PhD Position F/M [Cordi-S Campaign 2021 Grenoble] Computational analysis of metabolic strategies in pathogenic bacteria, Inria, Montbonnot-Saint-Martin (38)

Staff is localized on 5 campuses in Grenoble and Lyon, in close collaboration with labs, research and higher education institutions in Grenoble and Lyon, but also with the economic players in these areas.

Present in the fields of software, high-performance computing, Internet of things, image and data, but also simulation in oceanography and biology, it participates at the best level of international scientific achievements and collaborations in both Europe and the rest of the world.

The PhD student will carry out the project in the context of IBIS, an interdisciplinary research group involving researchers from Inria Grenoble – Rhône-Alpes and the Laboratoire Interdisciplinaire de Physique (CNRS/Université Grenoble Alpes), in close collaboration with the experimental group of Luiz Pedro Carvalho at the Francis Crick Institute (associated with UCL, Imperial College and King’s College) in London. This group is a world-leading expert on mycobacterial biochemistry, metabolism and antibiotic research.

The growth of heterotrophic microorganisms consists in the conversion of nutrients available in the environment into biomass. At the molecular level, this process involves a complex network of biochemical reactions involved in the uptake of the nutrients, their degradation into metabolite precursors, and the assembly of the latter into macromolecules that constitute the biomass of the cell (proteins, DNA, RNA, lipids, …). In recent years, constraint-based models have been developed that describe the above reaction networks in a simplified manner, as a linear system of balanced metabolic fluxes with additional flux inequalities expressing fundamental thermodynamic and biochemical constraints as well as experimental observations [1]. Constraint-based modeling of microbial growth has become a very effective tool for the analysis of the metabolic strategies of microorganisms as well as the redesign of these strategies for biotechnological or environmental purposes [2]. In particular, they provide an excellent framework for integrating heterogeneous, genome-scale datasets in a principled and efficient manner [3,4].

While constraint-based models have been mostly used for model organisms like the gut bacterium Escherichia coli or yeast, the formalism is also very well suited for the analysis of microorganisms that have been less well-studied but that are of large societal or economic interest. One example consists in the genus of mycobacteria, which includes Mycobacterium tuberculosis, the causative agent of tuberculosis [5]. Whereas different mycobacteria species have very similar metabolic capacities, they nevertheless show a bewildering variety of metabolic strategies, resulting in growth rates that vary over an order of magnitude (doubling times between 1 and 24 hours) [6]. In recent years, laboratories over the world have accumulated experimental datasets quantifying the physiology of different mycobacteria [7], but a fundamental understanding of the underlying metabolic strategies and their relation with pathogenicity is still in its infancy. Constraint-based modeling of mycobacterial metabolism is a promising approach for making sense of these data and gaining a better understanding of the relation between metabolism and pathogenicity.

The above considerations motivate a PhD proposal consisting of (i) the development of constraint-based models of a dozen of mycobacteria species, (ii) the integration of various high-throughput datasets for these mycobacteria available in the literature and in the laboratory of our experimental collaborator, and (iii) the computational and statistical analysis of the models to identify different metabolic strategies as well as their connection to pathogenicity.

[1] Palsson BO. Systems Biology: Constraint-based Reconstruction and Analysis. Cambridge University Press, Cambridge, 2015

[2] O’Brien EJ, Monk JM, Palsson BO. Using genome-scale models to predict biological capabilities. Cell, 161(5): 971–87, 2015

[3] Morin M, Ropers D, Cinquemani E, Portais JC, Enjalbert B, Cocaign-Bousquet M. The Csr system regulates Escherichia coli fitness by controlling glycogen accumulation and energy levels. mBio, 8(5): e01628-17, 2017

[4] Cinquemani E, Laroute V, Cocaign-Bousquet M, de Jong H, Ropers D. Estimation of time-varying growth, uptake and excretion rates from dynamic metabolomics data. Bioinformatics, 33(14): i301–10, 2017

[5] Rienksma RA, Schaap PJ, Martins dos Santos VAP, Suarez-Diez M. Modeling the metabolic state of Mycobacterium tuberculosis upon infection. Front Cell Infect Microbiol., 8:264, 2018

[6] Bachmann NL, Salamzade R, Manson AL, Whittington R, Sintchenko V, Earl AM, Marais BJ. Key transitions in the evolution of rapid and slow growing Mycobacteria identified by comparative genomics. Frontiers in microbiology, 10, 3019, 2020

[7] Agapova A, Serafini A, Petridis M, Hunt DM, Garza-Garcia A, Sohaskey CD, de Carvalho LPS. Flexible nitrogen utilisation by the metabolic generalist pathogen Mycobacterium tuberculosis. Elife, 8, e41129, 2019

The activities that the PhD student will carry out include the following:

1. Reconstruct constraint-based models for a dozen of different mycobacteria species, starting from available models for some species, the genome sequence of others, and the biomass composition (proteins, lipids, RNA, …) experimentally determined by the group of Luiz Carvalho at the Francis Crick Institute.
2. Using measured growth rates, metabolite uptake and secretion rates, as well as information on metabolite concentrations, apply metabolic flux analysis, flux variability analysis, and Monte Carlo sampling to estimate flux distributions compatible with the experimental observations.
3. Cluster the inferred flux distributions and cross-relate fluxes with experimentally determined protein concentrations. This will allow the formulation of hypotheses on regulatory mechanisms that could explain the large metabolic variability across species, as input for further testing by the group of Luiz Carvalho.

Applicants may come from different disciplinary backgrounds – bioinformatics, mathematical biology, microbiology, bioengineering, or biophysics. We expect them to be strongly motivated by interdisciplinary work combining the mathematical modeling of biological systems with the (statistical and computational) analysis of high-throughput experimental datasets. Basic knowledge in microbiology and previous experience with methods for data analysis and data integration would be appreciated. Good relational skills are very important for the project, as it will be carried out in an interdisciplinary and international environment.

• Subsidized meals
• Partial reimbursement of public transport costs
• Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.)
• Possibility of teleworking two days per week and flexible organization of working hours
• Professional equipment available (videoconferencing, loan of computer equipment, etc.)
• Social, cultural and sports events and activities
• Access to vocational training
• Social security coverage

Salary: 1982€ gross/month for 1st and 2^nd year. 2085€ gross/month for 3rd year.

Monthly salary after taxes : around 1596,05€ for 1st and 2^nd year. 1678,99€ for 3rd year. (medical insurance included, income tax excluded).