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.
The courses proposed in each module are described bellow.
Refrecher Courses (0 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
| 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 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. Syllabus: – Probability-Statistics: Descriptive statistics, Probability, Random variables, Distributions, Central Limit Theorem, Hypothesis testing, Linear regression, Probabilistic modeling, Tests – Machine Learning: Supervised learning, Unsupervised learning, Evolutionary algorithms – Modeling (regular languages): Regular expressions, Finite automata, Formal grammar – Introduction to discrete modeling and the use of formal mathematical structures to represent problems. – Data Analysis & Simulation: Introduction to Python, Introduction to R language, Introduction to Linux Theoretical and practical skills in computational and mathematical concepts applied in systems biology At the end of the course, the student should be able to: – perform basic statistical analysis on datasets – describe basic notions of machine learning – understand formal concepts used for describing biological problems – use Python and R for basic data analysis – be autonomous in a Unix/Linux environment 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 organized as follows: – Theoretical lectures on statistics and data analysis: 6h – Theoretical lectures concerning formal languages: 6h – Theoretical lectures concerning machine learning and evolutionary algorithms: 6h – Introduction to Python and data analysis: 6h (computer room) – Introduction to Unix and command line data manipulation: 6h (computer room) – Introduction to R and statistical analysis: 6h (computer room) – R/bioconductor packages for data analysis and simulation: 4h (computer room) |
| UE2.1 Biological Parts and Devices Course Objectives: This module will equip the student with broad knowledge of synthetic biology and engineering of genetic parts and devices. Students will learn how to identify the appropriate parts and use them to compose devices. Similarities and differences between different species (prokaryotes and eukaryotes) will be highlighted. The student will learn how to: – design nucleotide sequences of protein and RNA parts, – engineer synthetic genetic circuits based on proteins, – engineer synthetic genetic circuits based on RNAs, and – engineer synthetic genetic circuits based on nucleoprotein interactions Syllabus: – Design of promoters: Study state-of-the-art in promoter design, and discuss the engineering of repressors, activators and hybrid promoters. – Design of RNAs: Preliminary concepts on nucleotide recognition and cleavage, and engineering of RBSes and transcription terminators. – Design of circuits: We will review design principles and their application for building transcriptional networks with targeted behavior. – Characterization and optimization of devices: Debugging biological circuits, measuring noise (intrinsic and extrinsic), using directed evolution to optimize systems. Course Prerequisites: None Course Organization: The course module is organized as 11h of lectures and 9h of tutorials to introduce knowledge and methodological tools. Bibliography: A prescribed textbook is not required in this course. Primary literature materials and Powerpoint slides will be provided. However, students are encouraged to do some 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. – Kushwaha M, Rostain W, Prakash S, Duncan JN, Jaramillo A. Using RNA as Molecular Code for Programming Cellular Function. ACS Synth Biol. 2016, 5:795-809. |
| UE2.2 Genome Engineering Course Objectives: Genome engineering technologies are revolutionizing 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. 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…). 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 Course Prerequisites: None Course Organization: The course module is organized in 11h of lectures and 9h of tutorials 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. The purpose of this course class is 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. 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 optimized the production yields of metabolic pathways – Adapt metabolic engineering to build biosensor and biocomputing devices Course Prerequisites: None Course Organization: The course module is organized in 12h of lectures and 8h of tutorials to introduce metabolic engineering workflows. At the end of the course the student will be ask to use scientific workflows to design a pathway in a given chassis strain, to write an experimental plan to build and test the designed pathway and to evaluate the cost (man power and consumable) of performing the work. 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: – 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. |
