The Knowledge Engineering Review
Constructing a Knowledge Base for Gene Regulatory Dynamics by Formal Concept Analysis Methods
AB '08 Proceedings of the 3rd international conference on Algebraic Biology
The Journal of Machine Learning Research
Temporal Logic Patterns for Querying Qualitative Models of Genetic Regulatory Networks
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Qualitative system identification from imperfect data
Journal of Artificial Intelligence Research
The Knowledge Engineering Review
An immune-inspired approach to qualitative system identification of biological pathways
Natural Computing: an international journal
Evaluating theories of immunological memory using large-scale simulations
ICARIS'05 Proceedings of the 4th international conference on Artificial Immune Systems
Analyzing pathways using ASP-based approaches
ANB'10 Proceedings of the 4th international conference on Algebraic and Numeric Biology
Qualitative Reasoning for Biological Network Inference from Systematic Perturbation Experiments
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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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. Availability: All data and programs used are available on request. Contact: rdk@aber.ac.uk