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Construction and completion of flux balance models from pathway databases.

Bioinformatics (Oxford, England)

Latendresse M, Krummenacker M, Trupp M, Karp PD.
PMID: 22262672
Bioinformatics. 2012 Feb 01;28(3):388-96. doi: 10.1093/bioinformatics/btr681. Epub 2012 Jan 18.

MOTIVATION: Flux balance analysis (FBA) is a well-known technique for genome-scale modeling of metabolic flux. Typically, an FBA formulation requires the accurate specification of four sets: biochemical reactions, biomass metabolites, nutrients and secreted metabolites. The development of FBA models...

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Latendresse M, Krummenacker M, Trupp M, et al. Construction and completion of flux balance models from pathway databases. Bioinformatics. 2012;28(3):388-96doi: 10.1093/bioinformatics/btr681.
Latendresse, M., Krummenacker, M., Trupp, M., & Karp, P. D. (2012). Construction and completion of flux balance models from pathway databases. Bioinformatics (Oxford, England), 28(3), 388-96. https://doi.org/10.1093/bioinformatics/btr681
Latendresse, Mario, et al. "Construction and completion of flux balance models from pathway databases." Bioinformatics (Oxford, England) vol. 28,3 (2012): 388-96. doi: https://doi.org/10.1093/bioinformatics/btr681
Latendresse M, Krummenacker M, Trupp M, Karp PD. Construction and completion of flux balance models from pathway databases. Bioinformatics. 2012 Feb 01;28(3):388-96. doi: 10.1093/bioinformatics/btr681. Epub 2012 Jan 18. PMID: 22262672; PMCID: PMC3268246.
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Systemic Metabolomic Changes in Blood Samples of Lung Cancer Patients Identified by Gas Chromatography Time-of-Flight Mass Spectrometry.

Metabolites

Miyamoto S, Taylor SL, Barupal DK, Taguchi A, Wohlgemuth G, Wikoff WR, Yoneda KY, Gandara DR, Hanash SM, Kim K, Fiehn O.
PMID: 25859693
Metabolites. 2015 Apr 09;5(2):192-210. doi: 10.3390/metabo5020192.

Lung cancer is a leading cause of cancer deaths worldwide. Metabolic alterations in tumor cells coupled with systemic indicators of the host response to tumor development have the potential to yield blood profiles with clinical utility for diagnosis and...

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Miyamoto S, Taylor SL, Barupal DK, et al. Systemic Metabolomic Changes in Blood Samples of Lung Cancer Patients Identified by Gas Chromatography Time-of-Flight Mass Spectrometry. Metabolites. 2015;5(2):192-210doi: 10.3390/metabo5020192.
Miyamoto, S., Taylor, S. L., Barupal, D. K., Taguchi, A., Wohlgemuth, G., Wikoff, W. R., Yoneda, K. Y., Gandara, D. R., Hanash, S. M., Kim, K., & Fiehn, O. (2015). Systemic Metabolomic Changes in Blood Samples of Lung Cancer Patients Identified by Gas Chromatography Time-of-Flight Mass Spectrometry. Metabolites, 5(2), 192-210. https://doi.org/10.3390/metabo5020192
Miyamoto, Suzanne, et al. "Systemic Metabolomic Changes in Blood Samples of Lung Cancer Patients Identified by Gas Chromatography Time-of-Flight Mass Spectrometry." Metabolites vol. 5,2 (2015): 192-210. doi: https://doi.org/10.3390/metabo5020192
Miyamoto S, Taylor SL, Barupal DK, Taguchi A, Wohlgemuth G, Wikoff WR, Yoneda KY, Gandara DR, Hanash SM, Kim K, Fiehn O. Systemic Metabolomic Changes in Blood Samples of Lung Cancer Patients Identified by Gas Chromatography Time-of-Flight Mass Spectrometry. Metabolites. 2015 Apr 09;5(2):192-210. doi: 10.3390/metabo5020192. PMID: 25859693; PMCID: PMC4495369.
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Pharmacometabolomics informs Pharmacogenomics.

Metabolomics : Official journal of the Metabolomic Society

Neavin D, Kaddurah-Daouk R, Weinshilboum R.
PMID: 27516730
Metabolomics. 2016 Jul;12(7). doi: 10.1007/s11306-016-1066-x. Epub 2016 Jul 01.

