M1 - Integrative Biology of Interactions (B2I)

"General linear models with 1 or more random explanatory variables: from the translation of the figure that answers the biological question to the statistical model, i.e. taking into account numerous effects and knowing how to interpret them.

general properties seen through regression and 1-factor ANOVA (R2, F, ddl, least squares, likelihood, diagnosis, validation, goodness of fit, interpretation of effect sizes); nested and cross-factor ANOVA, multiple regression (notion of parameter and effects, and interaction)

incorporation of the dependence of explanatory random variables, confounding of effects (quantitative for multiple regression, and unbalanced designs for ANOVAs)".

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The general aim of this course is to develop the concepts needed to study symbiotic interactions, whether parasitic or mutualistic. To this end, we will address the specificities and ubiquity of the parasitic way of life in the tree of life. The defense mechanisms of host organisms, the notions of favoritism and manipulation, the consequences of host-symbiont interactions on life-history traits and the influence of these interactions on the diversification of organisms will be addressed.

Practical work will provide an opportunity to explore these concepts in greater depth on some major models of interactions involving symbionts (viruses, bacteria, unicellular and multicellular eukaryotes) and a variety of hosts (unicellular and multicellular).

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The general aim is to consolidate the ecological foundations acquired by students, and to give them the tools to mobilize them in an integrative way to interpret the functioning of ecological systems. The course includes: 1) lectures covering the concepts of ecology from population to macro-ecological scales, with examples of applications that place the discipline in the current ecological and societal context; 2) practical work and tutorials focusing on tools (sampling strategies, modelling, data analysis); 3) field courses in which students are invited to ask themselves relevant scientific questions based on observation in a given situation, and to mobilize their knowledge to answer them in a reasoned way.

Summary content of the EU :

- CM: History of the emergence of concepts in ecology; Population dynamics / metapopulations; Biotic interactions and food webs; Ecology of communities, meta-communities; Ecology of ecosystems / functional ecology; Notions of macroecology / biogeography; Global change and ecosystem functioning;

- Field: Integrative analysis of ecosystem functioning in real-life situations ;

- TD/TP: sampling and experimentation strategies in ecology; modeling in population/meta-population dynamics, community/meta-community ecology, food webs; biodiversity measurements (alpha, beta, etc.)."

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"The overall aim is to consolidate students' evolutionary biology foundations, covering both (i) macro-evolutionary phenomena, and the general methods used to analyze them, and (ii) micro-evolutionary processes, with an emphasis on the population genetics approach. The aim of this course is both to provide a common foundation of solid knowledge in evolutionary biology, and to illustrate the applications of the discipline to students' future fields of specialization. Teaching includes: 1) lectures on evolutionary concepts; 2) practical work in two main forms: 2a. sessions focusing on the use of tools (phylogeny) and on the mathematical formalization of evolutionary processes (population genetics), and 2b: sessions built around group work, enabling students, depending on their career path and professional objectives, to delve deeper into a particular theme (fundamental question or application of evolutionary biology)."

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English TD courses aimed at professional autonomy in the English language.

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Generalized linear mixed models + methodology and experimental protocols to take account of biological reality: non-normal distribution and pseudo-replication

Protocol optimization, power and uncontrolled 1st order risk: variable transformation, polynomial regression, link function, likelihood, model selection

Deviance analysis and goodness of fit

Incorporation of blocks, repeated measurements over time, consideration of spatial and temporal correlation, over-dispersion

Graphical representation of predictions.

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The aim of this course is to provide the statistical foundations needed to follow all the more advanced modules in the curriculum, so it's a general refresher. Descriptive statistics are reviewed (quantile, cumulative frequency polygon, sample estimators), simple tests are introduced, essential graphs for univariate and multivariate data are presented, the general principle of a statistical test, hypothesis design, the notion of p-value, first and second species risk are presented. In practical exercises, students are also brought up to speed in the R environment.

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"Lectures (57 h)

 Reminders: from genes (DNA) to functional units of genes (proteins): genes, transcription and translation (3H)

 DNA: enzymes for DNA manipulation, PCR and cloning (6h)

 Genomes: genetic mapping, genome sequencing, genome annotation, gene function identification, prokaryotic and eukaryotic organelle genomes, eukaryotic nuclear genomes, viral genomes and mobile genetic elements (12h)

 Gene expression: the role of DNA-binding proteins, transcriptional, post-transcriptional, translational and post-translational regulation; methods for studying these different levels of regulation and protein-protein, protein-RNA, protein-DNA interactions (15h)

 Gene expression in response to stress, during cell differentiation or development, epigenetics (9h)

 Genome replication, mutations and DNA repair, recombination, transposition, editing and horizontal transfer (6h)

 How genomes evolve (6h)

 Practical work (24h)

 RNAseq analysis: RNA extraction, sequencing on the Bioenvironnement platform, differential expression analysis on Galaxy, enrichment analysis, validation of some differentially expressed genes by RT-q-PCR".

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The individual M1 internship lasts around three months, and must be carried out in a research laboratory or a non-academic structure, depending on the course concerned. It enables students to gain professional experience in the field of biodiversity, evolution or ecology. It can be carried out in a local, national or international structure, on a subject validated by the teaching staff to fit in with the objectives of the course followed by the student.

Evaluation : The preparation of the internship is a graded exercise based on a written document and a presentation of the internship project. The internship work is assessed at a public presentation before a jury, during which the content of the dissertation and the quality of the answers to the jury's questions are evaluated. The student's behavior and dynamism during the internship are assessed by the internship supervisor.

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 "The aim of this course is to consolidate students' grounding in the integrative biology of interactions, particularly through approaches in ecology and/or evolution. To achieve this, students will work with other courses to define a research topic and question(s), defining relevant hypotheses and justifying a data acquisition and analysis strategy to test these hypotheses.

Synthetic content of the EU:

- Autonomous tutored work: identification of a relevant scientific question; bibliographical synthesis to establish the state of the art, placed in an interaction biology context, and to justify the scientific hypotheses; proposal and justification of a methodological approach (materials and methods) to test the proposed hypotheses.

Type of subject:

The topics can be based on any question identified by the students (in groups of 3/4), and validated by the teaching team, and draw on different approaches to suit the expectations of the different courses. For example, students may propose a field or experimental sampling strategy, a meta-analysis of literature data, an analysis of sequences retrieved from GenBank, an analysis of occurrence data retrieved from GBIF, etc.

In all cases, projects must involve a genuine data acquisition strategy, identified, justified and described by the students in the materials and methods requested in M1S2, with a provisional timetable for the project's progress and identification of the tasks that each student will carry out within each group as part of the project's implementation in M2S3. Projects must also be financially realistic, with a provisional budget, and must be able to be finalized within the time available in M2S3.

Assessment of knowledge:

Teaching is based on a problem-based learning approach, and students are assessed on how they progress in constructing their approach (40% of CC), as well as on their ability to present and defend their project at a final oral (60% of the overall mark)."

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