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2. Mitigating biomass composition uncertainties in flux balance analysis using ensemble representations.

Author

Choi, Yoon-Mi, Choi, Dong-Hyuk, Lee, Yi, Koduru, Lokanand, Lewis, Nathan, Lakshmanan, Meiyappan, and Lee, Dong-Yup

Subjects
Biomass equation, Ensemble modeling, Flux balance analysis (FBA), Macromolecular composition, Parsimonious flux balance analysis (pFBA)
Abstract

The biomass equation is a critical component in genome-scale metabolic models (GEMs): it is used as the de facto objective function in flux balance analysis (FBA). This equation accounts for the quantities of all known biomass precursors that are required for cell growth based on the macromolecular and monomer compositions measured at certain conditions. However, it is often reported that the macromolecular composition of cells could change across different environmental conditions and thus the use of the same single biomass equation in FBA, under multiple conditions, is questionable. Herein, we first investigated the qualitative and quantitative variations of macromolecular compositions of three representative host organisms, Escherichia coli, Saccharomyces cerevisiae and Cricetulus griseus, across different environmental/genetic variations. While macromolecular building blocks such as RNA, protein, and lipid composition vary notably, changes in fundamental biomass monomer units such as nucleotides and amino acids are not appreciable. We also observed that flux predictions through FBA is quite sensitive to macromolecular compositions but not the monomer compositions. Based on these observations, we propose ensemble representations of biomass equation in FBA to account for the natural variation of cellular constituents. Such ensemble representations of biomass better predicted the flux through anabolic reactions as it allows for the flexibility in the biosynthetic demands of the cells. The current study clearly highlights that certain component of the biomass equation indeed vary across different conditions, and the ensemble representation of biomass equation in FBA by accounting for such natural variations could avoid inaccuracies that may arise from in silico simulations.

Published
2023

5. Publisher Correction: MEMOTE for standardized genome-scale metabolic model testing

Author

Lieven, Christian, Beber, Moritz E, Olivier, Brett G, Bergmann, Frank T, Ataman, Meric, Babaei, Parizad, Bartell, Jennifer A, Blank, Lars M, Chauhan, Siddharth, Correia, Kevin, Diener, Christian, Dräger, Andreas, Ebert, Birgitta E, Edirisinghe, Janaka N, Faria, José P, Feist, Adam M, Fengos, Georgios, Fleming, Ronan MT, García-Jiménez, Beatriz, Hatzimanikatis, Vassily, van Helvoirt, Wout, Henry, Christopher S, Hermjakob, Henning, Herrgård, Markus J, Kaafarani, Ali, Kim, Hyun Uk, King, Zachary, Klamt, Steffen, Klipp, Edda, Koehorst, Jasper J, König, Matthias, Lakshmanan, Meiyappan, Lee, Dong-Yup, Lee, Sang Yup, Lee, Sunjae, Lewis, Nathan E, Liu, Filipe, Ma, Hongwu, Machado, Daniel, Mahadevan, Radhakrishnan, Maia, Paulo, Mardinoglu, Adil, Medlock, Gregory L, Monk, Jonathan M, Nielsen, Jens, Nielsen, Lars Keld, Nogales, Juan, Nookaew, Intawat, Palsson, Bernhard O, Papin, Jason A, Patil, Kiran R, Poolman, Mark, Price, Nathan D, Resendis-Antonio, Osbaldo, Richelle, Anne, Rocha, Isabel, Sánchez, Benjamín J, Schaap, Peter J, Sheriff, Rahuman S Malik, Shoaie, Saeed, Sonnenschein, Nikolaus, Teusink, Bas, Vilaça, Paulo, Vik, Jon Olav, Wodke, Judith AH, Xavier, Joana C, Yuan, Qianqian, Zakhartsev, Maksim, and Zhang, Cheng

Subjects
Biological Sciences, Industrial Biotechnology
Abstract

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Published
2020

9. A Consensus Genome-scale Reconstruction of Chinese Hamster Ovary Cell Metabolism

Author

Hefzi, Hooman, Ang, Kok Siong, Hanscho, Michael, Bordbar, Aarash, Ruckerbauer, David, Lakshmanan, Meiyappan, Orellana, Camila A, Baycin-Hizal, Deniz, Huang, Yingxiang, Ley, Daniel, Martinez, Veronica S, Kyriakopoulos, Sarantos, Jiménez, Natalia E, Zielinski, Daniel C, Quek, Lake-Ee, Wulff, Tune, Arnsdorf, Johnny, Li, Shangzhong, Lee, Jae Seong, Paglia, Giuseppe, Loira, Nicolas, Spahn, Philipp N, Pedersen, Lasse E, Gutierrez, Jahir M, King, Zachary A, Lund, Anne Mathilde, Nagarajan, Harish, Thomas, Alex, Abdel-Haleem, Alyaa M, Zanghellini, Juergen, Kildegaard, Helene F, Voldborg, Bjørn G, Gerdtzen, Ziomara P, Betenbaugh, Michael J, Palsson, Bernhard O, Andersen, Mikael R, Nielsen, Lars K, Borth, Nicole, Lee, Dong-Yup, and Lewis, Nathan E

