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Computation 2019, 7(1), 3; https://doi.org/10.3390/computation7010003

The Impact of Stochasticity and Its Control on a Model of the Inflammatory Response

1
School of Pharmacy and Pharmaceutical Sciences, SUNY at Buffalo, Buffalo, NY 14214, USA
2
Biomedical Engineering Department, Rutgers the State University of New Jersey, Piscataway, NJ 08854, USA
3
Department of Control and Dynamical Systems, California Institute of Technology, Pasadena, CA 91125, USA
4
Department of Surgery, University of Pittsburgh, Pittsburgh, PA 15213, USA
5
Center for Inflammation and Regenerative Modeling, McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA 15219, USA
6
Chemical & Biochemical Engineering Department, Rutgers the State University of New Jersey, Piscataway, NJ 08854, USA
*
Author to whom correspondence should be addressed.
Received: 23 October 2018 / Revised: 4 December 2018 / Accepted: 27 December 2018 / Published: 28 December 2018
(This article belongs to the Special Issue Computational Modeling in Inflammation and Regenerative Medicine)
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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. View Full-Text
Keywords: inflammation; mathematical model; stochasticity; noise inflammation; mathematical model; stochasticity; noise
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Mavroudis, P.D.; Scheff, J.D.; Doyle, J.C.; Vodovotz, Y.; Androulakis, I.P. The Impact of Stochasticity and Its Control on a Model of the Inflammatory Response. Computation 2019, 7, 3.

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