Séminaire mensuel virtuel (les vendredi à 13h)

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2 avril 2021: Francis Mairet (Ifremer / Nantes) et Antrea Pavlou (IBIS / Grenoble)

13h00 - 13h30 -- Francis Mairet (Ifremer, Nantes) -- Optimal proteome allocation determines temperature dependence of microbial growth laws. -- .
Although the effect of temperature on microbial growth has been widely studied, the role of proteome allocation in bringing about temperature-induced changes remains elusive. In this talk, I will present a coarse-grained model of microbial growth - including the processes of temperature-sensitive protein unfolding and chaperone-assisted (re)folding - that we develop to tackle this problem. We determine the proteome sector allocation that maximizes balanced growth rate as a function of nutrient limitation and temperature. Calibrated with quantitative proteomic data for Escherichia coli, the model allows us to clarify general principles of temperature-dependent proteome allocation and formulate growth laws. The same activation energy for metabolic enzymes and ribosomes leads to an Arrhenius increase in growth rate at constant proteome composition over a large range of temperatures, whereas at extreme temperatures resources are diverted away from growth to chaperone-mediated stress responses. Our approach points at risks and possible remedies for the use of ribosome content to characterize complex ecosystems with temperature variation.

13h30 - 14h00 -- Antrea Pavlou (IBIS / Grenoble) -- Insights into bacterial resource allocation in dynamically changing environments using a combination of experimental and mathematical approaches..
with E. Cinquemani, H. Geiselmann, H. de Jong
The relationship between bacterial growth and the environment has been well characterized over the last 50 years. In most studies, however, bacteria are maintained at steady-state growth even though in reality they are rarely in a constant environment. To investigate bacterial adaptation in changing environments, we track growth and gene expression of single- cell bacteria growing in a microfluidic device in changing environments. We examine the behavior of specific ribosomal and metabolic genes in this context using fluorescent protein tags. The experimental results provide a detailed view of resource allocation strategies of bacteria in dynamically changing environments and are helpful in testing the predictions made by resource allocation models of bacterial growth.

12 mars 2021: Samuel Chaffron (Combi team, LS2N, Nantes) et Stéphanie Chevalier (LRI, Paris-Saclay)

13h00 - 13h30 -- Samuel Chaffron (Combi team, LS2N, Nantes) -- Environmental vulnerability of the global ocean plankton community interactome. -- slides.
Marine plankton form complex communities of interacting organisms at the base of the food web, which sustain oceanic biogeochemical cycles, and help regulate climate. Though global surveys are starting to reveal ecological drivers underlying planktonic community structure, and predicted climate change responses, it is unclear how community-scale species interactions will be affected by climate change. Here we leveraged Tara Oceans sampling to infer a global ocean cross-domain plankton co-occurrence network – the community interactome – and used niche modeling to assess its vulnerabilities to environmental change. Globally, this revealed a plankton interactome self-organized latitudinally into marine biomes (Trades, Westerlies, Polar), and more connected poleward. Integrated niche modeling revealed biome-specific community interactome responses to environmental change, and forecasted most affected lineages for each community. These results provide baseline approaches to assess community structure and organismal interactions under climate scenarios, while identifying plausible plankton bioindicators for ocean monitoring of climate change.

13h30 - 14h00 -- Stéphanie Chevalier (Lifeware / Inria Saclay) -- Synthesis of Boolean networks from single-cell differentiation data.
Processes like cell differentiation and cancerisation have dynamical properties around the notion of trajectory (succession of changes in gene state), non-reachability (bifurcating event) and stability (differentiated cell). Single-cell data on such behaviors are now quite widely available but dynamical modelling with them remains too complex to be commonly leveraged. I will present the approach we develop to automatically infer dynamical models from such data and prior knowledge on gene interactions. The inference method consists in formulating the inference as a Boolean satisfiability problem, described as a logic program containing both the modelling formalism (Most Permissive Boolean network - MPBN) and the data on the biological process (prior knowledge, experimental measurements, dynamics, hypotheses). Several constraints have been implemented in Answer-Set Programming to ensure the desired dynamical properties, and thanks to this logic modeling it is now possible to exhaustively enumerate the MPBN compatible with the constraints of cell differentiation behaviors. In order to leverage single-cell data, I firstly run classification and trajectory reconstruction methods, then data are translated into logical form to describe the cells dynamics. I will present preliminary results obtained for a large-scale modeling of hematopoiesis from cell-scale transcriptomic sequencing data (single-cell RNA-seq data). Potential influences between genes and proteins are extracted from the SIGNOR database, which brings more than 5500 components (genes, proteins and complexes).

