Developing a logical model of yeast metabolism

Reiser, PGK, King, RD, Muggleton, SH, Bryant, CH ORCID:, Oliver, SG and Kell, DB 2001, 'Developing a logical model of yeast metabolism' , Electronic Transactions in Artificial Intelligence, 5 (B) , pp. 223-244.

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With the completion of the sequencing of genomes of increasing numbers of organisms, the focus of biology is moving to determining the role of these genes (functional genomics). To this end it is useful to view the cell as a biochemical machine: it consumes simple molecules to manufacture more complex ones by chaining together biochemical reactions into long sequences referred to as em metabolic pathways. Such metabolic pathways are not linear but often interesect to form complex networks. Genes play a fundamental role in these networks by providing the information to synthesise the enzymes that catalyse biochemical reactions. Although developing a complete model of metabolism is of fundamental importance to biology and medicine, the size and complexity of the network has proven beyond the capacity of human reasoning. This paper presents the first results of the Robot Scientist research programme that aims to automatically discover the function of genes in the metabolism of the yeast em Saccharomyces cerevisiae. Results include: (1) the first logical model of metabolism;(2) a method to predict phenotype by deductive inference; and (3) a method to infer reactions and gene function by aductive inference. We describe the em in vivo experimental set-up which will allow these em in silico predictions to be automatically tested by a laboratory robot.

Item Type: Article
Themes: Subjects / Themes > Q Science > QA Mathematics > QA075 Electronic computers. Computer science
Subjects / Themes > Q Science > QH Natural history > QH301 Biology
Subjects outside of the University Themes
Schools: Schools > School of Computing, Science and Engineering
Schools > School of Computing, Science and Engineering > Salford Innovation Research Centre
Journal or Publication Title: Electronic Transactions in Artificial Intelligence
Publisher: Royal Swedish Academy of Sciences
Refereed: Yes
ISSN: 14033534
Related URLs:
Depositing User: Dr Chris H. Bryant
Date Deposited: 16 Feb 2009 15:00
Last Modified: 16 Feb 2022 08:16

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