ERA-IB 5. ECOYEAST SJH: Mastering the economics of adaptation through constraint-based modeling in yeast (Hubbard)
Lead Research Organisation:
University of Manchester
Department Name: School of Biological Sciences
Abstract
Living cells evolved a remarkable ability to adapt to environmental conditions, or to withstand mutations. In biotechnology, this compromises success in metabolic engineering and causes instability of engineered strains. "Functional genomics" has allowed the cost-effective measurement of many of the components of the cell. However, we still mostly fail to understand how their interactions lead to cellular function and adaptation. It becomes clear, however, that physics and (bio)chemistry impose strong constraints on adaptation and evolution. Such constraints limit the total amount of protein that a cell can synthesize, and impact on how it should partition that limited resource over its processes to optimize fitness ("cellular economics"). Such knowledge is important to come with better metabolic engineering strategies that take into account the impact of novel genes and pathways on cellular economics, to develop processes with high yields that enable cost-effective bio-based chemicals and biofuels. In this proposal we will develop a modeling framework that will allow the integration of large data sets into comprehensive mechanistic models. These models are of genome-scale and will be able to compute the costs and benefits of implementing metabolic engineering strategies. The economic models will be used to provide proof-of-concept in two ways: (i) as tools for data integration and interpretation of adaptive responses; (ii) as predictive tool, through optimisation to predict more realistic theoretical yields and through exploration of metabolic engineering scenarios. This will be tested by a user case provided by our industrial partners, DSM and Roquette, involving succinate production, a versatile C4 diacid with a lot of potential applications, e.g. in polymers and resins.
Technical Summary
Living cells evolved a remarkable ability to adapt to environmental conditions, or to withstand mutations. In biotechnology, this compromises success in metabolic engineering and causes instability of engineered strains. "Functional genomics" has allowed the cost-effective measurement of many of the components of the cell. However, we still mostly fail to understand how their interactions lead to cellular function and adaptation. It becomes clear, however, that physics and (bio)chemistry impose strong constraints on adaptation and evolution. Such constraints limit the total amount of protein that a cell can synthesize, and impact on how it should partition that limited resource over its processes to optimize fitness ("cellular economics"). Such knowledge is important to come with better metabolic engineering strategies that take into account the impact of novel genes and pathways on cellular economics, to develop processes with high yields that enable cost-effective bio-based chemicals and biofuels. In this proposal we will develop a modeling framework that will allow the integration of large data sets into comprehensive mechanistic models. These models are of genome-scale and will be able to compute the costs and benefits of implementing metabolic engineering strategies. The economic models will be used to provide proof-of-concept in two ways: (i) as tools for data integration and interpretation of adaptive responses; (ii) as predictive tool, through optimisation to predict more realistic theoretical yields and through exploration of metabolic engineering scenarios. This will be tested by a user case provided by our industrial partners, DSM and Roquette, involving succinate production, a versatile C4 diacid with a lot of potential applications, e.g. in polymers and resins.
Planned Impact
Expected results and patents - we will deliver a methodology for large-scale genomics data
integration and modeling, that can be used and will be tested by our industrial partners. The
approach is generic and therefore of general use in industrial biotechnology. No patents are
expected from the modeling methodology per se, but as it is a powerful enabling technology for
biological discovery and innovation, there might be IP on optimization strategies. More directly,
findings for specific or general strain and/or process development of the succinic acid process, or
more general yeast fermentation might be achieved.
Preliminary exploitation plan - knowledge and expertise developed in this project, as well as
software and models, will be valuable assets for follow up in public-private partnerships. Software
will be developed under open-source agreement for academic use. None of the academic partners
have the ambition at this moment to set up a company to provide modeling services. All groups
involved have a large network with industrial partners to team up with to implement and apply the
developed technologies.
integration and modeling, that can be used and will be tested by our industrial partners. The
approach is generic and therefore of general use in industrial biotechnology. No patents are
expected from the modeling methodology per se, but as it is a powerful enabling technology for
biological discovery and innovation, there might be IP on optimization strategies. More directly,
findings for specific or general strain and/or process development of the succinic acid process, or
more general yeast fermentation might be achieved.
Preliminary exploitation plan - knowledge and expertise developed in this project, as well as
software and models, will be valuable assets for follow up in public-private partnerships. Software
will be developed under open-source agreement for academic use. None of the academic partners
have the ambition at this moment to set up a company to provide modeling services. All groups
involved have a large network with industrial partners to team up with to implement and apply the
developed technologies.
