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

Abstract

Enabled by rapid advances in computational sciences, in silico logical modeling of complex and large biological networks is more and more feasible making it an increasingly popular approach among biologists. Automated high-throughput, drug target identification is one of the primary goals of this in silico network biology. Targets identified in this way are then used to mine a library of drug chemical compounds in order to identify appropriate therapies. While identification of drug targets is exhaustively feasible on small networks, it remains computationally difficult on moderate and larger models. Moreover, there are several important constraints such as off-target effects, efficacy and safety that should be integrated into the identification of targets if the intention is translation to the clinical space. Here we introduce numerical constraints whereby efficacy is represented by efficiency in response and robustness of outcome. This paper introduces an algorithm that relies on a Constraint Satisfaction (CS) technique to efficiently compute the Minimal Intervention Sets (MIS) within a set of often complex clinical safety constraints with the aim of identifying the smallest least invasive set of targets pharmacologically accessible for therapy that most efficiently and reliably achieve the desired outcome.

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

“…Of the 19 original network models 15 , we focus here specifically on model 18, which we believe supports the most biologically plausible predicted immune response dynamics based on criteria described in 17 . Stating the search for idealized intervention sets as a constraint satisfaction problem 18 , we identified 30 intervention solutions 15 that prompt immune signaling predicted by the network model to migrate from a pattern of persistent immune hyperactivation (cytokine storm) to one that more closely resembles an idealized immune resting state. These solutions were computed under conditions of low viral load to highlight best case performance and because they would be used in concert with an effective antiviral treatment.…”

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

“…Predicted behaviors were compared to quantitative measurement of these constructs to determine the logic models that best reflected characteristic patient trajectories leading either to discontinuation or adherence over time. The resulting knowledge was applied to the simulation and design of potential intervention strategies which would reverse discontinuation trajectories ( Sedghamiz et al, 2019b ).…”

“…MIS are the most compact combinations of targetable behavioral mediators that if modulated simultaneously or in sequence could disrupt persistent non-adherence in favor of resuming ET. These intervention strategies are also ranked according to the expected promptness of response and robustness to noise ( Sedghamiz et al, 2019b ).…”

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

“…For instance, K 2 ({1, 4}) defines how entity v 2 behaves when both its activator v 1 and inhibitor v 4 are present simultaneously (which is an increase in its expression since K 2 ({1, 4}) = 1). Adapted from Figure 1A, Sedghamiz et al (2019).…”

“…Dashed edges highlight the interactions that were marked by the model checker not necessary in order to reproduce the three steady states reported in Garg et al (2008). Adapted from Figure 5A, Sedghamiz et al (2019).…”