A wealth of computational methods has been developed to address problems in systems biology, such as modeling gene expression.
A wealth of computational methods has been developed to help analyze biological properties revealed by reconstructed metabolic models.
The advent of rapid sequencing technologies, the collection and sharing of omics data in electronic databases, and the development of a wealth of computational tools in systems biology have allowed the reconstruction of metabolic networks on a genome-wide scale.
Chemistry is indeed an immense and rapidly growing discipline with a wealth of disparate computational and database resources which are currently largely isolated and inaccessible to truly integrative queries across the entirety of the chemical (deep) web.
However, such effects had not been reproduced in the wealth of prior computational studies examining the effect of electrical shocks on whole ventricular models, due to the inherent lack of structural detail in those models.
However, despite a wealth of biochemical, structural and computational data, the way in which GTP hydrolysis is activated and regulated is still a controversial topic and well-designed simulations can play an important role in resolving and rationalizing the experimental data.
The wealth of information derived from these computational analyses could aid future studies aimed at discovering/developing anti-cancer agents.
Overall, in addition to fiber diffraction, which over the years has provided a wealth of useful structural information, 104) computational studies of monomeric and oligomeric structures of various fragments have clearly shown structural flexibility and oligomerization complexity, for example, Aβ10−35, Aβ16−35, Aβ17−42, Aβ10−35 monomers, Aβ21−30, Aβ13−23,(91) and−28, Aβ14−234−23.
A central theme of systems biology is the use of computational modeling to reconstruct complex systems from a wealth of reductionist, molecular data (e.g., gene/protein expression, signal transduction activity, metabolic activity, etc).
Given the wealth of novel biodegradation pathways obtained using computational prediction methods, it is necessary to evaluate their relative feasibility.
However, despite wealth of data on co-localized transcripts, computational methods thus far fail to detect such signals in a reliable manner.
