M1 - Integrative Biology of Interactions (B2I)

"General linear models with 1 or more explanatory random variables: from translating the figure that answers the biological question to the statistical model, i.e., taking into account many 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 ANOVA)".

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The general objective of this course is to deepen the concepts necessary for the study of symbiotic interactions, whether parasitic or mutualistic. For that we will approach the specificities but also the ubiquity of the parasitic way of life in the tree of life. The defense mechanisms of host organisms, the notions of promotion and manipulation, the consequences of host-symbiont interactions on life history traits as well as the influence of these interactions in the diversification of organisms will be discussed.

The practical work will be an opportunity to deepen these concepts on some major models of interactions involving symbionts (viruses, bacteria, unicellular and multicellular eukaryotes) and hosts (uni- and multicellular).

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The general objective is to consolidate the basic knowledge of ecology acquired by the students, and to give them the tools to mobilize it in an integrative way to interpret the functioning of ecological systems. The courses include: 1) lectures on the concepts of ecology from the population scale to macroecological scales, with examples of applications that place the discipline in the current ecological and societal context; 2) practical and directed work focused on tools (sampling strategies, modeling, data analysis); 3) field courses during which students are invited to ask themselves relevant scientific questions based on observation in a situation, and to mobilize their knowledge in order to respond to them in an argumentative manner.

Synthetic content of the EU :

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

- Field: Integrative Analysis of Ecosystem Functioning in Situations;

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

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"The general objective is to consolidate the students' bases in evolutionary biology, by approaching both (i) macro-evolutionary phenomena, and the general methods used for their analysis and (ii) micro-evolutionary processes by insisting on the population genetic approach. The objective of this course is to provide a common base of solid knowledge in evolutionary biology and to illustrate the applications of the discipline to the students' future fields of specialization. The teaching includes: 1) lectures on evolutionary concepts; 2) practical work in two main forms: 2a. sessions focused on the use of tools (phylogeny) and on the mathematical formalization of evolutionary processes (population genetics) as well as 2b: sessions built around group work, allowing students, depending on their career path and professional objectives, to go deeper into a particular theme (fundamental question or application of evolutionary biology)."

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English tutorials aimed at professional autonomy in the English language.

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Generalized linear mixed models + methodology and experimental protocols to take into account a biological reality: non-normal law 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, taking into account spatial and temporal correlation, over-dispersion

Graphical representation of predictions.

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The objective of this course is to provide the necessary basis in statistics to follow all the more elaborate modules of the curriculum, so it is a general refresher. Descriptive statistics are reviewed (quantile, polygon of cumulative frequencies, estimators from samples), simple tests are presented, essential graphs for univariate and multivariate data are presented, the general principle of a statistical test, the hypothesis plan, 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, identification of gene function, 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 to study 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 Bioenvironment platform, differential expression analysis on Galaxy, enrichment analysis, validation of some differentially expressed genes by RT-q-PCR".

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The individual internship in M1 lasts about three months and must be carried out, depending on the course, in a research laboratory or a structure in the non-academic sector. It allows the student to acquire 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 in order to meet 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 defense of the internship project. The internship work is evaluated during a public defense before a jury during which the content of the thesis and the quality of the answers to the jury's questions are evaluated. The behavior and dynamism of the student during the internship are evaluated by the internship supervisor.

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 "The objective of this course is to consolidate the students' bases in integrative biology of interactions, in particular through approaches in ecology and/or evolution. For this, students will work, in relation with other courses, to define a subject and research question(s), by defining relevant hypotheses in an argued way, and by justifying a strategy of acquisition and analysis of data allowing to test these hypotheses.

Synthetic content of the EU:

- Independent tutored work: identification of a relevant scientific question; bibliographic synthesis allowing to realize the state of the art, placed in a context of biology of interactions, and to justify the scientific hypotheses; proposal and justification of a methodological approach (material and methods) to test the proposed hypotheses.

Type of topics:

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

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

Methods of control of knowledge:

The teaching is based on a problem-based learning approach, and students are evaluated on the way they progress in building their approach (40% of CC), as well as on their ability to present and defend their project during a final oral (60% of the overall grade)."

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