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Artificial neurons built on synthetic gene networks have potentialapplications ranging from complex cellular decision-making to bioreactorregulation. Furthermore, due to the high information throughput of naturalsystems, it provides an interesting candidate for biologically-basedsupercomputing and analog simulations of traditionally intractable problems. Inthis paper, we propose an architecture for constructing multicellular neuralnetworks and programmable nonlinear systems. We design an artificial neuronbased on gene regulatory networks and optimize its dynamics for modularity.Using gene expression models, we simulate its ability to perform arbitrarylinear classifications from multiple inputs. Finally, we construct a two-layerneural network to demonstrate scalability and nonlinear decision boundaries anddiscuss future directions for utilizing uncontrolled neurons in computationaltasks.

Towards Multicellular Biological Deep Neural Nets Based on Transcriptional Regulation