Bow-tie architectures in biological and artificial neural networks: Implications for network evolution and assay design

Seth Hilliard; Karen Mosoyan; Sergio Branciamore; Grigoriy Gogoshin; Alvin Zhang; Diana L Simons; Russell C Rockne; Peter P Lee; Andrei S Rodin

doi:10.1016/j.isci.2023.106041

Bow-tie architectures in biological and artificial neural networks: Implications for network evolution and assay design

iScience. 2023 Jan 25;26(2):106041. doi: 10.1016/j.isci.2023.106041. eCollection 2023 Feb 17.

Authors

Seth Hilliard¹, Karen Mosoyan¹, Sergio Branciamore¹, Grigoriy Gogoshin¹, Alvin Zhang², Diana L Simons², Russell C Rockne¹, Peter P Lee², Andrei S Rodin¹

Affiliations

¹ Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope National Medical Center, 1500 East Duarte Road, Duarte, CA 91010, USA.
² Department of Immuno-Oncology, Beckman Research Institute, City of Hope National Medical Center, 1500 East Duarte Road, Duarte, CA 91010, USA.

Abstract

Modern artificial neural networks (ANNs) have long been designed on foundations of mathematics as opposed to their original foundations of biomimicry. However, the structure and function of these modern ANNs are often analogous to real-life biological networks. We propose that the ubiquitous information-theoretic principles underlying the development of ANNs are similar to the principles guiding the macro-evolution of biological networks and that insights gained from one field can be applied to the other. We generate hypotheses on the bow-tie network structure of the Janus kinase - signal transducers and activators of transcription (JAK-STAT) pathway, additionally informed by the evolutionary considerations, and carry out ANN simulation experiments to demonstrate that an increase in the network's input and output complexity does not necessarily require a more complex intermediate layer. This observation should guide novel biomarker discovery-namely, to prioritize sections of the biological networks in which information is most compressed as opposed to biomarkers representing the periphery of the network.

Keywords: Artificial intelligence; Molecular network; Neural networks.