  Selective control in a population is the ability to control a member of the
population while leaving the other members relatively unaffected. The concept
of selective control is developed using cell death or apoptosis in
heterogeneous cell populations as an example. Apoptosis signaling in
heterogeneous cells is described by an ensemble of gene networks with identical
topology but different link strengths. Selective control depends on the
statistics of signaling in the ensemble of networks and we analyse the effects
of superposition, non-linearity and feedback on these statistics. Parallel
pathways promote normal statistics while series pathways promote skew
distributions which in the most extreme cases become log-normal. We also show
that feedback and non-linearity can produce bimodal signaling statistics, as
can discreteness and non-linearity. Two methods for optimizing selective
control are presented. The first is an exhaustive search method and the second
is a linear programming based approach. Though control of a single gene in the
signaling network yields little selectivity, control of a few genes typically
yields higher levels of selectivity. The statistics of gene combinations
susceptible to selective control is studied and is used to identify general
control strategies. We found that selectivity is promoted by acting on the
least sensitive nodes in the case of weak populations, while selective control
of robust populations is optimized through perturbations of more sensitive
nodes. High throughput experiments with heterogeneous cell lines could be
designed in an analogous manner, with the further possibility of incorporating
the selectivity optimization process into a closed-loop control system.
