The in silico study and reverse engineering of regulatory networks has gained in recognition as an insightful tool for the qualitative study of biological mechanisms that underlie a broad range of complex illness. In the creation of reliable network models, the integration of prior mechanistic knowledge with experimentally observed behavior is hampered by the disparate nature and widespread sparsity of such measurements. The former challenges conventional regression-based parameter fitting while the latter leads to large sets of highly variable network models that are equally compliant with the data. In this paper, we propose a bounded Constraint Satisfaction (CS) based model checking framework for parameter set identification that readily accommodates partial records and the exponential complexity of this problem. We introduce specific criteria to describe the biological plausibility of competing multi-valued regulatory networks that satisfy all the constraints and formulate model identification as a multi-objective optimization problem. Optimization is directed at maximizing structural parsimony of the regulatory network by mitigating excessive control action selectivity while also favoring increased state transition efficiency and robustness of the network's dynamic response. The framework's scalability, computational time and validity is demonstrated on several well-established and well-studied biological networks.
A major complication in COVID-19 infection consists in the onset of acute respiratory distress fueled by a dysregulation of the host immune network that leads to a run-away cytokine storm. Here, we present an in silico approach that captures the host immune system’s complex regulatory dynamics, allowing us to identify and rank candidate drugs and drug pairs that engage with minimal subsets of immune mediators such that their downstream interactions effectively disrupt the signaling cascades driving cytokine storm. Drug–target regulatory interactions are extracted from peer-reviewed literature using automated text-mining for over 5000 compounds associated with COVID-induced cytokine storm and elements of the underlying biology. The targets and mode of action of each compound, as well as combinations of compounds, were scored against their functional alignment with sets of competing model-predicted optimal intervention strategies, as well as the availability of like-acting compounds and known off-target effects. Top-ranking individual compounds identified included a number of known immune suppressors such as calcineurin and mTOR inhibitors as well as compounds less frequently associated for their immune-modulatory effects, including antimicrobials, statins, and cholinergic agonists. Pairwise combinations of drugs targeting distinct biological pathways tended to perform significantly better than single drugs with dexamethasone emerging as a frequent high-ranking companion. While these predicted drug combinations aim to disrupt COVID-induced acute respiratory distress syndrome, the approach itself can be applied more broadly to other diseases and may provide a standard tool for drug discovery initiatives in evaluating alternative targets and repurposing approved drugs.
e18063 Background: Epithelial-to- mesenchymal transition (EMT) plays a key role not only in cancer invasiveness and progression but also in treatment resistance. Experimental models suggest that epithelial and mesenchymal phenotypes may represent steady states supported by the regulatory logic of intracellular signaling networks. Using known regulatory interactions between intracellular signaling molecules as a framework, we explored EMT in silico with the aim of identifying potentially novel means of inhibiting metastasis in ovarian cancer. Methods: A mathematical model connecting 27 genes involved in EMT through 153 regulatory interactions was assembled from the Mogrify, Reactome, String and Pathway Studio (PS) databases, the last of which used the MedScan natural language processing (NLP) engine to process over 8,000 full-text publications. Logic model parameters dictating the regulatory response dynamics of the EMT circuit were constrained to values that accurately predicted the proteomic profiles characterized as either epithelial or mesenchymal in 30 cell lines in the publicly available MaxQB database. Results: A total of 94,672 candidate models predicted the protein expression in stable epithelial and mesenchymal cells equally well, clustering into 24 classes of slightly different EMT transition kinetics. Discrete event simulations based on these models identified 7 unique combinations, involving the concurrent modulation of at least 3 gene products capable of effecting this transition successfully even in the presence of noise. Every combination required inactivation of ZEB2, along with 2 other manipulations. Inactivation of TP53, HOXA5, or IRF7 was required in 3 combinations, while inactivation of ICAM1 was required in 2 cases. Only BCL2 was required to be activated in any of these scenarios. Interestingly, BCL2 inhibition is now applied clinically in some hematologic cancers and proving effective in patient-derived cell lines for high-grade serous ovarian cancer when combined with MEK inhibition. Conclusions: This work suggests that EMT might at least in part be driven by normal regulatory interactions between specific proteins in a network of pathways, without the requirement for additional mutations or alterations in the underlying circuitry. Maintenance of ZEB2 or TP53 activation as well as BCL2 inhibition may thus represent promising avenues for future research in arresting EMT and associated chemo-resistance.
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