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DistributedFBA.jl: high-level, high-performance flux balance analysis in Julia.

Bioinformatics (Oxford, England)

Heirendt L, Thiele I, Fleming RMT.
PMID: 28453682
Bioinformatics. 2017 May 01;33(9):1421-1423. doi: 10.1093/bioinformatics/btw838.

MOTIVATION: Flux balance analysis and its variants are widely used methods for predicting steady-state reaction rates in biochemical reaction networks. The exploration of high dimensional networks with such methods is currently hampered by software performance limitations.RESULTS: DistributedFBA.jl is a...

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Heirendt L, Thiele I, Fleming RMT. DistributedFBA.jl: high-level, high-performance flux balance analysis in Julia. Bioinformatics. 2017;33(9):1421-1423doi: 10.1093/bioinformatics/btw838.
Heirendt, L., Thiele, I., & Fleming, R. M. T. (2017). DistributedFBA.jl: high-level, high-performance flux balance analysis in Julia. Bioinformatics (Oxford, England), 33(9), 1421-1423. https://doi.org/10.1093/bioinformatics/btw838
Heirendt, Laurent, et al. "DistributedFBA.jl: high-level, high-performance flux balance analysis in Julia." Bioinformatics (Oxford, England) vol. 33,9 (2017): 1421-1423. doi: https://doi.org/10.1093/bioinformatics/btw838
Heirendt L, Thiele I, Fleming RMT. DistributedFBA.jl: high-level, high-performance flux balance analysis in Julia. Bioinformatics. 2017 May 01;33(9):1421-1423. doi: 10.1093/bioinformatics/btw838. PMID: 28453682; PMCID: PMC5408791.
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Coexistence of competing metabolic pathways in well-mixed populations.

Physical review. E

Fernández L, Amado A, Campos PR, Ferreira FF.
PMID: 27300918
Phys Rev E. 2016 May;93(5):052401. doi: 10.1103/PhysRevE.93.052401. Epub 2016 May 05.

Understanding why strains with different metabolic pathways that compete for a single limiting resource coexist is a challenging issue within a theoretical perspective. Previous investigations rely on mechanisms such as group or spatial structuring to achieve a stable coexistence...

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Fernández L, Amado A, Campos PR, et al. Coexistence of competing metabolic pathways in well-mixed populations. Phys Rev E. 2016;93(5):052401doi: 10.1103/PhysRevE.93.052401.
Fernández, L., Amado, A., Campos, P. R., & Ferreira, F. F. (2016). Coexistence of competing metabolic pathways in well-mixed populations. Physical review. E, 93(5), 052401. https://doi.org/10.1103/PhysRevE.93.052401
Fernández, Lenin, et al. "Coexistence of competing metabolic pathways in well-mixed populations." Physical review. E vol. 93,5 (2016): 052401. doi: https://doi.org/10.1103/PhysRevE.93.052401
Fernández L, Amado A, Campos PR, Ferreira FF. Coexistence of competing metabolic pathways in well-mixed populations. Phys Rev E. 2016 May;93(5):052401. doi: 10.1103/PhysRevE.93.052401. Epub 2016 May 05. PMID: 27300918.
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A comparison of aggregate p-value methods and multivariate statistics for self-contained tests of metabolic pathway analysis.

PloS one

Mitchell MW.
PMID: 25927705
PLoS One. 2015 Apr 30;10(4):e0125081. doi: 10.1371/journal.pone.0125081. eCollection 2015.

For pathway analysis of genomic data, the most common methods involve combining p-values from individual statistical tests. However, there are several multivariate statistical methods that can be used to test whether a pathway has changed. Because of the large...

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Mitchell MW. A comparison of aggregate p-value methods and multivariate statistics for self-contained tests of metabolic pathway analysis. PLoS One. 2015;10(4):e0125081doi: 10.1371/journal.pone.0125081.
Mitchell, M. W. (2015). A comparison of aggregate p-value methods and multivariate statistics for self-contained tests of metabolic pathway analysis. PloS one, 10(4), e0125081. https://doi.org/10.1371/journal.pone.0125081
Mitchell, Matthew W. "A comparison of aggregate p-value methods and multivariate statistics for self-contained tests of metabolic pathway analysis." PloS one vol. 10,4 (2015): e0125081. doi: https://doi.org/10.1371/journal.pone.0125081
Mitchell MW. A comparison of aggregate p-value methods and multivariate statistics for self-contained tests of metabolic pathway analysis. PLoS One. 2015 Apr 30;10(4):e0125081. doi: 10.1371/journal.pone.0125081. eCollection 2015. PMID: 25927705; PMCID: PMC4415974.
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MOST-visualization: software for producing automated textbook-style maps of genome-scale metabolic networks.

