Control of cellular signal transduction networks using a stochastic search algorithm

Cellular activities, such as gene expression, are mediated by diverse signal transduction cascades. Effective regulation of these pathways requires not only the understanding of individual pathways, but also the knowledge of their interactions. With a closed-loop optimization modality to circumvent the need for detailed information of biological signaling and regulatory networks, we demonstrate cellular induction towards a desired phenotypic response by efficiently searching through a large parametric space of chemical effectors. A transcription factor, nuclear factor kappa B, was chosen as the model system to explore this approach. Only tens of tests out of one million possible trials were needed to ascertain the most potent combination of these cytokines using the Gur Game, a stochastic search algorithm, in the feedback loop. Several underlying interactions of the signal transduction pathways were revealed in human embryonic kidney 293T cells. This closed-loop optimization approach offers a methodology to control, and eventually understand complex biological systems.