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36 results on '"Sánchez, Benjamín J."'

2. Combining mechanistic and machine learning models for predictive engineering and optimization of tryptophan metabolism.

Author

Zhang, Jie, Petersen, Søren D, Radivojevic, Tijana, Ramirez, Andrés, Pérez-Manríquez, Andrés, Abeliuk, Eduardo, Sánchez, Benjamín J, Costello, Zak, Chen, Yu, Fero, Michael J, Martin, Hector Garcia, Nielsen, Jens, Keasling, Jay D, and Jensen, Michael K

Subjects
Saccharomyces cerevisiae, Amino Acids, Tryptophan, Biosensing Techniques, Biochemical Phenomena, Genotype, Phenotype, Algorithms, Models, Biological, Metabolic Networks and Pathways, Metabolic Engineering, Machine Learning, Models, Biological
Abstract

Through advanced mechanistic modeling and the generation of large high-quality datasets, machine learning is becoming an integral part of understanding and engineering living systems. Here we show that mechanistic and machine learning models can be combined to enable accurate genotype-to-phenotype predictions. We use a genome-scale model to pinpoint engineering targets, efficient library construction of metabolic pathway designs, and high-throughput biosensor-enabled screening for training diverse machine learning algorithms. From a single data-generation cycle, this enables successful forward engineering of complex aromatic amino acid metabolism in yeast, with the best machine learning-guided design recommendations improving tryptophan titer and productivity by up to 74 and 43%, respectively, compared to the best designs used for algorithm training. Thus, this study highlights the power of combining mechanistic and machine learning models to effectively direct metabolic engineering efforts.

Published
2020

3. 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

5. Yeast9: A Consensus Yeast Metabolic Model Enables Quantitative Analysis of Cellular Metabolism By Incorporating Big Data

Author

Zhang, Chengyu, primary, Sánchez, Benjamín J., additional, Li, Feiran, additional, Eiden, Cheng Wei Quan, additional, Scott, William T., additional, Liebal, Ulf W., additional, Blank, Lars M., additional, Mengers, Hendrik G., additional, Anton, Mihail, additional, Rangel, Albert Tafur, additional, Mendoza, Sebastián N., additional, Zhang, Lixin, additional, Nielsen, Jens, additional, Lu, Hongzhong, additional, and Kerkhoven, Eduard J., additional

Published
2023
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6. CRI-SPA: a high-throughput method for systematic genetic editing of yeast libraries

Author

Cachera, Paul, primary, Olsson, Helén, additional, Coumou, Hilde, additional, Jensen, Mads L, additional, Sánchez, Benjamín J, additional, Strucko, Tomas, additional, van den Broek, Marcel, additional, Daran, Jean-Marc, additional, Jensen, Michael K, additional, Sonnenschein, Nikolaus, additional, Lisby, Michael, additional, and Mortensen, Uffe H, additional

Published
2023
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9. CRI-SPA: a high-throughput method for systematic genetic editing of yeast libraries

Author

Cachera, Paul, Olsson, Helén, Coumou, Hilde, Jensen, Mads L, Sánchez, Benjamín J., Strucko, Tomas, van den Broek, Marcel, Daran, Jean-Marc, Jensen, Michael K., Sonnenschein, Nikolaus, Lisby, Michael, Mortensen, Uffe H., Cachera, Paul, Olsson, Helén, Coumou, Hilde, Jensen, Mads L, Sánchez, Benjamín J., Strucko, Tomas, van den Broek, Marcel, Daran, Jean-Marc, Jensen, Michael K., Sonnenschein, Nikolaus, Lisby, Michael, and Mortensen, Uffe H.

Abstract

Biological functions are orchestrated by intricate networks of interacting genetic elements. Predicting the interaction landscape remains a challenge for systems biology and new research tools allowing simple and rapid mapping of sequence to function are desirable. Here, we describe CRI-SPA, a method allowing the transfer of chromosomal genetic features from a CRI-SPA Donor strain to arrayed strains in large libraries of Saccharomyces cerevisiae. CRI-SPA is based on mating, CRISPR-Cas9-induced gene conversion, and Selective Ploidy Ablation. CRI-SPA can be massively parallelized with automation and can be executed within a week. We demonstrate the power of CRI-SPA by transferring four genes that enable betaxanthin production into each strain of the yeast knockout collection (≈4800 strains). Using this setup, we show that CRI-SPA is highly efficient and reproducible, and even allows marker-free transfer of genetic features. Moreover, we validate a set of CRI-SPA hits by showing that their phenotypes correlate strongly with the phenotypes of the corresponding mutant strains recreated by reverse genetic engineering. Hence, our results provide a genome-wide overview of the genetic requirements for betaxanthin production. We envision that the simplicity, speed, and reliability offered by CRI-SPA will make it a versatile tool to forward systems-level understanding of biological processes.

