Alexis Pietak, Michael Levin

Regulatory networks such as gene regulatory networks (GRNs) are critically important for efforts in biomedicine and synthetic biology. They have classically been viewed as mechanistic, “clockwork-like” systems, assumed to require direct changes to network topology via genetic modification to effect significant, stable changes in their output functions. This perspective limits therapeutic approaches, suggesting a need for alternative conceptual framing. Here we show how regulatory networks can behave as analog computational agents to perform sophisticated information processing, driven by patterns of stimulus inputs, without a change in network topology. We introduce and develop a new conceptual and computational framework for working with regulatory networks called the Regulatory Network Machine (RNM). Given a regulatory network model, our RNM framework enables the construction of detailed maps that embody the “software-like” nature of a regulatory network, providing easy identification of the specific interventions necessary to achieve desired outcomes. We demonstrate the use of our RNM framework to gain insights into important biological examples including yeast osmoadaptation, PI3K/AKT/mTor cross-signaling cascades, and embryonic stem cell differentiation. Importantly, we show how system-level outcomes can be induced in a biological system without requiring genetic rewiring. Our RNM approach also elucidates system factors that support the innate computational capabilities of regulatory networks, and ascertains the interventions that provide the most control for the least amount of effort. Ultimately, we hope to use insights gained from our RNM framework to expand the horizons of biomedicine, providing an effective avenue to move beyond “single-factor, single treatment” and “one-constant-dose” biomedical paradigms.

Read the full article at: www.researchgate.net

• Papers