INTRODUCTION: The initial decades of the 21OBJECTIVES: This "Brief Review" will outline some of the challenges and opportunities associated with studies in which metabolomic data have been merged with genomics in an attempt to gain novel insight into mechanisms...

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Neavin D, Kaddurah-Daouk R, Weinshilboum R. Pharmacometabolomics informs Pharmacogenomics. Metabolomics. 2016;12(7)doi: 10.1007/s11306-016-1066-x.
Neavin, D., Kaddurah-Daouk, R., & Weinshilboum, R. (2016). Pharmacometabolomics informs Pharmacogenomics. Metabolomics : Official journal of the Metabolomic Society, 12(7), . https://doi.org/10.1007/s11306-016-1066-x
Neavin, Drew, et al. "Pharmacometabolomics informs Pharmacogenomics." Metabolomics : Official journal of the Metabolomic Society vol. 12,7 (2016). doi: https://doi.org/10.1007/s11306-016-1066-x
Neavin D, Kaddurah-Daouk R, Weinshilboum R. Pharmacometabolomics informs Pharmacogenomics. Metabolomics. 2016 Jul;12(7). doi: 10.1007/s11306-016-1066-x. Epub 2016 Jul 01. PMID: 27516730; PMCID: PMC4976774.
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Novel plasma biomarker of atenolol-induced hyperglycemia identified through a metabolomics-genomics integrative approach.

Metabolomics : Official journal of the Metabolomic Society

de Oliveira FA, Shahin MH, Gong Y, McDonough CW, Beitelshees AL, Gums JG, Chapman AB, Boerwinkle E, Turner ST, Frye RF, Fiehn O, Kaddurah-Daouk R, Johnson JA, Cooper-DeHoff RM.
PMID: 28217400
Metabolomics. 2016 Aug;12(8). doi: 10.1007/s11306-016-1076-8. Epub 2016 Jul 13.

INTRODUCTION: While atenolol is an effective antihypertensive agent, its use is also associated with adverse events including hyperglycemia and incident diabetes that may offset the benefits of blood pressure lowering. By combining metabolomic and genomic data acquired from hypertensive...

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de Oliveira FA, Shahin MH, Gong Y, et al. Novel plasma biomarker of atenolol-induced hyperglycemia identified through a metabolomics-genomics integrative approach. Metabolomics. 2016;12(8)doi: 10.1007/s11306-016-1076-8.
de Oliveira, F. A., Shahin, M. H., Gong, Y., McDonough, C. W., Beitelshees, A. L., Gums, J. G., Chapman, A. B., Boerwinkle, E., Turner, S. T., Frye, R. F., Fiehn, O., Kaddurah-Daouk, R., Johnson, J. A., & Cooper-DeHoff, R. M. (2016). Novel plasma biomarker of atenolol-induced hyperglycemia identified through a metabolomics-genomics integrative approach. Metabolomics : Official journal of the Metabolomic Society, 12(8), . https://doi.org/10.1007/s11306-016-1076-8
de Oliveira, Felipe A, et al. "Novel plasma biomarker of atenolol-induced hyperglycemia identified through a metabolomics-genomics integrative approach." Metabolomics : Official journal of the Metabolomic Society vol. 12,8 (2016). doi: https://doi.org/10.1007/s11306-016-1076-8
de Oliveira FA, Shahin MH, Gong Y, McDonough CW, Beitelshees AL, Gums JG, Chapman AB, Boerwinkle E, Turner ST, Frye RF, Fiehn O, Kaddurah-Daouk R, Johnson JA, Cooper-DeHoff RM. Novel plasma biomarker of atenolol-induced hyperglycemia identified through a metabolomics-genomics integrative approach. Metabolomics. 2016 Aug;12(8). doi: 10.1007/s11306-016-1076-8. Epub 2016 Jul 13. PMID: 28217400; PMCID: PMC5310671.
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Pharmacometabolomic signature links simvastatin therapy and insulin resistance.

Metabolomics : Official journal of the Metabolomic Society

Elbadawi-Sidhu M, Baillie RA, Zhu H, Chen YI, Goodarzi MO, Rotter JI, Krauss RM, Fiehn O, Kaddurah-Daouk R.
PMID: 29732238
Metabolomics. 2017 Jan;13. doi: 10.1007/s11306-016-1141-3. Epub 2016 Dec 23.