Subjects
Biochemistry and Cell Biology, Biological Sciences, Industrial Biotechnology, Bioengineering, Animals, CHO Cells, Consensus, Cricetinae, Cricetulus, Genome, Humans, Metabolic Networks and Pathways, Recombinant Proteins, CHO, Chinese hamster ovary, biotherapeutic protein production, genome-scale model, metabolic network, systems biology, Biochemistry and cell biology
Abstract

Chinese hamster ovary (CHO) cells dominate biotherapeutic protein production and are widely used in mammalian cell line engineering research. To elucidate metabolic bottlenecks in protein production and to guide cell engineering and bioprocess optimization, we reconstructed the metabolic pathways in CHO and associated them with >1,700 genes in the Cricetulus griseus genome. The genome-scale metabolic model based on this reconstruction, iCHO1766, and cell-line-specific models for CHO-K1, CHO-S, and CHO-DG44 cells provide the biochemical basis of growth and recombinant protein production. The models accurately predict growth phenotypes and known auxotrophies in CHO cells. With the models, we quantify the protein synthesis capacity of CHO cells and demonstrate that common bioprocess treatments, such as histone deacetylase inhibitors, inefficiently increase product yield. However, our simulations show that the metabolic resources in CHO are more than three times more efficiently utilized for growth or recombinant protein synthesis following targeted efforts to engineer the CHO secretory pathway. This model will further accelerate CHO cell engineering and help optimize bioprocesses.

Published
2016

10. Recon 2.2: from reconstruction to model of human metabolism

Author

Swainston, Neil, Smallbone, Kieran, Hefzi, Hooman, Dobson, Paul D, Brewer, Judy, Hanscho, Michael, Zielinski, Daniel C, Ang, Kok Siong, Gardiner, Natalie J, Gutierrez, Jahir M, Kyriakopoulos, Sarantos, Lakshmanan, Meiyappan, Li, Shangzhong, Liu, Joanne K, Martínez, Veronica S, Orellana, Camila A, Quek, Lake-Ee, Thomas, Alex, Zanghellini, Juergen, Borth, Nicole, Lee, Dong-Yup, Nielsen, Lars K, Kell, Douglas B, Lewis, Nathan E, and Mendes, Pedro

Subjects
Biotechnology, Genetics, Human Genome, Nutrition, Generic health relevance, Human, Metabolism, Modelling, Reconstruction, Model, Systems biology, Analytical Chemistry, Biochemistry and Cell Biology, Clinical Sciences
Abstract

IntroductionThe human genome-scale metabolic reconstruction details all known metabolic reactions occurring in humans, and thereby holds substantial promise for studying complex diseases and phenotypes. Capturing the whole human metabolic reconstruction is an on-going task and since the last community effort generated a consensus reconstruction, several updates have been developed.ObjectivesWe report a new consensus version, Recon 2.2, which integrates various alternative versions with significant additional updates. In addition to re-establishing a consensus reconstruction, further key objectives included providing more comprehensive annotation of metabolites and genes, ensuring full mass and charge balance in all reactions, and developing a model that correctly predicts ATP production on a range of carbon sources.MethodsRecon 2.2 has been developed through a combination of manual curation and automated error checking. Specific and significant manual updates include a respecification of fatty acid metabolism, oxidative phosphorylation and a coupling of the electron transport chain to ATP synthase activity. All metabolites have definitive chemical formulae and charges specified, and these are used to ensure full mass and charge reaction balancing through an automated linear programming approach. Additionally, improved integration with transcriptomics and proteomics data has been facilitated with the updated curation of relationships between genes, proteins and reactions.ResultsRecon 2.2 now represents the most predictive model of human metabolism to date as demonstrated here. Extensive manual curation has increased the reconstruction size to 5324 metabolites, 7785 reactions and 1675 associated genes, which now are mapped to a single standard. The focus upon mass and charge balancing of all reactions, along with better representation of energy generation, has produced a flux model that correctly predicts ATP yield on different carbon sources.ConclusionThrough these updates we have achieved the most complete and best annotated consensus human metabolic reconstruction available, thereby increasing the ability of this resource to provide novel insights into normal and disease states in human. The model is freely available from the Biomodels database (http://identifiers.org/biomodels.db/MODEL1603150001).

