Diffuser et promouvoir la culture en mathématiques et en informatique déployée dans les sciences agronomiques à INRAE et rassembler la communauté des maths-info INRAE.
Using resource allocation-based models to define advanced therapeutic strategies for stopping cancerous cell proliferation
Cancer metabolism is one of the oldest areas of research in cancer biology,
predating the discovery of oncogenes and tumor suppressors by some 50 years. The field is
based on the principle that metabolic activities are altered in cancer cells relative to normal
cells, and that these alterations support the acquisition and maintenance of malignant
properties. Because some altered metabolic features are observed quite generally across
many cancer types, reprogrammed metabolism is considered a hallmark of cancer. The key
questions driving research in the field should be devoted to identifying key metabolic
candidates whose inactivation might severely impair tumor cells while sparing normal cells
for therapeutic benefits. Unfortunately, the high metabolic adaptability of tumor cells to find
alternative pathways frequently frustrates recent therapeutic interventions.
We thus need to explore in some detail the possibilities for cancerous cells to grow, and
determine if cancer cells are able to multiply using the resources available in their micro
environment, in the presence or absence of specific therapeutic agents. To address this
problem, an approach is to use whole-cell modeling to account not only for metabolic
activities, but also for other cellular functions that can be impacted during cancer cells
adaptation.Among whole-cell modeling techniques, the constraint-based modeling (CBM)
framework, and especially, the resource balance analysis (RBA) framework (developed in
MaIAGE, see [1-3]) is particularly promising to tackle this challenge since it offers a good
trade-off between prediction accuracy of cell phenotypes and numerical tractability .