Ph.D. Program
Prospective students who have completed a bachelor’s degree may apply for direct admission to the post-bachelor’s Ph.D. program. The post-bachelor’s Ph.D. requires a total of 64 credits, consisting of lecture/laboratory courses and research or seminar courses. While there is a set of required core courses, the precise course of study will be determined in consultation with the student’s academic advisor, and will reflect the student’s background and interests.
Prospective students who have completed a bachelor’s degree as well as a master’s degree in disciplines related to the field: biological science, computer science, physical science, mathematics, or engineering may apply for direct admission to the post-master’s Ph.D. program. The post-master’s Ph.D. requires 32 credits, consisting of satisfactory fulfillment of the core course requirements with a minimum of four lecture/laboratory courses, as recommended by the student’s two academic advisors. Admission requirements for the Ph.D. candidacy are the same for the post-bachelor’s Ph.D.
In order to be admitted to Ph.D. candidacy (by the end of the second year) students must demonstrate mastery of the core subject matter (no lower than a “B” in core courses) and successfully complete a qualifying examination, which has both written and oral components.
Core Courses
Post-bachelor Ph.D. students are expected to fulfill all of the core course requirements listed below. Fulfillment of core course equivalents will be determined based on documented previous academic and/or work experience. When either past work or an alternate course has been accepted as a core equivalent, the student’s advisors will recommend other courses to fulfill the requirements. This applies as well to Post-master Ph.D. students. Post-master Ph.D. students are expected to fulfill the same core course requirements as Post-bachelor Ph.D. students if the student has not satisfied all of the following core courses:
ENG BE 561: Protein and DNA Sequence Analysis
Presents fundamental concepts from molecular biology and molecular genetics. Teaches how to make biological inferences from DNA and protein sequences using mathematical and computer science techniques. Pair-wise sequence comparison; extension to multiple sequence alignment and conserved sequence pattern recognition; phylogenetic trees identification of coding regions; fragment assembly. Mathematical models and computational algorithms for genetic regulation. An introduction to protein 3-dimentional structure prediction. 4 cr.
ENG BE 768: Biological Database Systems
Describes relational data models and database management systems; teaches the theories and techniques of constructing relational databases to store various biological data, including sequences, structures, genetic linkages and maps, and signal pathways. Introduces relational database query language SQL and the ORACLE database management system, with an emphasis on answering biologically important questions. Summarizes currently existing biological databases. Describes Web based programming tools to make databases accessible. Addresses questions in data integration and security. The future directions for biological database development are also discussed. 4cr.
ENG BE 777: Computational Genomics I
A case-study approach to current topics in computational genomics. Mathematical and engineering tools for analyzing genomic data are reviewed. The relationships between sequence, structure, and function in complex biological networks are studied using quantitative modeling. Whole genome analysis is performed. Completion of a series of projects emphasizing real-life data, integrated approaches, practical applications, hands-on analysis, and collaboration. Course projects aim at improving current approaches and involve C and/or PERL programming to interface with existing software packages. The course will be offered in a computer laboratory equipped with one laptop per student. 4 cr.
ENG BF 778: Physical Chemistry for Systems Biology
This course introduces students to quantitative modeling in bioinformatics and systems biology. We begin with basic principles of statistical thermodynamics, chemical kinetics, with selected applications in biomolecular systems. Next we describe molecular driving forces in biology, and computation with biomolecular structures. Finally we discuss quantitative models of biomolecular networks, and design principles of biological circuits. 4 cr.
ENG BF 821: Bioinformatics Graduate Seminar
This two-semester sequence is required for all students. The Journal club affords students opportunity to present advanced papers in Computational Biology and Bioinformatics. The papers are chosen to cover recent breakthroughs in Genomics, computational biology, high-throughput biology, analysis methods, computational modeling, databases, theory and Bioinformatics. Faculty involvement leads discussion on current issues and research topics in Bioinformatics. 2 cr.
CAS BI 552: Molecular Biology I
Synthesis, structure, and function of biologically important macromolecules (DNA, RNA, and proteins). Regulation and control of the synthesis of RNA and proteins. Introduction to molecular biology of eukaryotes. Discussion of molecular biological techniques, including genetics and recombinant DNA techniques. 4 cr.
LAW BF 752: Legal and Ethical Issues of Science and Technology
Research Advisors
The process of selection of two Ph.D. research advisors will rely upon development of a collaborative research project directed by two faculty members, one computational and the other either physical/chemical or biological/biochemical. Students should expect to identify potential advisors primarily based upon published research descriptions, academic advising, lab rotations, and participation in research discussion meetings to be scheduled during the Fall semester.
Core Knowledge
Our goal is for students to attain a common core of knowledge, with particular emphasis on their ability to integrate knowledge from biological and mathematical disciplines. In particular, proficiency will be required at the level of the core courses.
As examples of courses of study, a student with a strong undergraduate degree in biology should be able to pass that part of the qualifying exam with little or no formal course work. However, the courses in genome analysis and database systems will probably need to be taken, and a quantitative course such as Molecular and Cellular Systems Analysis, along with its prerequisite, differential equations, would be desirable. The qualifying exams could be taken with as few as four formal courses. A student with a background in computer science and mathematics would very likely have to take courses in biochemistry, molecular biology and cell biology. These would follow a two semester chemistry course offered the summer before the first year, specifically designed to prepare students for molecular and cell biology.
Specialized Courses
These courses are only a small set of the courses from which students who require or desire formal course work can choose. For example, BU has more than 20 faculty in computer science and computer engineering with a full range of courses, for those wishing to become more specialized in computer science. Similarly, we have an unusually wide range of biology and chemistry courses, both on the Charles River Campus and the Medical Campus.
Tutorials in Computation
Beyond the training in information sciences that will be achieved through course work and research experience, the students will be able to augment their practical skills and obtain training in the use of the computational resources that are available at the university through regularly scheduled, professional tutorials.
The tutorials are offered throughout the academic year in three to four hour sessions, repeated several times during the year. All of the tutorials are also available on the Web. This provides great flexibility in selection of specific training topics and in scheduling training in such a way that it does not interfere with other course work and research activities. The tutorials cover parallel programming techniques and languages; computational methods and algorithms, image manipulation and printing, data analysis and plotting; computer animation; scientific visualization; and application programming for the World Wide Web. The tutorials also introduce new equipment and software as it becomes available.