Published
2016

15. Mammalian Systems Biotechnology Reveals Global Cellular Adaptations in a Recombinant CHO Cell Line

Author

Yusufi, Faraaz Noor Khan, Lakshmanan, Meiyappan, Ho, Ying Swan, Loo, Bernard Liat Wen, Ariyaratne, Pramila, Yang, Yuansheng, Ng, Say Kong, Tan, Tessa Rui Min, Yeo, Hock Chuan, Lim, Hsueh Lee, Ng, Sze Wai, Hiu, Ai Ping, Chow, Chung Ping, Wan, Corrine, Chen, Shuwen, Teo, Gavin, Song, Gao, Chin, Ju Xin, Ruan, Xiaoan, Sung, Ken Wing Kin, Hu, Wei-Shou, Yap, Miranda Gek Sim, Bardor, Muriel, Nagarajan, Niranjan, and Lee, Dong-Yup

Published
2017
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30. Publisher Correction: MEMOTE for standardized genome-scale metabolic model testing

Author

Lieven, Christian [0000-0001-5377-4091], Beber, Moritz E. [0000-0003-2406-1978], Olivier, Brett G. [0000-0002-5293-5321], Ataman, Meric [0000-0002-7942-9226], Babaei, Parizad [0000-0001-9411-0427], Bartell, Jennifer A. [0000-0003-2750-9678], Blank, Lars M. [0000-0003-0961-4976], Chauhan, Siddharth [0000-0001-6674-895X], Correia, Kevin [0000-0001-7130-1765], Diener, Christian [0000-0002-7476-0868], Dräger, Andreas [0000-0002-1240-5553], Ebert, Birgitta E. [0000-0001-9425-7509], Edirisinghe, Janaka N. [0000-0003-2493-234X], Faria, José P. [0000-0001-9302-7250], Feist, Adam M. [0000-0002-8630-4800], Fengos, Georgios [0000-0001-8110-8424], Fleming, Ronan M. T. [0000-0001-5346-9812], García-Jiménez, Beatriz [0000-0002-8129-6506], Hatzimanikatis, Vassily [0000-0001-6432-4694], Van Helvoirt, Wout [0000-0002-9143-9726], Henry, Christopher S. [0000-0001-8058-9123], Hermjakob, Henning [0000-0001-8479-0262], Herrgård, Markus J. [0000-0003-2377-9929], Kaafarani, Ali [0000-0002-2805-310X], Kim, Hyun Uk [0000-0001-7224-642X], King, Zachary [0000-0003-1238-1499], Klamt, Steffen [0000-0003-2563-7561], Klipp, Edda [0000-0002-0567-7075], Koehorst, Jasper J. [0000-0001-8172-8981], König, Matthias [0000-0003-1725-179X], Lakshmanan, Meiyappan [0000-0003-2356-3458], Lee, Dong-Yup [0000-0003-0901-708X], Lee, Sang Yup [0000-0003-0599-3091], Lee, Sunjae [0000-0002-6428-5936], Lewis, Nathan E. [0000-0001-7700-3654], Liu, Filipe [0000-0001-8701-2984], Ma, Hongwu [0000-0001-5325-2314], Mahadevan, Radhakrishnan [0000-0002-1270-9063], Maia, Paulo [0000-0002-0848-8683], Mardinoglu, Adil [0000-0002-4254-6090], Medlock, Gregory L. [0000-0002-1571-0801], Monk, Jonathan M. [0000-0002-3895-8949], Nielsen, Jens [0000-0002-9955-6003], Nielsen, Lars K. [0000-0001-8191-3511], Nogales, Juan [0000-0002-4961-0833], Palsson, Bernhard Ø [0000-0003-2357-6785], Papin, Jason A. [0000-0002-2769-5805], Patil, Kiran R. [0000-0002-6166-8640], Poolman, Mark [0000-0002-3972-5418], Price, Nathan D. [0000-0002-4157-0267], Resendis-Antonio, Osbaldo [0000-0001-5220-541X], Richelle, Anne [0000-0003-1491-114X], Rocha, Isabel [0000-0001-9494-3410], Sánchez, Benjamín J. [0000-0001-6093-4110], Schaap, Peter J. [0000-0002-4346-6084], Sheriff, Rahuman S Malik [0000-0003-0705-9809], Shoaie, Saeed [0000-0001-5834-4533], Sonnenschein, Nikolaus [0000-0002-7581-4936], Teusink, Bas [0000-0003-3929-0423], Vilaça, Paulo [0000-0002-1098-5849], Vik, Jon Olav [0000-0002-7778-4515], Wodke, Judith A. H. [0009-0009-9712-060X], Xavier, Joana C. [0000-0001-9242-8968], Zakhartsev, Maksim [0000-0002-7973-9902], Zhang, Cheng [0000-0002-3721-8586], Lieven, Christian, Beber, Moritz E., Olivier, Brett G., Bergmann, Frank T., Ataman, Meric, Babaei, Parizad, Bartell, Jennifer A., Blank, Lars M., Chauhan, Siddharth, Correia, Kevin, Diener, Christian, Dräger, Andreas, Ebert, Birgitta E., Edirisinghe, Janaka N., Faria, José P., Feist, Adam M., Fengos, Georgios, Fleming, Ronan M. T., García-Jiménez, Beatriz, Hatzimanikatis, Vassily, Van Helvoirt, Wout, Henry, Christopher S., Hermjakob, Henning, Herrgård, Markus J., Kaafarani, Ali, Kim, Hyun Uk, King, Zachary, Klamt, Steffen, Klipp, Edda, Koehorst, Jasper J., König, Matthias, Lakshmanan, Meiyappan, Lee, Dong-Yup, Lee, Sang Yup, Lee, Sunjae, Lewis, Nathan E., Liu, Filipe, Ma, Hongwu, Machado, Daniel, Mahadevan, Radhakrishnan, Maia, Paulo, Mardinoglu, Adil, Medlock, Gregory L., Monk, Jonathan M., Nielsen, Jens, Nielsen, Lars K., Nogales, Juan, Nookaew, Intawat, Palsson, Bernhard