Published
2023

10. CRI-SPA: a high-throughput method for systematic genetic editing of yeast libraries

Author

Cachera, Paul (author), Olsson, Helén (author), Coumou, Hilde (author), Jensen, Mads L. (author), Sánchez, Benjamín J. (author), Strucko, Tomas (author), van den Broek, M.A. (author), Daran, J.G. (author), Jensen, Michael K. (author), Sonnenschein, Nikolaus (author), Lisby, Michael (author), Mortensen, Uffe H. (author), Cachera, Paul (author), Olsson, Helén (author), Coumou, Hilde (author), Jensen, Mads L. (author), Sánchez, Benjamín J. (author), Strucko, Tomas (author), van den Broek, M.A. (author), Daran, J.G. (author), Jensen, Michael K. (author), Sonnenschein, Nikolaus (author), Lisby, Michael (author), and Mortensen, Uffe H. (author)

Abstract

Biological functions are orchestrated by intricate networks of interacting genetic elements. Predicting the interaction landscape remains a challenge for systems biology and new research tools allowing simple and rapid mapping of sequence to function are desirable. Here, we describe CRI-SPA, a method allowing the transfer of chromosomal genetic features from a CRI-SPA Donor strain to arrayed strains in large libraries of Saccharomyces cerevisiae. CRI-SPA is based on mating, CRISPR-Cas9-induced gene conversion, and Selective Ploidy Ablation. CRI-SPA can be massively parallelized with automation and can be executed within a week. We demonstrate the power of CRI-SPA by transferring four genes that enable betaxanthin production into each strain of the yeast knockout collection (≈4800 strains). Using this setup, we show that CRI-SPA is highly efficient and reproducible, and even allows marker-free transfer of genetic features. Moreover, we validate a set of CRI-SPA hits by showing that their phenotypes correlate strongly with the phenotypes of the corresponding mutant strains recreated by reverse genetic engineering. Hence, our results provide a genome-wide overview of the genetic requirements for betaxanthin production. We envision that the simplicity, speed, and reliability offered by CRI-SPA will make it a versatile tool to forward systems-level understanding of biological processes., BT/Industriele Microbiologie

Published
2023
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11. CRI-SPA:a high-throughput method for systematic genetic editing of yeast libraries

Author

Cachera, Paul, Olsson, Helén, Coumou, Hilde, Jensen, Mads L, Sánchez, Benjamín j, Strucko, Tomas, Van den broek, Marcel, Daran, Jean-marc, Jensen, Michael k, Sonnenschein, Nikolaus, Lisby, Michael, Mortensen, Uffe h, Cachera, Paul, Olsson, Helén, Coumou, Hilde, Jensen, Mads L, Sánchez, Benjamín j, Strucko, Tomas, Van den broek, Marcel, Daran, Jean-marc, Jensen, Michael k, Sonnenschein, Nikolaus, Lisby, Michael, and Mortensen, Uffe h