INTRODUCTION: Statins, widely prescribed drugs for treatment of cardiovascular disease, inhibit the biosynthesis of low density lipoprotein cholesterol (LDL-C). Despite providing major benefits, sub populations of patients experience adverse effects, including muscle myopathy and development of type II diabetes...

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Elbadawi-Sidhu M, Baillie RA, Zhu H, et al. Pharmacometabolomic signature links simvastatin therapy and insulin resistance. Metabolomics. 2016;13doi: 10.1007/s11306-016-1141-3.
Elbadawi-Sidhu, M., Baillie, R. A., Zhu, H., Chen, Y. I., Goodarzi, M. O., Rotter, J. I., Krauss, R. M., Fiehn, O., & Kaddurah-Daouk, R. (2017). Pharmacometabolomic signature links simvastatin therapy and insulin resistance. Metabolomics : Official journal of the Metabolomic Society, 13. https://doi.org/10.1007/s11306-016-1141-3
Elbadawi-Sidhu, Mona, et al. "Pharmacometabolomic signature links simvastatin therapy and insulin resistance." Metabolomics : Official journal of the Metabolomic Society vol. 13 (2017). doi: https://doi.org/10.1007/s11306-016-1141-3
Elbadawi-Sidhu M, Baillie RA, Zhu H, Chen YI, Goodarzi MO, Rotter JI, Krauss RM, Fiehn O, Kaddurah-Daouk R. Pharmacometabolomic signature links simvastatin therapy and insulin resistance. Metabolomics. 2017 Jan;13. doi: 10.1007/s11306-016-1141-3. Epub 2016 Dec 23. PMID: 29732238; PMCID: PMC5931366.
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Pharmacometabolomic Assessment of Metformin in Non-diabetic, African Americans.

Frontiers in pharmacology

Rotroff DM, Oki NO, Liang X, Yee SW, Stocker SL, Corum DG, Meisner M, Fiehn O, Motsinger-Reif AA, Giacomini KM, Kaddurah-Daouk R.
PMID: 27378919
Front Pharmacol. 2016 Jun 14;7:135. doi: 10.3389/fphar.2016.00135. eCollection 2016.

Millions of individuals are diagnosed with type 2 diabetes mellitus (T2D), which increases the risk for a plethora of adverse outcomes including cardiovascular events and kidney disease. Metformin is the most widely prescribed medication for the treatment of T2D;...

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Rotroff DM, Oki NO, Liang X, et al. Pharmacometabolomic Assessment of Metformin in Non-diabetic, African Americans. Front Pharmacol. 2016;7:135doi: 10.3389/fphar.2016.00135.
Rotroff, D. M., Oki, N. O., Liang, X., Yee, S. W., Stocker, S. L., Corum, D. G., Meisner, M., Fiehn, O., Motsinger-Reif, A. A., Giacomini, K. M., & Kaddurah-Daouk, R. (2016). Pharmacometabolomic Assessment of Metformin in Non-diabetic, African Americans. Frontiers in pharmacology, 7135. https://doi.org/10.3389/fphar.2016.00135
Rotroff, Daniel M, et al. "Pharmacometabolomic Assessment of Metformin in Non-diabetic, African Americans." Frontiers in pharmacology vol. 7 (2016): 135. doi: https://doi.org/10.3389/fphar.2016.00135
Rotroff DM, Oki NO, Liang X, Yee SW, Stocker SL, Corum DG, Meisner M, Fiehn O, Motsinger-Reif AA, Giacomini KM, Kaddurah-Daouk R. Pharmacometabolomic Assessment of Metformin in Non-diabetic, African Americans. Front Pharmacol. 2016 Jun 14;7:135. doi: 10.3389/fphar.2016.00135. eCollection 2016. PMID: 27378919; PMCID: PMC4906013.
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Pharmacometabolomic Assessments of Atenolol and Hydrochlorothiazide Treatment Reveal Novel Drug Response Phenotypes.

CPT: pharmacometrics & systems pharmacology

Rotroff DM, Shahin MH, Gurley SB, Zhu H, Motsinger-Reif A, Meisner M, Beitelshees AL, Fiehn O, Johnson JA, Elbadawi-Sidhu M, Frye RF, Gong Y, Weng L, Cooper-DeHoff RM, Kaddurah-Daouk R.
PMID: 26783503
CPT Pharmacometrics Syst Pharmacol. 2015 Nov;4(11):669-79. doi: 10.1002/psp4.12017. Epub 2015 Oct 29.