Ø, Papin, Jason A., Patil, Kiran R., Poolman, Mark, Price, Nathan D., Resendis-Antonio, Osbaldo, Richelle, Anne, Rocha, Isabel, Sánchez, Benjamín J., Schaap, Peter J., Sheriff, Rahuman S Malik, Shoaie, Saeed, Sonnenschein, Nikolaus, Teusink, Bas, Vilaça, Paulo, Vik, Jon Olav, Wodke, Judith A. H., Xavier, Joana C., Yuan, Qianqian, Zakhartsev, Maksim, Zhang, Cheng, Lieven, Christian [0000-0001-5377-4091], Beber, Moritz E. [0000-0003-2406-1978], Olivier, Brett G. [0000-0002-5293-5321], Ataman, Meric [0000-0002-7942-9226], Babaei, Parizad [0000-0001-9411-0427], Bartell, Jennifer A. [0000-0003-2750-9678], Blank, Lars M. [0000-0003-0961-4976], Chauhan, Siddharth [0000-0001-6674-895X], Correia, Kevin [0000-0001-7130-1765], Diener, Christian [0000-0002-7476-0868], Dräger, Andreas [0000-0002-1240-5553], Ebert, Birgitta E. [0000-0001-9425-7509], Edirisinghe, Janaka N. [0000-0003-2493-234X], Faria, José P. [0000-0001-9302-7250], Feist, Adam M. [0000-0002-8630-4800], Fengos, Georgios [0000-0001-8110-8424], Fleming, Ronan M. T. [0000-0001-5346-9812], García-Jiménez, Beatriz [0000-0002-8129-6506], Hatzimanikatis, Vassily [0000-0001-6432-4694], Van Helvoirt, Wout [0000-0002-9143-9726], Henry, Christopher S. [0000-0001-8058-9123], Hermjakob, Henning [0000-0001-8479-0262], Herrgård, Markus J. [0000-0003-2377-9929], Kaafarani, Ali [0000-0002-2805-310X], Kim, Hyun Uk [0000-0001-7224-642X], King, Zachary [0000-0003-1238-1499], Klamt, Steffen [0000-0003-2563-7561], Klipp, Edda [0000-0002-0567-7075], Koehorst, Jasper J. [0000-0001-8172-8981], König, Matthias [0000-0003-1725-179X], Lakshmanan, Meiyappan [0000-0003-2356-3458], Lee, Dong-Yup [0000-0003-0901-708X], Lee, Sang Yup [0000-0003-0599-3091], Lee, Sunjae [0000-0002-6428-5936], Lewis, Nathan E. [0000-0001-7700-3654], Liu, Filipe [0000-0001-8701-2984], Ma, Hongwu [0000-0001-5325-2314], Mahadevan, Radhakrishnan [0000-0002-1270-9063], Maia, Paulo [0000-0002-0848-8683], Mardinoglu, Adil [0000-0002-4254-6090], Medlock, Gregory L. [0000-0002-1571-0801], Monk, Jonathan M. [0000-0002-3895-8949], Nielsen, Jens [0000-0002-9955-6003], Nielsen, Lars K. [0000-0001-8191-3511], Nogales, Juan [0000-0002-4961-0833], Palsson, Bernhard Ø [0000-0003-2357-6785], Papin, Jason A. [0000-0002-2769-5805], Patil, Kiran R. [0000-0002-6166-8640], Poolman, Mark [0000-0002-3972-5418], Price, Nathan D. [0000-0002-4157-0267], Resendis-Antonio, Osbaldo [0000-0001-5220-541X], Richelle, Anne [0000-0003-1491-114X], Rocha, Isabel [0000-0001-9494-3410], Sánchez, Benjamín J. [0000-0001-6093-4110], Schaap, Peter J. [0000-0002-4346-6084], Sheriff, Rahuman S Malik [0000-0003-0705-9809], Shoaie, Saeed [0000-0001-5834-4533], Sonnenschein, Nikolaus [0000-0002-7581-4936], Teusink, Bas [0000-0003-3929-0423], Vilaça, Paulo [0000-0002-1098-5849], Vik, Jon Olav [0000-0002-7778-4515], Wodke, Judith A. H. [0009-0009-9712-060X], Xavier, Joana C. [0000-0001-9242-8968], Zakhartsev, Maksim [0000-0002-7973-9902], Zhang, Cheng [0000-0002-3721-8586], Lieven, Christian, Beber, Moritz E., Olivier, Brett G., Bergmann, Frank T., Ataman, Meric, Babaei, Parizad, Bartell, Jennifer A., Blank, Lars M., Chauhan, Siddharth, Correia, Kevin, Diener, Christian, Dräger, Andreas, Ebert, Birgitta E., Edirisinghe, Janaka N., Faria, José P., Feist, Adam M., Fengos, Georgios, Fleming, Ronan M. T., García-Jiménez, Beatriz, Hatzimanikatis, Vassily, Van Helvoirt, Wout, Henry, Christopher S., Hermjakob, Henning, Herrgård, Markus J., Kaafarani, Ali, Kim, Hyun Uk, King, Zachary, Klamt, Steffen, Klipp, Edda, Koehorst, Jasper J., König, Matthias, Lakshmanan, Meiyappan, Lee, Dong-Yup, Lee, Sang Yup, Lee, Sunjae, Lewis, Nathan E., Liu, Filipe, Ma, Hongwu, Machado, Daniel, Mahadevan, Radhakrishnan, Maia, Paulo, Mardinoglu, Adil, Medlock, Gregory L., Monk, Jonathan M., Nielsen, Jens, Nielsen, Lars K., Nogales, Juan, Nookaew, Intawat, Palsson, Bernhard Ø, Papin, Jason A., Patil, Kiran R., Poolman, Mark, Price, Nathan D., Resendis-Antonio, Osbaldo, Richelle, Anne, Rocha, Isabel, Sánchez, Benjamín J., Schaap, Peter J., Sheriff, Rahuman S Malik, Shoaie, Saeed, Sonnenschein, Nikolaus, Teusink, Bas, Vilaça, Paulo, Vik, Jon Olav, Wodke, Judith A. H., Xavier, Joana C., Yuan, Qianqian, Zakhartsev, Maksim, and Zhang, Cheng

