Next Article in Journal
Advanced Markov-Based Machine Learning Framework for Making Adaptive Trading System
Previous Article in Journal
Thermal Behavior of a Building with Incorporated Phase Change Materials in the South and the North Wall
Previous Article in Special Issue
The Role of Dimensionality in Understanding Granuloma Formation
Open AccessArticle

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

School of Pharmacy and Pharmaceutical Sciences, SUNY at Buffalo, Buffalo, NY 14214, USA
Biomedical Engineering Department, Rutgers the State University of New Jersey, Piscataway, NJ 08854, USA
Department of Control and Dynamical Systems, California Institute of Technology, Pasadena, CA 91125, USA
Department of Surgery, University of Pittsburgh, Pittsburgh, PA 15213, USA
Center for Inflammation and Regenerative Modeling, McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA 15219, USA
Chemical & Biochemical Engineering Department, Rutgers the State University of New Jersey, Piscataway, NJ 08854, USA
Author to whom correspondence should be addressed.
Computation 2019, 7(1), 3;
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)
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
Show Figures

Figure 1

MDPI and ACS Style

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.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Search more from Scilit
Back to TopTop