Post-Doctoral position - ANR Covid-19 PullCov (Pull the Covid-19 replicative catalytic core apart)

Type de poste
Niveau d'étude minimal
Dates
Durée du poste
Contrat renouvelable
Contrat non renouvelable
Date de prise de fonction
Date de fin de validité de l'annonce
Localisation
Adresse

1 Rue Thomas Becket
76130 Mont-Saint-Aignan
France

Contacts
Lina Soualmia
Cécilia Zanni-Merck
Email du/des contacts
cecilia.zanni-merck@insa-rouen.fr
lina.soualmia@chu-rouen.fr
Description

In the framework of the ANR (French National Research Agency) project PullCoVoApart (Pull the Covid-19 replicative catalytic core apart), LITIS (https://www.litislab.fr) is recruiting a post-doctoral researcher for 12 months, the position is to be filled as soon as possible.

To apply, please send a detailed CV and an application to lina.soualmia@chu-rouen.fr and cecilia.zanni-merck@insa-rouen.fr.

Abstract
Antiviral strategies targeting replication machines have proven their worth, with for example the success stories of the cure of Hepatitis C Virus-infected patients or Human Immunodeficiency Virus treatments. One of the prerequisite is a detailed knowledge of the structure and function of these multi-protein complexes allowing the RNA genome replication. The coronavirus (CoV) genome is a positive and single-stranded RNA, the largest among RNA viruses (~30-kb) and paradoxically, with a genetic stability superior to the other RNA viruses. It is now established that it is the 3'-5' exonuclease activity encoded by the CoVs that enables correcting errors during the genome replication. This proofreading activity partly explains the absence of effect of ribavirin on patients infected with SARS-CoV or MERS-CoV. More generally, future anti-CoV strategies will need to incorporate this unique property for RNA viruses (+).

The PullCoVapart project, funded by ANR, the French National Research Agency, is an interdisciplinary project that combines methods in artificial intelligence with protein biochemistry in order to formally model the RNA polymerase behavior of this new coronavirus to be able to predict it, by simulation.

Keywords
Knowledge representation and modelling; Spatio/Temporal reasoning; Ontologies; Textmining; Biomedical Applications; Open data.

Objectives
The three main objectives of the PullCoVapart project are to (1) reconstitute, in vitro, the replication complex of the COVID-19; (2) model its replication activity in silico and finally (3) compare the in silico results with the in vitro experimentations to validate them (or not). In particular, this post-doc project will need to address the 2nd main objective, by proposing a unified formal model of the available knowledge in published resources about COVID-19, and to enrich them by eliciting knowledge from state-of-the-art scientific literature using text mining techniques founded on the new corpus (LitCovid) from the National Library of Medicine (https://www.ncbi.nlm.nih.gov/research/coronavirus/) and the newly published "Coronavirus Infectious Disease Ontology" (http://bioportal.bioontology.org/ontologies/CIDO).

Moreover, it will also be necessary to identify the dynamic properties of the COVID-19 RNA polymerase, which is more complex than just representing simple biomedical concepts. Thus, temporal logics for reasoning will also need to be taken into account. Verification and validation of the obtained models will also need to be performed.

Expected profile :

Background and skills required:

• PhD in computer science/bioinformatics with relevant skills in semantic technologies (including the development of ontologies and reasoning models).

• Excellent communications skills, able to discuss with scientists with different backgrounds (mainly the researchers of other two partners of the project, biologists and
computer scientists expert in simulation).

Equipe adhérente personne morale SFBI
Equipe Non adhérente