Special Issue "Feature Papers 2013"

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A special issue of Cells (ISSN 2073-4409).

Deadline for manuscript submissions: closed (15 September 2013)

Special Issue Editor

Guest Editor
Dr. Alexander E. Kalyuzhny

Neuroscience, UMN Twin Cities, 6-145 Jackson Hall, 321 Church St SE, Minneapolis, MN 55455, USA
Website | E-Mail
Phone: +1 612 624 2991
Interests: physiology of pain; antinociceptive brainstem circuit; cellular localization, trafficking and oligomerization of opioid receptors; drugs of abuse; cytokines and cytokine receptors

Special Issue Information

Submission

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. Papers will be published continuously (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as 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 refereed through a peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Cells 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 500 CHF (Swiss Francs). English correction and/or formatting fees of 250 CHF (Swiss Francs) will be charged in certain cases for those articles accepted for publication that require extensive additional formatting and/or English corrections.

Published Papers (5 papers)

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Research

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Open AccessArticle Macroscopic Quantum-Type Potentials in Theoretical Systems Biology
Cells 2014, 3(1), 1-35; doi:10.3390/cells3010001
Received: 27 July 2013 / Revised: 18 November 2013 / Accepted: 28 November 2013 / Published: 30 December 2013
Cited by 1 | PDF Full-text (1340 KB) | HTML Full-text | XML Full-text
Abstract
We review in this paper the use of the theory of scale relativity and fractal space-time as a tool particularly well adapted to the possible development of a future genuine systems theoretical biology. We emphasize in particular the concept of quantum-type potentials, since,
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We review in this paper the use of the theory of scale relativity and fractal space-time as a tool particularly well adapted to the possible development of a future genuine systems theoretical biology. We emphasize in particular the concept of quantum-type potentials, since, in many situations, the effect of the fractality of space—or of the underlying medium—can be reduced to the addition of such a potential energy to the classical equations of motion. Various equivalent representations—geodesic, quantum-like, fluid mechanical, stochastic—of these equations are given, as well as several forms of generalized quantum potentials. Examples of their possible intervention in high critical temperature superconductivity and in turbulence are also described, since some biological processes may be similar in some aspects to these physical phenomena. These potential extra energy contributions could have emerged in biology from the very fractal nature of the medium, or from an evolutive advantage, since they involve spontaneous properties of self-organization, morphogenesis, structuration and multi-scale integration. Finally, some examples of applications of the theory to actual biological-like processes and functions are also provided. Full article
(This article belongs to the Special Issue Feature Papers 2013)
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Open AccessArticle An Enhanced ELISPOT Assay for Sensitive Detection of Antigen-Specific T Cell Responses to Borrelia burgdorferi
Cells 2013, 2(3), 607-620; doi:10.3390/cells2030607
Received: 11 July 2013 / Revised: 30 August 2013 / Accepted: 4 September 2013 / Published: 13 September 2013
Cited by 3 | PDF Full-text (452 KB) | HTML Full-text | XML Full-text
Abstract
Lyme Borreliosis is an infectious disease caused by the spirochete Borrelia burgdorferi that is transmitted through the bite of infected ticks. Both B cell-mediated humoral immunity and T cell immunity develop during natural Borrelia infection. However, compared with humoral immunity, the T cell
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Lyme Borreliosis is an infectious disease caused by the spirochete Borrelia burgdorferi that is transmitted through the bite of infected ticks. Both B cell-mediated humoral immunity and T cell immunity develop during natural Borrelia infection. However, compared with humoral immunity, the T cell response to Borrelia infection has not been well elucidated. In this study, a novel T cell-based assay was developed and validated for the sensitive detection of antigen-specific T cell response to B. burgdorferi. Using interferon-g as a biomarker, we developed a new enzyme-linked immunospot method (iSpot Lyme™) to detect Borrelia antigen-specific effector/memory T cells that were activated in vivo by exposing them to recombinant Borrelia antigens ex vivo. To test this new method as a potential laboratory diagnostic tool, we performed a clinical study with a cohort of Borrelia positive patients and healthy controls. We demonstrated that the iSpot Lyme assay has a significantly higher specificity and sensitivity compared with the Western Blot assay that is currently used as a diagnostic measure. A comprehensive evaluation of the T cell response to Borrelia infection should, therefore, provide new insights into the pathogenesis, diagnosis, treatment and monitoring of Lyme disease. Full article
(This article belongs to the Special Issue Feature Papers 2013)
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Open AccessArticle Spatial Rule-Based Modeling: A Method and Its Application to the Human Mitotic Kinetochore
Cells 2013, 2(3), 506-544; doi:10.3390/cells2030506
Received: 29 March 2013 / Revised: 5 June 2013 / Accepted: 25 June 2013 / Published: 2 July 2013
Cited by 6 | PDF Full-text (9966 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
A common problem in the analysis of biological systems is the combinatorial explosion that emerges from the complexity of multi-protein assemblies. Conventional formalisms, like differential equations, Boolean networks and Bayesian networks, are unsuitable for dealing with the combinatorial explosion, because they are designed
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A common problem in the analysis of biological systems is the combinatorial explosion that emerges from the complexity of multi-protein assemblies. Conventional formalisms, like differential equations, Boolean networks and Bayesian networks, are unsuitable for dealing with the combinatorial explosion, because they are designed for a restricted state space with fixed dimensionality. To overcome this problem, the rule-based modeling language, BioNetGen, and the spatial extension, SRSim, have been developed. Here, we describe how to apply rule-based modeling to integrate experimental data from different sources into a single spatial simulation model and how to analyze the output of that model. The starting point for this approach can be a combination of molecular interaction data, reaction network data, proximities, binding and diffusion kinetics and molecular geometries at different levels of detail. We describe the technique and then use it to construct a model of the human mitotic inner and outer kinetochore, including the spindle assembly checkpoint signaling pathway. This allows us to demonstrate the utility of the procedure, show how a novel perspective for understanding such complex systems becomes accessible and elaborate on challenges that arise in the formulation, simulation and analysis of spatial rule-based models. Full article
(This article belongs to the Special Issue Feature Papers 2013)
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Open AccessArticle A New Integrated Lab-on-a-Chip System for Fast Dynamic Study of Mammalian Cells under Physiological Conditions in Bioreactor
Cells 2013, 2(2), 349-360; doi:10.3390/cells2020349
Received: 1 April 2013 / Revised: 29 April 2013 / Accepted: 16 May 2013 / Published: 27 May 2013
Cited by 6 | PDF Full-text (1004 KB) | HTML Full-text | XML Full-text
Abstract
For the quantitative analysis of cellular metabolism and its dynamics it is essential to achieve rapid sampling, fast quenching of metabolism and the removal of extracellular metabolites. Common manual sample preparation methods and protocols for cells are time-consuming and often lead to the
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For the quantitative analysis of cellular metabolism and its dynamics it is essential to achieve rapid sampling, fast quenching of metabolism and the removal of extracellular metabolites. Common manual sample preparation methods and protocols for cells are time-consuming and often lead to the loss of physiological conditions. In this work, we present a microchip-bioreactor setup which provides an integrated and rapid sample preparation of mammalian cells. The lab-on-a-chip system consists of five connected units that allow sample treatment, mixing and incubation of the cells, followed by cell separation and simultaneous exchange of media within seconds. This microsystem is directly integrated into a bioreactor for mammalian cell cultivation. By applying overpressure (2 bar) onto the bioreactor, this setup allows pulsation free, defined, fast, and continuous sampling. Experiments evince that Chinese Hamster Ovary cells (CHO-K1) can be separated from the culture broth and transferred into a new medium efficiently. Furthermore, this setup permits the treatment of cells for a defined time (9 s or 18 s) which can be utilized for pulse experiments, quenching of cell metabolism, and/or another defined chemical treatment. Proof of concept experiments were performed using glutamine containing medium for pulse experiments. Continuous sampling of cells showed a high reproducibility over a period of 18 h. Full article
(This article belongs to the Special Issue Feature Papers 2013)

