Robustness and lethality in multilayer biological molecular networks

Xueming Liu (), Enrico Maiorino, Arda Halu, Kimberly Glass, Rashmi B. Prasad, Joseph Loscalzo, Jianxi Gao () and Amitabh Sharma ()
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Xueming Liu: Huazhong University of Science and Technology
Enrico Maiorino: Brigham and Women’s Hospital, Harvard Medical School
Arda Halu: Brigham and Women’s Hospital, Harvard Medical School
Kimberly Glass: Brigham and Women’s Hospital, Harvard Medical School
Rashmi B. Prasad: Lund University Diabetes Centre, CRC
Joseph Loscalzo: Brigham and Women’s Hospital, Harvard Medical School
Jianxi Gao: Rensselaer Polytechnic Institute
Amitabh Sharma: Brigham and Women’s Hospital, Harvard Medical School

Nature Communications, 2020, vol. 11, issue 1, 1-12

Abstract: Abstract Robustness is a prominent feature of most biological systems. Most previous related studies have been focused on homogeneous molecular networks. Here we propose a comprehensive framework for understanding how the interactions between genes, proteins and metabolites contribute to the determinants of robustness in a heterogeneous biological network. We integrate heterogeneous sources of data to construct a multilayer interaction network composed of a gene regulatory layer, a protein–protein interaction layer, and a metabolic layer. We design a simulated perturbation process to characterize the contribution of each gene to the overall system’s robustness, and find that influential genes are enriched in essential and cancer genes. We show that the proposed mechanism predicts a higher vulnerability of the metabolic layer to perturbations applied to genes associated with metabolic diseases. Furthermore, we find that the real network is comparably or more robust than expected in multiple random realizations. Finally, we analytically derive the expected robustness of multilayer biological networks starting from the degree distributions within and between layers. These results provide insights into the non-trivial dynamics occurring in the cell after a genetic perturbation is applied, confirming the importance of including the coupling between different layers of interaction in models of complex biological systems.

Date: 2020
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DOI: 10.1038/s41467-020-19841-3

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