Abstract

An amendment to this paper has been published and can be accessed via a link at the top of the paper. MEMOTE for standardized genome-scale metabolic model testing (http://hdl.handle.net/10261/230245) Nature Biotechnology, Volume 38, Issue 3, Pages 272 - 276, 1 March 2020

Published
2020

32. Enhancing Microbiome Research through Genome-Scale Metabolic Modeling

Author

Ankrah, Nana Y. D., primary, Bernstein, David B., additional, Biggs, Matthew, additional, Carey, Maureen, additional, Engevik, Melinda, additional, García-Jiménez, Beatriz, additional, Lakshmanan, Meiyappan, additional, Pacheco, Alan R., additional, Sulheim, Snorre, additional, and Medlock, Gregory L., additional

Published
2021
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36. Multi‐omics profiling of a CHO cell culture system unravels the effect of culture pH on cell growth, antibody titer, and product quality

Author

Lee, Alison P., primary, Kok, Yee Jiun, additional, Lakshmanan, Meiyappan, additional, Leong, Dawn, additional, Zheng, Lu, additional, Lim, Hsueh Lee, additional, Chen, Shuwen, additional, Mak, Shi Ya, additional, Ang, Kok Siong, additional, Templeton, Neil, additional, Salim, Taha, additional, Wei, Xiaona, additional, Gifford, Eric, additional, Tan, Andy Hee‐Meng, additional, Bi, Xuezhi, additional, Ng, Say Kong, additional, Lee, Dong‐Yup, additional, Ling, Wai Lam W., additional, and Ho, Ying Swan, additional

Published
2021
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39. Multi-omics profiling of a CHO cell culture system unravels the effect of culture pH on cell growth, antibody titer and product quality

Author

Lee, Alison, primary, Kok, Yee Jiun, additional, Lakshmanan, Meiyappan, additional, Leong, Dawn, additional, Zheng, Lu, additional, Lim, Hsueh Lee, additional, Chen, Shuwen, additional, Mak, Shi Ya, additional, Ang, Kok Siong, additional, Templeton, Neil, additional, Salim, Taha, additional, Wei, Xiaona, additional, Gifford, Eric, additional, Tan, Andy Hee-Meng, additional, Bi, Xuezhi, additional, Ng, Say Kong, additional, Lee, Dong-Yup, additional, Ling, Wai Lam, additional, and Ho, Ying Swan, additional

