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Our lab uses a systems biology approach to the study of combinatorial control in biology and pharmacology. Combined drug interventions are an increasingly common therapeutic approach to complex diseases, for example in cancer. Drugs are, however, usually developed individually and only later combined empirically in the clinic based on their known effects as single-therapy agents. We are interested in the problem of inducing selective cancer cell death. We have developed and validated search algorithms to discover optimal combinations of three or more drugs that would be infeasible to identify by fully combinatorial searches.

In our procedure the optimization is not carried out in silico, but directly in an in vivo high-throughput system, where the response to therapeutic combinations is used as information to guide the system toward improved combinations using an iterative algorithm. System-wide molecular measurements (for example metabolomics and transcriptomics) and models of metabolism and of signal transduction can also be incorporated in these algorithms. It is useful to view the information processing by our experimental cellular systems as biological computations, since the algorithms we use are indeed often derived from algorithms that are implemented in silico in other scientific fields.

Cells are regulated by networks of controllers having many targets, and targets affected by many controllers, but these “many-to-many” combinatorial control systems are poorly understood. We have analyzed distinct cellular networks (transcription factors, microRNAs, and protein kinases) and a drug-target network. Certain network properties seem universal across systems and species, suggesting the existence of common control strategies in biology. These findings suggest a completely new approach to pharmacological control of biological systems. Molecular tools, such as kinase inhibitors, are now available to test if therapeutic combinations may benefit from being designed with biomimetic properties, such as “many-to-many” targeting, very wide concurrent coverage of the target set, and redundancy of incoming links per target.

We also use the fruit fly (Drosophila) and cell lines to study metabolic alterations caused by aging and hypoxia, using high-throughput physiological measurements, NMR metabolomics and models of metabolism.

Our multi-disciplinary team is composed of biomedical and computational scientists, and we have close collaborations with physicists, engineers, bioengineers and clinicians.

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Page last modified on June 28, 2017, at 06:33 AM