Special Issue "Successes of Systems Biology and Future Challenges"
QuicklinksA special issue of Cells (ISSN 2073-4409).
Deadline for manuscript submissions: closed (31 March 2013)
Special Issue Editors
Guest Editor
Dr. Nathan R. Brady
Research Group of Systems Biology of Cell Death Mechanisms, German Cancer Research Center (DKFZ), and Department of Surgery, Medical Faculty, University of Heidelberg, BioQuant, INF 267, 69120 Heidelberg, Germany
Website: http://bradylabs.bioquant.uni-heidelberg.de
E-Mail: n.brady@dkfz.de
Phone: +49 6221 5451 357
Interests: autophagy; autophagy receptors; BH3-only proteins; apoptosis; quantitative microscopy; systems biology; dynamic modeling of signal transduction; data-driven modeling
Guest Editor
Dr. Sehyo Charley Choe
Research Group of Systems Biology of Cell Death Mechanisms, German Cancer Research Center (DKFZ), and Department of Surgery, Medical Faculty, University of Heidelberg, BioQuant, INF 267, 69120 Heidelberg, Germany
Website: http://bradylabs.bioquant.uni-heidelberg.de
E-Mail: charley.choe@bioquant.uni-heidelberg.de
Phone: +49 6221 5451324
Interests: complex adaptive many-body systems; agent-based modeling; ODE modeling; spatio-temporal multi-scale modeling; Systems Biology; network theory; apoptosis; autophagy
Special Issue Information
Dear Colleagues,
Research in the post-genomic era is generating an unprecedented amount of genetic information, with the promise to achieve effective, targeted and personalized therapies against human disease. However, predicting the contribution of changes at the genomic and transcriptomic levels to multi-factorial diseases, such as cancer, requires that biology undergoes a transformation into a reproducibly quantitative science, with organism-specific resolution. To that end, over the past decade fruitful inter-disciplinary approaches have developed through collaborations between bench molecular biologists and scientists from computational and mathematical fields, launching the integrative framework of Systems Biology.
In addition to genomics, other fields, such as metabolomics, proteomics, and high-content microscopy have emerged as driving forces for the quantitative investigation of spatial and temporal dynamics of cellular physiology and pathophysiology. Simultaneously, integrative mathematical modeling approaches and tools have been applied to permit not only the investigation of biological heterogeneity and spatio-temporal complexity, but develop hypothesis-driven investigation into emergent behavior. Remarkable successes have been achieved in the quantitative study of biological processes, from identification of point mutations within genes and the subsequent understanding of their impact on protein-protein network functionality and organismal physiological consequences. However, many challenges remain ahead for the ongoing reshaping of our current approach to biology into a modernistic Systems Biology.
To that end, the goal of this Special Issue is to build an Open Access forum to discuss the advancement of Systems Biology, through both original work and review articles. We are welcoming contributions for all aspects of molecular systems biology, including topics from genes to organism, data collection to data analysis, and correlative to predictive mathematical modeling.
Dr. Nathan R. Brady
Dr. Sehyo Charley Choe
Guest Editors
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. For the first couple of issues the Article Processing Charge (APC) will be waived for well-prepared manuscripts. 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.
Keywords
- systems biology
- network modeling
- data-driven modeling
- multi-scalability
- quantitative biology
- cell-to-cell variability
- genomics
- proteomics
- personalized medicine
Published Papers (3 papers)
 Cells 2013, 2(2), 284-293; doi:10.3390/cells2020284
Received: 20 February 2013; in revised form: 3 April 2013 / Accepted: 15 April 2013 / Published: 29 April 2013
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 Cells 2013, 2(2), 306-329; doi:10.3390/cells2020306
Received: 26 February 2013; in revised form: 22 April 2013 / Accepted: 27 April 2013 / Published: 10 May 2013
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Cells 2013, 2(2), 393-413; doi:10.3390/cells2020393
Received: 4 April 2013; in revised form: 6 May 2013 / Accepted: 15 May 2013 / Published: 31 May 2013
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Planned Papers
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: Reverse Engineering Cellular Networks Using Information Theoretic Methods
Authors: Alejandro F Villaverde 1, John Ross 2, Julio R. Banga1
Affiliations: 1 BioProcess Engineering, IIM-CSIC, Vigo, Spain;
2 Department of Chemistry, Stanford University, Stanford, CA, USA; E-Mail: afvillaverde@iim.csic.es
Abstract: Building mathematical models of cellular networks lies at the core of systems biology. It involves, among other tasks, the reconstruction of the structure of interactions between molecular components, which is known as network inference or reverse engineering. Information theory can help in the goal of extracting as much information as possible from the available data. A large number of methods founded on these concepts have been proposed in the literature, not only in biology journals, but in a wide range of areas. Their critical comparison is difficult due to the different focuses and the adoption of different terminologies. Here we attempt to review the existing methodologies and clarify their differences. While some of these methods have achieved notable success, many challenges remain, among which we can mention: incomplete measurements, noisy data, counter-intuitive behaviour emerging from nonlinear relations or feedback loops, computational burden of dealing with large data sets, or non-uniqueness of the solutions.
Last update: 7 December 2012