Speaker: Dr Zibo Chen, Westlake University
Title: "A synthetic protein-level neural network in mammalian cells"
Abstract: Artificial neural networks provide a powerful paradigm for information processing that has transformed diverse fields. Within living cells, genetically encoded synthetic molecular networks could, in principle, harness principles of neural computation to classify molecular signals. Here, we combine de novo designed protein heterodimers and engineered viral proteases to implement a synthetic protein circuit that performs winner-take-all neural network computation. This “perceptein” circuit includes modules that compute weighted sums of input protein concentrations through reversible binding interactions, and allow for self-activation and mutual inhibition of protein components using irreversible proteolytic cleavage reactions. Altogether, these interactions comprise a network of 310 chemical reactions stemming from 8 expressed protein species. The complete system achieves signal classification with tunable decision boundaries in mammalian cells. These results demonstrate how engineered protein-based networks can enable programmable signal classification in living cells.
Biography: Zibo Chen obtained his B.Sc. degree in Life Sciences with First Class Honours from National University of Singapore (2013). He received his Ph.D. degree in biochemistry with David Baker and Frank DiMaio at the University of Washington (2013-2018) and did postdoctoral work in synthetic biology with Michael Elowitz at Caltech (2019-2022), before joining Weslake University in 2022. Research in the Chen lab focuses on programming biology using proteins as the coding language.