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Computational Molecular Biology

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A Survey of Practical Models, Algorithms, and Numerical Methods

There are many excellent computational biology resources now available for learning about methods that have been developed to address specific biological systems, but comparatively little attention has been paid to training aspiring computational biologists to handle new and unanticipated problems. This text is intended to fill that gap by teaching students how to reason about developing formal mathematical models of biological systems that are amenable to computational analysis.

Deciphering the Ends of DNA

Molecular biologist Elizabeth Blackburn--one of Time magazine’s 100 “Most Influential People in the World” in 2007--made headlines in 2004 when she was dismissed from the President’s Council on Bioethics after objecting to the council’s call for a moratorium on stem cell research and protesting the suppression of relevant scientific evidence in its final report. But it is Blackburn’s groundbreaking work on telomeric DNA, which launched the field of telomere research, that will have the more profound and long-lasting effect on science and society.

In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between supervised learning (in which all training examples are labeled) and unsupervised learning (in which no label data are given). Interest in SSL has increased in recent years, particularly because of application domains in which unlabeled data are plentiful, such as images, text, and bioinformatics.

From Concepts to Nuts and Bolts

Research in systems biology requires the collaboration of researchers from diverse backgrounds, including biology, computer science, mathematics, statistics, physics, and biochemistry. These collaborations, necessary because of the enormous breadth of background needed for research in this field, can be hindered by differing understandings of the limitations and applicability of techniques and concerns from different disciplines.

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