M1 - Integrative Biology of Interactions (B2I)

"General linear models with one or more explanatory random variables: translating the figure that answers the biological question into a statistical model—that is, taking into account numerous effects and knowing how to interpret them"

general properties examined through regression and one-way ANOVA (R², F, ddl, least squares, likelihood, diagnostics, validation, goodness of fit, interpretation of effect sizes); ANOVA with nested and crossed factors, multiple regression (concepts of parameters, effects, and interactions)

"accounting for the dependence of explanatory random variables, confounding effects (quantitative for multiple regression, and unbalanced designs for ANOVA)"

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The overall objective of this course unit is to deepen students’ understanding of the concepts necessary for studying symbiotic interactions, whether parasitic or mutualistic. To this end, we will examine both the specific characteristics and the ubiquity of the parasitic lifestyle across the tree of life. We will discuss the defense mechanisms of host organisms, the concepts of favoring and manipulation, the consequences of host-symbiont interactions on life history traits, and the influence of these interactions on the diversification of organisms.

The lab sessions will provide an opportunity to explore these concepts in greater depth using examples of major interactions involving symbionts (viruses, bacteria, and unicellular and multicellular eukaryotes) and a variety of hosts (both unicellular and multicellular).

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The overall objective is to reinforce the students’ foundational knowledge of ecology and to provide them with the tools to apply this knowledge in an integrated manner to interpret the functioning of ecological systems. The curriculum includes: 1) lectures covering ecological concepts ranging from the population level to macroecological scales, supported by practical examples that place the discipline within the current ecological and societal context; 2) practical and guided exercises focused on tools (sampling strategies, modeling, data analysis); 3) fieldwork sessions during which students are encouraged to ask relevant scientific questions based on in-situ observations and to apply their knowledge to answer these questions in a well-reasoned manner.

Summary of the learning unit:

- 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;

- Fieldwork: Integrative analysis of ecosystem functioning in real-world settings;

- Tutorials/Lab Sessions: sampling and experimental strategies in ecology; modeling of population dynamics/metapopulations, community/metacommunity ecology, and food webs; measures of biodiversity (alpha, beta, etc.)."

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"The overall objective is to strengthen students’ foundational knowledge of 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 both to provide a solid common foundation of knowledge in evolutionary biology and to illustrate the discipline’s applications to the students’ future fields of specialization. The course includes: 1) lectures on evolutionary concepts; 2) laboratory sessions in two main formats: 2a. sessions focused on the use of analytical tools (phylogenetics) and on the mathematical formalization of evolutionary processes (population genetics), as well as 2b: sessions structured around group work, allowing students—based on their academic background and career goals—to explore a specific topic in greater depth (a fundamental question or an application of evolutionary biology)."

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English tutorial courses designed to foster professional proficiency in English.

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Generalized Linear Mixed Models + Methodology and Experimental Protocols to Account for Biological Reality: Non-Normal Distributions and Pseudo-Replication

Protocol Optimization, Power, and Uncontrolled Type I 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, and overdispersion

Graphical representation of the predictions.

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The objective of this course unit is to provide the necessary foundation in statistics to follow the more advanced modules in the curriculum; it is therefore a general refresher course. Descriptive statistics are reviewed (quantiles, cumulative frequency polygons, sample-based estimators); simple tests are introduced; essential graphs for univariate and multivariate data are presented; and the general principles of statistical testing, hypothesis testing, the concept of the p-value, and Type I and Type II errors are covered. In lab sessions, students also receive training in the R environment.

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"Lectures (57 hours)

 Review: From Genes (DNA) to the Functional Units of Genes (Proteins): Genes, Transcription, and Translation (3H)

 DNA: Enzymes for DNA Manipulation, PCR, and Cloning (6 hours)

 Genomes: genetic mapping, genome sequencing, genome annotation, gene function identification, prokaryotic genomes and eukaryotic organelle genomes, eukaryotic nuclear 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, and epigenetics (9 hours)

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

 How Do Genomes Evolve? (6 hours)

 Practical Work (24 hours)

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

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The M1 individual internship lasts approximately three months and, depending on the student’s program track, must be completed in a research laboratory or a non-academic organization. It allows students to gain professional experience in the fields of biodiversity, evolution, or ecology. It may be carried out at a local, national, or international organization, on a topic approved by the faculty to ensure it aligns with the specific objectives of the student’s program.

Assessment: Preparation for the internship is a graded assignment based on a written document and a presentation of the internship proposal. The internship work is assessed during a public presentation before a panel, during which the content of the report and the quality of the student’s responses to the panel’s questions are evaluated. The student’s conduct and initiative during the internship are assessed by the internship supervisor.

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 "The objective of this course unit is to strengthen students’ foundational knowledge of the integrative biology of interactions, particularly through approaches in ecology and/or evolution. To this end, students will work—in conjunction with other programs—to define a research topic and question(s), formulate well-reasoned and relevant hypotheses, and justify a strategy for data collection and analysis that will enable them to test these hypotheses.

Summary of the learning unit:

- Supervised independent work: identification of a relevant scientific question; a literature review to assess the state of the art within the context of interaction biology and to justify the scientific hypotheses; proposal and justification of a methodological approach (materials and methods) to test the proposed hypotheses.

Topic types:

Topics may address any issue identified by the students (in groups of 3 or 4) and approved by the teaching staff, and may draw on various approaches to align with the learning objectives of the different programs.  For example, students may propose a field sampling or experimental strategy, 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, projects must include a concrete data collection strategy—identified, justified, and described by the students in the “Materials and Methods” section required for the M1S2 course—along with a projected timeline for the project’s progress and a breakdown of the tasks each student will perform within each group as part of the project’s implementation during the M2S3 course. Projects must also be financially realistic and include a projected budget, and they must be capable of being completed within the time available during the M2S3 semester.

Assessment Procedures:

"The course is based on a problem-based learning approach; students are evaluated on their progress in developing their methodology (40% of the grade), as well as on their ability to present and defend their project during a final oral presentation (60% of the overall grade)."

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