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Glucocorticoids are steroid hormones which, among other functions, exert an antiinflammatory effect. Endogenous glucocorticoids are normally secreted by the adrenal gland in discrete bursts. It is becoming increasingly evident that this pulsatile secretion pattern, leading to ultradian rhythms of plasma glucocorticoid levels, may have important downstream regulatory effects on glucocorticoid-responsive genes. Mathematical modeling of this system can compliment recent experimental data and quantitatively evaluate hypothesized mechanistic underpinnings of differential pulsatile signal transduction. In this paper, we describe an integrated model of pulsatile secretion of glucocorticoids by the hypothalamic–pituitary–adrenal (HPA) axis and the pharmacodynamic effect of glucocorticoids. This model is used to investigate the difference in transcriptional responses to pulsatile and constant glucocorticoid exposure. Nonlinearity in ligand-receptor kinetics leads to the differential expression of glucocorticoid-responsive genes in response to different patterns of glucocorticoid secretion, even when the total amount of glucocorticoid exposure is held constant. Understanding the implications of ultradian rhythms in glucocorticoids is important in studying the dysregulation of HPA axis function leading to altered glucocorticoid secretion patterns in disease. [ABSTRACT FROM AUTHOR]

Background: Inflammation is a highly complex biological response evoked by many stimuli. A persistent challenge in modeling this dynamic process has been the (nonlinear) nature of the response that precludes the single-variable assumption. Systems-based approaches offer a promising possibility for understanding inflammation in its homeostatic context. In order to study the underlying complexity of the acute inflammatory response, an agent-based framework is developed that models the emerging host response as the outcome of orchestrated interactions associated with intricate signaling cascades and intercellular immune system interactions. Methodology/Principal Findings: An agent-based modeling (ABM) framework is proposed to study the nonlinear dynamics of acute human inflammation. The model is implemented using NetLogo software. Interacting agents involve either inflammation-specific molecules or cells essential for the propagation of the inflammatory reaction across the system. Spatial orientation of molecule interactions involved in signaling cascades coupled with the cellular heterogeneity are further taken into account. The proposed in silico model is evaluated through its ability to successfully reproduce a self-limited inflammatory response as well as a series of scenarios indicative of the nonlinear dynamics of the response. Such scenarios involve either a persistent (non)infectious response or innate immune tolerance and potentiation effects followed by perturbations in intracellular signaling molecules and cascades. Conclusions/Significance: The ABM framework developed in this study provides insight on the stochastic interactions of the mediators involved in the propagation of endotoxin signaling at the cellular response level. The simulation results are in accordance with our prior research effort associated with the development of deterministic human inflammation models that include transcriptional dynamics, signaling, and physiological components. The hypothetical scenarios explored in this study would potentially improve our understanding of how manipulating the behavior of the molecular species could manifest into emergent behavior of the overall system. [ABSTRACT FROM AUTHOR]

Human survival from injury requires an appropriate inflammatory and immune response. We describe the circulating leukocyte transcriptome after severe trauma and burn injury, as well as in healthy subjects receiving low-dose bacterial endotoxin, and show that these severe stresses produce a global reprioritization affecting >80% of the cellular functions and pathways, a truly unexpected "genomic storm." In severe blunt trauma, the early leukocyte genomic response is consistent with simultaneously increased expression of genes involved in the systemic inflammatory, innate immune, and compensatory antiinflammatory responses, as well as in the suppression of genes involved in adaptive immunity. Furthermore, complications like nosocomial infections and organ failure are not associated with any genomic evidence of a second hit and differ only in the magnitude and duration of this genomic reprioritization. The similarities in gene expression patterns between different injuries reveal an apparently fundamental human response to severe inflammatory stress, with genomic signatures that are surprisingly far more common than different. Based on these transcriptional data, we propose a new paradigm for the human immunological response to severe injury.