Achieving hypertension (HTN) control and mitigating the adverse health effects associated with HTN continues to be a global challenge. Some individuals respond poorly to current HTN therapies, and mechanisms for response variation remain poorly understood. We used a nontargeted...

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Rotroff DM, Shahin MH, Gurley SB, et al. Pharmacometabolomic Assessments of Atenolol and Hydrochlorothiazide Treatment Reveal Novel Drug Response Phenotypes. CPT Pharmacometrics Syst Pharmacol. 2015;4(11):669-79doi: 10.1002/psp4.12017.
Rotroff, D. M., Shahin, M. H., Gurley, S. B., Zhu, H., Motsinger-Reif, A., Meisner, M., Beitelshees, A. L., Fiehn, O., Johnson, J. A., Elbadawi-Sidhu, M., Frye, R. F., Gong, Y., Weng, L., Cooper-DeHoff, R. M., & Kaddurah-Daouk, R. (2015). Pharmacometabolomic Assessments of Atenolol and Hydrochlorothiazide Treatment Reveal Novel Drug Response Phenotypes. CPT: pharmacometrics & systems pharmacology, 4(11), 669-79. https://doi.org/10.1002/psp4.12017
Rotroff, D M, et al. "Pharmacometabolomic Assessments of Atenolol and Hydrochlorothiazide Treatment Reveal Novel Drug Response Phenotypes." CPT: pharmacometrics & systems pharmacology vol. 4,11 (2015): 669-79. doi: https://doi.org/10.1002/psp4.12017
Rotroff DM, Shahin MH, Gurley SB, Zhu H, Motsinger-Reif A, Meisner M, Beitelshees AL, Fiehn O, Johnson JA, Elbadawi-Sidhu M, Frye RF, Gong Y, Weng L, Cooper-DeHoff RM, Kaddurah-Daouk R. Pharmacometabolomic Assessments of Atenolol and Hydrochlorothiazide Treatment Reveal Novel Drug Response Phenotypes. CPT Pharmacometrics Syst Pharmacol. 2015 Nov;4(11):669-79. doi: 10.1002/psp4.12017. Epub 2015 Oct 29. PMID: 26783503; PMCID: PMC4716583.
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Pharmacometabolomics reveals that serotonin is implicated in aspirin response variability.

CPT: pharmacometrics & systems pharmacology

Ellero-Simatos S, Lewis JP, Georgiades A, Yerges-Armstrong LM, Beitelshees AL, Horenstein RB, Dane A, Harms AC, Ramaker R, Vreeken RJ, Perry CG, Zhu H, Sànchez CL, Kuhn C, Ortel TL, Shuldiner AR, Hankemeier T, Kaddurah-Daouk R.
PMID: 25029353
CPT Pharmacometrics Syst Pharmacol. 2014 Jul 16;3:e125. doi: 10.1038/psp.2014.22.

While aspirin is generally effective for prevention of cardiovascular disease, considerable variation in drug response exists, resulting in some individuals displaying high on-treatment platelet reactivity. We used pharmacometabolomics to define pathways implicated in variation of response to treatment. We...

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Ellero-Simatos S, Lewis JP, Georgiades A, et al. Pharmacometabolomics reveals that serotonin is implicated in aspirin response variability. CPT Pharmacometrics Syst Pharmacol. 2014;3:e125doi: 10.1038/psp.2014.22.
Ellero-Simatos, S., Lewis, J. P., Georgiades, A., Yerges-Armstrong, L. M., Beitelshees, A. L., Horenstein, R. B., Dane, A., Harms, A. C., Ramaker, R., Vreeken, R. J., Perry, C. G., Zhu, H., Sànchez, C. L., Kuhn, C., Ortel, T. L., Shuldiner, A. R., Hankemeier, T., & Kaddurah-Daouk, R. (2014). Pharmacometabolomics reveals that serotonin is implicated in aspirin response variability. CPT: pharmacometrics & systems pharmacology, 3e125. https://doi.org/10.1038/psp.2014.22
Ellero-Simatos, S, et al. "Pharmacometabolomics reveals that serotonin is implicated in aspirin response variability." CPT: pharmacometrics & systems pharmacology vol. 3 (2014): e125. doi: https://doi.org/10.1038/psp.2014.22
Ellero-Simatos S, Lewis JP, Georgiades A, Yerges-Armstrong LM, Beitelshees AL, Horenstein RB, Dane A, Harms AC, Ramaker R, Vreeken RJ, Perry CG, Zhu H, Sànchez CL, Kuhn C, Ortel TL, Shuldiner AR, Hankemeier T, Kaddurah-Daouk R. Pharmacometabolomics reveals that serotonin is implicated in aspirin response variability. CPT Pharmacometrics Syst Pharmacol. 2014 Jul 16;3:e125. doi: 10.1038/psp.2014.22. PMID: 25029353; PMCID: PMC4120016.
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Improved Sparse Multi-Class SVM and Its Application for Gene Selection in Cancer Classification.

