Offre en lien avec l’Action/le Réseau : – — –/– — –

Laboratoire/Entreprise : Lorraine Research Laboratory in Computer Science a
Durée : 36 mois
Contact : sabeur.aridhi@loria.fr
Date limite de publication : 2023-06-30

Contexte :
We propose a fully funded 3-years PhD position in computer science with application to biomolecule analysis. The proposed position is funded by the Lorraine Université d’Excellence (LUE) through a multidisciplinary project that involves 2 researchers in computer science and 4 researchers in microbiology. A PhD thesis in microbiology will be conducted in parallel.

Context and motivations

Bacteriocins are antimicrobial peptides of bacterial origin with a very high economic potential in the agri-food sector. They are used in biopreservation/biocontrol applications to fight against undesirable microorganisms in the agronomy and food industry. LIBio has recently developed a technology based on the selection of two strains of the lactic acid bacterium Carnobacterium maltaromaticum producing anti-Listeria monocytogenes bacteriocins [9]. These strains inhibit the growth of this pathogen in cheeses when added to the manufacturing milk to produce the antimicrobial agents in the cheese matrix. These remarkable properties have led to a patent [10] that has very recently been licensed to a ferment producer. However, like the vast majority of biopreservation technologies, the effect is at best bacteriostatic: there is little or no decay of the pathogenic bacteria, which can then be maintained at low concentrations in the food. The biopreservation technologies described in the literature are based on engineering approaches that do not take advantage of the properties of the microbial communities forming the microbiomes of food products. Yet microbiome engineering is among the 12 promising technologies that could transform food systems over the next decade [11]. Indeed, in the case of biopreservation, assemblies of microorganisms could allow obtaining communities producing multiple antimicrobial agents and moreover being able to occupy the ecological niche of the undesirable microorganism to exclude it more efficiently. However, knowledge in the field of microbial community engineering is insufficient to fully exploit their potential. Indeed, due to the complexity of microbial communities, there is no available method to predict microbial community structure based on the knowledge of the ecological properties of microorganisms. Moreover, assembling microorganisms whose properties is to produce antimicrobial agents is a major difficulty because these agents can lead to the mutual exclusion of the microorganisms producing them.

Positioning

In a microbial ecosystem in which members produce antimicrobial substances like bacteriocins, three actors can be considered: the bacteriocin-producing microorganism (P) and the microorganisms sensitive (S) and resistant (R) to this bacteriocin. It was experimentally shown that in simple ecosystems mixing three such actors, all three actors are able to maintain equilibrium [12]. In these systems, S is more competitive than R because it does not pay the cost of resistance, and R is more competitive than P because it does not pay the cost of bacteriocin production. This cyclical relationship between P, S, and R is similar to that of the popular game « rock paper scissors » where no one player has an advantage over the other two: each player can overtake one player and each can be defeated by another. These simple experimental systems suggest that it is possible to implement engineering tools to predict the structure of complex communities based on the interactive properties of microorganisms. Thanks to the emergence of high throughput investigation methods, it is now possible to produce interaction data between large sets of microorganisms and thus reconstitute models of microorganism interaction networks. [6] [7] Lately, Ramia et al. [4] [5] built the interaction network corresponding to 73 Carnobacterium maltaromaticum strains. Like previously, the graph is sender-determined and also shows a highly nested structure [7], which means that it is different from a randomly built network with the same number of nodes and edges. The results also show that the competitive interaction network is very dense making C. maltaromaticum a very interesting model to develop community engineering approaches producing high performance antimicrobial substances cocktails for the fight against undesirable microorganisms. This project will use the data published in Ramia et al. [5] and will try to provide a rather computer science approach to the study of those interaction graph properties.

The originality of this project is that it will make it possible to integrate experimental variables describing the properties of interaction between microorganisms for the prediction of community structure which is not possible by existing methods.

Sujet :
Objectives of the thesis

The main goal of the thesis is to use advanced machine learning and graph-based approaches in order to predict the long-term community structure in microbiological ecosystems [3] [1]. Particularly, it aims at providing approaches to deduce diversity directly from the static, inner properties of the interaction graph the entities are involved in. The practical objectives of this interdisciplinary PhD project, which will be carried out in collaboration with researchers from the Laboratoire d’Ingénierie des Biomolécules (LIBio), are as follows:

to study existing research works on the analysis of interaction networks and long-term diversity prediction in bacteria.
to propose machine learning and graph-based approaches in order to learn models that are able to predict diversity based on the interaction graphs. In this context, regression methods could be used to learn the relation between the interaction graph properties and the diversity.
to study how graph embedding could help in predicting the level of development for each strain. In this context, we aim to study the impact of graph embedding methods on the prediction results. A specific embedding method could be proposed in the context of this project.

Profil du candidat :
Required qualification: Candidates must have a master degree in computer science. Good programming skills in a procedural language are essential. Experience of machine learning and graph mining is also desirable but not essential. A strong interest in bioinformatics would also be highly desirable.

Formation et compétences requises :
Required qualification: Candidates must have a master degree in computer science. Good programming skills in a procedural language are essential. Experience of machine learning and graph mining is also desirable but not essential. A strong interest in bioinformatics would also be highly desirable.

