. 2019, doi:10.1038/s41596-019-0215-x. – Ollion J, Elez M, Robert L. High-throughput detection and tracking of cells and intracellular spots in mother machine experiments. Nature Protocols. 2019, doi:10.1038/s41596-019-0216-9. – Elez M, Murray AW, Bi LJ, Zhang XE, Matic I, Radman M. Seeing mutations in living cells. Curr Biol. 2010, 20:1432-1437. – Wang P, Robert L, Pelletier J, Dang WL, Taddei F, Wright A, Jun S. Robust growth of Escherichia coli. Curr Biol. 2010, 20:1099-103. |
| UE3.3 Computational DNA Nanotechnology Course Objectives: – Understand the basic principles of DNA self-assembly and DNA computing. – Understand basic applications of DNA nanotechnology in biology and medicine. – Learn how to design basic DNA nanostructures. Syllabus: – Presentation of DNA self-assembly, theory and experiments. – Biomedical applications of DNA self-assembly : reading and discussion. – Supervised project on thed design of a DNA origami. Course Prerequisites: – basics of computer science (complexity, automata theory, langage theory) – basics of the biology of DNA Course Organization: This class starts by a brief presentation of the computational aspects of DNA self-assembly and the experimental technique of DNA origami. This presentation is followed by a reading session in which the students are encouraged to present their favorite aspects in detail to their peers. The class ends with a practical assignment in the DNA origami design software https://www.ens-lyon.fr/ensnano/, in which the students are encouraged to design a shape having a high probability of folding in a test tube. Bibliography: 1. Fan Hong, Fei Zhang, Yan Liu, Hao Yan: DNA Origami: Scaffolds for Creating Higher Order Structures, Chem. Rev. 2017, 117, 20, 12584–12640 2. Adleman, L. M. (1994). Molecular computation of solutions to combinatorial problems. Science. 266 (5187): 1021–1024. 3. Diverse and robust molecular algorithms using reprogrammable DNA self-assembly Damien Woods, David Doty, Cameron Myhrvold, Joy Hui, Felix Zhou, Peng Yin, Erik Winfree Nature 567:366—372, 2019 4. Non-cooperatively assembling large structures Pierre-Étienne Meunier, Damien Regnault DNA25: The 25th International Conference on DNA Computing and Molecular Programming Springer LNCS 11648:120—139, 2019 |
| UE3.4 Computational Protein Design Course Objectives: This course provides an overview of the techniques used in computational protein design, from molecular modelling to in silico combinatorial library design, including those practical aspects associated with the integration of such computational techniques into a protein engineering project. The course covers various aspects of the field, including an overview of protein molecular modelling and dynamics, protein activity modelling, protein engineering, and protein design techniques: – Molecular modelling: atomistic, descriptor-based, and knowledge-based models; solvent representations, AI for protein structure prediction. – Force field-based techniques: energy minimization and molecular dynamics. – Docking techniques: protein-protein, protein-peptide, and protein-small molecule interactions. – Descriptor-based modelling of biological activities: quantitative structure-activity relationship models. – Knowledge-based modelling: rotamer libraries, large-scale analysis of propensities. – Machine-learning and deep learning modeling. – Protein design: search algorithms, combinatorial optimization, and library design. – Design of protein-based properties for synthetic biology applications: novel enzymatic, regulatory, and signalling activities. Course Prerequisites: The course prerequisites are a basic knowledge of molecular and structural biology, bioinformatics and thermodynamics. Course Organization: The course is organised in 6 sessions: 1. Introduction to molecular modelling 2. Modelling biological activity of molecular structures 3. Introduction to biomolecular design 4. Hands-on exercise on computational tools for protein design. 5. Modelling protein interactions through docking techniques 6. Hands-on exercise on computational tools for molecular docking. Bibliography: – Carbonell P, Trosset JY. Computational protein design methods for synthetic biology. Methods Mol Biol. 2015, 1244:3-21. – Gainza-Cirauqui P, Correia BE. Computational protein design-the next generation tool to expand synthetic biology applications. Curr Opin Biotechnol. 