Published
2021
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42. MEMOTE for standardized genome-scale metabolic model testing

Author

Lieven, Christian, primary, Beber, Moritz E., additional, Olivier, Brett G., additional, Bergmann, Frank T., additional, Ataman, Meric, additional, Babaei, Parizad, additional, Bartell, Jennifer A., additional, Blank, Lars M., additional, Chauhan, Siddharth, additional, Correia, Kevin, additional, Diener, Christian, additional, Dräger, Andreas, additional, Ebert, Birgitta E., additional, Edirisinghe, Janaka N., additional, Faria, José P., additional, Feist, Adam M., additional, Fengos, Georgios, additional, Fleming, Ronan M. T., additional, García-Jiménez, Beatriz, additional, Hatzimanikatis, Vassily, additional, van Helvoirt, Wout, additional, Henry, Christopher S., additional, Hermjakob, Henning, additional, Herrgård, Markus J., additional, Kaafarani, Ali, additional, Kim, Hyun Uk, additional, King, Zachary, additional, Klamt, Steffen, additional, Klipp, Edda, additional, Koehorst, Jasper J., additional, König, Matthias, additional, Lakshmanan, Meiyappan, additional, Lee, Dong-Yup, additional, Lee, Sang Yup, additional, Lee, Sunjae, additional, Lewis, Nathan E., additional, Liu, Filipe, additional, Ma, Hongwu, additional, Machado, Daniel, additional, Mahadevan, Radhakrishnan, additional, Maia, Paulo, additional, Mardinoglu, Adil, additional, Medlock, Gregory L., additional, Monk, Jonathan M., additional, Nielsen, Jens, additional, Nielsen, Lars Keld, additional, Nogales, Juan, additional, Nookaew, Intawat, additional, Palsson, Bernhard O., additional, Papin, Jason A., additional, Patil, Kiran R., additional, Poolman, Mark, additional, Price, Nathan D., additional, Resendis-Antonio, Osbaldo, additional, Richelle, Anne, additional, Rocha, Isabel, additional, Sánchez, Benjamín J., additional, Schaap, Peter J., additional, Malik Sheriff, Rahuman S., additional, Shoaie, Saeed, additional, Sonnenschein, Nikolaus, additional, Teusink, Bas, additional, Vilaça, Paulo, additional, Vik, Jon Olav, additional, Wodke, Judith A. H., additional, Xavier, Joana C., additional, Yuan, Qianqian, additional, Zakhartsev, Maksim, additional, and Zhang, Cheng, additional

Published
2020
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43. On the impact of biomass composition in constraint-based flux analysis

Author

Lakshmanan, Meiyappan, Long, Sichang, Ang, Kok Siong, Lewis, Nathan, and Lee, Dong-Yup

Abstract

The biomass equation is a critical component in genome-scale metabolic models (GEMs): It is one of most widely used objective functions within constraint-based flux analysis formulation, describing cellular driving force under the growth condition. The equation accounts for the quantities of all known biomass precursors that are required for cell growth. Most often than not, published GEMs have adopted relevant information from other species to derive the biomass equation when any of the macromolecular composition is unavailable. Thus, its validity is still questionable. Here, we investigated the qualitative and quantitative aspects of biomass equations from GEMs of eight different yeast species. Expectedly, most yeast GEMs borrowed macromolecular compositions from the model yeast, Saccharomyces cerevisiae . We further confirmed that the biomass compositions could be markedly different even between phylogenetically closer species and none of the high throughput omics data such as genome, transcriptome and proteome provided a good estimate of relative amino acid abundances. Upon varying the stoichiometric coefficients of biomass components, subsequent flux simulations demonstrated how predicted in silico growth rates change with the carbon substrates used. Furthermore, the internal fluxes through individual reactions are highly sensitive to all components in the biomass equation. Overall, the current analysis clearly highlight that biomass equation need to be carefully drafted from relevant experiments, and the in silico simulation results should be appropriately interpreted to avoid any inaccuracies.

