Kim Hyobin, Muñoz Stalin, Osuna Pamela, Gershenson Carlos
Biotech Research and Innovation Centre (BRIC), University of Copenhagen (UCPH), 2200 Copenhagen, Denmark.
Novo Nordisk Foundation Center for Stem Cell Biology, DanStem, Faculty of Health Sciences, University of Copenhagen, 2200 Copenhagen, Denmark.
Entropy (Basel). 2020 Sep 4;22(9):986. doi: 10.3390/e22090986.
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.
稳健性和可进化性是生物网络进化的基本属性。为了确定一个生物网络是否稳健和/或可进化，需要比较其在突变前后的功能。然而，随着网络规模的增长，这有时会带来高昂的计算成本。在这里，我们开发了一种预测方法，无需明确比较功能即可估计生物网络的稳健性和可进化性。我们在生物系统的布尔网络模型中测量反脆弱性，并将其用作预测指标。当一个系统从外部扰动中受益时，就会出现反脆弱性。通过原始生物网络和突变生物网络之间反脆弱性的差异，我们训练了一个卷积神经网络（CNN）并对其进行测试，以对稳健性和可进化性的属性进行分类。我们发现我们的CNN模型成功地对这些属性进行了分类。因此，我们得出结论，我们的反脆弱性度量可以用作生物网络稳健性和可进化性的预测指标。