Review

Jump to: Research

Open AccessReview Systems Biology as an Integrated Platform for Bioinformatics, Systems Synthetic Biology, and Systems Metabolic Engineering
Cells 2013, 2(4), 635-688; doi:10.3390/cells2040635
Received: 16 April 2013 / Revised: 12 September 2013 / Accepted: 19 September 2013 / Published: 11 October 2013
Cited by 2 | PDF Full-text (3105 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Systems biology aims at achieving a system-level understanding of living organisms and applying this knowledge to various fields such as synthetic biology, metabolic engineering, and medicine. System-level understanding of living organisms can be derived from insight into: (i) system structure and the mechanism
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Systems biology aims at achieving a system-level understanding of living organisms and applying this knowledge to various fields such as synthetic biology, metabolic engineering, and medicine. System-level understanding of living organisms can be derived from insight into: (i) system structure and the mechanism of biological networks such as gene regulation, protein interactions, signaling, and metabolic pathways; (ii) system dynamics of biological networks, which provides an understanding of stability, robustness, and transduction ability through system identification, and through system analysis methods; (iii) system control methods at different levels of biological networks, which provide an understanding of systematic mechanisms to robustly control system states, minimize malfunctions, and provide potential therapeutic targets in disease treatment; (iv) systematic design methods for the modification and construction of biological networks with desired behaviors, which provide system design principles and system simulations for synthetic biology designs and systems metabolic engineering. This review describes current developments in systems biology, systems synthetic biology, and systems metabolic engineering for engineering and biology researchers. We also discuss challenges and future prospects for systems biology and the concept of systems biology as an integrated platform for bioinformatics, systems synthetic biology, and systems metabolic engineering. Full article
(This article belongs to the Special Issue Feature Papers 2013)

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