2020/02/04 by Hyobin Kim, Stalin Muñoz, Kim, Hyobin +5 · 1 citation
Biochemistry, Genetics and Molecular Biology · #Adaptation and Self-Organizing Systems (nlin.AO) #Bioinformatics and Genomic Networks #Cellular Automata and Lattice Gases (nlin.CG) #Evolution and Genetic Dynamics #FOS: Biological sciences #FOS: Computer and information sciences #FOS: Physical sciences #Gene Regulatory Network Analysis #Machine Learning (cs.LG) #Molecular Networks (q-bio.MN)
paper · pdf · doi:10.48550/arxiv.2002.01571
openalex publication_date 2020/02/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Robustness and evolvability are essential properties to the evolution of biological networks. To determine if a biological network is robust and/or evolvable, it is required to compare its functions before and after mutations. However, this sometimes takes a high computational cost as the network size grows. Here we develop a predictive method to estimate the robustness and evolvability of biological networks without an explicit comparison of functions. We measure antifragility in Boolean network models of biological systems and use this as the predictor. Antifragility occurs when a system benefits from external perturbations. By means of the differences of antifragility between the original and mutated biological networks, we train a convolutional neural network (CNN) and test it to classify the properties of robustness and evolvability. We found that our CNN model successfully classified the properties. Thus, we conclude that our antifragility measure can be used as a predictor of the robustness and evolvability of biological networks.