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Habilitation à diriger des recherches

Modelling biochemical reaction networks in bacteria – From data to models and back

Delphine Ropers 1, 2
1 IBIS - Modeling, simulation, measurement, and control of bacterial regulatory networks
LAPM - Laboratoire Adaptation et pathogénie des micro-organismes [Grenoble], Inria Grenoble - Rhône-Alpes, Institut Jean Roget
2 MICROCOSME - Analyse, ingénierie et contrôle des micro-organismes
Inria Grenoble - Rhône-Alpes, UGA - Université Grenoble Alpes
Abstract : With the advent of new technologies, experimental data in biology has exploded in size and complexity. It is now possible to simultaneously quantify different components of the cell at metabolic, transcriptomic, proteomic, and phenotypic levels. Connecting these different multi-scale and dynamic datasets provides an integrated view of cellular growth and informs us about the underlying molecular networks of genes, RNAs, proteins and metabolites that control the adaptation of the cell to the environment. This is the perspective offered by math-ematical modelling and computer simulation, allowing the association of different microscopic and macroscopic scales. This is a difficult problem however, because of the noise and the heterogeneity of the data, and of the size and the nonlinearity of the models. As a consequence, a large number of datasets are only partially analysed and underexploited. This manuscript describes the work I have carried out to improve the utilization of experimental data to gain a better understanding of the adaptation of bacterial growth to a changing environment. This work has been carried out within the Ibis project-team (Inria, Université Grenoble Alpes) with my colleagues, especially the students that I have had the chance to supervise. After the introductory Chapter 1, I describe in Chapter 2 the modelling of cellular networks using ordinary differential equations as well as simplification and approximation of the models depending on the nature of the available data and the questions addressed. These principles are applied in Chapter 3 to the qualitative analysis of the dynamics of gene networks in the context of the carbon starvation response in Escherichia colibacteria. With the general trend of biology becoming increasingly quantitative, modelling studies require obtaining reliable gene expression and metabolomic data, the analysis of which requires the development of suitable methods described in Chapter 4. Chapter 5 examines the strong link between the activity of the cellular gene expression machinery and bacterial growth rate. This understanding is used to develop a synthetic strain of E. coliwhose growth control makes it possible to divert the flow of precursors for growth towards the bioproduction of molecules of biotechnological interest. In Chapter 6, large-scale reconstructions of central carbon metabolism are used as platforms to interpret datasets regarding the post-transcriptional regulation of central carbon metabolisminE. coli. Chapter 7 is dedicated to the genome-scale analysis of mRNA decay by means of dynamic transcriptomics data. I describe in Chapter 8 ongoing and future projects towards the integrative analysis of microbial growth and resource allocation strategies. The scientific developments of these projects are expected to shape my own research activity in the coming years and that of the future project-team, under creation, that I will lead.
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Habilitation à diriger des recherches
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https://hal.inria.fr/tel-03252736
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Submitted on : Monday, June 7, 2021 - 7:16:41 PM
Last modification on : Friday, January 21, 2022 - 3:11:02 AM
Long-term archiving on: : Wednesday, September 8, 2021 - 7:54:22 PM

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Delphine Ropers. Modelling biochemical reaction networks in bacteria – From data to models and back. Bioinformatics [q-bio.QM]. Université Claude Bernard Lyon I, 2021. ⟨tel-03252736⟩

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