Systems approaches and algorithms for discovery of combinatorial therapies

TL;DR

This paper reviews current and emerging systems biology approaches and algorithms for designing combinatorial therapies to control complex cellular networks, emphasizing the role of high-throughput data and control landscape optimization.

Contribution

It introduces new algorithmic strategies for identifying optimal control parameters in cellular networks based on control landscape analysis and incomplete biological knowledge.

Findings

Algorithms for control landscape optimization are effective with high-throughput data.

Systems biology approaches enhance the design of combinatorial therapies.

Comparison with engineering optimization reveals unique challenges in medical applications.

Abstract

Effective therapy of complex diseases requires control of highly non-linear complex networks that remain incompletely characterized. In particular, drug intervention can be seen as control of signaling in cellular networks. Identification of control parameters presents an extreme challenge due to the combinatorial explosion of control possibilities in combination therapy and to the incomplete knowledge of the systems biology of cells. In this review paper we describe the main current and proposed approaches to the design of combinatorial therapies, including the empirical methods used now by clinicians and alternative approaches suggested recently by several authors. New approaches for designing combinations arising from systems biology are described. We discuss in special detail the design of algorithms that identify optimal control parameters in cellular networks based on a quantitative characterization of control landscapes, maximizing utilization of incomplete knowledge of the state and structure of intracellular networks. The use of new technology for high-throughput measurements is key to these new approaches to combination therapy and essential for the characterization of control landscapes and implementation of the algorithms. Combinatorial optimization in medical therapy is also compared with the combinatorial optimization of engineering and materials science and similarities and differences are delineated.