### 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 language | English |
---|---|

Pages (from-to) | 2017-2026 |

Number of pages | 9 |

Journal | Bioinformatics |

Volume | 21 |

DOIs | |

Publication status | Published - Jan 2005 |

### Keywords

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

### Cite this

*Bioinformatics*,

*21*, 2017-2026. https://doi.org/10.1093/bioinformatics/bti255

**On the use of qualitative reasoning to simulate and identify metabolic pathways.** / King, R. D.; Garrett, S. M.; Coghill, George MacLeod.

Research output: Contribution to journal › Article

*Bioinformatics*, vol. 21, pp. 2017-2026. https://doi.org/10.1093/bioinformatics/bti255

}

TY - JOUR

T1 - On the use of qualitative reasoning to simulate and identify metabolic pathways

AU - King, R. D.

AU - Garrett, S. M.

AU - Coghill, George MacLeod

PY - 2005/1

Y1 - 2005/1

N2 - 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.

AB - 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.

KW - TRYPANOSOMA-BRUCEI

KW - NETWORKS

KW - ENCYCLOPEDIA

KW - KINETICS

KW - GENES

KW - CELL

U2 - 10.1093/bioinformatics/bti255

DO - 10.1093/bioinformatics/bti255

M3 - Article

VL - 21

SP - 2017

EP - 2026

JO - Bioinformatics

JF - Bioinformatics

SN - 1367-4803

ER -