High-fidelity discrete modeling of the HPA axis: a study of regulatory plasticity in biology

Abstract

BackgroundThe hypothalamic-pituitary-adrenal (HPA) axis is a central regulator of stress response and its dysfunction has been associated with a broad range of complex illnesses including Gulf War Illness (GWI) and Chronic Fatigue Syndrome (CFS). Though classical mathematical approaches have been used to model HPA function in isolation, its broad regulatory interactions with immune and central nervous function are such that the biological fidelity of simulations is undermined by the limited availability of reliable parameter estimates.MethodHere we introduce and apply a generalized discrete formalism to recover multiple stable regulatory programs of the HPA axis using little more than connectivity between physiological components. This simple discrete model captures cyclic attractors such as the circadian rhythm by applying generic constraints to a minimal parameter set; this is distinct from Ordinary Differential Equation (ODE) models, which require broad and precise parameter sets. Parameter tuning is accomplished by decomposition of the overall regulatory network into isolated sub-networks that support cyclic attractors. Network behavior is simulated using a novel asynchronous updating scheme that enforces priority with memory within and between physiological compartments.ResultsConsistent with much more complex conventional models of the HPA axis, this parsimonious framework supports two cyclic attractors, governed by higher and lower levels of cortisol respectively. Importantly, results suggest that stress may remodel the stability landscape of this system, favoring migration from one stable circadian cycle to the other. Access to each regime is dependent on HPA axis tone, captured here by the tunable parameters of the multi-valued logic. Likewise, an idealized glucocorticoid receptor blocker alters the regulatory topology such that maintenance of persistently low cortisol levels is rendered unstable, favoring a return to normal circadian oscillation in both cortisol and glucocorticoid receptor expression.ConclusionThese results emphasize the significance of regulatory connectivity alone and how regulatory plasticity may be explored using simple discrete logic and minimal data compared to conventional methods.Electronic supplementary materialThe online version of this article (10.1186/s12918-018-0599-1) contains supplementary material, which is available to authorized users.

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

“…Intuitively, each K i (I a ) is a propositional formula consisting of one or more literals. The disjunction of K i (I a ) defines the state level image (e.g., yit) of an entity (Sedghamiz et al, 2018). For instance, the equivalent propositional formula for v 4 in Figure 1A in an unsimplified form might be written as;…”

Immune Network Modeling Predicts Specific Nasopharyngeal and Peripheral Immune Dysregulation in Otitis-Prone Children

“…Immune network response was represented using a discrete logical formalism (e.g., if expression of protein A is High, then expression of protein B should become Low) designed to qualitatively recapitulate the dynamic behaviors of immune regulation typically described using more complex continuous kinetic models [ (31)(32)(33)(34)(35); i.e., expressed in units of change in concentration or abundance per unit time]. Each entity in the network is represented as a switch which assumes a particular level of activation depending on the signals it receives.…”

“…In addition, decisional input weights were used to capture the dynamic response of a given immune component under all possible combinations of input signals. To accommodate often sparse and partially observed experimental data we redefined the conventional goal-directed search for parameter values to one where resting state and time course data were applied as constraints; combinations of parameter values were retained if they supported model predictions that complied with observed experimental data (35,36). Regulatory parameters for the immune network model were selected based on adherence to a qualitative summary of results previously reported by our group (Supplementary Table 1).…”

“…As such, any given model constitutes a fully deterministic ensemble of transition rules dictating the dynamic response of the immune network under a given set of conditions. Model parameters including activation thresholds and weights dictating the logical rules for the resolution of competing input signals were derived using a constraint-satisfaction formalism as previously described (35). Model parameterization was conducted with the aim of enforcing strict compliance with known stable states while also minimizing divergence from transient states observed experimentally during response to AOM infection in vivo and in vitro polyclonal stimulation of PBMCs.…”

Culturing Adult Stem Cells for Cell-Based Therapeutics: Neuroimmune Applications

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