Constraint-based network model of pathogen–immune system interactions

Abstract

Pathogenic bacteria such as Bordetella bronchiseptica modulate host immune responses to enable their establishment and persistence; however, the immune response is generally successful in clearing these bacteria. Here, we model the dynamic outcome of the interplay between host immune components and B. bronchiseptica virulence factors. The model extends our previously published interaction network of B. bronchiseptica and includes the existing experimental information on the relative timing of IL10 and IFNg activation in the form of qualitative inequalities. The current model improves the previous one in two directions: (i) by augmenting the network with new nodes with specific function in T helper cell differentiation and effector mechanisms and (ii) by using a dynamic approach that allows us to quantify node states and mechanisms revealed to be important from our previous model. The model makes predictions about the time scales of each process, the activity thresholds of each node and novel regulatory interactions. For example, the model predicts that the activity threshold of IL4 is higher than that of IL12 and that pro-inflammatory cytokines regulate the activity of Th2 cells. Some of these predictions are supported by the literature, and many can serve as targets of future experiments.

Computational and Experimental Analysis Reveals a Requirement for Eosinophil-Derived IL-13 for the Development of Allergic Airway Responses in C57BL/6 Mice

“…In the study of immunological responses, this approach has been implemented in small networks for the analysis of T cell activation and anergy, for the analysis of lymphocyte subsets, and we have used this approach to analyze the pathogenesis of Bordetellae infections (10,(71)(72)(73). In this study, we assembled the literature on the immune components involved in case of the i.p.…”

Modeling approaches for qualitative and semi-quantitative analysis of cellular signaling networks

“…However, many successful applications in Systems Biology have demonstrated that (i) qualitative modeling provides a suitable framework to deal not only with the often coarse-grained biological knowledge but also with the typically qualitative information (trends) contained in many biological datasets; and (ii) that the analysis of qualitative models (with or without experimental data) may uncover important network and system’s properties on the basis of a given network topology or/and other qualitative knowledge. Accordingly, models that are only based on qualitative data and network topology have been shown to be predictive tools that are able to provide constructive hypotheses (see, e.g., [116] and references therein). In this sense, key achievements of qualitative modeling approaches in large-scale signaling networks (that could arguably not have been achieved by ODE modeling) are (i) capturing and formalizing qualitative knowledge [61,67,73], (ii) getting a broader understanding of the network function (e.g., input–output behavior) generated by tens or hundreds of interacting signaling molecules [59,70,71], (iii) assessing experimental data in the context of large signaling networks [20,60], and, more recently, (iv) the identification or/and training of large signaling networks based on high-throughput measurements [64,101].…”

A comparative study of qualitative and quantitative dynamic models of biological regulatory networks

“…If the value of the majority of the kinetic parameters is known, comparison of the modeling results with available experimental observations can help to estimate the value of the remaining parameters or constrain the ranges of parameter values [14]. As a detailed, quantitative model with many parameters has a high degree of uncertainty when many of the parameter values are not known, taking the opposite route of transitioning to more abstract models that have fewer parameters can also be beneficial [8,69].…”

“…This approximation leads to hybrid models, wherein the production rates are described by logical functions and the degradation rates are considered to be linear. These models are suitable for systems for which partial knowledge of parameter values is available, and have been widely used in the literature [10][11][12][13][14][15]. In the extreme idealization case of qualitative models, called Boolean models, biological entities are characterized by binary (ON or OFF) variables and their interactions are usually expressed using the logic operators AND, OR, and NOT [16,17].…”