Adresse d’emploi :
Lorraine Research Laboratory in Computer Science and its Applications (LORIA), Nancy, France

Document attaché : 202305240952_PROJET-DE-THESE-LORIA-LUE.pdf

Offre en lien avec l’Action/le Réseau : DOING/– — –

Laboratoire/Entreprise : University of Montpellier. LIRMM computer science
Durée : 3 years
Contact : pierre.larmande@ird.fr
Date limite de publication : 2023-06-23

Contexte :
To meet the challenges of the global demand for food in a context of climate change, a better understanding of agronomically important traits, such as yield, quality, and resistance to abiotic and biotic stresses is crucial to improve crops production capacities. Deciphering molecular mechanisms that drive a particular trait is one of the most critical research areas in biology. However, these genotype-phenotype interactions are difficult to identify because they occur at different molecular levels in the plant and are strongly influenced by environmental factors (i.e., climate change). For biologists, it is difficult to search for relevant information as it is often dispersed in several databases on the Internet each with different data models, scales or distinct means of access. Today’s major challenges are related to the development of methods to integrate these heterogeneous data and to enrich biological knowledge. The scientists also need methods to dig into this mass of data and to highlight relevant information that identifies key genes. To this end, we developed the AgroLD [1] platform which is a knowledge graph that uses Semantic Web technologies to integrate heterogeneous agronomic data from the genome to the phenome (i.e., from the set of genes to the set of phenotypes observed in a plant organism). AgroLD is actively developed. As of today, AgroLD contains more than 900 million triples resulting from the integration of around 100 datasets gathered in 33 named graphs.

Sujet :
The thesis is proposed under the frame of the DIG-AI ANR project which aims to develop machine learning methods combined with knowledge graphs such as AgroLD to study the molecular interactions driving the phenotype development in crops.

Objective 1: The current challenges are related to the development of methods for functional analysis of genes and in particular to methods for prioritization of candidate genes. Indeed, the data integrated from databases are incomplete, heterogeneous, insufficient to infer genes function with good accuracy. One of the first objectives of the thesis will be the development of knowledge extraction methods to extract functional information on genes in scientific documents.

Objective 2: The recent success of graph neural networks (GNNs) suggests the possibility of systematically incorporating multiple sources of information into a heterogeneous network and learning the nonlinear relationship between phenotypes and genes [2]. However, knowledge graphs like AgroLD can be complex and contain interference information. Therefore, as proposed by [3, 4], some GNN models could reduce the influence of noisy data on the overall prediction effect by assigning low weights to unreliable nodes/edges. The second objective will be to develop an adapted approach to the AgroLD context by building meaningful representations from the high dimensional and complex gene data.

Objective 3: Finally, based on previous candidate gene studies in the biomedical field [5, 6] and because inferring gene regulatory networks (GRN) can be formulated as a link prediction problem in Graph Neural Networks (GNN) [7], the third objective will be to apply GNN models to implement candidate gene prioritization and GRN methods to answer biological questions related to adaptation of crops to drought stress and plant diseases.

References

1. Venkatesan A, Tagny Ngompe G, Hassouni NE, Chentli I, Guignon V, Jonquet C, et al. Agronomic Linked Data (AgroLD): A knowledge-based system to enable integrative biology in agronomy. PLOS ONE. 2018;13:1–17.
2. Zhang X-M, Liang L, Liu L, Tang M-J. Graph Neural Networks and Their Current Applications in Bioinformatics. Front Genet. 2021;12.
3. Neil D, Briody J, Lacoste A, Sim A, Creed P, Saffari A. Interpretable Graph Convolutional Neural Networks for Inference on Noisy Knowledge Graphs. ArXiv181200279 Cs Stat. 2018.
4. Li X, Saude J. Explain Graph Neural Networks to Understand Weighted Graph Features in Node Classification. ArXiv200200514 Cs. 2020.
5. Alshahrani M, Hoehndorf R. Semantic Disease Gene Embeddings (SmuDGE): phenotype-based disease gene prioritization without phenotypes. Bioinform. 2018;34:i901–7.
6. Chen J, Althagafi A, Hoehndorf R. Predicting candidate genes from phenotypes, functions and anatomical site of expression. Bioinformatics. 2021;37:853–60.
7. Gligorijević V, Barot M, Bonneau R. deepNF: deep network fusion for protein function prediction. Bioinformatics. 2018;34:3873–81.

Profil du candidat :
Expected profile:
The candidate must have the equivalent of a BAC+5 degree from a University or Engineering School, with specialization in applied mathematics, data science-related, graph theory and machine learning fields. A good understanding of molecular biology and bioinformatics is a plus. We are expecting applicants to have a solid background in programming (Python). The candidate must have a good understanding of English.

Formation et compétences requises :

How to apply:
Applications have to be send before June 23th 2023 and require the following documents:
1) Motivation letter
2) 2-pages max CV
3) M1, M2 academic transcripts
4) references if possible
to be sent by mail to: pierre.larmande@ird.fr and francois.scharffe@umontpellier.fr

Adresse d’emploi :
Link to the full description: https://sites.google.com/site/larmandepierre/positions/phd-in-computational-biology-and-bioinformatics