Published
2019
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44. MEMOTE for standardized genome-scale metabolic model testing

Author

Lieven, Christian, Beber, Moritz Emanuel, Olivier, Brett G., Bergmann, Frank T., Ataman, Meric, Babaei, Parizad, Bartell, Jennifer A., Blank, Lars M., Chauhan, Siddharth, Correia, Kevin, Diener, Christian, Dräger, Andreas, Ebert, Birgitta E., Edirisinghe, Janaka N., Faria, José P., Feist, Adam M., Fengos, Georgios, Fleming, Ronan M. T., García-Jiménez, Beatriz, Hatzimanikatis, Vassily, van Helvoirt, Wout, Henry, Christopher S., Hermjakob, Henning, Herrgard, Markus J., Kaafarani, Ali, Kim, Hyun Uk, King, Zachary, Klamt, Steffen, Klipp, Edda, Koehorst, Jasper J., König, Matthias, Lakshmanan, Meiyappan, Lee, Dong-Yup, Lee, Sang Yup, Lee, Sunjae, Lewis, Nathan E., Liu, Filipe, Ma, Hongwu, Machado, Daniel, Mahadevan, Radhakrishnan, Maia, Paulo, Mardinoglu, Adil, Medlock, Gregory L., Monk, Jonathan M., Nielsen, Jens, Nielsen, Lars Keld, Nogales, Juan, Nookaew, Intawat, Palsson, Bernhard O., Papin, Jason A., Patil, Kiran Raosaheb, Poolman, Mark, Price, Nathan D., Resendis-Antonio, Osbaldo, Richelle, Anne, Rocha, Isabel, Sánchez, Benjamín José, Schaap, Peter J., Malik Sheriff, Rahuman S., Shoaie, Saeed, Sonnenschein, Nikolaus, Teusink, Bas, Vilaca, Paulo, Vik, Jon Olav, Wodke, Judith A. H., Xavier, Joana C., Yuan, Qianqian, Zakhartsev, Maksim, Zhang, Cheng, Lieven, Christian, Beber, Moritz Emanuel, Olivier, Brett G., Bergmann, Frank T., Ataman, Meric, Babaei, Parizad, Bartell, Jennifer A., Blank, Lars M., Chauhan, Siddharth, Correia, Kevin, Diener, Christian, Dräger, Andreas, Ebert, Birgitta E., Edirisinghe, Janaka N., Faria, José P., Feist, Adam M., Fengos, Georgios, Fleming, Ronan M. T., García-Jiménez, Beatriz, Hatzimanikatis, Vassily, van Helvoirt, Wout, Henry, Christopher S., Hermjakob, Henning, Herrgard, Markus J., Kaafarani, Ali, Kim, Hyun Uk, King, Zachary, Klamt, Steffen, Klipp, Edda, Koehorst, Jasper J., König, Matthias, Lakshmanan, Meiyappan, Lee, Dong-Yup, Lee, Sang Yup, Lee, Sunjae, Lewis, Nathan E., Liu, Filipe, Ma, Hongwu, Machado, Daniel, Mahadevan, Radhakrishnan, Maia, Paulo, Mardinoglu, Adil, Medlock, Gregory L., Monk, Jonathan M., Nielsen, Jens, Nielsen, Lars Keld, Nogales, Juan, Nookaew, Intawat, Palsson, Bernhard O., Papin, Jason A., Patil, Kiran Raosaheb, Poolman, Mark, Price, Nathan D., Resendis-Antonio, Osbaldo, Richelle, Anne, Rocha, Isabel, Sánchez, Benjamín José, Schaap, Peter J., Malik Sheriff, Rahuman S., Shoaie, Saeed, Sonnenschein, Nikolaus, Teusink, Bas, Vilaca, Paulo, Vik, Jon Olav, Wodke, Judith A. H., Xavier, Joana C., Yuan, Qianqian, Zakhartsev, Maksim, and Zhang, Cheng

