2019/12/24 by Sihao Huang, Huang, Sihao · 2 voices
Biochemistry, Genetics and Molecular Biology · Computer Science · #Cell Image Analysis Techniques #Emerging Technologies (cs.ET) #FOS: Biological sciences #FOS: Computer and information sciences #Gene Regulatory Network Analysis #Molecular Networks (q-bio.MN) #Neural and Evolutionary Computing (cs.NE) #Single-cell and spatial transcriptomics #cs.ET #cs.NE #q-bio.MN
paper · pdf · doi:10.48550/arxiv.1912.11423
openalex publication_date 2019/12/24 · arxiv published 2019/12/24 · openalex created_date 2020/01/10 · arxiv updated 2020/01/30 · openalex updated_date 2026/07/28
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.