"I have always believed that, in order to achieve insightful and novel scientific results, scientists should integrate and make use of different scientific disciplines"
I’m Anis Mansouri, I’m from Algeria. I’ve started my university studies in biology at Ferhat Abbas University (Sétif, Algeria), then I have moved to Paris to pursue my studies at Paris University where I obtained my bachelor’s degree in Cell Biology and Physiology (2018) followed by a master’s degree in Bioinformatics/In Silico Drug Design (2020).
I have always believed that, in order to achieve insightful and novel scientific results, scientists should integrate and make use of different scientific disciplines when carrying out their studies. Therefore, I enrolled for the E-MUSE ESR7 project because it perfectly met my expectations, since it involves scientists from various horizons (Physicists, Biologists, Bioinfomaticians…), with whom I will permanently interact in order to exchange ideas and take advantage of their suggestions to handle different biological problems.
We owe the mouth-watering Parmigiano that we enjoy with a glass of good wine to tiny microscopic artisans, who cooperate harmoniously to craft delicious and healthy food products.
Microorganisms have specific but complementary and interdependent roles during food-making processes. They create a functional network where different species/strains are related, share many substrates, products, and enzymes, and have different tasks in common. On the other hand and at the sub-cellular level, each specific microorganism has its own molecular networks that ensure its functions, like signalling networks, regulatory networks and metabolic networks between enzymes and metabolites.
In order to study these inter and intra-species interactions and predict their outcome, we are using machine learning and network theory approaches to reconstruct and analyse these networks. These approaches allow the integration of multi-omics data (genomics, transcriptomics, metabolomics…) and, thus, allow a more realistic representation of the complex biological processes that govern food making.
Discovery of putative antimicrobial resistance-related genes in Escherichia coli through a network-based analysis.
Combination of transcriptomics and network theory approaches to create context-specific genome-scale metabolic networks.
Machine learning approaches for a straightforward inference of metabolite levels in cheese from microbial gene expression.