M1 - Integrative Biology of Interactions (B2I)

General linear models with one or more random explanatory variables: from translating 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 viewed through regression and one-factor ANOVA (R2, F, ddl, least squares, likelihood, diagnosis, validation, goodness of fit, interpretation of effect sizes); nested and crossed factor ANOVA, multiple regression (concept of parameters and effects, and interaction)

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

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The overall objective of this course unit is to explore the concepts necessary for studying symbiotic interactions, whether parasitic or mutualistic. To this end, we will examine the specific characteristics and ubiquity of the parasitic lifestyle in the tree of life. The defense mechanisms of host organisms, the concepts of facilitation and manipulation, the consequences of host-symbiote interactions on life history traits, and the influence of these interactions on the diversification of organisms will be discussed.

The practical work will provide an opportunity to explore these concepts in greater depth using certain major models of interactions involving symbionts (viruses, bacteria, unicellular and multicellular eukaryotes) and various hosts (unicellular and multicellular).

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The overall objective is to consolidate the foundations in ecology acquired by students and to give them the tools they need to apply them in an integrated way to interpret the functioning of ecological systems. The course includes: 1) lectures on ecological concepts from the population scale to the macroecological scale, using examples of applications that place the discipline in the current ecological and societal context; 2) practical and supervised work focused on tools (sampling strategies, modeling, data analysis); 3) field teaching, during which students are encouraged to ask relevant scientific questions based on their observations in the field and to use their knowledge to answer them in a reasoned manner.

Summary of EU content:

- CM: History of the emergence of concepts in ecology; Population dynamics/metapopulations; Biotic interactions and food webs; Community ecology, metacommunities; Ecosystem ecology/functional ecology; Concepts of macroecology/biogeography; Global change and ecosystem functioning;

- Field: Integrative analysis of ecosystem functioning in situ;

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

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The overall objective is to consolidate students' foundations in evolutionary biology by addressing both (i) macroevolutionary phenomena and the general methods used to analyze them, and (ii) microevolutionary processes with an emphasis on the population genetics approach. This course unit aims to provide a solid foundation of knowledge in evolutionary biology and to illustrate the applications of the discipline to students' future areas of specialization. The course includes: 1) lectures on the concepts of evolution; 2) practical work in two main forms: 2a. sessions focused on the use of tools (phylogeny) and the mathematical formalization of evolutionary processes (population genetics); and 2b: sessions built around group work, allowing students, depending on their background and professional goals, to explore a particular topic in depth (fundamental question or application of evolutionary biology).

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English tutorial courses aimed at developing professional autonomy in the English language.

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

Protocol optimization, power, and uncontrolled type I risk: variable transformation, polynomial regression, link function, likelihood, model selection

Deviance and goodness-of-fit analysis

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

Graphical representation of predictions.

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The objective of this course unit is to provide the necessary statistical foundations for following the more advanced modules in the curriculum; it is therefore a general refresher course. Descriptive statistics are reviewed (quantiles, cumulative frequency polygons, sample estimators), simple tests are presented, essential graphs for univariate and multivariate data are presented, and the general principle of a statistical test, hypothesis testing, the concept of p-value, and Type I and Type II errors are presented. In practical work, students are also brought up to speed in the R environment.

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Lectures (57 hours)

 Review: From genes (DNA) to functional units of genes (proteins): genes, transcription, and translation (3 hours)

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

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

 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, and protein-DNA interactions (15 hours)

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

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

 How do genomes evolve? (6 hours)

 Practical work (24 hours)

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

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The individual M1 internship lasts approximately three months and, depending on the program, must be completed in a research laboratory or a non-academic organization. It allows students to gain professional experience in the field of biodiversity, evolution, or ecology. It can be carried out in a local, national, or international organization, on a topic approved by the teaching team so as to fit in with the objectives specific to the program followed by the student.

Assessment: Preparation for the internship is assessed on the basis of a written document and a presentation of the internship project. The internship work is assessed during a public presentation before a panel, during which the content of the dissertation and the quality of the responses to the panel's questions are evaluated. The student's behavior and enthusiasm during the internship are assessed by the internship supervisor.

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 The objective of this course unit is to consolidate students' foundations in integrative biology of interactions, particularly through approaches in ecology and/or evolution. To this end, students will work, in conjunction with other courses, to define a research topic and question(s), formulating relevant hypotheses and justifying a data acquisition and analysis strategy for testing these hypotheses.

Summary of EU content:

- Independent work under supervision: identification of a relevant scientific question; bibliographic review to establish the state of the art in the context of interaction biology and justify scientific hypotheses; proposal and justification of a methodological approach (materials and methods) to test the proposed hypotheses.

Types of topics:

Topics may cover any issue identified by students (in groups of 3/4) and approved by the teaching team, and may be based on different approaches to suit the requirements of different courses.  For example, students may propose a field sampling or experimentation 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 students in the materials and methods required for M1S2, with a provisional schedule 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. Projects must also be financially realistic and include a provisional budget, and must be able to be completed within the time available in M2S3.

Assessment methods:

Teaching is based on a problem-based learning approach. Students are assessed on how they progress in developing their approach (40% of the final grade), as well as on their ability to present and defend their project in a final oral exam (60% of the final grade).

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