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

Abstract

Biological networks are widely reported to be robust to both external and internal perturbations. However, the exact mechanisms and design principles that enable robustness are not yet fully understood. Here we investigated dynamic and structural robustness in biological networks with regards to phenotypic distribution and plasticity. We use two different approaches to simulate these networks: a computationally inexpensive, parameter-independent continuous model, and an ODE-based parameter-agnostic framework (RACIPE), both of which yield similar phenotypic distributions. Using perturbations to network topology and by varying network parameters, we show that multistable biological networks are structurally and dynamically more robust as compared to their randomized counterparts. These features of robustness are governed by an interplay of positive and negative feedback loops embedded in these networks. Using a combination of the number of negative and positive feedback loops weighted by their lengths and sign, we identified a metric that can explain the structural and dynamical robustness of these networks. This metric enabled us to compare networks across multiple sizes, and the network principles thus obtained can be used to identify fragilities in large networks without simulating their dynamics. Our analysis highlights a network topology based approach to quantify robustness in multistable biological networks.

The Arabidopsis transcription factor NLP2 regulates early nitrate responses and integrates nitrate assimilation with energy and carbon skeleton supply

“…Interestingly, the NLP2-TF 2/3 -dependent downstream events also contributed to the direct NLP2-dependent transcriptional wave, since 75% of the nlp2-2 nitrate-regulated DEGs bound by NLP2 were also predicted to be bound by at least one NLP2-TF 2 and/or NLP2-TF 3 member. These multi-tier regulations via TF 2/3 s acting as positive or negative feedback loops are consistent with recent observations underscoring the robustness of GRNs imparted by such feedback loops (Hebbar et al, 2021).…”