2018, 52:145-152. – Samish I. The Framework of Computational Protein Design. Methods Mol Biol. 2017, 1529:3-19 – Bagdad Y, Sisquellas M, Arthur M, Miteva MA. Machine Learning and Deep Learning Models for Predicting Noncovalent Inhibitors of AmpC β-Lactamase. ACS Omega. 2024;9(40):41334-41342. – Gyulkhandanyan A, Rezaie AR, Roumenina L, Lagarde N, Fremeaux-Bacchi V, Miteva MA, Villoutreix BO. Analysis of protein missense alterations by combining sequence- and structure-based methods. Mol Genet Genomic Med. 2020 Apr;8(4):e1166. – Rey J, Murail S, de Vries S, Derreumaux P, Tuffery P. PEP-FOLD4: a pH-dependent force field for peptide structure prediction in aqueous solution. Nucleic Acids Res. 2023 Jul 5;51(W1):W432-W437. |
| UE3.5 Machine Learning in Synthetic Biology and Metabolic Engineering Course Objectives: At the end of this course, the student will be able to: – 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. – Understand cross-validation methods for classification and regression – Define the needs for experimental design and learning for classical problems in the construction of metabolic pathways and synthetic circuits developed in synthetic biology – 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) – Use Python libraries (such as pyDoE, Keras, Scikit-Learn) to create an experimental design and simple learning on a biological data set. – To be familiar with the use of learning methods in the fields of human health and image recognition. Course Prerequisites: Basic knowledge of Python programming and the use of Python libraries. Course Organization: Teaching 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. Bibliography: – Gricourt G, Meyer P, Duigou T, Faulon JL. Artificial Intelligence Methods and Models for Retro-Biosynthesis. ACS Synth Biol. 2024;13(8):2276-2294. – Faulon JL, Ahavi P, Hoang A, Reservoir Computing with Bacteria, bioRxiv 2024 DOI: 10.1101/2024.09.12.612674 – Faure 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 – Borkowski 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. – Pandi 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. – Carbonell 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. |
| UE3.6 Environmental Biotech and Upstream Processing Course Objectives: The development of a sustainable, bio-based economy that does not depend on fossil fuels for energy and commodities and has a low environmental impact is a major goal as well as a big societal challenge. Modern technologies have provided new ways for increasing knowledge, understanding bioprocesses and testing potential applications. This course will focus on topics like biodegradation and bioremediation of legacy as well as emerging pollutants (incl. heavy metals, pesticides and plastics) and microbial engineering for the use of renewable resources and their underlying biological and technological principles. It aims to give students clues to : – understand the input of microbial genome sequences and integrated -omics strategies to fill in knowledge gaps concerning degradation pathways – describe technological and analytical tools available in the field and envision the upstream processing (inputs, bottlenecks, gaps) – analyse scientific literature, interpret data – cross-analyze various scientific and methodological options and make strategic choices at the onset of a project – formulate accurate strategies to bring about a project to its goal, develop critical thinking toward results and outcomes, communicate at each stage with scientists and third-party – stick on the goals of sustainable development, scientific integrity and ethics Course Prerequisites: Students attending the course should have a regular background in fundamental and applied microbial molecular biology: regulation of gene expression, nucleic acid methods, basics in (bio)chemistry and microbial physiology (Bachelor of Sciences or equivalent). Being a transdisciplinary course, diverse backgrounds are welcomed, as long as willingness to investigate missing or new concepts is a goal of the attendee. Course Organization: The schedule is distributed between bona fide plenary lectures, seminars on selected hot-spot research topics developed at the Metabolic Genomics research units, and scientific literature discussions for understanding, analysing and developing further core concepts and their practical issues. The range of full-time scientists and university staff involved in the course accounts for a dynamic and wide-range mixing of ideas, methods and contextual ways of thinking, understanding and developing environmental bioprojects. Bibliography: – McCarty NS, Ledesma-Amaro R. Synthetic Biology Tools to Engineer Microbial Communities for Biotechnology. Trends Biotechnol. 