Abstract

Biological functions are orchestrated by intricate networks of interacting genetic elements. Predicting the interaction landscape remains a challenge for systems biology and new research tools allowing simple and rapid mapping of sequence to function are desirable. Here, we describe CRI-SPA, a method allowing the transfer of chromosomal genetic features from a CRI-SPA Donor strain to arrayed strains in large libraries of Saccharomyces cerevisiae. CRI-SPA is based on mating, CRISPR-Cas9-induced gene conversion, and Selective Ploidy Ablation. CRI-SPA can be massively parallelized with automation and can be executed within a week. We demonstrate the power of CRI-SPA by transferring four genes that enable betaxanthin production into each strain of the yeast knockout collection (≈4800 strains). Using this setup, we show that CRI-SPA is highly efficient and reproducible, and even allows marker-free transfer of genetic features. Moreover, we validate a set of CRI-SPA hits by showing that their phenotypes correlate strongly with the phenotypes of the corresponding mutant strains recreated by reverse genetic engineering. Hence, our results provide a genome-wide overview of the genetic requirements for betaxanthin production. We envision that the simplicity, speed, and reliability offered by CRI-SPA will make it a versatile tool to forward systems-level understanding of biological processes., Biological functions are orchestrated by intricate networks of interacting genetic elements. Predicting the interaction landscape remains a challenge for systems biology and new research tools allowing simple and rapid mapping of sequence to function are desirable. Here, we describe CRI-SPA, a method allowing the transfer of chromosomal genetic features from a CRI-SPA Donor strain to arrayed strains in large libraries of Saccharomyces cerevisiae. CRI-SPA is based on mating, CRISPR-Cas9-induced gene conversion, and Selective Ploidy Ablation. CRI-SPA can be massively parallelized with automation and can be executed within a week. We demonstrate the power of CRI-SPA by transferring four genes that enable betaxanthin production into each strain of the yeast knockout collection (≈4800 strains). Using this setup, we show that CRI-SPA is highly efficient and reproducible, and even allows marker-free transfer of genetic features. Moreover, we validate a set of CRI-SPA hits by showing that their phenotypes correlate strongly with the phenotypes of the corresponding mutant strains recreated by reverse genetic engineering. Hence, our results provide a genome-wide overview of the genetic requirements for betaxanthin production. We envision that the simplicity, speed, and reliability offered by CRI-SPA will make it a versatile tool to forward systems-level understanding of biological processes.

Published
2023

14. CRI-SPA – a mating based CRISPR-Cas9 assisted method for high-throughput genetic modification of yeast strain libraries

Author

Olsson, Helén, primary, Cachera, Paul, additional, Coumou, Hilde, additional, Jensen, Mads L., additional, Sánchez, Benjamín J., additional, Strucko, Tomas, additional, van den Broek, Marcel, additional, Daran, Jean-Marc, additional, Jensen, Michael K., additional, Sonnenschein, Nikolaus, additional, Lisby, Michael, additional, and Mortensen, Uffe H., additional

Published
2022
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16. 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

17. DebaryOmics: an integrative –omics study to understand the halophilic behaviour of Debaryomyces hansenii

Author

Navarrete, Clara, Sánchez, Benjamín J., Savickas, Simonas, Martínez, José L., Navarrete, Clara, Sánchez, Benjamín J., Savickas, Simonas, and Martínez, José L.

Abstract

Debaryomyces hansenii is a non-conventional yeast considered to be a well-suited option for a number of different industrial bioprocesses. It exhibits a set of beneficial traits (halotolerant, oleaginous, xerotolerant, inhibitory compounds resistant) which translates to a number of advantages for industrial fermentation setups when compared to traditional hosts. Although D. hansenii has been highly studied during the last three decades, especially in regards to its salt-tolerant character, the molecular mechanisms underlying this natural tolerance should be further investigated in order to broadly use this yeast in biotechnological processes. In this work, we performed a series of chemostat cultivations in controlled bioreactors where D. hansenii (CBS 767) was grown in the presence of either 1M NaCl or KCl and studied the transcriptomic and (phospho)proteomic profiles. Our results show that sodium and potassium trigger different responses at both expression and regulation of protein activity levels and also complemented previous reports pointing to specific cellular processes as key players in halotolerance, moreover providing novel information about the specific genes involved in each process. The phosphoproteomic analysis, the first of this kind ever reported in D. hansenii, also implicated a novel and yet uncharacterized cation transporter in the response to high sodium concentrations.

Published
2022

21. Benchmarking accuracy and precision of intensity-based absolute quantification of protein abundances in Saccharomyces cerevisiae

Author

Sánchez, Benjamín J., Lahtvee, Petri Jaan, Campbell, Kate, Kasvandik, Sergo, Yu, Rosemary, Domenzain, Iván, Zelezniak, Aleksej, Nielsen, Jens, Sánchez, Benjamín J., Lahtvee, Petri Jaan, Campbell, Kate, Kasvandik, Sergo, Yu, Rosemary, Domenzain, Iván, Zelezniak, Aleksej, and Nielsen, Jens

Abstract

Protein quantification via label-free mass spectrometry (MS) has become an increasingly popular method for predicting genome-wide absolute protein abundances. A known caveat of this approach, however, is the poor technical reproducibility, that is, how consistent predictions are when the same sample is measured repeatedly. Here, we measured proteomics data for Saccharomyces cerevisiae with both biological and inter-batch technical triplicates, to analyze both accuracy and precision of protein quantification via MS. Moreover, we analyzed how these metrics vary when applying different methods for converting MS intensities to absolute protein abundances. We demonstrate that our simple normalization and rescaling approach can perform as accurately, yet more precisely, than methods which rely on external standards. Additionally, we show that inter-batch reproducibility is worse than biological reproducibility for all evaluated methods. These results offer a new benchmark for assessing MS data quality for protein quantification, while also underscoring current limitations in this approach.

