Abstract
Advancements and challenges in computational biology.
Highlights
We have at our disposal large information-rich resources, and we are increasingly able to integrate and understand the vast quantities of data that they encompass
We have witnessed huge leaps in biological computing [2]
Formidable challenges include: the establishment of computer networks for surveillance of disease; mapping the pathways and biological networks associated with the initiation, growth and spread of cancer; predicting function and mutational dysfunction in disease from the structure of complex molecules; resolving the mechanisms of oncogenic mutations and the cellular network which is rewired in cancer; achieving accurate, efficient, and comprehensive dynamic models; and moving from artificial intelligence to the ‘‘connectome’’—the connections among all of the neurons of the brain
Summary
We have at our disposal large information-rich resources, and we are increasingly able to integrate and understand the vast quantities of data that they encompass. We have made big strides toward multiscale biological modeling, and we have a vastly more networked world of researchers and their data. Analysis of massive gene expression and proteomic data permitted the construction of comprehensive and predictive models for cellular pathways, as well as software for inferring interaction networks, and steps toward modeling of cells.
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