Special Issue "Successes of Systems Biology and Future Challenges"

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

Deadline for manuscript submissions: closed (31 March 2013)

Special Issue Editors

Guest Editor
Dr. Nathan R. Brady (Website)

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
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 (Website)

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
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. 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.

Keywords

  • systems biology
  • network modeling
  • data-driven modeling
  • multi-scalability
  • quantitative biology
  • cell-to-cell variability
  • genomics
  • proteomics
  • personalized medicine

Published Papers (5 papers)

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Review

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Open AccessReview Systems Biology: The Role of Engineering in the Reverse Engineering of Biological Signaling
Cells 2013, 2(2), 393-413; doi:10.3390/cells2020393
Received: 4 April 2013 / Revised: 6 May 2013 / Accepted: 15 May 2013 / Published: 31 May 2013
Cited by 5 | PDF Full-text (683 KB) | HTML Full-text | XML Full-text
Abstract
One of the principle tasks of systems biology has been the reverse engineering of signaling networks. Because of the striking similarities to engineering systems, a number of analysis and design tools from engineering disciplines have been used in this process. This review [...] Read more.
One of the principle tasks of systems biology has been the reverse engineering of signaling networks. Because of the striking similarities to engineering systems, a number of analysis and design tools from engineering disciplines have been used in this process. This review looks at several examples including the analysis of homeostasis using control theory, the attenuation of noise using signal processing, statistical inference and the use of information theory to understand both binary decision systems and the response of eukaryotic chemotactic cells. Full article
(This article belongs to the Special Issue Successes of Systems Biology and Future Challenges)
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Open AccessReview Reverse Engineering Cellular Networks with Information Theoretic Methods
Cells 2013, 2(2), 306-329; doi:10.3390/cells2020306
Received: 26 February 2013 / Revised: 22 April 2013 / Accepted: 27 April 2013 / Published: 10 May 2013
Cited by 8 | PDF Full-text (245 KB) | HTML Full-text | XML Full-text
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 [...] Read more.
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 some of the existing information theoretic methodologies for network inference, and clarify their differences. While some of these methods have achieved notable success, many challenges remain, among which we can mention dealing with incomplete measurements, noisy data, counterintuitive behaviour emerging from nonlinear relations or feedback loops, and computational burden of dealing with large data sets. Full article
(This article belongs to the Special Issue Successes of Systems Biology and Future Challenges)
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Other

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Open AccessConcept Paper Quantification of High-Molecular Weight Protein Platforms by AQUA Mass Spectrometry as Exemplified for the CD95 Death-Inducing Signaling Complex (DISC)
Cells 2013, 2(3), 476-495; doi:10.3390/cells2030476
Received: 8 May 2013 / Revised: 29 May 2013 / Accepted: 19 June 2013 / Published: 27 June 2013
Cited by 4 | PDF Full-text (2075 KB) | HTML Full-text | XML Full-text
Abstract
Contemporary quantitative mass spectrometry provides fascinating opportunities in defining the stoichiometry of high-molecular weight complexes or multiprotein platforms. The composition stoichiometry of multiprotein platforms is a key to understand the regulation of complex signaling pathways and provides a basis for constructing models [...] Read more.
Contemporary quantitative mass spectrometry provides fascinating opportunities in defining the stoichiometry of high-molecular weight complexes or multiprotein platforms. The composition stoichiometry of multiprotein platforms is a key to understand the regulation of complex signaling pathways and provides a basis for constructing models in systems biology. Here we present an improved AQUA technique workflow that we adapted for the quantitative mass spectrometry analysis of the stoichiometry of the CD95 (Fas/APO-1) death inducing signaling complex (DISC). The DISC is a high-molecular weight platform essential for the initiation of CD95-mediated apoptotic and non-apoptotic responses. For protein quantification, CD95 DISCs were immunoprecipitated and proteins in the immunoprecipitations were separated by one-dimensional gel electrophoresis, followed by protein quantification using the AQUA technique. We will discuss in detail AQUA analysis of the CD95 DISC focusing on the key issues of this methodology, i.e., selection and validation of AQUA peptides. The application of this powerful method allowed getting new insights into mechanisms of procaspase-8 activation at the DISC and apoptosis initiation [1]. Here we discuss the AQUA methodology adapted by us for the analysis of the CD95 DISC in more detail. This approach paves the way for the successful quantification of multiprotein complexes and thereby delineating the intrinsic details of molecular interactions. Full article
(This article belongs to the Special Issue Successes of Systems Biology and Future Challenges)
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Open AccessEssay Systems Biology — the Broader Perspective
Cells 2013, 2(2), 414-431; doi:10.3390/cells2020414
Received: 1 April 2013 / Revised: 17 May 2013 / Accepted: 5 June 2013 / Published: 19 June 2013
Cited by 1 | PDF Full-text (933 KB) | HTML Full-text | XML Full-text
Abstract
Systems biology has two general aims: a narrow one, which is to discover how complex networks of proteins work, and a broader one, which is to integrate the molecular and network data with the generation and function of organism phenotypes. Doing all [...] Read more.
Systems biology has two general aims: a narrow one, which is to discover how complex networks of proteins work, and a broader one, which is to integrate the molecular and network data with the generation and function of organism phenotypes. Doing all this involves complex methodologies, but underpinning the subject are more general conceptual problems about upwards and downwards causality, complexity and information storage, and their solutions provide the constraints within which these methodologies can be used. This essay considers these general aspects and the particular role of protein networks; their functional outputs are often the processes driving phenotypic change and physiological function—networks are, in a sense, the units of systems biology much as proteins are for molecular biology. It goes on to argue that the natural language for systems-biological descriptions of biological phenomena is the mathematical graph (a set of connected facts of the general form <state 1> [process] <state 2> (e.g., <membrane-bound delta> [activates] <notch pathway>). Such graphs not only integrate events at different levels but emphasize the distributed nature of control as well as displaying a great deal of data. The implications and successes of these ideas for physiology, pharmacology, development and evolution are briefly considered. The paper concludes with some challenges for the future. Full article
(This article belongs to the Special Issue Successes of Systems Biology and Future Challenges)
Open AccessEssay A Checklist for Successful Quantitative Live Cell Imaging in Systems Biology
Cells 2013, 2(2), 284-293; doi:10.3390/cells2020284
Received: 20 February 2013 / Revised: 3 April 2013 / Accepted: 15 April 2013 / Published: 29 April 2013
Cited by 2 | PDF Full-text (368 KB) | HTML Full-text | XML Full-text
Abstract
Mathematical modeling of signaling and gene regulatory networks has provided unique insights about systems behaviors for many cell biological problems of medical importance. Quantitative single cell monitoring has a crucial role in advancing systems modeling of molecular networks. However, due to the [...] Read more.
Mathematical modeling of signaling and gene regulatory networks has provided unique insights about systems behaviors for many cell biological problems of medical importance. Quantitative single cell monitoring has a crucial role in advancing systems modeling of molecular networks. However, due to the multidisciplinary techniques that are necessary for adaptation of such systems biology approaches, dissemination to a wide research community has been relatively slow. In this essay, I focus on some technical aspects that are often under-appreciated, yet critical in harnessing live cell imaging methods to achieve single-cell-level understanding and quantitative modeling of molecular networks. The importance of these technical considerations will be elaborated with examples of successes and shortcomings. Future efforts will benefit by avoiding some pitfalls and by utilizing the lessons collectively learned from recent applications of imaging in systems biology. Full article
(This article belongs to the Special Issue Successes of Systems Biology and Future Challenges)

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