In recently published work, Michael Levin at Tufts University's Allen Discovery Center shows that the standard models of Gene Regulatory Networks (GRNs) display at least six forms of learning, including operant conditioning. If verified in the lab, this discovery has the potential to reshape approaches to medical interventions, gain access to previously-unavailable (too toxic) drugs, and offer a new theoretical framework to molecular biology and medicine.

Levin proposes to develop theory and perform biological experiments to test the hypothesis that evolutionarily ancient (pre-neural) cellular mechanisms, such as molecular networks, could exhibit learning (a basic aspect of primitive cognition). They will leverage the tools of behavior science, using experiences (specific temporal regimes of stimulation), to control outcomes in gene regulatory networks (GRNs), with major advantages over traditional molecular rewiring (purely mechanist) approaches.

The specific aims are to produce a device and software that not only answer a specific biological question, but form a versatile platform for future advances by many other groups, enabling the discovery of training protocols for any type of cell, for other biomedical purposes. The big question to be addressed is: how can the tools of computational behavior science be brought to bear on molecular networks to solve key open problems in physiology and medicine?

Key needs and knowledge gaps will be addressed by 1) producing a new device and computer software that will 2) uncover effective mechanisms to address problems of drug toxicity, pharmacoresistance, sensitization, and unpredictability in cellular systems (with a focus on cancer physiology), thus 3) addressing a specific pressing biomedical need. Our project will specifically test the hypothesis that associative conditioning (and several other learning types) exist as a practically exploitable phenomenon in GRNs.