  The biochemical reaction networks that regulate living systems are all
stochastic to varying degrees. The resulting randomness affects biological
outcomes at multiple scales, from the functional states of single proteins in a
cell to the evolutionary trajectory of whole populations. Controlling how the
distribution of these outcomes changes over time -- via external interventions
like time-varying concentrations of chemical species -- is a complex challenge.
In this work, we show how counterdiabatic (CD) driving, first developed to
control quantum systems, provides a versatile tool for steering biological
processes. We develop a practical graph-theoretic framework for CD driving in
discrete-state continuous-time Markov networks. Though CD driving is limited to
target trajectories that are instantaneous stationary states, we show how to
generalize the approach to allow for non-stationary targets and local control
-- where only a subset of system states are targeted. The latter is
particularly useful for biological implementations where there may be only a
small number of available external control knobs, insufficient for global
control. We derive simple graphical criteria for when local versus global
control is possible. Finally, we illustrate the formalism with global control
of a genetic regulatory switch and local control in chaperone-assisted protein
folding. The derived control protocols in the chaperone system closely resemble
natural control strategies seen in experimental measurements of heat shock
response in yeast and E. coli.
