Towards Multicellular Biological Deep Neural Nets Based on Transcriptional Regulation
This work addresses the challenge of building biologically-based computing systems for applications like cellular decision-making and analog simulations, representing an incremental advance in synthetic biology.
The authors proposed an architecture for constructing multicellular neural networks using artificial neurons based on gene regulatory networks, simulating their ability to perform arbitrary linear classifications and demonstrating scalability with a two-layer network for nonlinear decision boundaries.
Artificial neurons built on synthetic gene networks have potential applications ranging from complex cellular decision-making to bioreactor regulation. Furthermore, due to the high information throughput of natural systems, it provides an interesting candidate for biologically-based supercomputing and analog simulations of traditionally intractable problems. In this paper, we propose an architecture for constructing multicellular neural networks and programmable nonlinear systems. We design an artificial neuron based on gene regulatory networks and optimize its dynamics for modularity. Using gene expression models, we simulate its ability to perform arbitrary linear classifications from multiple inputs. Finally, we construct a two-layer neural network to demonstrate scalability and nonlinear decision boundaries and discuss future directions for utilizing uncontrolled neurons in computational tasks.