AI3SD Video: AI and multi-omics discovery science: A case study in understanding ageing at a systems level
AI3SD Video: AI and multi-omics discovery science: A case study in understanding ageing at a systems level
Metabolism is central to all processes of life and the metabolome -- large-scale measurement of the quantities of small molecular entities in cells and tissues -- gives a readout of cellular functioning at a point in time. Harnessing metabolomic information together with transcriptomic information about gene expression allows for multi-level insights into genetic dysregulation and its cellular effects. I will describe a multi-omics approach based on genome-scale modelling that is able to integrate the two levels and provide insights into the systems-level deregulation of cellular function due to ageing by transforming the cellular reaction space into a constraint-based linear optimisation problem. Metabolic models such as these and their interpretation depends on publicly available data about small molecular metabolites. Chemical ontologies provide structured classifications of chemical entities that can be used for navigation and filtering of chemical space including in metabolic models. ChEBI is a prominent example of a chemical ontology, widely used in life science contexts including to annotate metabolites in genome-scale models, and recent work has involved using deep learning to automatically extend the ChEBI classification to a wider range of metabolites thus enhancing the benefit of genome-scale models for ageing systems research. Finally, I will discuss the role of artificial intelligence technologies in systems-level -omics research more generally.
Hastings, Janna
8aa73d04-52db-4017-8ff7-5b67b1c9a8d7
Frey, Jeremy G.
ba60c559-c4af-44f1-87e6-ce69819bf23f
Kanza, Samantha
b73bcf34-3ff8-4691-bd09-aa657dcff420
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f
1 March 2022
Hastings, Janna
8aa73d04-52db-4017-8ff7-5b67b1c9a8d7
Frey, Jeremy G.
ba60c559-c4af-44f1-87e6-ce69819bf23f
Kanza, Samantha
b73bcf34-3ff8-4691-bd09-aa657dcff420
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f
Hastings, Janna
(2022)
AI3SD Video: AI and multi-omics discovery science: A case study in understanding ageing at a systems level.
Frey, Jeremy G., Kanza, Samantha and Niranjan, Mahesan
(eds.)
AI4SD Network+ Conference, Chilworth Manor , Southampton, United Kingdom.
01 - 03 Mar 2022.
(doi:10.5258/SOTON/AI3SD0189).
Record type:
Conference or Workshop Item
(Other)
Abstract
Metabolism is central to all processes of life and the metabolome -- large-scale measurement of the quantities of small molecular entities in cells and tissues -- gives a readout of cellular functioning at a point in time. Harnessing metabolomic information together with transcriptomic information about gene expression allows for multi-level insights into genetic dysregulation and its cellular effects. I will describe a multi-omics approach based on genome-scale modelling that is able to integrate the two levels and provide insights into the systems-level deregulation of cellular function due to ageing by transforming the cellular reaction space into a constraint-based linear optimisation problem. Metabolic models such as these and their interpretation depends on publicly available data about small molecular metabolites. Chemical ontologies provide structured classifications of chemical entities that can be used for navigation and filtering of chemical space including in metabolic models. ChEBI is a prominent example of a chemical ontology, widely used in life science contexts including to annotate metabolites in genome-scale models, and recent work has involved using deep learning to automatically extend the ChEBI classification to a wider range of metabolites thus enhancing the benefit of genome-scale models for ageing systems research. Finally, I will discuss the role of artificial intelligence technologies in systems-level -omics research more generally.
Video
ai4sd_march_2022_day_1_JannaHastings
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Published date: 1 March 2022
Additional Information:
I am a computer scientist interested in developing artificial intelligence-based computational systems to support research across the biological and social sciences. I am particularly interested in the interface between data science, i.e. algorithms for deriving inferences and predictions based on structured and unstructured data, and knowledge science, i.e. research that amasses, integrates and harnesses what we already know and channels that back towards efforts to make novel discoveries, towards a genuinely cumulative discovery frontier. To this end I have actively contributed to research in computational knowledge representation and reasoning, to community-wide knowledge integration via building semantic standards, and to scientific discovery research using computational approaches across a range of domains.
Venue - Dates:
AI4SD Network+ Conference, Chilworth Manor , Southampton, United Kingdom, 2022-03-01 - 2022-03-03
Identifiers
Local EPrints ID: 468647
URI: http://eprints.soton.ac.uk/id/eprint/468647
PURE UUID: 7a86244c-246d-4b76-9fc5-f88071d9bc0c
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Date deposited: 19 Aug 2022 16:38
Last modified: 17 Mar 2024 03:51
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Author:
Janna Hastings
Editor:
Mahesan Niranjan
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