This project investigates novel computational methods and interventions that might alleviate the suffering caused by complex diseases. Our disease model is the selective killing of cancer cells, but the algorithms developed might also have more general uses for the therapy of other complex diseases. Emerging biological computing paradigms require 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 and thus as information processing. Identification of control parameters presents an extreme challenge due to the combinatorial explosion of control possibilities in combination therapy and to incomplete knowledge of the systems biology of cells. In this project, we design 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. We apply our methods to the control of signal processing that leads to the life/death decision of a cell. In many applications, this control has to be selective. For example, the response to a cytotoxic therapy targeted at cancer cells should ideally occur with minimal response in the normal cells. We define this desired response as selective cell death. The use of new technology for high-throughput measurements, which only recently has become available to academic researchers, is key to this research and essential for the characterization of control landscapes and implementation of the algorithms.