Bioinformatics (Oxford, England)

Kelley JJ, Maor S, Kim MK, Lane A, Lun DS.
PMID: 28430868
Bioinformatics. 2017 Aug 15;33(16):2596-2597. doi: 10.1093/bioinformatics/btx240.

SUMMARY: Visualization of metabolites, reactions and pathways in genome-scale metabolic networks (GEMs) can assist in understanding cellular metabolism. Three attributes are desirable in software used for visualizing GEMs: (i) automation, since GEMs can be quite large; (ii) production of...

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Kelley JJ, Maor S, Kim MK, et al. MOST-visualization: software for producing automated textbook-style maps of genome-scale metabolic networks. Bioinformatics. 2017;33(16):2596-2597doi: 10.1093/bioinformatics/btx240.
Kelley, J. J., Maor, S., Kim, M. K., Lane, A., & Lun, D. S. (2017). MOST-visualization: software for producing automated textbook-style maps of genome-scale metabolic networks. Bioinformatics (Oxford, England), 33(16), 2596-2597. https://doi.org/10.1093/bioinformatics/btx240
Kelley, James J, et al. "MOST-visualization: software for producing automated textbook-style maps of genome-scale metabolic networks." Bioinformatics (Oxford, England) vol. 33,16 (2017): 2596-2597. doi: https://doi.org/10.1093/bioinformatics/btx240
Kelley JJ, Maor S, Kim MK, Lane A, Lun DS. MOST-visualization: software for producing automated textbook-style maps of genome-scale metabolic networks. Bioinformatics. 2017 Aug 15;33(16):2596-2597. doi: 10.1093/bioinformatics/btx240. PMID: 28430868.
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Flow of Information in Biological Systems.

Seminars in cell & developmental biology

Masoudi-Nejad A, Zenil H.
PMID: 26987578
Semin Cell Dev Biol. 2016 Mar;51:1-2. doi: 10.1016/j.semcdb.2016.03.004.

No abstract available.

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Masoudi-Nejad A, Zenil H. Flow of Information in Biological Systems. Semin Cell Dev Biol. 2016;51:1-2doi: 10.1016/j.semcdb.2016.03.004.
Masoudi-Nejad, A., & Zenil, H. (2016). Flow of Information in Biological Systems. Seminars in cell & developmental biology, 511-2. https://doi.org/10.1016/j.semcdb.2016.03.004
Masoudi-Nejad, Ali, and Zenil, Hector. "Flow of Information in Biological Systems." Seminars in cell & developmental biology vol. 51 (2016): 1-2. doi: https://doi.org/10.1016/j.semcdb.2016.03.004
Masoudi-Nejad A, Zenil H. Flow of Information in Biological Systems. Semin Cell Dev Biol. 2016 Mar;51:1-2. doi: 10.1016/j.semcdb.2016.03.004. PMID: 26987578.
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Advancing Clinical Proteomics via Analysis Based on Biological Complexes: A Tale of Five Paradigms.

Journal of proteome research

Goh WW, Wong L.
PMID: 27454466
J Proteome Res. 2016 Sep 02;15(9):3167-79. doi: 10.1021/acs.jproteome.6b00402. Epub 2016 Aug 10.

Despite advances in proteomic technologies, idiosyncratic data issues, for example, incomplete coverage and inconsistency, resulting in large data holes, persist. Moreover, because of naïve reliance on statistical testing and its accompanying p values, differential protein signatures identified from such...