Organisations
- University of Manchester (Lead Research Organisation)
- Novo Nordisk Foundation (Collaboration)
- Warsaw University of Technology (Collaboration)
- Roquette Pharma (Collaboration)
- DSM (Collaboration)
- Free University of Amsterdam (Collaboration)
- UNIVERSITY OF LIVERPOOL (Collaboration)
- Delft University of Technology (TU Delft) (Collaboration)
Publications
Garcia-Albornoz M
(2020)
A proteome-integrated, carbon source dependent genetic regulatory network in Saccharomyces cerevisiae.
in Molecular omics
Jarnuczak AF
(2018)
A quantitative and temporal map of proteostasis during heat shock in Saccharomyces cerevisiae.
in Molecular omics
Szatkowska R
(2019)
Glycolytic flux in Saccharomyces cerevisiae is dependent on RNA polymerase III and its negative regulator Maf1.
in The Biochemical journal
Elsemman IE
(2022)
Whole-cell modeling in yeast predicts compartment-specific proteome constraints that drive metabolic strategies.
in Nature communications
Description | The recent acquisition of high quality quantatitive proteomics data on yeast samples grown on different carbon sources has been highly productive. We are using this data to feedback proteome levels for the modelling partners in Netherlands and Sweden, and also for own uses. It has helped us develop novel analysis pipelines to examine changes in metabolic pathways and novel visualisation approaches. A paper on this was submitted in 2018, was rejected from one journa, and is now accepted in Mol Omics. We also have high quality transcriptome and proteome data for yeast grown at different growth rates, which has been analysed and the lead collaborators in Netherlands are writing a manuscript.We are also exploring this data for fundamental aspects of transcription vs translation in control of gene expression and have reanalysed some new data, along with existing data concerning gene expression in yeast growing under different growth rates in chemostat. |
Exploitation Route | Using the protein quantitation data to extend genome scale metabolic models - this has now happened and a paper has been published |
Sectors | Chemicals,Manufacturing, including Industrial Biotechology,Pharmaceuticals and Medical Biotechnology |
Description | We held some joint project meetings with European and industrial partners which were productive, with sharing of expertise about genome scale metabolic modelling and the use of the proteomics data in this project appears to be bearing fruit. Industrial partners may integrate this into their own work, and we are working in their industrial yeast strains (with appropriate MTAs etc) though I am not aware this has reached a mature stage. A more mature genome-scale model has also now been developed from the project which is available, and is now published in Nature Communications. |
First Year Of Impact | 2021 |
Sector | Chemicals,Manufacturing, including Industrial Biotechology,Pharmaceuticals and Medical Biotechnology |
Impact Types | Economic |
Title | Whole-cell modeling in yeast predicts compartment-specific proteome constraints that drive metabolic strategies |
Description | Mass spec data associated with the publication in Nature Communications |
Type Of Material | Database/Collection of data |
Year Produced | 2022 |
Provided To Others? | Yes |
Impact | none yet - other than the paper |
URL | http://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD030003 |
Description | ERA-IB yeast proteomics and turnover studies |
Organisation | DSM |
Country | Netherlands |
Sector | Private |
PI Contribution | The collaboration was borne out of email discussions with a PI at VU who had seen our proteomics and transcriptomics work and wondered if we wanted to collaborate on a yeast systems biology project. We bring the bioinformatics, transcriptomics and proteomics expertise, and allows us to integrate our quantitative proteomics platform (developed on the LoLa grant to Hubbard/Beynon) to generate data for systems modelling efforts in NL and Denmark. |
Collaborator Contribution | Provision of expertise in modelling, with industrial partners providing industrially important yeast strains which are able to produce high yields of succinate. We aim to investigate this at the systems level through post-genomic science and genome scale modelling |
Impact | This project is in its early days but we now have acquired quantitiative proteomics data via the Liverpool partners and have improved our data analysis pipelines for estimation of protein turnover rates |
Start Year | 2015 |
Description | ERA-IB yeast proteomics and turnover studies |
Organisation | Delft University of Technology (TU Delft) |
Country | Netherlands |
Sector | Academic/University |
PI Contribution | The collaboration was borne out of email discussions with a PI at VU who had seen our proteomics and transcriptomics work and wondered if we wanted to collaborate on a yeast systems biology project. We bring the bioinformatics, transcriptomics and proteomics expertise, and allows us to integrate our quantitative proteomics platform (developed on the LoLa grant to Hubbard/Beynon) to generate data for systems modelling efforts in NL and Denmark. |
Collaborator Contribution | Provision of expertise in modelling, with industrial partners providing industrially important yeast strains which are able to produce high yields of succinate. We aim to investigate this at the systems level through post-genomic science and genome scale modelling |
Impact | This project is in its early days but we now have acquired quantitiative proteomics data via the Liverpool partners and have improved our data analysis pipelines for estimation of protein turnover rates |
Start Year | 2015 |
Description | ERA-IB yeast proteomics and turnover studies |
Organisation | Free University of Amsterdam |
Country | Netherlands |
Sector | Academic/University |
PI Contribution | The collaboration was borne out of email discussions with a PI at VU who had seen our proteomics and transcriptomics work and wondered if we wanted to collaborate on a yeast systems biology project. We bring the bioinformatics, transcriptomics and proteomics expertise, and allows us to integrate our quantitative proteomics platform (developed on the LoLa grant to Hubbard/Beynon) to generate data for systems modelling efforts in NL and Denmark. |