2019, 37:181-197. – de Lorenzo V. Seven microbial bio-processes to help the planet. Microb Biotechnol. 2017, 10:995-998. – Reuß DR, Commichau FM, Stülke J. The contribution of bacterial genome engineering to sustainable development. Microb Biotechnol. 2017, 10:1259-1263. – Revuelta JL, Buey RM, Ledesma-Amaro R, Vandamme EJ. Microbial biotechnology for the synthesis of (pro)vitamins, biopigments and antioxidants: challenges and opportunities. Microb Biotechnol. 2016, 9:564-567. |
| UE3.7 Downstream Processing for Industrial Biotechnologies Course Objectives: The objective of this course is to train managers capable of choosing and pre-sizing separation and purification steps of biomolecules, taking into account eco-design issues. At the end of this course, students will: – Have an overview of the separation techniques used in biotechnology – Know innovative techniques for bioproducts separation – Understand the problems of coupling separation processes – Knowing how to choose the most appropriate techniques – Know how to make mass balances to size some usual processes – Be aware of process eco-design Syllabus: “Downstream Processing” applied to industrial biotechnology is defined as “all unitary operations aimed at extracting and purifying biomolecules resulting from the fractionation or bioconversion of renewable materials”. It is an essential step in biotechnologies and biorefineries, which aim to substitute fossil resources and develop new, more sober and ecological production processes from vegetal biomass. Indeed, it is generally the most expensive and the most impacting on the environment because of the number of operations required to obtain the final product(s). The general objective of this course is to train executives capable of defining innovative, competitive and more environmentally friendly separation strategies by cleverly choosing and combining modern or more conventional technologies. After the presentation of a rather broad panorama of the usual techniques, a reasoned choice guide will be proposed as well as courses to learn how to optimize and pre-dimension certain processes that are essential in biorefineries such as membrane filtration, electrodialysis, ion exchange, adsorption, gas-liquid absorption, liquid-liquid extraction, distillation or preparative chromatography. Course Prerequisites: Basics in chemistry and physics (level L1 – Bac +1) Fundamentals of Chemical Engineering : mass balance, material and heat transfer Course Organization: This course is composed of lectures which present basics and general principles, illustrated with examples and industrial applications. Tutorials are associated with some lectures in order to learn how to carry out fundamental digital applications. Meanwhile, the students, will work in teams of 4 to 5 students (depending on the number of students), on a bibliographical study project around a case study in order to apply course lessons and take a step back. The aim will be to propose one or more separation strategies in the context of the production of a biosourced molecule (derived from plant co-products or obtained by biotechnology). The work will be presented to all (15 min presentation followed by 10 min discussion) during the last class. All courses are shared with the 3rd year students of CentraleSupelec in « Environment and Sustainable Production » Bibliography: – Aimar P, Daufin G. Séparations par membrane dans l’industrie alimentaire. Techniques de l’Ingénieur 2004, F3250. – Broust F, Girard P, Van De Steene L. Biocarburants de seconde génération et bioraffinerie. Techniques de l’Ingénieur 2013, RE110. – Chemat F, Fabiano-Tixier As, Abert-Vian M. Les six principes de l’éco-extraction du végétal, Techniques de l’Ingénieur 2018, J4922. – Chirat C. Bioraffineries lignocellulosiques: Extraction et valorisation des hémicelluloses. Techniques de l’Ingénieur 2019, RE279. – De Dardel F. Échange d’ions: Applications, Techniques de l’Ingénieur 2016, J2785. – Gésan-Guiziou G. Filtration membranaire (OI, NF, UF, MFT): Applications en agroalimentaire, Techniques de l’Ingénieur 2007, J2795. – Roux De Balmann H, Casademont E. Électrodialyse, Techniques de l’Ingénieur 2006, J2840. – Sun LM, Meunier F, Baron G. Adsorption: Procédés et applications. Techniques de l’Ingénieur 2015, J2731. – Veynachter B, Pottier P. Centrifugation et décantation. Techniques de l’Ingénieur 2007, F2730. |
