RNA : algorithms, evolution and design

Modern biology is being remade by a dizzying array of new technologies, a deluge of data, and an increasingly strong reliance on computation to guide and interpret experiments. In two areas of biology, computational methods have become central: predicting and designing the structure of biological molecules and inferring function from molecular evolution. In this thesis, I develop a number of algorithms for problems in these areas and combine them with experiment to provide biological insight. First, I study the problem of designing RNA sequences that fold into specific structures. To do so I introduce a novel computational problem on Hidden Markov Models (HMMs) and Stochastic Context Free Grammars (SCFGs). I show that the problem is NP-hard, resolving an open question for RNA secondary structure design, and go on to develop a number of approximation approaches. I then turn to the problem of inferring function from evolution. I develop an algorithm to identify regions in the genome that are serving two simultaneous functions: encoding a protein and encoding regulatory information. I first use this algorithm to find microRNA targets in both Drosophila and mammalian genes and show that conserved microRNA targeting in coding regions is widespread. Next, I identify a novel phenomenon where an accumulation of sequence repeats leads to surprisingly strong microRNA targeting, demonstrating a previously unknown role for such repeats. Finally, I address the problem of detecting more general conserved regulatory elements in coding DNA. I show that such elements are widespread in Drosophila and can be identified with high confidence, a result with important implications for understanding both biological regulation and the evolution of protein coding sequences.

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Mathematics.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2011

Author dc:creator

• Schnall-Levin, Michael (Michael Benjamin)

Advisor dc:contributor.advisor

• Bonnie Berger.

• Mathematics.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/67718

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/67718