Collaborator Contribution | Provision of expertise in modelling, with industrial partners providing industrially important yeast strains which are able to produce high yields of succinate. We aim to investigate this at the systems level through post-genomic science and genome scale modelling |
Impact | This project is in its early days but we now have acquired quantitiative proteomics data via the Liverpool partners and have improved our data analysis pipelines for estimation of protein turnover rates |
Start Year | 2015 |
Description | ERA-IB yeast proteomics and turnover studies |
Organisation | Novo Nordisk Foundation |
Country | Denmark |
Sector | Charity/Non Profit |
PI Contribution | The collaboration was borne out of email discussions with a PI at VU who had seen our proteomics and transcriptomics work and wondered if we wanted to collaborate on a yeast systems biology project. We bring the bioinformatics, transcriptomics and proteomics expertise, and allows us to integrate our quantitative proteomics platform (developed on the LoLa grant to Hubbard/Beynon) to generate data for systems modelling efforts in NL and Denmark. |
Collaborator Contribution | Provision of expertise in modelling, with industrial partners providing industrially important yeast strains which are able to produce high yields of succinate. We aim to investigate this at the systems level through post-genomic science and genome scale modelling |
Impact | This project is in its early days but we now have acquired quantitiative proteomics data via the Liverpool partners and have improved our data analysis pipelines for estimation of protein turnover rates |
Start Year | 2015 |
Description | ERA-IB yeast proteomics and turnover studies |
Organisation | Roquette Pharma |
Country | France |
Sector | Private |
PI Contribution | The collaboration was borne out of email discussions with a PI at VU who had seen our proteomics and transcriptomics work and wondered if we wanted to collaborate on a yeast systems biology project. We bring the bioinformatics, transcriptomics and proteomics expertise, and allows us to integrate our quantitative proteomics platform (developed on the LoLa grant to Hubbard/Beynon) to generate data for systems modelling efforts in NL and Denmark. |
Collaborator Contribution | Provision of expertise in modelling, with industrial partners providing industrially important yeast strains which are able to produce high yields of succinate. We aim to investigate this at the systems level through post-genomic science and genome scale modelling |
Impact | This project is in its early days but we now have acquired quantitiative proteomics data via the Liverpool partners and have improved our data analysis pipelines for estimation of protein turnover rates |
Start Year | 2015 |
Description | ERA-IB yeast proteomics and turnover studies |
Organisation | University of Liverpool |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | The collaboration was borne out of email discussions with a PI at VU who had seen our proteomics and transcriptomics work and wondered if we wanted to collaborate on a yeast systems biology project. We bring the bioinformatics, transcriptomics and proteomics expertise, and allows us to integrate our quantitative proteomics platform (developed on the LoLa grant to Hubbard/Beynon) to generate data for systems modelling efforts in NL and Denmark. |
Collaborator Contribution | Provision of expertise in modelling, with industrial partners providing industrially important yeast strains which are able to produce high yields of succinate. We aim to investigate this at the systems level through post-genomic science and genome scale modelling |
Impact | This project is in its early days but we now have acquired quantitiative proteomics data via the Liverpool partners and have improved our data analysis pipelines for estimation of protein turnover rates |
Start Year | 2015 |
Description | integrated bioinformatics to study the yeast chaperone |
Organisation | University of Liverpool |
Department | Centre for Proteome Research |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | Bioinformatics tools for this project have been exploited with various labs, including those at Manchester and at Liverpool, to improve a related manuscript on the effects of heat shock in Saccahromyces cerevisiae |
Collaborator Contribution | They provided proteomics facilities and data, which we integrated with bioinformatics networks to generate novel figures for a paper |
Impact | Andrew F. Jarnuczak, Manuel Garcia Albornoz, Claire E. Eyers, Christopher M. Grant, Simon J. Hubbard. A quantitative and temporal map of proteostasis during heat shock in Saccharomyces cerevisiae, 2018, Mol. Omics, 2018, 14, 37-52 |
Start Year | 2017 |
Description | yeast metabolic control bioinformatics with M Adamczyk |
Organisation | Warsaw University of Technology |
Country | Poland |
Sector | Academic/University |
PI Contribution | We helped process proteomics data and generate figures for a manuscript describing proteomics data collected at the University of Liverpool. We integrated the data into metabolic pathways and cross referenced it with transcription factor target data to generate figures for the manuscript, which we helped interpret and write. |
Collaborator Contribution | The collborators in Poland provided yeast samples from mutant strains to the University of Liverpool, and subsequently raw mass spectrometry data was sent to us. We processed the data, integrated this with known pathway information, and this was interpreted by the partners who led the manuscript - just accepted at Biochemical Journal, |
Impact | Roza Szatkowska1, Manuel Garcia-Albornoz2, Katarzyna Roszkowska1, Stephen W. Holman3a, Emil Furmanek1, Simon Hubbard2, Robert J. Beynon3, Malgorzata Adamczyk *1 (2019) Glycolytic flux in Saccharomyces cerevisiae is dependent on RNA polymerase III and its negative regulator Maf1, Biochem J. In press (we don't have a DOI yet) |
Start Year | 2017 |