Modeling cytokine regulatory network dynamics driving neuroinflammation in central nervous system disorders

Abstract

A central goal of pharmacological efforts to treat central nervous system (CNS) diseases is to develop systemic therapeutics that can restore CNS homeostasis. Achieving this goal requires a fundamental understanding of CNS function within the organismal context so as to leverage the mechanistic insights on the molecular basis of cellular and tissue functions towards novel drug target identification. The immune system constitutes a key link between the periphery and CNS, and many neurological disorders and neurodegenerative diseases are characterized by immune dysfunction. We review the salient opportunities for applying computational models to CNS disease research, and summarize relevant approaches from studies of immune function and neuroinflammation. While the accurate prediction of disease-related phenomena is often considered the central goal of modeling studies, we highlight the utility of computational modeling applications beyond making predictions, particularly for drawing counterintuitive insights from model-based analysis of multi-parametric and time series data sets.

Figure 1

Distributed control in multiscale networks coordinating CNS function. (A) CNS function is coupled to the function of multiple organs through efferents/afferents and endocrine/immune transmission through the blood. (B) Tissue scale interactions amongst multiple cell types in the CNS. (C) Molecular scale networks regulating the integration of molecular signaling with neuronal physiology.

Figure 2

Modeling disease dynamics. (A) Potential dynamics of disease progression. (B) Illustrative examples of dynamics in molecular levels, parameter sensitivity, and molecular regulatory network structure. (C) Illustrative trajectories of state transitions derived from statistical analysis of high-dimensional data and visualized in a reduced dimensional space. (D) Graphical representations of mathematical analysis to identify thresholds of disease - or separatrices - based on changes in initial conditions, variations in levels of system elements (e.g., cytokines x and y), and variations in parameters (e.g., network interaction coefficients).