Robustness and lethality in multilayer biological molecular networks

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.

Robustness in regulatory networks of Epithelial Mesenchymal Plasticity as a function of positive and negative feedback loops

“…Our methods only investigate robustness on the gene regulatory level, however it is important to understand how the interactions between genes, proteins and metabolites contribute to robustness in a multi-layer heterogenous biological network [12]. Moreover, merely counting the number of feedback loops in the network is insufficient to capture the overall network topology, as these feedback loops can be coupled to varying degrees.…”

“…Similarly, biological systems such as cells or organisms also have underlying networks, with interactions that have evolved over millions of years that can impart emergent properties such as robustness to the biological system. Examples of these networks include metabolic networks, protein interaction networks, gene regulatory networks (GRNs), etc [11,12,13,14]. The complexity of the interactions in these networks leads to emergent properties, which manifest as traits of the biological system.…”

“…For example, building on the concept of canalization by Waddington, Kauffman introduced a version of this concept to Boolean network modeling of gene regulatory networks by studying canalizing functions [17]. In the past decade, the mathematical frameworks for characterizing the robustness of networks have been studied in various settings, such as directed networks, embedded networks and many more [12,18,19]. Robustness has been widely investigated in linear control systems [20,21] but it remains very difficult to measure the robustness of complex non-linear systems [22].…”

Emergency of Tsallis statistics in fractal networks

“…The results obtained in the present work have an impact on the formulation of the best strategies for the information spread. In practice, it may have implications on Environment Sciences [ 41 ], since modifications in the local environment may evolve by locally transmitted effects to larger and distant areas [ 42 – 44 ]; Epidemiology, since viruses may evolve, by mutagenesis, to variants that optimize its transmission, a track that would prefer increasing the number of degrees of freedom by extending the period of virus transmission rather than increasing its transmission rate [ 45 , 46 ]; Sociology [ 8 ], since communication among individuals in the society can be made more or less effective by controlling the mechanisms of spreading, what may have an impact in policies and strategies to, e.g., combat fake-news and other irrational behaviours in social media [ 47 – 50 ]. In Computer Sciences [ 15 , 33 ], Biology [ 14 ], Physics [ 21 ], Economics [ 9 ], and Machine Learning [ 51 ], to name just a few.…”

Developing an Embedding, Koopman and Autoencoder Technologies-Based Multi-Omics Time Series Predictive Model (EKATP) for Systems Biology research

“…For example, we are still unclear on the impact of embedding dimensions from high-dimensional nonlinear space to low-dimensional linear space on predictive accuracy and the way to use high-performance computing to increase the efficiency of the EKATP. Applying the EKATP to network biological datasets ( Liu X. et al, 2020 ) is also the direction we need to continue the study. Thus, we will improve the EKATP from these perspectives in the distant future.…”