5 février 2021: Olivier Gandrillon (LBMC / ENS Lyon) et Aurélien Naldi (Lifeware / Inria Saclay)

13h00 - 13h30 -- Olivier Gandrillon (LBMC / ENS Lyon) -- A probabilistic dynamical framework for Gene Regulatory Network inference and simulation. Joined work with Matteo Bouvier, Alexey Koshkin, Fabien Crauste, Arnaud Bonnaffoux and Olivier Gandrillon.
In this talk, I will first recall our proposal for a GRN model that is simultaneously probabilistic, dynamical, and executable (Herbach et al. 2017; Bonnaffoux et al. 2019). It is specifically designed to reproduce and to predict the time-dependent evolution of the gene expression distributions that we observe at the single-cell level, for example during a differentiation process.
I will then address two open questions: (i) How do we compare a model's output to experimental data, that is single-cell-based gene expression distributions? and (ii) How do we compare the output of two different models? We will show that the main difficulty comes from the probabilistic nature of the model: two runs of the same model with the exact same parameter values will generate two different distributions.
I will present our current proposal and argue that there is no definitive answer to those questions and that more dedicated research is needed to answer those.
Herbach, U., Bonnaffoux, A., Espinasse, T., and Gandrillon, O. (2017). Inferring gene regulatory networks from single-cell data: a mechanistic approach. BMC Systems Biology 11, 105.
Bonnaffoux, A., Herbach, U., Richard, A., Guillemin, A., Gonin-Giraud, S., Gros, P.-A., and Gandrillon, O. (2019). WASABI: a dynamic iterative framework for gene regulatory network inference. BMC Bioinformatics 20, 220.

13h30 - 14h00 -- Aurélien Naldi (Lifeware / Inria Saclay) -- Kinetic assumptions in Boolean networks: a case for buffering. -- slides
Boolean networks are widely used to study complex biological systems, especially in absence of precise kinetic information. Their asynchronous interpretation has long been considered as more realistic than the synchronous one, as it removes some implicit kinetic assumptions. The "most permissive" semantics, which has been recently introduced, removes all known remaining assumptions and offers surprisingly good computational properties. However, this semantics also enables some dynamical behaviors which may conflict with the expected biological meaning of many models, in particular it can depend on hidden dual interactions between components of the network. We propose buffered network as a balance between the implicit kinetic assumptions of the asynchronous interpretation and the strong generalization of the most permissive semantics. Using this approach to refine the results obtained with the most permissive semantics, we identified some key analytical results which remain valid when we preserve the signs of all interactions, while others should be treated more carefully.

8 janvier 2021: Thomas E. Gorochowski (University of Bristol, UK) et Olivier Borkowski (Inria and Institut Pasteur)

13h00 - 13h30 -- Thomas E. Gorochowski (University of Bristol, UK) -- Using diverse sequencing technologies to accelerate genetic circuit design
Résumé: Synthetic genetic circuits are composed of many interconnected parts that must function together in concert to implement desired biological computations. A major challenge when developing new circuits is that genetic parts often display unexpected changes in their performance when used in new ways. Such changes can arise due to contextual effects or unintended interactions with the host cell. In this talk, I will demonstrate how we have been using a variety of sequencing technologies to tackle problem. First, I will show how RNA-sequencing can be used to measure the function of every transcriptional part making up large genetic circuits. This enables us to better understand why some designs fail and helps pinpoint the root cause. Then, I will present some recent work where we combined RNA-sequencing with ribosome profiling and RNA spike-in standards to enable the first large-scale characterization of transcriptional and translational parts in absolute units. Finally, I will discuss some new work that uses long-read nanopore sequencing to enable the characterization of thousands of genetic parts simultaneously to better understand their design constraints. Taken together, the methods presented provide a means for a more complete and quantitative view of the inner workings of genetic circuits and improves our understanding of the rules governing the effective construction of larger and more complex biological systems.

13h30 - 14h00 -- Olivier Borkowski (Inria and Institut Pasteur) -- A large-scale exploration of cell-free compositions to maximize protein production using active learning
Résumé: Lysate-based cell-free systems have become a major platform to study gene expression but batch-to-batch variation makes protein production difficult to predict. We described an active learning approach to explore a combinatorial space of ~4,000,000 cell-free compositions, maximizing protein production and identifying critical parameters involved in cell-free productivity. We also provided a one-step-method to achieve high quality predictions for protein production using minimal experimental effort regardless of the lysate quality. Eventually, we challenged our method with a collection of E. coli cell-free systems using various homemade cell lysates and lysates supplemented with antibiotics to alter the efficiency of transcription and translation processes.
Joint work with Mathilde Koch, Agnès Zettor, Amir Pandi, Angelo Cardoso Batista, Paul Soudier, and Jean-Loup Faulon at Génomique Métabolique, Genoscope, and Micalis Institute, INRAE, France

6 novembre 2020: Déborah Boyenval (I3S/Sparks) et Loïc Paulevé(CNRS/LaBRI/Formal Methods)