| UE3.8 Nanobiology Course Objectives: The objective of this course is to give a general background in nanosciences for biology and biotechnology applications. Syllabus: INTRODUCTION – Motivation and challenges for biology, health, nanotechnology, molecular storage – Principle of electrical detection – Experimental Setup – Analysis of data NANOPORES, NANOTUBES – Protein channels – Solid-states pores – Biomimetic channels – Hybrid pores – Nanotubes and Nanochannels DYNAMICS OF IONS TO BIOMOLECULES – Introduction physical concept: polymer conformation, size, flexibility and rididity, dilute and semi_dilute solution – Ions – Polymers – biomolecules APPLICATIONS – Protein folding – Ultrafast DNA and protein sequençing – Mass and size discrimination by nanopores – Biomarker and virus detection PRACTICAL COURSE – Lipid bilayer formation – Protein nanopore insertion into membrane – Electrical measurements Course Prerequisites: None Course Organization: 12h plenary courses followed by 8h experimental courses Bibliography: – Branton D, Deamer DW, Marziali A, Bayley H, Benner SA, Butler T, Di Ventra M, Garaj S, Hibbs A, Huang X, et al. The potential and challenges of nanopore sequencing. Nat Biotechnol 2008 26:1146-53. – Oukhaled A, Bacri L, Pastoriza-Gallego M, Betton JM, Pelta J. Sensing Proteins Through Nanopores: Fundamental to Applications. ACS Chem Biol. 2012, 7:1935−49. – Restrepo-Pérez L, Joo C, Dekker C. Paving the Way to Single Molecule Protein Sequencing. Nat Nanotechnol. 2018, 13:786−796. – Cressiot B, Ouldali H, Pastoriza-Gallego M, Bacri L, Van der Goot FG, Pelta J. Aerolysin, a Powerful Protein Sensor for Fundamental Studies and Development of Upcoming Applications. ACS Sens. 2019 4:530-548. – Ying YL, Long YT. Nanopore-Based Single-Biomolecule Interfaces: From Information to Knowledge. J Am Chem Soc. 2019 141:15720-15729. – Ratinho L, Meyer N, Greive S, Cressiot B, Pelta J. Nanopore Sensing of Protein and Peptide Conformation: toward Point-of-Care Technology, Nature Communications, 2025. |
| 3.9 Network Systems: Modeling and Analysis Course Objectives:Syllabus: Network analysis is widely used in system biology and precision medicine to gain a comprehensive understanding of molecular interactions. A network is a dynamical system represented structurally by a graph. The objective of this course is to provide the fundamental background in network analysis combining theoretical, computational and biological knowledge. Topological analysis: – Mastering the fundamental graph measurements with their algorithms, – Compute the different centralities and apply them in biological study, – Identify the class of a real/random networks to deduce their properties, – Apply a topological analysis on a biological case to discover relevant properties. Dynamical analysis: The student should master and practically use: – the basis Boolean network modelling, – the basis of Petri nets modelling, – the basis of neural network modelling, – the design of a network model based on data and literature with the resulting analysis. At the end of this course, the student will be able to perform a network analysis per se on a biological case including a topological study and a dynamical modelling. Course Prerequisites: Mathematical background, basic knowledge on programming language and algorithmics, molecular and cellular biology Course Organization: The training is mainly based on courses. Practical cases studies are given to students to deepen their understanding related to the learned notions. Students will also be subject to an oral presentation for validating their knowledge. |
