FET: Small: Rapid and Rational Drug-Cocktail Formulation and Discovery Via Electronic Circuits

Dartmouth College, Hanover NH

Investigators

Abstract

The project aims to create a foundational emerging technology tool for rapid drug-cocktail formulation in current pandemic, future pandemics, or for systems diseases such as cancer. The methodology is founded on fundamental similarities between biological circuits and networks and electronic circuits and networks. In particular, complex interactions between networks of molecules in cells can be exactly represented by mathematically equivalent interactions between networks of electronic circuit parts. Furthermore, high-throughput supercomputing drug-cocktail discovery is possible on electronic transistor circuits that map such equivalents to integrated-circuit electronic chips. Thus, in the case of the recent pandemic, complex interactions between viral circuits and immune circuits lead to optimal cocktails that optimize antiviral vs. immunosuppressant activity that are well fit by measured patient data. A two-course sequence on Biological Circuit Engineering is needed to demonstrate how to generalize the approach for other applications such as in biotechnology or in environmental monitoring. The teaching and research environments supported by this grant will host, support, and mentor underrepresented minorities via graduate and undergraduate programs. Electronic circuits intuitively visualize and quantitatively simulate biological systems with nonlinear differential equations that exhibit complicated dynamics. Such interactions can be probabilistic, noisy, nonlinear, asynchronous, and can instantiate highly interconnected networks. The project leverages deep similarities between common thermodynamic laws that govern probabilistic chemical reaction flux and probabilistic electronic current flow in transistors. Thus, the project enables supercomputing stochastic biological simulations on specialized cytomorphic chips with automatic incorporation of physical energy, stochastic, and molecular constraints, as well as of pathway feedback loops. Constraint incorporation, high-throughput search and learning, the fitting of measured biological data, and network robustness enable model-order reduction and good generalization of the foundational emerging technology tool for discovery. For example, stochastic chaos-like behavior in viral-immune interactions that has been recently discovered and characterized by the research team will be studied further. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.