The use of machine learning to discover regulatory networks controlling biological systems

Rossin Erbe; Jessica Gore; Kelly Gemmill; Daria A Gaykalova; Elana J Fertig

doi:10.1016/j.molcel.2021.12.011

The use of machine learning to discover regulatory networks controlling biological systems

Mol Cell. 2022 Jan 20;82(2):260-273. doi: 10.1016/j.molcel.2021.12.011. Epub 2022 Jan 10.

Authors

Rossin Erbe¹, Jessica Gore², Kelly Gemmill³, Daria A Gaykalova⁴, Elana J Fertig⁵

Affiliations

¹ Department of Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA; Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD, USA; Convergence Institute, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD, USA.
² Institute for Genome Sciences, University of Maryland Medical Center, Baltimore, MD, USA.
³ Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD, USA; Convergence Institute, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD, USA.
⁴ Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD, USA; Convergence Institute, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD, USA; Institute for Genome Sciences, University of Maryland Medical Center, Baltimore, MD, USA; Department of Otorhinolaryngology-Head and Neck Surgery, University of Maryland Medical Center, Baltimore, MD, USA; Marlene & Stewart Greenebaum Comprehensive Cancer Center, University of Maryland Medical Center, Baltimore, MD, USA.
⁵ Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD, USA; Convergence Institute, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD, USA; Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, USA; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA. Electronic address: ejfertig@jhmi.edu.

Abstract

Biological systems are composed of a vast web of multiscale molecular interactors and interactions. High-throughput technologies, both bulk and single cell, now allow for investigation of the properties and quantities of these interactors. Computational algorithms and machine learning methods then provide the tools to derive meaningful insights from the resulting data sets. One such approach is graphical network modeling, which provides a computational framework to explicitly model the molecular interactions within and between the cells comprising biological systems. These graphical networks aim to describe a putative chain of cause and effect between interacting molecules. This feature allows for determination of key molecules in a biological process, accelerated generation of mechanistic hypotheses, and simulation of experimental outcomes. We review the computational concepts and applications of graphical network models across molecular scales for both intracellular and intercellular regulatory biology, examples of successful applications, and the future directions needed to overcome current limitations.

Keywords: computational biology; genomics; machine learning; multiomics; networks.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Review

MeSH terms

Animals
Computational Biology*
Gene Expression Regulation
Gene Regulatory Networks*
Humans
Machine Learning*
Models, Biological
Protein Interaction Maps*
Research Design
Signal Transduction

Abstract

Publication types

MeSH terms

Grants and funding