Our main biological question is related to the understanding of cell decision-making prinicples. We view physiological and pathological tissues as multicellular systems of interacting decision-makers, such as immune cells, tumor cells, bacteria or tissue-specific progenitors. In turn, we translate this knowledge in medical solutions. The central theme of our medical applications is the interplay between immune cell plasticity and tumor/bacteria/progenitors cell decision-making.
Single cell decision-making has been a major focus of biology. However, in multicellular systems, the regulation of single cell decisions and their impact on the total system behaviour are far from being well understood. We view cell decision-making as a communication between the cell and its microenvironent. Cells can be viewed as signalling processing units, which receive signals and process them via interconnected pathways. The result of this information processing is the decision of cells for their fate, phenotype and generally speaking their future state. We regard information as an organising principle for the spatiotemporal evolution of multi-cellular biological systems. Using multiscale modeling, we try to delinate the design principles of cell decision-making. In particularly, we focus on specific cell fate determination mechanisms, such as Notch-Delta signalling, in developmental systems, e.g. retina.
Dr. D. W. Smithers said in 1962 that "cancer is no more a disease of cells than a traffic jam is a disease of cars. A lifetime of study of the internal-combustion engine would not help anyone understand our traffic problems". We regard tumors as the emergent behavior of interacting cellular decision-makers bearing the ability to adapt their microenvironmental challenges. Immune system is a crucial part of tumor’s microenviroment, where different immune cell types exhibit a large degree of phenotypic plasticity. In particular, de/activation of effector cells and phenotypic polarization of macrophages, from anti- to pro-tumoral types, are proven to be central to the prognosis and therapy of individual tumor. Understanding the dynamic interplay of two phenotypically plastic population, such as tumor and immune cells, is highly non trivial and requires mathematical modeling. This endeavor involves model calibration using comprehensive spatial data extracted from digitalized biopsy images along with other available data types. The vision is by understanding this complex interplay develop new prognostic methods and design new therapies.
Bacteria have developed an impressive ability to survive and propagate in highly diverse and changing environments by evolving phenotypic heterogeneity. The main reasons of bacterial resistance to antibiotics is their phenotypic heterogeneity. Quorum sensing, intercellular communication and inherent phenotypic plasticity mechanisms are major reasons for bacteria deciding over a broad spectrum of phenotypes. We focus on the plasticity of motility phenotypes, especially for Salmonella strains, and the associated impact on the infection potential of such populations. The goal is an improved phenotypic characterization of such infections and intelligent treatment strategies.
Some bacterial pathogens, such as Staphylococcus Aureus (SA), can establish life-long chronic/persistent infections in immunologically challenged hosts. Persistence is normally established after an acute infection period involving the activation of the immune system. S Chronic SA infection involves occupation of a particular tissue type or organ or modification of the intracellular environment. SA bacteria appear to adapt their immediate environment to favor survival and may hijack essential immunoregulatory mechanisms. In silico modelling allows for a better understanding of the chronic phase establishment and novel therapy design.
Immune dynamics balance is critical to the fate of renal transplantation grafts. Inflammatory responses may lead to rejection-based graft failure, whereas immuno-suppressive and anti-inflammatory conditions may induce fibrotic graft failure or even allow for opportunistic infections and oncogenetic events. We focus mainly on macrophage and T-cell responses and their corresponding plasticity in the context of graft fate. The synergy of image analysis, immunology and nephrology allow us to develop dynamic models for the prediction of graft rejection and treatment optimization