| 1. |
- Brunnsåker, Daniel, 1992, et al.
(author)
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High-throughput metabolomics for the design and validation of a diauxic shift model
- 2023
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In: NPJ systems biology and applications. - : Springer Nature. - 2056-7189. ; 9:1, s. 11-
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Journal article (peer-reviewed)abstract
- Saccharomyces cerevisiae is a very well studied organism, yet ∼20% of its proteins remain poorly characterized. Moreover, recent studies seem to indicate that the pace of functional discovery is slow. Previous work has implied that the most probable path forward is via not only automation but fully autonomous systems in which active learning is applied to guide high-throughput experimentation. Development of tools and methods for these types of systems is of paramount importance. In this study we use constrained dynamical flux balance analysis (dFBA) to select ten regulatory deletant strains that are likely to have previously unexplored connections to the diauxic shift. We then analyzed these deletant strains using untargeted metabolomics, generating profiles which were then subsequently investigated to better understand the consequences of the gene deletions in the metabolic reconfiguration of the diauxic shift. We show that metabolic profiles can be utilised to not only gaining insight into cellular transformations such as the diauxic shift, but also on regulatory roles and biological consequences of regulatory gene deletion. We also conclude that untargeted metabolomics is a useful tool for guidance in high-throughput model improvement, and is a fast, sensitive and informative approach appropriate for future large-scale functional analyses of genes. Moreover, it is well-suited for automated approaches due to relative simplicity of processing and the potential to make massively high-throughput.
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| 2. |
- Gower, Alexander, 1993
(author)
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Frameworks for Automated Discovery in Systems Biology
- 2024
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Licentiate thesis (other academic/artistic)abstract
- Systems biology is an integrationist approach to biological science, meaning we treat organisms as complex systems whose behaviour is dictated by the interaction of their constituent parts. Because eukaryotic organisms are extremely complex systems, research progress in systems biology can be slow. Recent advances in robotics, and more importantly in artificial intelligence (AI), offer great opportunity for automating scientific discovery in this field. Using the model organism Saccharomyces cerevisiae , baker’s yeast, this thesis explores: the philosophical and practical motivations for the use of automation in biological research; the structure of knowledge models, experi- mental data, and hypotheses in systems biology; and computational models of metabolism, a core component of systems biology. The first main contribution of this thesis is a set of ontologies and accompanying database software for enabling an autonomous discovery platform. The second main contribution is a first-order logic framework for modelling cellular physiology, which we call LGEM⁺. Abduction of hypotheses for improvement of knowledge models is enabled by LGEM⁺, which couples a set of predicates and clauses expressing biochemical reaction processes with an efficient automated theorem prover (ATP), iProver. Results from these studies show automated improvement of knowledge models in systems biology can be achieved using general purpose tools, in this case ATPs, by using a first-order logic formalism faithful to domain ontologies. More work is needed to integrate these techniques with laboratory robotics and inductive reasoning agents, building on the work presented in this thesis, to achieve the goal of autonomous discovery in systems biology.
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| 3. |
- Gower, Alexander, 1993, et al.
(author)
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LGEM+ : A First-Order Logic Framework for Automated Improvement of Metabolic Network Models Through Abduction
- 2023
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In: Discovery Science - 26th International Conference, DS 2023, Proceedings. - : Springer Nature. ; 14276 LNAI, s. 628-643
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Conference paper (peer-reviewed)abstract
- Scientific discovery in biology is difficult due to the complexity of the systems involved and the expense of obtaining high quality experimental data. Automated techniques are a promising way to make scientific discoveries at the scale and pace required to model large biological systems. A key problem for 21st century biology is to build a computational model of the eukaryotic cell. The yeast Saccharomyces cerevisiae is the best understood eukaryote, and genome-scale metabolic models (GEMs) are rich sources of background knowledge that we can use as a basis for automated inference and investigation. We present LGEM+, a system for automated abductive improvement of GEMs consisting of: a compartmentalised first-order logic framework for describing biochemical pathways (using curated GEMs as the expert knowledge source); and a two-stage hypothesis abduction procedure. We demonstrate that deductive inference on logical theories created using LGEM+, using the automated theorem prover iProver, can predict growth/no-growth of S. cerevisiae strains in minimal media. LGEM+ proposed 2094 unique candidate hypotheses for model improvement. We assess the value of the generated hypotheses using two criteria: (a) genome-wide single-gene essentiality prediction, and (b) constraint of flux-balance analysis (FBA) simulations. For (b) we developed an algorithm to integrate FBA with the logic model. We rank and filter the hypotheses using these assessments. We intend to test these hypotheses using the robot scientist Genesis, which is based around chemostat cultivation and high-throughput metabolomics.
