The Bioinformatics Research Laboratory (BIRL) focuses on developing models to understand and control the growth of highly proliferative cells, which has wide applications in biotechnology. These include health-related applications such as tumor growth and pathogen growth, as well as sectors like agro-food, cosmetics, pharmaceuticals, and bioenergy for the production of high-value molecules.

Axis 1: Therapeutic Research in Silico

The first axis of BIRL’s research deals with therapeutic research, focusing on identifying new chemical classes or new combinations of known drugs that can inhibit cellular proliferation. BIRL particularly explores the transfer of technology between cancer and malaria. This research uses molecular modeling and cheminformatics techniques to construct new chemical classes "de novo" or to search databases for chemical fragments from known anticancer molecules that can inhibit pathogen cellular growth. Another approach at the intersection of therapeutic research and bio-production is to control cellular energy metabolism. BIRL focuses on the respiro-fermentative shift (transition from a respiratory to a fermentative metabolism), observed when nutrients are abundant in certain cells, especially tumors (Warburg effect) or microorganisms like parasites, bacteria, yeasts (Crabtree effect). This metabolic shift allows rapid energy (ATP) production and biomass constituents (amino acids, nucleotides…). The team develops formal models of metabolic regulation to search for therapeutic combinations to reverse the Warburg effect in cancers. This project is in collaboration with Prof. G. Bernot from the I3S Laboratory at the University of Côte d’Azur (Sophia Antipolis) and a PhD student, R. Khooderam.

  • Cancer and Malaria

    BIRL is engaged in therapeutic research to identify new chemical classes or drug combinations that can inhibit cellular proliferation. This includes molecular modeling and cheminformatics techniques to design new chemical entities or search chemical databases for anticancer fragments that could inhibit pathogen cellular growth.

  • Metabolic Transition Modeling

    Controlling cellular metabolism is crucial for both therapeutic research (cancer, immune cells, glial cells) and bio-production processes. BIRL uses formal metabolic models employing R. Thomas’ formalism or software proof techniques implemented by Prof. G. Bernot and his team (I3S, University of Côte d’Azur) for biological network modeling. These methods eliminate model inconsistencies and identify sufficient and necessary action combinations to induce the desired cellular phenotype change. This project is done in collaboration with Prof. G. Bernot (I3S Sophia Antipolis) and PhD student R. Koodheraam (University of Mauritius).

Axis 2: Bioprocess Control and Analysis

The second research axis at BIRL focuses on the growth dynamics of cells. At the scale of an organ or a set of cells, the microenvironment, particularly nutrient abundance or scarcity, influences cell metabolism. Predicting the overall growth dynamics of the system while considering cell metabolic variability (mixotrophy) or medium heterogeneities allows studying the impact of metabolic or genetic diversity on total biomass production. This link between diversity and growth is key to theoretical ecology and central to all ecosystems, including within a bioreactor. This research project, initiated in 2018, is conducted in collaboration with Prof. I. Boussaada of the Laboratory of Complex Systems at IPSA (aeronautics school of the IONIS Group), members of the L2S and LGPM teams at CentraleSupélec, and the BIRL. It includes the MAC-BIORE project (Modeling, Automation, and Control of Bioreactors).

  • Microorganism Growth Dynamics

    This second research axis focuses on the growth dynamics of a population of cells, initially with a specific interest in microorganism growth in a bioreactor. The scientific aim is to study the impact of mixotrophy and medium heterogeneities on biomass production. The research, initiated in 2018, collaborates with Prof. I. Boussaada from the Complex System Dynamics Laboratory at IPSA (IONIS Group’s aeronautics school), members of the L2S and LGPM teams at CentraleSupélec, and BIRL. It includes the MAC-BIORE project.

  • Statistical Analysis in Oenology

    The facultative metabolic yeast Saccharomyces Cerevisiae is an ideal system for studying the respiro-fermentative shift observed during wine pressing. Physicochemical aspects of this metabolic shift depend on electron potential (pe) and proton potential (pH). Comparing physicochemical properties between grape juice and wine aims to identify indicators of this respiro-fermentative shift. This research collaborates with Prof. R. Marchal from the University of Reims Champagne Ardennes, focusing on controlling the dynamic evolution of must quality during pressing. This project uses analytical time series techniques to detect quality changes in must over time and pressing. It is conducted with Dr. O. Arkoun, a statistician affiliated with BIRL and the Raphaël Salem Mathematical Laboratory (University of Rouen-Normandy).