Elucidating the design principles of regulatory networks driving cellular decision-making has fundamental implications in mapping and eventually controlling cell-fate decisions. Despite being complex, these regulatory networks often only give rise to a few phenotypes. Previously, we identified two 'teams' of nodes in a small cell lung cancer regulatory network that constrained the phenotypic repertoire and aligned strongly with the dominant phenotypes obtained from network simulations (Chauhan et al., 2021). However, it remained elusive whether these 'teams' exist in other networks, and how do they shape the phenotypic landscape. Here, we demonstrate that five different networks of varying sizes governing epithelial-mesenchymal plasticity comprised of two 'teams' of players - one comprised of canonical drivers of epithelial phenotype and the other containing the mesenchymal inducers. These 'teams' are specific to the topology of these regulatory networks and orchestrate a bimodal phenotypic landscape with the epithelial and mesenchymal phenotypes being more frequent and dynamically robust to perturbations, relative to the intermediary/hybrid epithelial/ mesenchymal ones. Our analysis reveals that network topology alone can contain information about corresponding phenotypic distributions, thus obviating the need to simulate them. We propose 'teams' of nodes as a network design principle that can drive cell-fate canalization in diverse decision-making processes.

Elucidating the principles of cellular decision-making is of fundamental importance. These decisions are often orchestrated by underlying regulatory networks. While we understand the dynamics of simple network motifs, how do large networks lead to a limited number of phenotypes, despite their complexity, remains largely elusive. Here, we investigate five different networks governing epithelial-mesenchymal plasticity and identified a latent design principles in their topology that limits their phenotypic repertoire - the presence of two 'teams' of nodes engaging in a mutually inhibitory feedback loop, forming a toggle switch. These teams are specific to these networks and directly shape the phenotypic landscape and consequently the frequency and stability of terminal phenotypes vs. the intermediary ones. Our analysis reveals that network topology alone can contain information about phenotypic distributions it can lead to, thus obviating the need to simulate them. We unravel topological signatures that can drive canalization of cell-fates during diverse decision-making processes.

Understanding the dynamical hallmarks of network motifs is one of the fundamental aspects of systems biology. Positive feedback loops constituting one or two nodes -self-activation, toggle switch, and double activation loops -are the commonly observed motifs in regulatory networks underlying cell-fate decision systems. Their individual dynamics are well studied; they are capable of exhibiting bistability. However, studies across various biological systems suggest that such positive feedback loops are interconnected with one another, and design principles of coupled bistable motifs remain unclear. What happens to the bistability or multistability traits and the phenotypic space (collection of phenotypes exhibited by a system) due to the couplings? In this study, we explore a set of such interactions using discrete and continuous simulation methods. Our results suggest that the most frequent states in coupled networks follow the 'rules' within a motif (double activation, toggle switch) and those across the two motifs in terms of how the two motifs have been coupled. Moreover, 'hybrid' states can be observed, too, where one of the above-mentioned 'rules' can be compromised, leading to a more diverse phenotypic repertoire. Furthermore, adding direct and indirect selfactivations to these coupled networks can increase the frequency of multistability. Thus, our observations revealed specific dynamical traits exhibited by various coupled bistable motifs.