Boolean Modeling of Cellular and Molecular Pathways Involved in Influenza Infection

Abstract

Systems virology integrates host-directed approaches with molecular profiling to understand viral pathogenesis. Self-contained statistical approaches that combine expression profiles of genes with the available databases defining the genes involved in the pathways (gene-sets) have allowed characterization of predictive gene-signatures associated with outcome of the influenza virus (IV) infection. However, such enrichment techniques do not take into account interactions among pathways that are responsible for the IV infection pathogenesis. We investigate dendritic cell response to seasonal H1N1 influenza A/New Caledonia/20/1999 (NC) infection and infer the Boolean logic rules underlying the interaction network of ligand induced signaling pathways and transcription factors. The model reveals several novel regulatory modes and provides insights into mechanism of cross talk between NFκB and IRF mediated signaling. Additionally, the logic rule underlying the regulation of IL2 pathway that was predicted by the Boolean model was experimentally validated. Thus, the model developed in this paper integrates pathway analysis tools with the dynamic modeling approaches to reveal the regulation between signaling pathways and transcription factors using genome-wide transcriptional profiles measured upon influenza infection.

Mathematical modeling of ventilator-induced lung inflammation

“…To our knowledge, no mathematical models have described M1/M2 interactions specific to the immune response to VILI. Many models have examine the immune response to bacterial and viral infections, such as pneumonia (Schirm et al, 2016;Mochan et al, 2014;Smith et al, 2011), tuberculosis (Day et al, 2009;Raman et al, 2010;Segovia-Juarez et al, 2004), and influenza (Manchanda et al, 2014;Anderson et al, 2016;Hancioglu et al, 2007). Additionally, models related to smoking and asthma (Brown et al, 2011;Chernyavsky et al, 2014;Golov et al, 2017;Pothen et al, 2015), mechanical ventilation (Hickling, 1998;Marini et al, 1989;Pidaparti et al, 2013), and general inflammatory stress (Reynolds et al, 2010) have been developed, but these models generally deal with the mechanics of the airways, including airflow, pressure, and gas exchange, and how these mechanics respond to inflammation and particle inhalation without accounting for the various cells types involved in the immune response.…”

“…Additionally, models related to smoking and asthma (Brown et al, 2011;Chernyavsky et al, 2014;Golov et al, 2017;Pothen et al, 2015), mechanical ventilation (Hickling, 1998;Marini et al, 1989;Pidaparti et al, 2013), and general inflammatory stress (Reynolds et al, 2010) have been developed, but these models generally deal with the mechanics of the airways, including airflow, pressure, and gas exchange, and how these mechanics respond to inflammation and particle inhalation without accounting for the various cells types involved in the immune response. Models have also been developed to understand and analyze the molecular mechanisms that govern the phenotype switch that macrophages undergo from pro-inflammatory to anti-inflammatory, as well as other important subcellular pathways (Anderson et al, 2016;Braun et al, 2013;Maiti et al, 2014).…”

“…Common modeling approaches used in these papers include agent based modeling (Brown et al, 2011;Pothen et al, 2015;Segovia-Juarez et al, 2004), partial differential equations (Pidaparti et al, 2013;Reynolds et al, 2010), ordinary differential equations (Chernyavsky et al, 2014;Day et al, 2009;Hancioglu et al, 2007;Mochan et al, 2014;Schirm et al, 2016;Smith et al, 2011), and Boolean models (Anderson et al, 2016). Each technique has its advantages and disadvantages, but we choose to model the inflammatory response to VILI, specifically the resulting damage to epithelial cells, using a set of coupled ordinary differential equations (ODEs), which we describe further in the following section.…”

Mathematical and Computational Modeling in Complex Biological Systems

“…Anderson et al proposed an approach for integrating gene set enrichment methods with Boolean dynamic modeling to reveal the induction of a densely connected network of cellular (TFs) and molecular (ligands) signaling upon influenza virus infection of dendritic cell [72]. Kaderali and colleagues also inferred signaling pathways from gene knockdown data using Boolean networks with probabilistic Boolean threshold functions [73].…”

Frontline Science: c-Myc regulates P-selectin glycoprotein ligand-1 expression in monocytes during HIV-1 infection

“…The status of the system across time was simulated by repeatedly applying the Boolean rules for each node until a stationary state was found (e.g., no change in the activities of the following key nodes: p52, p65, STAT protein, CTCF, ERK, JNK, p38, GSH, GSSG, O 2 , and H 2 O 2 ). As the kinetics and time scales of the individual processes represented as edges are not known, a random order asynchronous update was selected, wherein the time scales of each regulatory process were randomly chosen in such a way that the node states were updated in a randomly selected order during each time step [27][28][29]. The asynchronous algorithm was…”