Computational Modeling in Inflammation and Regenerative Medicine

A special issue of Computation (ISSN 2079-3197). This special issue belongs to the section "Computational Biology".

Deadline for manuscript submissions: closed (15 March 2019) | Viewed by 11431

Special Issue Editors


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Guest Editor
Department of Surgery, University of Pittsburgh, W944 Biomedical Sciences Tower, 200 Lothrop St., Pittsburgh, PA 15213, USA
Interests: inflammation; computational modeling; systems biology; synthetic biology; trauma; sepsis; wound healing

E-Mail Website
Guest Editor
Department of Surgery, University of Pittsburgh, W944 Biomedical Sciences Tower, 200 Lothrop St., Pittsburgh, PA 15213, USA
Interests: trauma; sepsis; inflammation; predictive analytics; mathematical modeling

Special Issue Information

Dear Colleagues,

This Special Issue will consist of papers focused on computational modelling of the inflammatory response and its interactions with tissue damage, healing, and regeneration. Inflammation is a prototypical complex system, where the whole is often quite different from the sum of the parts. Properly regulated inflammation is necessary for both healing and regeneration. However, dysregulated inflammation is a feature of most, if not all, diseases affecting both developed and developing nations. Over the past 15 years or so, computational modelling has emerged as a novel approach to address the complexity of inflammation, wound healing, and tissue regeneration. This Special Issue aims to give a current perspective on the field.

Prof. Dr. Yoram Vodovotz
Dr. Rami Namas
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Computation is an international peer-reviewed open access monthly journal published by MDPI.

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Keywords

  • Inflammation
  • Wound healing
  • Regeneration
  • Mathematical modeling
  • Computational biology
  • Systems biology
  • Predictive analytics

Published Papers (2 papers)

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Research

17 pages, 3263 KiB  
Article
The Impact of Stochasticity and Its Control on a Model of the Inflammatory Response
by Panteleimon D. Mavroudis, Jeremy D. Scheff, John C. Doyle, Yoram Vodovotz and Ioannis P. Androulakis
Computation 2019, 7(1), 3; https://doi.org/10.3390/computation7010003 - 28 Dec 2018
Cited by 3 | Viewed by 5436
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Computational Modeling in Inflammation and Regenerative Medicine)
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17 pages, 3915 KiB  
Article
The Role of Dimensionality in Understanding Granuloma Formation
by Simeone Marino, Caitlin Hult, Paul Wolberg, Jennifer J. Linderman and Denise E. Kirschner
Computation 2018, 6(4), 58; https://doi.org/10.3390/computation6040058 - 14 Nov 2018
Cited by 9 | Viewed by 5131
Abstract
Within the first 2–3 months of a Mycobacterium tuberculosis (Mtb) infection, 2–4 mm spherical structures called granulomas develop in the lungs of the infected hosts. These are the hallmark of tuberculosis (TB) infection in humans and non-human primates. A cascade of immunological events [...] Read more.
Within the first 2–3 months of a Mycobacterium tuberculosis (Mtb) infection, 2–4 mm spherical structures called granulomas develop in the lungs of the infected hosts. These are the hallmark of tuberculosis (TB) infection in humans and non-human primates. A cascade of immunological events occurs in the first 3 months of granuloma formation that likely shapes the outcome of the infection. Understanding the main mechanisms driving granuloma development and function is key to generating treatments and vaccines. In vitro, in vivo, and in silico studies have been performed in the past decades to address the complexity of granuloma dynamics. This study builds on our previous 2D spatio-temporal hybrid computational model of granuloma formation in TB (GranSim) and presents for the first time a more realistic 3D implementation. We use uncertainty and sensitivity analysis techniques to calibrate the new 3D resolution to non-human primate (NHP) experimental data on bacterial levels per granuloma during the first 100 days post infection. Due to the large computational cost associated with running a 3D agent-based model, our major goal is to assess to what extent 2D and 3D simulations differ in predictions for TB granulomas and what can be learned in the context of 3D that is missed in 2D. Our findings suggest that in terms of major mechanisms driving bacterial burden, 2D and 3D models return very similar results. For example, Mtb growth rates and molecular regulation mechanisms are very important both in 2D and 3D, as are cellular movement and modulation of cell recruitment. The main difference we found was that the 3D model is less affected by crowding when cellular recruitment and movement of cells are increased. Overall, we conclude that the use of a 2D resolution in GranSim is warranted when large scale pilot runs are to be performed and if the goal is to determine major mechanisms driving infection outcome (e.g., bacterial load). To comprehensively compare the roles of model dimensionality, further tests and experimental data will be needed to expand our conclusions to molecular scale dynamics and multi-scale resolutions. Full article
(This article belongs to the Special Issue Computational Modeling in Inflammation and Regenerative Medicine)
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