This article is a correction to:
Multiplicity of Mathematical Modeling Strategies to Search for Molecular and Cellular Insights into Bacteria Lung Infection
Martina Cantone
Guido Santos
Pia Wentker
Xin Lai
Julio Vera- Laboratory of Systems Tumor Immunology, Department of Dermatology, Friedrich-Alexander University Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
A corrigendum on
Multiplicity of Mathematical Modeling Strategies to Search for Molecular and Cellular Insights into Bacteria Lung Infection
by Cantone, M., Santos, G., Wentker, P., Lai, X., and Vera, J. (2017). Front. Physiol. 8:645. doi: 10.3389/fphys.2017.00645
In the original article, there was a mistake in the legend for Figure 5 as published. In the legend the red color should represent bacteria and the blue color macrophages. The correct legend appears below.
In the original article, there was a mistake in the legend for Table 3 as published. In the caption the legend for the black color should refers to “appropriate” and the white color should refers to “poor.” The correct caption appears below.
Applicability: This table presents an illustrative guidance to select the best modeling framework to the biological scale of interest. Depending on the scale the applicability of the different frameworks can be poor (white) possible (gray) or appropriate (black).
In the original article, Kholodenko (2006) was not cited in the article. The citation has now been inserted in Introduction, fourth paragraph and should read:
A balanced immune response can be achieved via interacting immune cells that are controlled by intracellular regulatory networks of interacting molecules, such as cytokines, receptors, kinases, transcription factors, or non-coding RNAs. Such a system contains regulatory motifs, especially positive and negative feedback loops, which increase the complexity of the response and can provoke non-linear behaviors such as bistability and oscillation (Kholodenko, 2006). For patients with respiratory bacterial infections, severe pathological condition can emerge if their immune systems fail to quickly neutralize the infection and to avoid systemic spread of the pathogen. On the other hand, overwhelming host immune response to the pathogens is also dangerous and can impede the proper functioning of the lung and other organs. So, any new treatments using the combination of antibiotics and immunomodulatory drugs will be useful if they can help the patients to maintain a balanced immune response (Wentker et al., 2017), which is governed by the multi-level biological system (Eberhardt et al., 2016).
Conflict of Interest Statement
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
References
Keywords: systems biology, systems medicine, lung infection, mathematical modeling, Boolean network, ODE models, stochastic modeling, agent-based modeling
Citation: Cantone M, Santos G, Wentker P, Lai X and Vera J (2017) Corrigendum: Multiplicity of Mathematical Modeling Strategies to Search for Molecular and Cellular Insights into Bacteria Lung Infection. Front. Physiol. 8:817. doi: 10.3389/fphys.2017.00817
Received: 21 September 2017; Accepted: 04 October 2017;
Published: 30 October 2017.
Edited and reviewed by: Matteo Barberis, University of Amsterdam, Netherlands
Copyright © 2017 Cantone, Santos, Wentker, Lai and Vera. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Julio Vera, julio.vera-gonzalez@uk-erlangen.de
†These authors have contributed equally to this work.