Selective control of the apoptosis signaling network in heterogenous cell populations

Abstract

Background: 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. Control of apoptosis is essential in a variety of therapeutic, environments, including cancer where cancer cell death is a desired outcome and Alzheimers disease where neutron survival is the desired outcome. However, in both cases these responses must occur with minimal response in other cells exposed to treatment; that is, the response must be selective, Methodology and Principal Findings: 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 analyze the effects of superposition, non-linearity and feedbacon these statistics. Parallel pathways promote normal statistics while series pathways promote skew distribution which in the most extreme cases become log-normal. We also show that feedback and non-linearity can produce bimodal signalling statistics, as can discreteness and non-linearity. Two methods for optimizing selective control are presented. The first is an extraustive search method and the second is a linear programming based approach. Though contrel of a single gene in the signalling network yields little selectivity, control of a few genes typically yields higher levels of selectivity. The statistics of gene combinations susceptible to selective control in heterogeneous apoptosis networks is studied and is used to identify general control strategies. Conclusions and Significance: We have explored two methods for the study of selectivity in cell population. The first is an exhaustive search method limited to three node perturbations. The second is an effective linear model, based on interpolation of single node sensitivity, in which the selective combinations can be found by linear programming optimization. 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. © 2007 Calzolari et al.