2022/04/11 by Sara Maria Brancato, Francesco De Lellis, Brancato, Sara Maria +7
Biochemistry, Genetics and Molecular Biology · #CRISPR and Genetic Engineering #FOS: Biological sciences #FOS: Computer and information sciences #FOS: Electrical engineering #Gene Regulatory Network Analysis #Machine Learning (cs.LG) #Molecular Networks (q-bio.MN) #Quantitative Methods (q-bio.QM) #Systems and Control (eess.SY) #Viral Infectious Diseases and Gene Expression in Insects #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2204.04972
openalex publication_date 2022/04/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We investigate the problem of using a learning-based strategy to stabilize a synthetic toggle switch via an external control approach. To overcome the data efficiency problem that would render the algorithm unfeasible for practical use in synthetic biology, we adopt a sim-to-real paradigm where the policy is learnt via training on a simplified model of the toggle switch and it is then subsequently exploited to control a more realistic model of the switch parameterized from in-vivo experiments. Our in-silico experiments confirm the viability of the approach suggesting its potential use for in-vivo control implementations.