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Goh WW, Wong L. Advancing Clinical Proteomics via Analysis Based on Biological Complexes: A Tale of Five Paradigms. J Proteome Res. 2016;15(9):3167-79doi: 10.1021/acs.jproteome.6b00402.
Goh, W. W., & Wong, L. (2016). Advancing Clinical Proteomics via Analysis Based on Biological Complexes: A Tale of Five Paradigms. Journal of proteome research, 15(9), 3167-79. https://doi.org/10.1021/acs.jproteome.6b00402
Goh, Wilson Wen Bin, and Wong, Limsoon. "Advancing Clinical Proteomics via Analysis Based on Biological Complexes: A Tale of Five Paradigms." Journal of proteome research vol. 15,9 (2016): 3167-79. doi: https://doi.org/10.1021/acs.jproteome.6b00402
Goh WW, Wong L. Advancing Clinical Proteomics via Analysis Based on Biological Complexes: A Tale of Five Paradigms. J Proteome Res. 2016 Sep 02;15(9):3167-79. doi: 10.1021/acs.jproteome.6b00402. Epub 2016 Aug 10. PMID: 27454466.
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Recent advances in elementary flux modes and yield space analysis as useful tools in metabolic network studies.

World journal of microbiology & biotechnology

Horvat P, Koller M, Braunegg G.
PMID: 26066363
World J Microbiol Biotechnol. 2015 Sep;31(9):1315-28. doi: 10.1007/s11274-015-1887-1. Epub 2015 Jun 12.

A review of the use of elementary flux modes (EFMs) and their applications in metabolic engineering covered with yield space analysis (YSA) is presented. EFMs are an invaluable tool in mathematical modeling of biochemical processes. They are described from...

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Horvat P, Koller M, Braunegg G. Recent advances in elementary flux modes and yield space analysis as useful tools in metabolic network studies. World J Microbiol Biotechnol. 2015;31(9):1315-28doi: 10.1007/s11274-015-1887-1.
Horvat, P., Koller, M., & Braunegg, G. (2015). Recent advances in elementary flux modes and yield space analysis as useful tools in metabolic network studies. World journal of microbiology & biotechnology, 31(9), 1315-28. https://doi.org/10.1007/s11274-015-1887-1
Horvat, Predrag, et al. "Recent advances in elementary flux modes and yield space analysis as useful tools in metabolic network studies." World journal of microbiology & biotechnology vol. 31,9 (2015): 1315-28. doi: https://doi.org/10.1007/s11274-015-1887-1
Horvat P, Koller M, Braunegg G. Recent advances in elementary flux modes and yield space analysis as useful tools in metabolic network studies. World J Microbiol Biotechnol. 2015 Sep;31(9):1315-28. doi: 10.1007/s11274-015-1887-1. Epub 2015 Jun 12. PMID: 26066363.
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Dynamic metabolic engineering: New strategies for developing responsive cell factories.

Biotechnology journal

Brockman IM, Prather KL.
PMID: 25868062
Biotechnol J. 2015 Sep;10(9):1360-9. doi: 10.1002/biot.201400422. Epub 2015 Apr 13.

Metabolic engineering strategies have enabled improvements in yield and titer for a variety of valuable small molecules produced naturally in microorganisms, as well as those produced via heterologous pathways. Typically, the approaches have been focused on up- and downregulation...

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Brockman IM, Prather KL. Dynamic metabolic engineering: New strategies for developing responsive cell factories. Biotechnol J. 2015;10(9):1360-9doi: 10.1002/biot.201400422.
Brockman, I. M., & Prather, K. L. (2015). Dynamic metabolic engineering: New strategies for developing responsive cell factories. Biotechnology journal, 10(9), 1360-9. https://doi.org/10.1002/biot.201400422
Brockman, Irene M, and Prather, Kristala L J. "Dynamic metabolic engineering: New strategies for developing responsive cell factories." Biotechnology journal vol. 10,9 (2015): 1360-9. doi: https://doi.org/10.1002/biot.201400422
Brockman IM, Prather KL. Dynamic metabolic engineering: New strategies for developing responsive cell factories. Biotechnol J. 2015 Sep;10(9):1360-9. doi: 10.1002/biot.201400422. Epub 2015 Apr 13. PMID: 25868062; PMCID: PMC4629492.
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Integration of CellDesigner and SABIO-RK.

In silico biology

Funahashi A, Jouraku A, Matsuoka Y, Kitano H.
PMID: 17822394
In Silico Biol. 2007;7(2):S81-90.

Understanding the logic and dynamics of gene-regulatory and biochemical networks is a major challenge for systems biology. To facilitate this research topic, we have developed CellDesigner to visualize, model and simulate biochemical networks. CellDesigner allows the users to easily...

