Group: Statistical physics and interdisciplinary applications. Research area: biophysics, cellular behaviour, collective phenomena

Department

Department of Applied Science and Technology (DISAT), Politecnico of Turin

Main research activities/topics/projects

Cell populations adapt to an environment on at least two different levels: (i) via intra-cellular regulation (e.g. metabolic, signaling, genetic), which governs essential maintenance, biosynthetic and possibly replicative pro-cesses; and (ii) via extra-cellular mechanisms (e.g. sens-ing, signaling, motility), necessary to harvest information and control exchanges with the medium. The latter level prompts inter-cellular interactions and introduces an eco-logical dimension to multi-cellular systems, whereby cells can be seen as agents that need to meet certain require-ments while jointly modulating a shared environment. The way in which the two layers integrate is a key de-terminant of adaptation, viability, and ultimately fitness. This raises a rather basic question: is a viable en-vironment the result of the straightforward aggregation of a large number of autonomous actions by individual cells, or is it rather an emergent property of the collective behavior of many interacting cells? 

 Perhaps surprisingly, recent experiments on cancer cell cultures suggest that collective effects, notably imbalances in the dynamic and heterogeneous inter-cellular exchange network through which cells regulate the microenvironment, play the central role for the viability of the population and the sustainability of the medium. More precisely, interactions appear to aim at stabilizing the environment in the short term while optimizing the efficiency of nutrient usage over longer timescales. Theoretical models (developed by members of the Statistical physics and interdisciplinary applications group at Politecnico) are currently able to reproduce empirical data only within a static framework where different timescales are ignored. The dynamical aspect, which is critical in experiments, is not currently accounted for. 

 We are interested in developing dynamical agent-based mathematical models of cell populations that can capture the subtle interplay of short- vs long-term objectives observed in the population. These models can provide a first quantitative insight into the emergence of sustainable collective strategies for fitness and growth in multi-cellular systems. 

 Working language

 English

Special entry requirements

Knowledge of statistical physics; good programming skills (eg Python, Julia); analytic (mathematical) skills; experience with agent-based models would be a major plus

Duration in months (min-max)

PhD sandwich: 2-6