Every cell can be thought of as a computing device that takes a variety of physical and chemical stimuli as inputs, and combines them using an analog circuit of biomolecular interactions to generate outputs that aid its survival and perform its physiological function. Building such circuits from the bottom up, using basic biophysical principles of molecular recognition and chemical dynamics, provides insights into the underlying physiochemical design principles of biological systems, and allows predictive construction of useful circuits with chosen inputs and outputs that go beyond the ones found in nature. This project will develop a new class of fast biological circuitry that can detect a variety of chosen inputs, such as light and small molecule chemicals, and rapidly (and controllably) respond by generating cell survival and function-determining outputs. The principles being interrogated in this project should enable the design of artificial circuits for a variety of applications including controllable biomanufacturing of pharmaceuticals and biodegradation of pollutants. The project will offer training and research opportunities for several undergraduate students especially from the underrepresented communities via involvement of the local SACNAS chapter at Rutgers University started by the PI. This project will also support teams built by undergraduates to participate in tri-state area biomolecular design competitions.
Several synthetic biological circuits using transcriptional networks in living cells, to enable biological computation with living cells, have been built in the last decade. However, the slow timescales for transcription-based circuit performance (hours to days), heterogeneity and lack of precise control over the levels of biological components, limit the speed, applicability and robustness of transcriptional circuits. The speed of the circuit is especially critical for applications where rapid sensing-and-response is required. This proposal's goal is the development of protease enzyme-based circuits using computationally designed/engineered proteases that can be made responsive to chosen input stimuli such as small molecule chemicals and light, and mutually orthogonal with respect to their substrate selectivity. Well-characterized, mutually orthogonal and diverse class of chemical/light-inducers of dimerization domains (as sensors), designed split-protease enzymes (as actuators), and a redesigned cell survival-regulating enzyme (as output) are being used to construct, characterize and optimize elementary logic gates and circuit elements. This project involves optimization of the emergent properties of circuit elements using chemical dynamics modeling methods that will allow combining multiple input signals in complex ways. Overall, this research develops a combined computational and experimental design strategy to construct stimulus-responsive, protease-based logic gates in cells, and involves optimizing their performance using a mathematical treatment of their emergent behavior in a chemical reaction network modeling framework. This project is jointly supported by the Molecular Biophysics Cluster of the Molecular and Cellular Biosciences Division in the Directorate for Biological Sciences and Cellular and Biochemical Engineering Cluster in the Chemical, Bioengineering, Environmental and Transport System Division of Engineering Directorate.
