Antifragility Predicts the Robustness and Evolvability of Biological Networks through Multi-Class Classification with a Convolutional Neural Network

Hyobin Kim, Stalin Muñoz, Pamela Osuna, Carlos Gershenson

Research output: Contribution to journalArticle

Abstract

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.
Original languageEnglish
Article number986
JournalEntropy
Volume22
Issue number9
DOIs
Publication statusPublished - 4 Sep 2020

Keywords

  • Robustness
  • Evolvability
  • Antifragility
  • Complexity
  • Prediction
  • Boolean networks
  • Gene Regulatory Networks
  • Convolutional neural networks

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