One of the most important roles of cells is performing their cellular tasks properly for survival. Cells usually achieve robust functionality, for example cell-fate decision-making and signal transduction, through multiple layers of regulation involving many genes. Despite the combinatorial complexity of gene regulation, its quantitative behavior has been typically studied on the basis of experimentally-verified core gene regulatory circuitry, composed of a small set of important elements. It is still unclear how such a core circuit operates in the presence of many other regulatory molecules and in a crowded and noisy cellular environment. Here we report a new computational method, named random circuit perturbation (RACIPE), for interrogating the robust dynamical behavior of a gene regulatory circuit even without accurate measurements of circuit kinetic parameters. RACIPE generates an ensemble of random kinetic models corresponding to a fixed circuit topology, and utilizes statistical tools to identify generic properties of the circuit. By applying RACIPE to simple toggle-switch-like motifs, we observed that the stable states of all models converge to experimentally observed gene state clusters even when the parameters are strongly perturbed. RACIPE was further applied to a proposed 22-gene network of the Epithelial-to-Mesenchymal transition (EMT), from which we identified four experimentally observed gene states, including the states that are associated with two different types of hybrid Epithelial/Mesenchymal phenotypes. Our results suggest that dynamics of a gene circuit is mainly determined by its topology, not by detailed circuit parameters. Our work provides a theoretical foundation for circuit-based systems biology modeling. We anticipate RACIPE to be a powerful tool to predict and decode circuit design principles in an unbiased manner, and to quantitatively evaluate the robustness and heterogeneity of gene expression.

The gain of cellular motility via the epithelial-mesenchymal transition (EMT) is considered crucial in the metastatic cascade. Cells undergoing EMT to varying extents are launched into the bloodstream as single circulating tumor cells (CTCs) or multi-cellular clusters. The frequency and size distributions of these multicellular clusters has been recently measured, but the underlying mechanisms enabling these different modes of migration remain poorly understood. We present a biophysical model that couples the epithelialmesenchymal phenotypic transition and cell migration to explain these different modes of cancer cell migration. With this reduced physical model, we identify a transition from individual migration to clustered cell migration that is regulated by the rate of EMT and the degree of cooperativity between cells during migration. This single cell to clustered migration transition can robustly recapitulate cluster size distributions observed experimentally across several cancer types, thus suggesting the existence of common features in the mechanisms of cell migration during metastasis. Furthermore, we identify three main mechanisms that can facilitate the formation and dissemination of large clusters: first, mechanisms that prevent a complete EMT and instead increase the population of hybrid Epithelial/Mesenchymal (E/M) cells; second, multiple intermediate E/M states that give rise to heterogeneous clusters formed by cells with different epithelial-mesenchymal traits; and third, non-cell-autonomous induction of EMT via cell-tocell signaling that gives rise to spatial correlations among cells in a tissue. Overall, this biophysical model represents a first step toward bridging the gap between the molecular and biophysical understanding of EMT and various modes of cancer cell migration, and highlights that a complete EMT might not be required for metastasis. Statement of significanceThe Epithelial-Mesenchymal Transition (EMT) confers motility and invasive traits to cancer cells. These cells can then enter the circulatory system both as single cells or as multi-cellular clusters to initiate metastases.We develop a biophysical model to investigate how EMT at the single cell level can give rise to a solitary or clustered cell migration. This model quantitatively reproduces cluster size distributions reported in human circulation and mouse models, therefore suggesting similar mechanisms in cancer cell migration across different cancer types. Moreover, we show that a partial EMT to a hybrid epithelial/mesenchymal cell state is sufficient to explain both single cell and clustered migration, therefore questioning the necessity of a complete EMT for cancer metastasis.

Abstract:The selection of amino acid identities that encode new interactions between two-component signaling (TCS) proteins remains a significant challenge. Recent work constructed a coevolutionary landscape that can be used to select mutations to maintain signal transfer interactions between partner TCS proteins without introducing signal transfer between nonpartners (crosstalk). A bigger challenge is to introduce mutations between non-natural partner TCS proteins using the landscape to enhance, suppress, or have a neutral effect on their basal signal transfer rates. This study focuses on the selection of mutations to a response regulator (RR) from Bacilus subtilis and its effect on phosphotransfer with a histidine kinase (HK) from Escherichia Coli. Twelve single-point mutations of the RR protein are selected from the landscape and experimentally expressed to directly test the theoretical predictions on the effect of signal transfer. Differential Scanning Calorimetry is used to monitor any protein stability effects caused by the mutations, which could be detrimental to proper protein function. Of these proteins, seven mutants successfully perturb phosphoryl transfer activity in the computationally predicted manner between the TCS proteins. Furthermore, brute-force exhaustive mutagenesis approaches indicate that only 1% of mutations result in enhanced activity. In comparison, of the six mutations predicted to enhance phosphotransfer, two mutations exhibit a significant enhancement while two mutations are comparable to the wild-type. Thus co-evolutionary landscape theory offers significant improvement over traditional large-scale mutational studies in the efficiency of selecting mutations for protein engineering and design.All rights reserved. No reuse allowed without permission.(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.

Metabolic plasticity enables cancer cells to switch their metabolism phenotypes between glycolysis and oxidative phosphorylation (OXPHOS) during tumorigenesis and metastasis. However, it is still largely unknown how cancer cells orchestrate gene regulation to balance their glycolysis and OXPHOS activities for better survival. Here, we establish a theoretical framework to model the coupling of gene regulation and metabolic pathways in cancer. Our modeling results demonstrate a direct association between the activities of AMPK and HIF-1, master regulators of OXPHOS and glycolysis respectively, with the activities of three metabolic pathways: glucose oxidation, glycolysis and fatty acid oxidation (FAO). Guided by the model, we develop metabolic pathway signatures to quantify the activities of glycolysis, FAO and the citric acid cycle of tumor samples by evaluating the expression levels of enzymes involved in corresponding processes. The association of AMPK/HIF-1 activity with metabolic pathway activity, predicted by the model and verified by analyzing the gene expression and metabolite abundance data of patient samples, is further validated by in vitro studies of aggressive triple negative breast cancer cell lines.