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. Can we find protocols to assist this driving via an auxiliary field that would always keep the system on the desired trajectory? In this talk, I will present a method to control the set of trajectories in stochastic networks that has close analogies with quantum adiabatic protocols (transitionless quantum driving). We will explore the implementation of these ideas with two example applications in biology: i) the evolution of 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). This could have wide potential applications, from therapeutic strategies against complex diseases to crop design in response to climate change.  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.