LibSBML: an API Library for SBML

Bornstein, B.; Keating, Sarah M.; Jouraku, Akiya; Hucka, Michael

doi:10.1093/bioinformatics/btn051

Cited by 358 publications

(302 citation statements)

References 3 publications

Supporting

Mentioning

295

Contrasting

Order By: Relevance

“…The four models-with known biomass representations and nutrient limitations-were imported to MATLAB s using libSBML (Bornstein et al, 2008) and solved with GLPK (GNU Linear Programming Kit) (Makhorin, 2001).…”

Section: Resultsmentioning

confidence: 99%

Flux balance analysis: A geometric perspective

Smallbone

Simeonidis

2009

Journal of Theoretical Biology

View full text Add to dashboard Cite

a b s t r a c tAdvances in the field of bioinformatics have led to reconstruction of genome-scale networks for a number of key organisms. The application of physicochemical constraints to these stoichiometric networks allows researchers, through methods such as flux balance analysis, to highlight key sets of reactions necessary to achieve particular objectives. The key benefits of constraint-based analysis lie in the minimal knowledge required to infer systemic properties. However, network degeneracy leads to a large number of flux distributions that satisfy any objective; moreover, these distributions may be dominated by biologically irrelevant internal cycles. By examining the geometry underlying the problem, we define two methods for finding a unique solution within the space of all possible flux distributions; such a solution contains no internal cycles, and is representative of the space as a whole. The first method draws on typical geometric knowledge, but cannot be applied to large networks because of the high computational complexity of the problem. Thus a second method, an iteration of linear programs which scales easily to the genome scale, is defined. The algorithm is run on four recent genome-scale models, and unique flux solutions are found. The algorithm set out here will allow researchers in flux balance analysis to exchange typical solutions to their models in a reproducible format. Moreover, having found a single solution, statistical analyses such as correlations may be performed.

show abstract

Section: Resultsmentioning

confidence: 99%

Flux balance analysis: A geometric perspective

Smallbone

Simeonidis

2009

Journal of Theoretical Biology

View full text Add to dashboard Cite

show abstract

“…( 3 ) Clustering: k -means or largest common subgraph. The proposed methods were implemented in C++ (gcc4.1.2 +O3) with Boost C++ library and libSBML 15) , and were evaluated using 32 SBML predefined rate law functions 6) . Pipeline scheduling and arithmetic resource sharing were done by list scheduling policy 7),13) .…”

Section: Evaluation and Discussionmentioning

confidence: 99%

Automatic Pipeline Construction Focused on Similarity of Rate Law Functions for an FPGA-based Biochemical Simulator

Yamada

Ogawa

Ooya

et al. 2010

IPSJ Transactions on System LSI Design Methodology

View full text Add to dashboard Cite

For FPGA-based scientific simulation systems, hardware design technique that can reduce required amount of hardware resources is a key issue, since the size of simulation target is often limited by the size of the FPGA. Focusing on FPGA-based biochemical simulation, this paper proposes hardware design methodology which finds and combines common datapath for similar rate law functions appeared in simulation target models, so as to generate area-effective pipelined hardware modules. In addition, similarity-based clustering techniques of rate law functions are also presented in order to alleviate negative effects on performance for combined pipelines. Empirical evaluation with a practical biochemical model reveals that our method enables the simulation with 66% of the original hardware resources at a reasonable cost of 20% performance overhead.

show abstract

“…The constructors deal with specifics of particular file format. The use of libSBML (Bornstein et al, 2008) enables convenient way of SBML model conversion to ModeRator inner data format. SBML files prior to Level 3 does not support storing of chemical formulas.…”

Section: Moderator -A Software Tool For Comparisonmentioning

confidence: 99%

Comparative Analysis of Biochemical Network Reconstructions

Mednis¹

2016

BJMC

View full text Add to dashboard Cite

Abstract. Reconstruction of genome-scale metabolic network is a result of assembling various information sources about all biochemical reactions expected in the metabolic network of interest. Despite the efforts of leading bio-models databases to make comparison of biochemical networks by elements names obsolete, interest from researchers in using string similarity metrics in comparison of metabolites names has been growing. Multiple challenges in comparison of reconstructions are discussed in this article and an insight into current approach of metabolic model comparison has been given. The discussion of challenges and attempts to solve them are followed by the author's proposed algorithm for models comparison that can be particularly useful in case of reconstructions. Special attention is given to the use of metabolites names and chemical formulas. The author's proposed algorithm has been implemented in a software tool ModeRator. The article is concluded with use cases of the comparison algorithm and the software tool.

show abstract

LibSBML: an API Library for SBML

Abstract: LibSBML 3 was released in August 2007. Source code, binaries and documentation are freely available under LGPL open-source terms from http://sbml.org/software/libsbml.

Cited by 358 publications

References 3 publications

Flux balance analysis: A geometric perspective

Flux balance analysis: A geometric perspective

Automatic Pipeline Construction Focused on Similarity of Rate Law Functions for an FPGA-based Biochemical Simulator

Comparative Analysis of Biochemical Network Reconstructions

Contact Info

Product

Resources

About