Computation of Robust Minimal Intervention Sets in Multi-Valued Biological Regulatory Networks

A regulatory pathway model of neuropsychological disruption in Havana syndrome

Introduction

In 2016 diplomatic personnel serving in Havana, Cuba, began reporting audible sensory phenomena paired with onset of complex and persistent neurological symptoms consistent with brain injury. The etiology of these Anomalous Health Incidents (AHI) and subsequent symptoms remains unknown. This report investigates putative exposure-symptom pathology by assembling a network model of published bio-behavioral pathways and assessing how dysregulation of such pathways might explain loss of function in these subjects using data available in the published literature. Given similarities in presentation with mild traumatic brain injury (mTBI), we used the latter as a clinically relevant means of evaluating if the neuropsychological profiles observed in Havana Syndrome Havana Syndrome might be explained at least in part by a dysregulation of neurotransmission, neuro-inflammation, or both.

Method

Automated text-mining of >9,000 publications produced a network consisting of 273 documented regulatory interactions linking 29 neuro-chemical markers with 9 neuropsychological constructs from the Brief Mood Survey, PTSD Checklist, and the Frontal Systems Behavior Scale. Analysis of information flow through this network produced a set of regulatory rules reconciling to within a 6% departure known mechanistic pathways with neuropsychological profiles in N = 6 subjects.

Results

Predicted expression of neuro-chemical markers that jointly satisfy documented pathways and observed symptom profiles display characteristically elevated IL-1B, IL-10, NGF, and norepinephrine levels in the context of depressed BDNF, GDNF, IGF1, and glutamate expression (FDR < 5%). Elevations in CRH and IL-6 were also predicted unanimously across all subjects. Furthermore, simulations of neurological regulatory dynamics reveal subjects do not appear to be "locked in" persistent illness but rather appear to be engaged in a slow recovery trajectory.

Discussion

This computational analysis of measured neuropsychological symptoms in Havana-based diplomats proposes that these AHI symptoms may be supported in part by disruption of known neuroimmune and neurotransmission regulatory mechanisms also associated with mTBI.

Old drugs, new tricks: leveraging known compounds to disrupt coronavirus-induced cytokine storm

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.

Network Modeling of Complex Time-Dependent Changes in Patient Adherence to Adjuvant Endocrine Treatment in ER+ Breast Cancer

Early patient discontinuation from adjuvant endocrine treatment (ET) is multifactorial and complex: Patients must adapt to various challenges and make the best decisions they can within changing contexts over time. Predictive models are needed that can account for the changing influence of multiple factors over time as well as decisional uncertainty due to incomplete data. AtlasTi8 analyses of longitudinal interview data from 82 estrogen receptor-positive (ER+) breast cancer patients generated a model conceptualizing patient-, patient-provider relationship, and treatment-related influences on early discontinuation. Prospective self-report data from validated psychometric measures were discretized and constrained into a decisional logic network to refine and validate the conceptual model. Minimal intervention set (MIS) optimization identified parsimonious intervention strategies that reversed discontinuation paths back to adherence. Logic network simulation produced 96 candidate decisional models which accounted for 75% of the coordinated changes in the 16 network nodes over time. Collectively the models supported 15 persistent end-states, all discontinued. The 15 end-states were characterized by median levels of general anxiety and low levels of perceived recurrence risk, quality of life (QoL) and ET side effects. MIS optimization identified 3 effective interventions: reducing general anxiety, reinforcing pill-taking routines, and increasing trust in healthcare providers. Increasing health literacy also improved adherence for patients without a college degree. Given complex regulatory networks’ intractability to end-state identification, the predictive models performed reasonably well in identifying specific discontinuation profiles and potentially effective interventions.

Bio-ModelChecker: Using Bounded Constraint Satisfaction to Seamlessly Integrate Observed Behavior With Prior Knowledge of Biological Networks

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.