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.
Gene regulatory networks (GRNs) are critically important for efforts in biomedicine and biotechnology. Here, we introduce the Regulatory Network Machine framework, demonstrating how GRNs behave as analog computers capable of sophisticated information processing.
Cells can compensate a disruptive change in one ion channel by compensatory changes in other channels. This work has simulated the adaptation of a multicellular aggregate of non-excitable cells to the electrophysiological perturbation produced by the external blocking of a cation channel. In the biophysical model employed, the researchers consider that this blocking provokes a cell depolarization that opens a voltage-gated calcium channel, thus allowing toxic Ca2+ levels...
Broadly considered, morphogenesis is the ability of groups of cells to build complex, functional anatomical structures. A multiscale agent-based model of morphogenesis that quantitatively examined the impact of stress sharing (where stress is a physiological parameter reflecting error in a homeostatic loop) on the ability to reach target morphology was constructed and analyzed. The research found stress sharing improves the morphogenetic efficiency of multicellular collectives; populations with stress sharing reached anatomical targets faster. Moreover, stress sharing influenced the future fate of distant cells in the multi-cellular collective, enhancing cells’ movement and their radius of influence, consistent with the hypothesis that stress sharing works to increase cohesiveness of collectives. These analyses support an important role for stress sharing in natural and engineered systems that seek robust large-scale behaviors to emerge from the activity of their competent components.
The hypothesis explored is that collective intelligence is not only the province of groups of animals, and that an important symmetry exists between the behavioral science of swarms and the competencies of cells and other biological systems at different scales. The implications of this approach are outlined, as is the possible impact of tools from the field of diverse intelligence for regenerative medicine and synthetic bioengineering.
