Control of biological processes

Time is a major constraint for life. Biological systems need to adapt and respond accurately to the changes in their environment. Having fine-grained control in nonequilibrium processes via external interventions like time-varying concentrations of chemical species is a complex challenge. In this talk, I will present a method to control the set of trajectories in stochastic networks that has close conceptual analogies with quantum adiabatic protocols (transitionless quantum driving). We will explore the implementation of these ideas with two example applications in biology: i) ecological populations under selective pressure and ii) in the context of protein folding biophysics.

In the first example, I will show how to manipulate the evolutionary dynamics of cell populations by controlling selection [1] (e.g., dosage control of drugs). In the second part, I will first show a general method of such control for Markovian processes using a network theory of master equation systems [2]. I will illustrate how control can be achieved in natural settings with an example of cell response to heat shock, by upregulating molecular chaperones that act as recovery agents for misfolded proteins in cells.

[1] Iram et al., Controlling the speed and trajectory of evolution with counterdiabatic driving, Nature Physics 17, 135 (2021). 

[2] Ilker et al., Shortcuts in stochastic systems and control of biophysical processes, Phys. Rev. X 12, 021048 (2022).