| 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 Ethical Questions of 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 To do this, 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. Gouverner 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. 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 modeling (Flux Balance Analysis, Resource Balance Analysis, etc.) to dynamical modeling (Ordinary Differential Equations, etc.) are at the core of this course. 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 Course Prerequisites: Several optional modules from Master 1 BS and BIP are highly recommended: Systems Biology I Systems Biology II Cellular Economics Course Organization: The course module is organized in 11h of lectures and 9h of tutorials 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) – use this chip to follow single cells growth, fluorescent protein expression and mutation arising in single cells by using fluorescent microscopy – 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 Course Prerequisites: None Course Organization: – 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, mounting the device on the microscope, preparing automatic image acquisition 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 Inference and Modeling of Biological Networks Course Objectives: Representing the complexity of biological regulatory systems as networks enables the analysis of the network’s topology and underlying structure. The increasing abundance of high-throughput « omics » data and recent advances in machine learning have allowed important progress in biological networks inference. Computational modelling of biological networks provide the necessary means to study the emerging behavior of the system under various conditions. Thus, a passage from static to dynamic, executable networks is essential for in depth system analysis. The main objective is to provide an overview of bottom up and top down approaches for biological networks inference and also present to students state of the art methodology and tools for discrete dynamical model construction and analysis. Learning outcomes: Theoretical and practical knowledge of basic concepts and notions in Computational Systems Biology At the end of the course, the student is expected to: – master the fundamental concepts of biological networks – be familiar with all relevant pathway databases – analyze network structure and perform basic topological analysis – use omics data and machine learning to inferred a co-regulatory network – use discrete based dynamical analysis to model biological networks – be able to complete a small project concerning network inference and/or dynamical modelling combining theoretical and practical knowledge acquired during the course Course Prerequisites: Solid background in biology, knowledge of discrete mathematics, and computational analyses. Basic knowledge of R. Course Organization: The course will address both static networks with a focus on co-regulatory network inference using machine learning and expression data, and dynamic models of biological mechanisms with a focus on discrete computational modelling. The presented tools and methodologies will be coupled with practical hands on sessions and an individual project. The course comprises of 8h of theoretical lectures on computational inference and modelling of biological networks and 12h of practical sessions (6h machine learning for network inference and 6h discrete dynamical modelling). The student will have to complete a relevant project, write a report and make a presentation for the final evaluation. |
| UE3.4 Computational Protein Design Course Objectives: This course provides an overview of the techniques used in computational protein design, from molecular modeling 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 modeling and dynamics, protein activity modeling, protein engineering, and protein design techniques: – Molecular modeling: atomistic, descriptor-based, and knowledge-based models; solvent representations. – Force field-based techniques: energy minimization and molecular dynamics. – Docking techniques: protein-protein, protein-peptide, protein-nucleotides, and protein-small molecule interactions. – Descriptor-based modeling of biological activities: quantitative structure-activity relationship models. – Knowledge-based modeling: rotamer libraries, large-scale analysis of propensities. – Protein design: search algorithms, combinatorial optimization, and library design. – Design of protein-based devices for synthetic biology applications: biosensors, novel enzymatic, regulatory, and signaling activities. Course Prerequisites: Basic knowledge of molecular and structural biology, bioinformatics and thermodynamics. Course Organization: The course is organized in 6 sessions: 1. Introduction to molecular modeling 2. Modeling biological activity of molecular structures 3. Introduction to biomolecular design 4. Hands-on exercise on computational tools for protein design. 5. Modeling protein interactions through docking techniques 6. Hands-on exercise on computational tools for molecular docking. Evaluation: The course will be evaluated through two practical projects on computational molecular modeling (protein design and docking). The projects will be organized on groups of 1 or 2 students. Each team will write a report detailing the methodology used in the project and the obtained results. Bibliography: – Carbonell P. Metabolic Pathway Design: A Practical Guide. Springer. 2019. – 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. – Moroy G, Martiny VY, Vayer P, Villoutreix BO, Miteva MA. Toward in silico structure-based ADMET prediction in drug discovery. Drug Discov Today. 2012, 17:44-55. – Martiny VY, Carbonell P, Lagorce D, Villoutreix BO, Moroy G, Miteva MA. In silico mechanistic profiling to probe small molecule binding to sulfotransferases. PLoS One. 2013, 8:e73587. – Li X, Zhang Z, Song J. Computational enzyme design approaches with significant biological outcomes: progress and challenges. Comput Struct Biotechnol J. 2012, 2:e201209007. |