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| 4. |
- Kronström, Filip, 1995, et al.
(author)
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RIMBO - An Ontology for Model Revision Databases
- 2023
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In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). - 1611-3349 .- 0302-9743. ; 14276 LNAI, s. 523-534
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Conference paper (peer-reviewed)abstract
- The use of computational models is growing throughout most scientific domains. The increased complexity of such models, as well as the increased automation of scientific research, imply that model revisions need to be systematically recorded. We present RIMBO (Revisions for Improvements of Models in Biology Ontology), which describes the changes made to computational biology models. The ontology is intended as the foundation of a database containing and describing iterative improvements to models. By recording high level information, such as modelled phenomena, and model type, using controlled vocabularies from widely used ontologies, the same database can be used for different model types. The database aims to describe the evolution of models by recording chains of changes to them. To make this evolution transparent, emphasise has been put on recording the reasons, and descriptions, of the changes. We demonstrate the usefulness of a database based on this ontology by modelling the update from version 8.4.1 to 8.4.2 of the genome-scale metabolic model Yeast8, a modification proposed by an abduction algorithm, as well as thousands of simulated revisions. This results in a database demonstrating that revisions can successfully be modelled in a semantically meaningful and storage efficient way. We believe such a database is necessary for performing automated model improvement at scale in systems biology, as well as being a useful tool to increase the openness and traceability for model development. With minor modifications the ontology can also be used in other scientific domains. The ontology is made available at https://github.com/filipkro/rimbo and will be continually updated.
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| 5. |
- Kronström, Filip, et al.
(author)
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RIMBO - An Ontology for Model Revision Databases
- 2023
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In: Discovery Science - 26th International Conference, DS 2023, Proceedings. - : Springer Nature. ; 14276 LNAI, s. 523-534
-
Conference paper (peer-reviewed)abstract
- The use of computational models is growing throughout most scientific domains. The increased complexity of such models, as well as the increased automation of scientific research, imply that model revisions need to be systematically recorded. We present RIMBO (Revisions for Improvements of Models in Biology Ontology), which describes the changes made to computational biology models. The ontology is intended as the foundation of a database containing and describing iterative improvements to models. By recording high level information, such as modelled phenomena, and model type, using controlled vocabularies from widely used ontologies, the same database can be used for different model types. The database aims to describe the evolution of models by recording chains of changes to them. To make this evolution transparent, emphasise has been put on recording the reasons, and descriptions, of the changes. We demonstrate the usefulness of a database based on this ontology by modelling the update from version 8.4.1 to 8.4.2 of the genome-scale metabolic model Yeast8, a modification proposed by an abduction algorithm, as well as thousands of simulated revisions. This results in a database demonstrating that revisions can successfully be modelled in a semantically meaningful and storage efficient way. We believe such a database is necessary for performing automated model improvement at scale in systems biology, as well as being a useful tool to increase the openness and traceability for model development. With minor modifications the ontology can also be used in other scientific domains. The ontology is made available at https://github.com/filipkro/rimbo and will be continually updated.
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| 6. |
- Reder, Gabriel, 1992, et al.
(author)
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Genesis-DB: a database for autonomous laboratory systems
- 2023
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In: Bioinformatics Advances. - 2635-0041. ; 3:1
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Journal article (peer-reviewed)abstract
- Artificial intelligence (AI)-driven laboratory automation - combining robotic labware and autonomous software agents - is a powerful trend in modern biology. We developed Genesis-DB, a database system designed to support AI-driven autonomous laboratories by providing software agents access to large quantities of structured domain information. In addition, we present a new ontology for modeling data and metadata from autonomously performed yeast microchemostat cultivations in the framework of the Genesis robot scientist system. We show an example of how Genesis-DB enables the research life cycle by modeling yeast gene regulation, guiding future hypotheses generation and design of experiments. Genesis-DB supports AI-driven discovery through automated reasoning and its design is portable, generic, and easily extensible to other AI-driven molecular biology laboratory data and beyond.
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