Published
2021

22. 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

23. 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 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 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., 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, 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 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., 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

Published
2020

26. 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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27. Predictive engineering and optimization of tryptophan metabolism in yeast through a combination of mechanistic and machine learning models

Author

Zhang, Jie, primary, Petersen, Søren D., additional, Radivojevic, Tijana, additional, Ramirez, Andrés, additional, Pérez, Andrés, additional, Abeliuk, Eduardo, additional, Sánchez, Benjamín J., additional, Costello, Zachary, additional, Chen, Yu, additional, Fero, Mike, additional, Martin, Hector Garcia, additional, Nielsen, Jens, additional, Keasling, Jay D., additional, and Jensen, Michael K., additional

Published
2019
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35. HIPPO:An Iterative Reparametrization Method for Identificationand Calibration of Dynamic Bioreactor Models of Complex Processes.

Author

Sánchez, Benjamín J., Soto, Daniela C., Jorquera, Héctor, Gelmi, Claudio A., and Pérez-Correa, José R.

Subjects
*PARAMETERIZATION, *BIOREACTORS, *ITERATIVE methods (Mathematics), *HEURISTIC algorithms, *PARAMETER estimation, *FERMENTATION
Abstract

Unstructured,dynamic bioreactor models of complex processes usuallypossess many nonidentifiable, insensitive, or statistically nonsignificantparameters. However, an exhaustive search to find a reduced set ofidentifiable parameters is computationally demanding. We developeda heuristic iterative procedure for parameter optimization (HIPPO),generic and free of symbolic mathematical manipulations, to help bioprocessengineers to find and estimate a set of significant parameters, thusobtaining reliable models. This methodology includes local sensitivity,significance and identifiability analysis, metaheuristic optimization,and fitting performance assessments. We tested this method with twobioreactor models: a microalgal fed-batch bioreactor (MFB) 12-parametermodel and a solid substrate fermentation (SSF) 14-parameter model.Applying this procedure, we were able to find a MFB reparametrizationwith 4 fitting parameters after 479 iterations and a SSF reparametrizationwith 6 fitting parameters after 918 iterations. In both cases, allfitting parameters were statistically significant and accurately estimatedand models had a better fit than the originally fitted 12- and 14-parametermodels. The heuristic was programmed using MATLAB and is freely availableto the research community (http://www.systemsbiology.cl/tools/). [ABSTRACT FROM AUTHOR]

Published
2014
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36. DebaryOmics: an integrative -omics study to understand the halophilic behaviour of Debaryomyces hansenii.

Author

Navarrete C, Sánchez BJ, Savickas S, and Martínez JL

Subjects
Ion Transport, Potassium metabolism, Proteomics, Sodium metabolism, Debaryomyces genetics
Abstract

Debaryomyces hansenii is a non-conventional yeast considered to be a well-suited option for a number of different industrial bioprocesses. It exhibits a set of beneficial traits (halotolerant, oleaginous, xerotolerant, inhibitory compounds resistant) which translates to a number of advantages for industrial fermentation setups when compared to traditional hosts. Although D. hansenii has been highly studied during the last three decades, especially in regards to its salt-tolerant character, the molecular mechanisms underlying this natural tolerance should be further investigated in order to broadly use this yeast in biotechnological processes. In this work, we performed a series of chemostat cultivations in controlled bioreactors where D. hansenii (CBS 767) was grown in the presence of either 1M NaCl or KCl and studied the transcriptomic and (phospho)proteomic profiles. Our results show that sodium and potassium trigger different responses at both expression and regulation of protein activity levels and also complemented previous reports pointing to specific cellular processes as key players in halotolerance, moreover providing novel information about the specific genes involved in each process. The phosphoproteomic analysis, the first of this kind ever reported in D. hansenii, also implicated a novel and yet uncharacterized cation transporter in the response to high sodium concentrations., (© 2021 The Authors. Microbial Biotechnology published by Society for Applied Microbiology and John Wiley & Sons Ltd.)

Published
2022
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