13h00 - 13h30 -- Déborah Boyenval (I3S/Sparks) -- Étude des checkpoints du cycle cellulaire : spécification et vérification -- slides
Résumé: Le cycle cellulaire est par définition une succession d'évènements conduisant à la duplication sans erreur de l'ADN (phase S) et l'équitable division d'une cellule mère en deux cellules filles (phase M). Au cours de la progression du cycle cellulaire (G1-S-G2-M), l'intégrité de l'ADN est garantie notamment par les checkpoints. Nos travaux montrent que la modélisation discrète du cycle cellulaire permet de modéliser proprement la notion fondamentale de checkpoint. Un nouveau modèle multivalué du cycle cellulaire est présenté en suivant le formalisme de René Thomas. Le modèle se focalise sur la succession des évènements de régulation qui représente le cycle cellulaire. On y montre que plusieurs permutations de ces évènements sont admissibles, tout en permettant néanmoins de dégager des évènements clefs non permutables qui caractérisent les checkpoints. Cette étude a été rendue possible grâce à l'usage de deux types de méthodes formelles dédiées aux réseaux de régulation multivalués: la logique de Hoare "génétiquement modifiée" et le model-checking pour CTL. L'outil TotemBioNet combine efficacement ces deux approches formelles pour identifier exhaustivement les paramètres dynamiques des modèles compatibles avec nos définitions du cycle cellulaire et de ses checkpoints.

13h30 - 14h00 -- Loïc Paulevé (CNRS/LaBRI/Formal Methods) -- Most Permissive Boolean Networks in practice -- slides
Résumé: Logical modeling, notably with Boolean Networks (BNs), is a well-established approach that enables reasoning on the qualitative dynamics of networks. However, (a)synchronous Boolean network, besides being costly to analyze, can preclude the prediction of certain behaviors observed in quantitative systems.
Most Permissive Boolean Networks offer the formal guarantee not to miss any behavior achievable by a quantitative model following the same logic. Moreover, MPBNs significantly reduce the complexity of dynamical analysis, enabling to model genome-scale networks.
In this talk, after an overview of the motivation and properties of MPBNs, I'll focus on their practical usage for the analysis of models of biological networks.
Related material:

2 octobre 2020: Caroline Baroukh (INRA Toulouse) et Anaïs Baudot (MMG Marseille)

13h00 - 13h30 -- Caroline Baroukh (INRA Toulouse) -- Modélisation métabolique des interactions plantes-pathogènes
Résumé: Les outils de la biologie des systèmes, et plus particulièrement la modélisation métabolique, sont parfaitement adaptés pour étudier l’interaction métabolique hôte-pathogène. En effet, ils permettent de formaliser les systèmes complexes de manière rigoureuse, d’avoir une vision globale et générique, de faire des bilans matières et surtout de faire un lien entre physiologie observée (croissance, excrétion de facteur de virulence, déplétion des substrats) et données génomiques (génome, transcriptome, protéome). Ces approches ont déjà fait leur preuve dans le domaine des biotechnologies et de la biologie de synthèse pour l’optimisation de la production de molécules d’intérêts industriels. Leur adaptation au domaine de la pathologie des plantes peut aider à déchiffrer les stratégies de virulence de pathogènes de plante.
Après une brève présentation des techniques de modélisation utilisées, deux exemples de l’apport de la modélisation métabolique pour la pathologie des plantes seront présentés. Le premier exemple est la reconstruction et la modélisation semi-automatique des réseaux métaboliques des souches du complexe d’espèces Ralstonia solanacearum, bactéries pathogènes provoquant le flétrissement de nombreuses plantes. L’étude in silico a montré que l’architecture des réseaux métaboliques semble liée à la phylogénie des souches, ainsi qu’au style de vie particulier de certaines souches. Le second exemple est la reconstruction du réseau métabolique de Xylella fastidiosa (souche CFBP8418), phytopathogène bactérien responsables de nombreuses maladies dont l’«Olive Scorch » en Italie. L’étude in silico du métabolisme de cette souche a permis de révéler certaines particularités métaboliques qui impactent fortement la robustesse du pathogène et qui pourrait expliquer en partie sa croissance fastidieuse.

13h30 - 14h00 -- Anaïs Baudot (MMG Marseille) -- A Multi-Objective Genetic Algorithm to Find Active Modules in Multiplex Biological Networks
Résumé: The identification of subnetworks of interest - or active modules - by integrating biological networks with molecular profiles is a key resource to inform on the processes perturbed in different cellular conditions. We here propose MOGAMUN, a Multi-Objective Genetic Algorithm to identify active modules in multiplex biological networks. MOGAMUN optimizes the scores of the nodes (e.g., their differential expression) and the density of interactions from multiplex networks. Multiplex networks are composed of different layers of physical and functional relationships between genes and proteins. Each layer is associated to its own meaning, topology, and biases; the multiplex framework allows exploiting this diversity of biological networks.