Cancer informatics

Huang L, Zhang HH, Zeng ZB, Bushel PR.
PMID: 23966761
Cancer Inform. 2013 Aug 04;12:143-53. doi: 10.4137/CIN.S10212. eCollection 2013.

BACKGROUND: Microarray techniques provide promising tools for cancer diagnosis using gene expression profiles. However, molecular diagnosis based on high-throughput platforms presents great challenges due to the overwhelming number of variables versus the small sample size and the complex nature...

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Huang L, Zhang HH, Zeng ZB, et al. Improved Sparse Multi-Class SVM and Its Application for Gene Selection in Cancer Classification. Cancer Inform. 2013;12:143-53doi: 10.4137/CIN.S10212.
Huang, L., Zhang, H. H., Zeng, Z. B., & Bushel, P. R. (2013). Improved Sparse Multi-Class SVM and Its Application for Gene Selection in Cancer Classification. Cancer informatics, 12143-53. https://doi.org/10.4137/CIN.S10212
Huang, Lingkang, et al. "Improved Sparse Multi-Class SVM and Its Application for Gene Selection in Cancer Classification." Cancer informatics vol. 12 (2013): 143-53. doi: https://doi.org/10.4137/CIN.S10212
Huang L, Zhang HH, Zeng ZB, Bushel PR. Improved Sparse Multi-Class SVM and Its Application for Gene Selection in Cancer Classification. Cancer Inform. 2013 Aug 04;12:143-53. doi: 10.4137/CIN.S10212. eCollection 2013. PMID: 23966761; PMCID: PMC3740816.
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Augmentation of Physician Assessments with Multi-Omics Enhances Predictability of Drug Response: A Case Study of Major Depressive Disorder.

IEEE computational intelligence magazine

Athreya A, Iyer R, Neavin D, Wang L, Weinshilboum R, Kaddurah-Daouk R, Rush J, Frye M, Bobo W.
PMID: 30467458
IEEE Comput Intell Mag. 2018 Aug;13(3):20-31. doi: 10.1109/MCI.2018.2840660. Epub 2018 Jul 20.

This work proposes a "

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Athreya A, Iyer R, Neavin D, et al. Augmentation of Physician Assessments with Multi-Omics Enhances Predictability of Drug Response: A Case Study of Major Depressive Disorder. IEEE Comput Intell Mag. 2018;13(3):20-31doi: 10.1109/MCI.2018.2840660.
Athreya, A., Iyer, R., Neavin, D., Wang, L., Weinshilboum, R., Kaddurah-Daouk, R., Rush, J., Frye, M., & Bobo, W. (2018). Augmentation of Physician Assessments with Multi-Omics Enhances Predictability of Drug Response: A Case Study of Major Depressive Disorder. IEEE computational intelligence magazine, 13(3), 20-31. https://doi.org/10.1109/MCI.2018.2840660
Athreya, Arjun, et al. "Augmentation of Physician Assessments with Multi-Omics Enhances Predictability of Drug Response: A Case Study of Major Depressive Disorder." IEEE computational intelligence magazine vol. 13,3 (2018): 20-31. doi: https://doi.org/10.1109/MCI.2018.2840660
Athreya A, Iyer R, Neavin D, Wang L, Weinshilboum R, Kaddurah-Daouk R, Rush J, Frye M, Bobo W. Augmentation of Physician Assessments with Multi-Omics Enhances Predictability of Drug Response: A Case Study of Major Depressive Disorder. IEEE Comput Intell Mag. 2018 Aug;13(3):20-31. doi: 10.1109/MCI.2018.2840660. Epub 2018 Jul 20. PMID: 30467458; PMCID: PMC6241311.
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