Abstract
The dysregulation of inflammation, normally a self-limited response that initiates healing, is a critical component of many diseases. Treatment of inflammatory disease is hampered by an incomplete understanding of the complexities underlying the inflammatory response, motivating the application of systems and computational biology techniques in an effort to decipher this complexity and ultimately improve therapy. Many mathematical models of inflammation are based on systems of deterministic equations that do not account for the biological noise inherent at multiple scales, and consequently the effect of such noise in regulating inflammatory responses has not been studied widely. In this work, noise was added to a deterministic system of the inflammatory response in order to account for biological stochasticity. Our results demonstrate that the inflammatory response is highly dependent on the balance between the concentration of the pathogen and the level of biological noise introduced to the inflammatory network. In cases where the pro- and anti-inflammatory arms of the response do not mount the appropriate defense to the inflammatory stimulus, inflammation transitions to a different state compared to cases in which pro- and anti-inflammatory agents are elaborated adequately and in a timely manner. In this regard, our results show that noise can be both beneficial and detrimental for the inflammatory endpoint. By evaluating the parametric sensitivity of noise characteristics, we suggest that efficiency of inflammatory responses can be controlled. Interestingly, the time period on which parametric intervention can be introduced efficiently in the inflammatory system can be also adjusted by controlling noise. These findings represent a novel understanding of inflammatory systems dynamics and the potential role of stochasticity thereon.
Highlights
Inflammation is the dynamic response of the body to adverse stimuli, such as infection and injury, that helps drive wound healing and pathogen clearance to restore homeostasis via a coordinated cascade of interlinked responses [1]
The primary EBMs used in the inflammation modeling are ordinary differential equation (ODE)-based [10,11,12,13,14], and their level of detail can vary from models incorporating descriptions of all well-accepted constituents of the inflammatory response [15] to more reduced models of whole-organism inflammation [16,17,18]
For the four-variable model, as all to be sufficiently large that 50% of the simulations led to the same steady state as in simulations lacking variables have roughly the same magnitude, all of the noise levels were set to be equal so that σi =
Summary
Inflammation is the dynamic response of the body to adverse stimuli, such as infection and injury, that helps drive wound healing and pathogen clearance to restore homeostasis via a coordinated cascade of interlinked responses [1]. Computation 2019, 7, 3 involves transcriptional activation of inflammatory genes in multiple cell types, afferent and efferent neural signaling, central secretion of hormones such as cortisol and epinephrine, and the production of hormone-like inflammatory mediators (cytokines/chemokines) as well as “danger” signals secreted from stressed or damaged tissues [1,2,3]. These inter-communicating systems are designed to produce a self-limited inflammatory response that can become activated to the correct degree under appropriate circumstances, perform its function, and resolve. Agent-based approaches, on the other hand, consist of viewing the inflammatory system as an aggregation of inflammatory agents which can be classified into populations or agent classes based on similar agent-rules [19,20,21]
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