On the use of qualitative reasoning to simulate and identify metabolic pathways

R. D. King, S. M. Garrett, George MacLeod Coghill

Research output: Contribution to journalArticle

44 Citations (Scopus)

Abstract

Motivation: Perhaps the greatest challenge of modern biology is to develop accurate in silico models of cells. To do this we require computational formalisms for both simulation (how according to the model the state of the cell evolves over time) and identification (learning a model cell from observation of states). We propose the use of qualitative reasoning (QR) as a unified formalism for both tasks. The two most commonly used alternative methods of modelling biochemical pathways are ordinary differential equations (ODEs), and logical/graph-based (LG) models.

Results: The QR formalism we use is an abstraction of ODEs. It enables the behaviour of many ODEs, with different functional forms and parameters, to be captured in a single QR model. QR has the advantage over LG models of explicitly including dynamics. To simulate biochemical pathways we have developed 'enzyme' and 'metabolite' QR building blocks that fit together to form models. These models are finite, directly executable, easy to interpret and robust. To identify QR models we have developed heuristic chemoinformatics graph analysis and machine learning procedures. The graph analysis procedure is a series of constraints and heuristics that limit the number of ways metabolites can combine to form pathways. The machine learning procedure is generate-and-test inductive logic programming. We illustrate the use of QR for modelling and simulation using the example of glycolysis.

Original languageEnglish
Pages (from-to)2017-2026
Number of pages9
JournalBioinformatics
Volume21
DOIs
Publication statusPublished - Jan 2005

Keywords

  • TRYPANOSOMA-BRUCEI
  • NETWORKS
  • ENCYCLOPEDIA
  • KINETICS
  • GENES
  • CELL

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