Published
2020

45. MEMOTE for standardized genome-scale metabolic model testing

Author

Research Council of Norway, Innovation Fund Denmark, European Commission, National Institutes of Health (US), German Research Foundation, Novo Nordisk Foundation, W. M. Keck Foundation, Ministerio de Economía y Competitividad (España), Knut and Alice Wallenberg Foundation, Federal Ministry of Education and Research (Germany), Bill & Melinda Gates Foundation, National Research Foundation of Korea, Rural Development Administration (South Korea), Swiss National Science Foundation, University of Oxford, European Research Council, Washington Research Foundation, National Institute of General Medical Sciences (US), Lieven, Christian, Beber, Moritz E., Olivier, Brett G., Bergmann, Frank T., Ataman, Meric, Babaei, Parizad, Bartell, Jennifer A., Blank, Lars M., Chauhan, Siddharth, Correia, Kevin, Diener, Christian, Dräger, Andreas, Ebert, Birgitta E., Edirisinghe, Janaka N., Faria, José P., Feist, Adam M., Fengos, Georgios, Fleming, Ronan M. T., García-Jiménez, Beatriz, Hatzimanikatis, Vassily, Van Helvoirt, Wout, Henry, Christopher S., Hermjakob, Henning, Herrgård, Markus J., Kaafarani, Ali, Kim, Hyun Uk, King, Zachary, Klamt, Steffen, Klipp, Edda, Koehorst, Jasper J., König, Matthias, Lakshmanan, Meiyappan, Lee, Dong-Yup, Lee, Sang Yup, Lee, Sunjae, Lewis, Nathan E., Liu, Filipe, Ma, Hongwu, Machado, Daniel, Mahadevan, Radhakrishnan, Maia, Paulo, Mardinoglu, Adil, Medlock, Gregory L., Monk, Jonathan M., Nielsen, Jens, Nielsen, Lars K., Nogales, Juan, Nookaew, Intawat, Palsson, Bernhard Ø, Papin, Jason A., Patil, Kiran R., Poolman, Mark, Price, Nathan D., Resendis-Antonio, Osbaldo, Richelle, Anne, Rocha, Isabel, Sánchez, Benjamín J., Schaap, Peter J., Malik Sheriff, Rahuman S., Shoaie, Saeed, Sonnenschein, Nikolaus, Teusink, Bas, Vilaça, Paulo, Vik, Jon Olav, Wodke, Judith A. H., Xavier, Joana C., Yuan, Qianqian, Zakhartsev, Maksim, Zhang, Cheng, Research Council of Norway, Innovation Fund Denmark, European Commission, National Institutes of Health (US), German Research Foundation, Novo Nordisk Foundation, W. M. Keck Foundation, Ministerio de Economía y Competitividad (España), Knut and Alice Wallenberg Foundation, Federal Ministry of Education and Research (Germany), Bill & Melinda Gates Foundation, National Research Foundation of Korea, Rural Development Administration (South Korea), Swiss National Science Foundation, University of Oxford, European Research Council, Washington Research Foundation, National Institute of General Medical Sciences (US), Lieven, Christian, Beber, Moritz E., Olivier, Brett G., Bergmann, Frank T., Ataman, Meric, Babaei, Parizad, Bartell, Jennifer A., Blank, Lars M., Chauhan, Siddharth, Correia, Kevin, Diener, Christian, Dräger, Andreas, Ebert, Birgitta E., Edirisinghe, Janaka N., Faria, José P., Feist, Adam M., Fengos, Georgios, Fleming, Ronan M. T., García-Jiménez, Beatriz, Hatzimanikatis, Vassily, Van Helvoirt, Wout, Henry, Christopher S., Hermjakob, Henning, Herrgård, Markus J., Kaafarani, Ali, Kim, Hyun Uk, King, Zachary, Klamt, Steffen, Klipp, Edda, Koehorst, Jasper J., König, Matthias, Lakshmanan, Meiyappan, Lee, Dong-Yup, Lee, Sang Yup, Lee, Sunjae, Lewis, Nathan E., Liu, Filipe, Ma, Hongwu, Machado, Daniel, Mahadevan, Radhakrishnan, Maia, Paulo, Mardinoglu, Adil, Medlock, Gregory L., Monk, Jonathan M., Nielsen, Jens, Nielsen, Lars K., Nogales, Juan, Nookaew, Intawat, Palsson, Bernhard Ø, Papin, Jason A., Patil, Kiran R., Poolman, Mark, Price, Nathan D., Resendis-Antonio, Osbaldo, Richelle, Anne, Rocha, Isabel, Sánchez, Benjamín J., Schaap, Peter J., Malik Sheriff, Rahuman S., Shoaie, Saeed, Sonnenschein, Nikolaus, Teusink, Bas, Vilaça, Paulo, Vik, Jon Olav, Wodke, Judith A. H., Xavier, Joana C., Yuan, Qianqian, Zakhartsev, Maksim, and Zhang, Cheng

Abstract

Reconstructing metabolic reaction networks enables the development of testable hypotheses of an organism’s metabolism under different conditions1. State-of-the-art genome-scale metabolic models (GEMs) can include thousands of metabolites and reactions that are assigned to subcellular locations. Gene–protein–reaction (GPR) rules and annotations using database information can add meta-information to GEMs. GEMs with metadata can be built using standard reconstruction protocols2, and guidelines have been put in place for tracking provenance and enabling interoperability, but a standardized means of quality control for GEMs is lacking3. Here we report a community effort to develop a test suite named MEMOTE (for metabolic model tests) to assess GEM quality.

Published
2020

50. Multi‐omics profiling of CHO parental hosts reveals cell line‐specific variations in bioprocessing traits

Author

Lakshmanan, Meiyappan, primary, Kok, Yee Jiun, additional, Lee, Alison P., additional, Kyriakopoulos, Sarantos, additional, Lim, Hsueh Lee, additional, Teo, Gavin, additional, Poh, Swan Li, additional, Tang, Wen Qin, additional, Hong, Jongkwang, additional, Tan, Andy Hee‐Meng, additional, Bi, Xuezhi, additional, Ho, Ying Swan, additional, Zhang, Peiqing, additional, Ng, Say Kong, additional, and Lee, Dong‐Yup, additional

Published
2019
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