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Funahashi A, Jouraku A, Matsuoka Y, et al. Integration of CellDesigner and SABIO-RK. In Silico Biol. 2007;7(2):S81-90
Funahashi, A., Jouraku, A., Matsuoka, Y., & Kitano, H. (2007). Integration of CellDesigner and SABIO-RK. In silico biology, 7(2), S81-90.
Funahashi, Akira, et al. "Integration of CellDesigner and SABIO-RK." In silico biology vol. 7,2 (2007): S81-90.
Funahashi A, Jouraku A, Matsuoka Y, Kitano H. Integration of CellDesigner and SABIO-RK. In Silico Biol. 2007;7(2):S81-90. PMID: 17822394.
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SBMLmerge, a system for combining biochemical network models.

Genome informatics. International Conference on Genome Informatics

Schulz M, Uhlendorf J, Klipp E, Liebermeister W.
PMID: 17503356
Genome Inform. 2006;17(1):62-71.

The Systems Biology Markup Language (SBML) is an XML-based format for representing mathematical models of biochemical reaction networks, and it is likely to become a main standard in the systems biology community. As published mathematical models in cell biology...

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Schulz M, Uhlendorf J, Klipp E, et al. SBMLmerge, a system for combining biochemical network models. Genome Inform. 2006;17(1):62-71
Schulz, M., Uhlendorf, J., Klipp, E., & Liebermeister, W. (2006). SBMLmerge, a system for combining biochemical network models. Genome informatics. International Conference on Genome Informatics, 17(1), 62-71.
Schulz, Marvin, et al. "SBMLmerge, a system for combining biochemical network models." Genome informatics. International Conference on Genome Informatics vol. 17,1 (2006): 62-71.
Schulz M, Uhlendorf J, Klipp E, Liebermeister W. SBMLmerge, a system for combining biochemical network models. Genome Inform. 2006;17(1):62-71. PMID: 17503356.
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Network medicine: a network-based approach to human disease.

Nature reviews. Genetics

Barabási AL, Gulbahce N, Loscalzo J.
PMID: 21164525
Nat Rev Genet. 2011 Jan;12(1):56-68. doi: 10.1038/nrg2918.

Given the functional interdependencies between the molecular components in a human cell, a disease is rarely a consequence of an abnormality in a single gene, but reflects the perturbations of the complex intracellular and intercellular network that links tissue...

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Barabási AL, Gulbahce N, Loscalzo J. Network medicine: a network-based approach to human disease. Nat Rev Genet. 2011;12(1):56-68doi: 10.1038/nrg2918.
Barabási, A. L., Gulbahce, N., & Loscalzo, J. (2011). Network medicine: a network-based approach to human disease. Nature reviews. Genetics, 12(1), 56-68. https://doi.org/10.1038/nrg2918
Barabási, Albert-László, et al. "Network medicine: a network-based approach to human disease." Nature reviews. Genetics vol. 12,1 (2011): 56-68. doi: https://doi.org/10.1038/nrg2918
Barabási AL, Gulbahce N, Loscalzo J. Network medicine: a network-based approach to human disease. Nat Rev Genet. 2011 Jan;12(1):56-68. doi: 10.1038/nrg2918. PMID: 21164525; PMCID: PMC3140052.
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Computational identification of cellular networks and pathways.

Molecular bioSystems

Markowetz F, Troyanskaya OG.
PMID: 17579773
Mol Biosyst. 2007 Jul;3(7):478-82. doi: 10.1039/b617014p. Epub 2007 May 14.

In this article we highlight recent developments in computational functional genomics to identify networks of functionally related genes and proteins based on diverse sources of genomic data. Our specific focus is on statistical methods to identify genetic networks. We...

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Markowetz F, Troyanskaya OG. Computational identification of cellular networks and pathways. Mol Biosyst. 2007;3(7):478-82doi: 10.1039/b617014p.
Markowetz, F., & Troyanskaya, O. G. (2007). Computational identification of cellular networks and pathways. Molecular bioSystems, 3(7), 478-82. https://doi.org/10.1039/b617014p
Markowetz, Florian, and Troyanskaya, Olga G. "Computational identification of cellular networks and pathways." Molecular bioSystems vol. 3,7 (2007): 478-82. doi: https://doi.org/10.1039/b617014p
Markowetz F, Troyanskaya OG. Computational identification of cellular networks and pathways. Mol Biosyst. 2007 Jul;3(7):478-82. doi: 10.1039/b617014p. Epub 2007 May 14. PMID: 17579773.
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Showing 1 to 12 of 204 entries