Representation, Simulation, and Hypothesis Generation in Graph and Logical Models of Biological Networks
Representation, Simulation, and Hypothesis Generation in Graph and Logical Models of Biological Networks
This chapter presents a discussion of metabolic modeling from graph theory and logical modeling perspectives. These perspectives are closely related and focus on the coarse structure of metabolism, rather than the finer details of system behavior. The models have been used as background knowledge for hypothesis generation by Robot Scientists using yeast as a model eukaryote, where experimentation and machine learning are used to identify additional knowledge to improve the metabolic model. The logical modeling concept is being adapted to cell signaling and transduction biological networks.
- University of Bristol United Kingdom
- Aberystwyth University United Kingdom
Logic, Computational Biology, Models, Biological, 004, Phenotype, Computer Graphics, Humans, Metabolic Networks and Pathways
Logic, Computational Biology, Models, Biological, 004, Phenotype, Computer Graphics, Humans, Metabolic Networks and Pathways
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).5 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average
Citations provided by BIP!Popularity provided by BIP!