Selective control of the apoptosis signaling network in heterogeneous cell populations

TL;DR

This paper develops a framework for selectively controlling apoptosis in heterogeneous cell populations by analyzing gene network statistics and proposing optimization methods, enabling targeted interventions with minimal effects on non-target cells.

Contribution

It introduces a novel approach to selective control in gene networks, analyzing the effects of network topology and feedback, and presents two optimization strategies for targeted gene control.

Findings

Control of a few genes increases selectivity.

Feedback and non-linearity can create bimodal signaling distributions.

Strategies differ for weak versus robust populations.

Abstract

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.