| UE3.5 Design of Experiments and Machine Learning in Synthetic Biology 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 – Understand experimental design methods including exhaustive, factorial, and optimized design of experiments (DoE) applied to molecular and cellular biology. – 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 R (such as pwr) and 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: This UE is optional the prerequisites are the UEs of the common core of the M2 SSB master (“refresher courses” and “compulsory courses”). Students who follow this course must have a basic knowledge of Python programming and the use of Python libraries. Course Organization: Teaching in this module 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 expzeriment 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: – 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. BioRxiv. 2019; doi: https://doi.org/10.1101/751669 – 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. – Jervis AJ, Carbonell P, Vinaixa M, Dunstan MS, Hollywood KA, Robinson CJ, Rattray NJW, Yan C, Swainston N, Currin A, Sung R, Toogood H, Taylor S, Faulon JL, Breitling R, Takano E, Scrutton NS. Machine Learning of Designed Translational Control Allows Predictive Pathway Optimization in Escherichia coli. ACS Synth Biol. 2019, 8:127-136. – 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 challenge for the next years. High-throughput technologies have provided new ways to screen for knowledge acquisition, understanding of bioprocesses and testing potential applications. This course will focus on topics such as those covering biocatalysis, bioremediation, engineering for the use of renewable resources and their underlying biological and technological principles. It aims to give students clues to : – (re)investigate the historical milestones and breakthrough developments in biotechnology with an emphasis on the scientific and technological contexts – describe technological and analytical tools available in the field and envision the upstream processing (inputs, bottlenecks, gaps) – analyze 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 to 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 and integrated plenary courses covering seminars on selected hot-spot research topics developed at the Metabolic Genomics research units and scientific literature discussions for understanding, analyzing and developing further the 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. – Agathos SN, Boon N. Editorial overview: Environmental biotechnology. Curr Opin Biotechnol. 2015, 33:v-vii. |
| UE3.7 Industrial Biotech and Downstream Processing Course Objectives: The objective of this course is to train managers capable of choosing and pre-sizing separation and purification steps of biomolecules/bioenergy, 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 processes – Be aware of process eco-design Course Prerequisites: Basics in chemistry and physics (level L1 – Bac +1) Fundamentals of Chemical Engineering : mass balance, material and heat transfer Course Organization: Shared courses with M2 PBA students and 3A CS – OBT students: – Panorama of separation techniques (3h lecture) – Comparison of separation techniques (1h30 lecture – 1h30 tutorial) – Combination of separation techniques – strategy – case studies (1h30 lecture – 1h30 tutorial) – Process eco-conception (2h lecture) Shared courses with 3A CS – OBT students only: – Focus on conventional separation processes (1h30 lecture – 1h30 tutorial) – Focus on membrane processes (1h30 lecture – 1h30 tutorial) – Focus on chromatography and ion exchange (1h30 lecture – 1h30 tutorial) 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 – 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 Pratical 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. – Majd S, Yusko EC, Billeh YN, Macrae MX, Yang J, Mayer M. Applications of biological pores in nanomedicine, sensing, and nanoelectronics. Curr Opin Biotechnol. 2010 21:439-76. – 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. |
| 3.9 Network Systems: Modeling and Analysis Course Objectives: 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 modeling, – the basis of Petri nets modeling, – the basis of neural network modeling, – 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 modeling. 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 Statistical Analysis of Large Scale Gene Expression Data Course Objectives: The objective of this course is to develop the skills needed to understand the statistical methods used to analyze large scale gene expression data, to be able to analyze these data and to develop a critical mind when reading statistical results published in the scientific literature. Covered topics: – Data presentation (microarrays and RNAseq) – Descriptive statistics and inferential statistics – Data normalization – Statistical tests for differential analysis – Multiple testing – Gene set enrichment analysis – Clustering – Supervised classification Course Prerequisites: None Course Organization: Methods will be illustrated by the practical analysis of real public datasets. Bibliography: – Korpelainen, Tuimala, Somervuo, Huss and Wong. RNA-seq data analysis – A practical approach. Chapman and Hall/CRC. 2014. – McLachlan, Do and Ambroise. Analyzing Microarray Gene Expression Data. John Wiley & Sons, Inc. 2004. – Parmigiani, Garett, Irizarry and Zeger. The analysis of gene expression data – methods and software. Springer. 2003. |