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Methods, software and technology

Section edited by Nitin Baliga and Pedro Mendes

This section covers the development and refinement of novel computational, statistical and experimental methods for the analysis of biological systems.

Page 2 of 11
  1. Content type: Research Article

    Early afterdepolarizations (EADs) are pathological voltage oscillations during the repolarization phase of cardiac action potentials (APs). EADs are caused by drugs, oxidative stress or ion channel disease, an...

    Authors: Philipp Kügler, M.A.K. Bulelzai and André H. Erhardt

    Citation: BMC Systems Biology 2017 11:42

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  2. Content type: Research Article

    Parameter optimisation is a critical step in the construction of computational biology models. In eye movement research, computational models are increasingly important to understanding the mechanistic basis o...

    Authors: Eleftherios Avramidis and Ozgur E. Akman

    Citation: BMC Systems Biology 2017 11:40

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  3. Content type: Research Article

    Parameter estimation in systems biology is typically done by enforcing experimental observations through an objective function as the parameter space of a model is explored by numerical simulations. Past studi...

    Authors: Cihan Oguz, Layne T. Watson, William T. Baumann and John J. Tyson

    Citation: BMC Systems Biology 2017 11:30

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  4. Content type: Research Article

    Mathematical models are used to gain an integrative understanding of biochemical processes and networks. Commonly the models are based on deterministic ordinary differential equations. When molecular counts ar...

    Authors: Luis U. Aguilera, Christoph Zimmer and Ursula Kummer

    Citation: BMC Systems Biology 2017 11:26

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  5. Content type: Methodology Article

    Systems Biology continues to produce increasingly large models of complex biochemical reaction networks. In applications requiring, for example, parameter estimation, the use of agent-based modelling approache...

    Authors: Thomas J. Snowden, Piet H. van der Graaf and Marcus J. Tindall

    Citation: BMC Systems Biology 2017 11:17

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  6. Content type: Research article

    Gene regulatory networks with dynamics characterized by multiple stable states underlie cell fate-decisions. Quantitative models that can link molecular-level knowledge of gene regulation to a global understan...

    Authors: Brian K. Chu, Margaret J. Tse, Royce R. Sato and Elizabeth L. Read

    Citation: BMC Systems Biology 2017 11:14

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  7. Content type: Software

    Ensemble modeling is a promising approach for obtaining robust predictions and coarse grained population behavior in deterministic mathematical models. Ensemble approaches address model uncertainty by using pa...

    Authors: David M. Bassen, Michael Vilkhovoy, Mason Minot, Jonathan T. Butcher and Jeffrey D. Varner

    Citation: BMC Systems Biology 2017 11:10

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  8. Content type: Methodology Article

    Obtaining accurate estimates of biological or enzymatic reaction rates is critical in understanding the design principles of a network and how biological processes can be experimentally manipulated on demand. ...

    Authors: Robert W. Smith, Britta Helwig, Adrie H. Westphal, Eran Pel, Maximilian Hörner, Hannes M. Beyer, Sophia L. Samodelov, Wilfried Weber, Matias D. Zurbriggen, Jan Willem Borst and Christian Fleck

    Citation: BMC Systems Biology 2016 10:110

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  9. Content type: Research Article

    Despite the increasing availability of high performance computing capabilities, analysis and characterization of stochastic biochemical systems remain a computational challenge. To address this challenge, the ...

    Authors: Min K. Roh and Bernie J. Daigle Jr.

    Citation: BMC Systems Biology 2016 10:109

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  10. Content type: Research Article

    Many problems in biomedicine and other areas of the life sciences can be characterized as control problems, with the goal of finding strategies to change a disease or otherwise undesirable state of a biologica...

    Authors: David Murrugarra, Alan Veliz-Cuba, Boris Aguilar and Reinhard Laubenbacher

    Citation: BMC Systems Biology 2016 10:94

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  11. Content type: Software

    The increased availability of high-throughput datasets has revealed a need for reproducible and accessible analyses which can quantitatively relate molecular changes to phenotypic behavior. Existing tools for ...

    Authors: Edwin F. Juarez, Roy Lau, Samuel H. Friedman, Ahmadreza Ghaffarizadeh, Edmond Jonckheere, David B. Agus, Shannon M. Mumenthaler and Paul Macklin

    Citation: BMC Systems Biology 2016 10:92

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  12. Content type: Methodology Article

    The estimation of intracellular flux through traditional metabolic flux analysis (MFA) using an overdetermined system of equations is a well established practice in metabolic engineering. Despite the continued...

    Authors: Stanislav Sokolenko, Marco Quattrociocchi and Marc G. Aucoin

    Citation: BMC Systems Biology 2016 10:91

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  13. Content type: Research article

    Chronic wounds are often colonized by consortia comprised of different bacterial species growing as biofilms on a complex mixture of wound exudate. Bacteria growing in biofilms exhibit phenotypes distinct from...

    Authors: Poonam Phalak, Jin Chen, Ross P. Carlson and Michael A. Henson

    Citation: BMC Systems Biology 2016 10:90

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  14. Content type: Software

    Many large data compendia on context-specific high-throughput genomic and regulatory data have been made available by international research consortia such as ENCODE, TCGA, and Epigenomics Roadmap. The use of ...

    Authors: Robert Pesch and Ralf Zimmer

    Citation: BMC Systems Biology 2016 10:76

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  15. Content type: Methodology Article

    Canonical correlation analysis (CCA) is a multivariate statistical method which describes the associations between two sets of variables. The objective is to find linear combinations of the variables in each d...

    Authors: Ines Wilms and Christophe Croux

    Citation: BMC Systems Biology 2016 10:72

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Page 2 of 11

2016 Journal Metrics

  • Citation Impact
    2.303 - 2-year Impact Factor
    2.526 - 5-year Impact Factor
    0.735 - Source Normalized Impact per Paper (SNIP)
    1.144 - SCImago Journal Rank (SJR)

    1233 Usage Factor

    Social Media Impact
    134 mentions