Category: Postdoc

Post-doctoral fellowship : Modelling evolution of disease-associated traits under local competition with conspecifics (M/F)

Type d’offre : Post-doctorant
Lieu de travail : Sophia-Antipolis
Thème de recherche : Santé, biologie et planète numériques
Projet : BIOCORE
Responsable scientifique : frederic.grognard@inria.fr
Date limite de candidature: 31/12/2015
Mission
He – She will be in charge of mathematical developments in order to understand how disease-associated traits evolve in fungal pathogens of plants. The main question will be how correlations between disease-associated traits could emerge from resource allocation models.

The post-doc will take place in collaboration with the UMR IAM of INRA Nancy in the framework of the ANR Funfit project (Funfit : A trait-based approach linking individual Fitness of Fungal plant pathogens to ecological strategies).

Funfit Modelling group: Frédéric Grognard (Biocore, Inria Sophia Antipolis), Fabien Halkett (UMR IAM, INRA Nancy), Ludovic Mailleret (UMR ISA, INRA Sophia Antipolis), Frédéric Hamelin (UMR IGEPP, Agrocampus Ouest, Rennes), Frédéric Fabre (UMR SAVE, INRA Bordeaux)

 
Job offer description
Fungi are among the most frequent damaging agents of plants, in natural and managed ecosystems. In recent years, they have been identified as a major cause of emerging diseases in the context of global change, especially through the establishment of alien species in new areas. Understanding this fast-moving epidemiological context is a key issue and will require greater emphasis on integrative and predictive approaches.

The study of disease-associated traits evolution is an active research field. Beyond the classical virulence-transmission trade-off, we are particularly interested in how correlations between disease-associated traits could emerge from resource allocation models.

 

In that context, the post-doctoral fellow will be asked to work in two complementary directions:

1) to build optimal control models on resource allocation strategies to investigate the evolutionary mechanisms responsible for the detrimental effect of fungal pathogen development on its host plant. Modelling assumptions will be based on the specific features of fungal infection (such as mycelium growth, resource extraction from host, sporulation, etc). Ultimately we aim at linking fungi fitness with disease associated traits that can be measured in experiments.

2) to study the evolution of traits in pathogen populations along an epidemic wave/colonisation gradient. This second direction, which will be the main one for the post-doctoral fellow, will consist in identifying traits involved in fungal pathogen adaptation at the population level, by comparing populations submitted to different selection pressures.

We will first develop models extending the ones developed in the first direction to account for situations where different fungi of the same species can infect the host.  Here, we aim at accounting for epidemiological situations in which host saturation is possible. In this new model, the amount of available healthy hosts will depend on local disease prevalence. Studying host co-infection, it is expected that competition will be shown to be the driving force behind the evolution through branching to the coexistence of subspecies characterized by different traits (e.g. different length of latent periods). Analytical results on small-scale models are expected.

 

Fungi traits and strategies will tend to be defined by the values of functions of time. The tools that will then naturally be central in the work of the post-doc will be evolutionary dynamics of function-valued traits and dynamic games theory.

 

References

J.A.J. Metz, M. Gyllenberg (2001) How should we define fitness in structured metapopulation models? Including an application to the calculation of evolutionary stable dispersal strategies. Proceedings of the Royal Society B: Biological Sciences, 268:499-508

U.Dieckmann, M. Heino, K. Parvinen (2006). The adaptive dynamics of function-valued traits. Journal of Theoretical Biology, 241 :370-389

A.R. Akhmetzhanov, F. Grognard, L. Mailleret (2011) Optimal life-history strategies in seasonal consumer-resource dynamics. Evolution, Wiley-Blackwell, 65:3113—3125.

 
Skills and profile
The post-doctoral fellow should either have a PhD thesis in Applied Mathematics with a strong knowledge in dynamic game theory, or a PhD thesis in evolutionary biology with a strong theoretical component.
 
Benefits
Gross Salary per month : 2621 euros
Starting date: Between October 1st, 2015 and February 1st, 2016 Contract Duration: 20 monthsBusiness Restaurant on site
 
Additional information
Place of work: Inria Sophia Antipolis Méditerranée

Required documents and sending of the application

Please send your detailed Resume and Covering letter showing your interest and letters of recommendation.

  1. directly on the web site Inria
  2. by email to :M. Frédéric Grognard, Researher in the BIOCORE team: frederic.grognard@inria.fr

 Applications will be admitted until the position is filled

 

Inria’s disabilities policy: All positions at the institute are open to disabled people.

Post-Doctoral fellowship: Control and optimal control of bacterial growth

Subject: Control and optimal control of bacterial growth.

Duration: 12 months

Location: INRIA Sophia-Antipolis (South of France, near Nice)
Contact: Gouze Jean-Luc:   gouze@sophia.inria.fr


Santé, biologie et planète numérique

Tags: Gene networks, biological models, control, dynamical systems, computational biology, numerical simulation


The study of genetic regulatory networks has taken a qualitative leap through the use of modern genomic techniques that allow simultaneous measurement of the expression levels of all genes of an organism. In addition to high-throughput experimental methods, approaches in mathematics and computer science will be indispensable for analyzing the dynamics of genetic regulatory networks. BIOCORE team applies mathematical and computational methods from Control Theory and Dynamical Systems to the study of models of genetic networks and more general biological networks (metabolic networks, signaling networks…).

The general goal of this work is to design control strategies for improving product yield and productivity in E coli bacteria.

The Gene Expression Machinery of this bacterium has been modified (by techniques of genetic engineering) to obtain a strain where a chemical inducer controls the expression of RNA polymerase (an enzyme needed for the expression of the genes).

A simplified dynamical model of this controlled GEM will be developed (with our partners) and studied.

We will notably characterize the transients towards steady states and their duration, as well as the

dependence of these properties on the concentrations of nutrients and inducer in the medium. Moreover, the possibility to externally adjust transients by choosing appropriate nutrients and inducer concentrations provides control parameters for, in the first place, the satisfaction of some biological constraints on the

variables and dynamical behavior , and secondly, the optimization of product yield. Using

optimal control, computer simulation and optimization, we will design control laws, possibly including

feedback. The above analysis will also be carried out for fed-batch cultures in a fermenter,

the condition most relevant for industrial biotechnological processes.

The work is done in collaboration with IBIS Inria Team (Grenoble) within the BIO-INFORMATIQUE ANR RESET project (see https://project.inria.fr/reset/).
We are looking for an applied mathematician with a  background in the analysis of dynamical systems, and familiar with control theory (and optimal control). In addition, we expect a strong motivation to work on biological applications in genomics.


Web page of JL Gouze http://www-sop.inria.fr/members/Jean-Luc.Gouze/JLGouze-eng.html
Web page of M. Chaves http://www-sop.inria.fr/members/Madalena.Chaves

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(English) PostDoc : Control of multiple loops models of genetic regulatory networks

avant le 16 mars (premier tour)!!

Control of multiple loops models of genetic regulatory networks