Special Issue "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: 15 March 2019

Special Issue Editors

Guest Editor
Prof. Dr. Yoram Vodovotz

Department of Surgery, University of Pittsburgh, W944 Biomedical Sciences Tower, 200 Lothrop St., Pittsburgh, PA 15213, USA
Website | E-Mail
Phone: 412-647-5609
Interests: inflammation; computational modeling; systems biology; synthetic biology; trauma; sepsis; wound healing
Guest Editor
Dr. Rami Namas

Department of Surgery, University of Pittsburgh, W944 Biomedical Sciences Tower, 200 Lothrop St., Pittsburgh, PA 15213, USA
Website | E-Mail
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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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 quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 350 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

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

Published Papers (1 paper)

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Research

Open AccessArticle The Role of Dimensionality in Understanding Granuloma Formation
Computation 2018, 6(4), 58; https://doi.org/10.3390/computation6040058 (registering DOI)
Received: 8 October 2018 / Revised: 4 November 2018 / Accepted: 9 November 2018 / Published: 14 November 2018
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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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