Logic-based models in systems biology: a predictive and parameter-free network analysis method.
Highly complex molecular networks, which play fundamental roles in almost all cellular processes, are known to be dysregulated in a number of diseases, most notably in cancer. As a consequence, there is a critical need to develop practical methodologies for constructing and analysing molecular networks at a systems level. Mathematical models built with continuous differential equations are an ideal methodology because they can provide a detailed picture of a network's dynamics. To be predictive, however, differential equation models require that numerous parameters be known a priori and this information is almost never available. An alternative dynamical approach is the use of discrete logic-based models that can provide a good approximation of the qualitative behaviour of a biochemical system without the burden of a large parameter space. Despite their advantages, there remains significant resistance to the use of logic-based models in biology. Here, we address some common concerns and provide a brief tutorial on the use of logic-based models, which we motivate with biological examples.
Center for Computational Medicine & Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA. mlwynn@umich.edu
Michelle L
ML
Wynn
Nikita
N
Consul
Sofia D
SD
Merajver
Santiago
S
Schnell
D000818:Animals; D017209:Apoptosis / Q000235:genetics / Q000502:physiology; D049109:Cell Proliferation; D015179:Colorectal Neoplasms / Q000235:genetics / Q000378:metabolism / Q000473:pathology; D004249:DNA Damage; D053263:Gene Regulatory Networks; D006801:Humans; D007700:Kinetics; D016015:Logistic Models; D053858:Metabolic Networks and Pathways; D008954:Models, Biological; D049490:Systems Biology