| UE3.10 Rational Protein Engineering Course Objectives: The objectives of this course are to provide students with an understanding of practical aspects developed in the laboratory for the study of proteins and, through concrete examples of recombinant proteins, illustrate the experimental methods for protein design and gene modification by mutagenesis in order to obtain the desired function by controlling the structure / function relationships. At the end of the course, students will be able to: – Perform wet lab experiments for the study of proteins – Remodel genes by site-directed and / or random mutagenesis – Compare in vivo and in vitro studies of the activity of the wild type protein and its mutants Correlation of experimental results with the theoretical model developed using the skills acquired in the module “Computational protein design” is strongly encouraged. Course Prerequisites: None Course Organization: This module will be organized in 3 sessions scheduled over 3 days on the basis of experimental constraints. |
| UE3.11 Experimental Design and Statistical Analysis for Next Generation Sequencing Course Objectives:Syllabus:Course Prerequisites: Next-generation sequencing is emerging as a powerful tool for genotype-phenotype mapping. The objective of the course is to train the students with the analysis of next-generation sequencing for different applications of next-generation sequencing: Genome sequencing, Deep mutational scanning, and RNA sequencing. In the course, the students will learn how to analyse real experimental data, which will help them develop a critical understanding to design high-throughput experiments and interpret data when reading results published in the scientific literature. Syllabus: Covered topics: – Next generation sequencing technologies(illumina, long-read sequencing). – NGS applications (Genome sequencing, Deep mutational scanning, RNA-seq) – Bioinformatics (Read mapping and filtering for analysis). – Calculating interpretable scores (fitness, enrichment, fold change) to interpret mutation/gene effects – Data normalisation – Statistical tests for differential analysis – Interpreting errors and noise in data – Clustering, principal component analysis – Data visualisation – Simulations of dynamics Course Organization: Methods will be illustrated by the practical analysis of real published experimental datasets. Course Prerequisites: None |
| UE3.12 Cell-Free Systems Course Objectives: The objectives of this course are to provide students with an understanding of practical aspects developed in the laboratory for the study of cell-free systems and, through concrete examples, illustrate experimental methods to increase protein production in cell-free. At the end of the course, students will be able to: – Know the diversity and the different usages of cell-free systems – Know-how to prepare cell-free systems from cell cultures and cell extracts – Understand the element required to obtain a functional transcription translation (TX-TL) system. – Use the automated protocol hosted on the Cell-Free Biofondry at MICALIS – Know-how to optimise the productivity of cell-free systems Syllabus: This course class is composed of 8h of lecture and 12h of practical work using the MICALIS cell-free biofoundry. The practical work will include the usage of the biofoundry software platform to maximize protein productivity in cell-free. Course Prerequisites: Basic knowledge of molecular biology Course Organization: Teaching in this EU will include 8 hrs of lectures with simple exercises (TD) given during the courses on specific points and 12hrs of practical work (TP). The practical work will consist of expressing a protein of interest using the Cell-Free biofoundries hosted at the Micalis institute. Bibliography: – Soudier P, et al. ACS Synthetic Biology, 2022, 11(8):2578-2588 DOI: 10.1021/acssynbio.2c00138 – Pandi A, et al. Nat Commun., 2022, 13(1):3876. DOI: 10.1038/s41467-022-31245-z – Batista AC , et al. Engineering Biology 5 (1), 10-19. DOI: 10.1049/enb2.12004 – Soudier P, et al. Methods Mol Biol. 2022, 2433:303-323. DOI: 10.1007/978-1-0716-1998-8_19 – Sabeti Azad M, et al. J. Vis. Exp. 2022, (186), e64236 DOI: 10.3791/64236 – Pandi A, et al. Nat Commun., 2022, 13(1):3876. DOI: 10.1038/s41467-022-31245-z – Borkowski O, et al. Nature Communications, 11(1): 1872, 2020. DOI: 10.1038/s41467-020-15798-5 – Pandi A, et al. Nature Communications, 10: 3880, 2019. DOI: 1038/s41467-019-11889-0 – Pandi A, ey al. ACS Synth Biol. 8(8):1952-1957, 2019. DOI: 10.1021/acssynbio.9b00160 – Voyvodic PL, et al. Nature Communications, 10(1):1697, 2019. DOI: 10.1038/s41467-019-09722-9 |