Request to establish a new Ph.D. program in Bioinformatics and Computational Biology

Date: January 27, 2011
To: College of Computing & Informatics
From: Clarence Greene, Faculty Governance Administrative Assistant
Approved On: January 14, 2011
Implementation Date: 2011

Note: Deletions are strikethroughs. Insertions are underlined.

Catalog Copy

Department of Bioinformatics & Genomics

Ph.D. in Bioinformatics and Computational Biology

Department of Bioinformatics and Genomics

365 Bioinformatics Building

704-687-8541

bioinformatics.uncc.edu

Chair

Dr. Lawrence Mays

Graduate Faculty

Xiuxia Du, Assistant Professor

Anthony Fodor, Assistant Professor

Cynthia Gibas, Associate Professor

Jun-tao Guo, Assistant Professor

Dennis Livesay, Associate Professor

Ann Loraine, Associate Professor

Jessica Schlueter, Assistant Professor

Shannon Schlueter, Assistant Professor

Susan Sell, Professor

Zhengchang Su, Assistant Professor

Jennifer Weller, Associate Professor

PH.D. IN BIOINFORMATICS AND COMPUTATIONAL BIOLOGY

The Department of Bioinformatics and Genomics admits students seeking the Ph.D. degree in Bioinformatics and Computational Biology.

Ph.D. in Bioinformatics and Computational Biology Requirements

Students will be required to demonstrate competence in the areas of biochemistry, cellular and molecular biology, genetics, statistics, and computer science related to core concepts in bioinformatics. The student’s advisor and at least one other dissertation committee member must be faculty in the Bioinformatics track. Students must complete at least 72 post-baccalaureate credit hours. This includes at least 18 hours of dissertation research and at least 9 hours of course work completed at UNC Charlotte. Students are expected to acquire a sufficiently broad body of technical knowledge in the discipline as well as a deep understanding of a specialized area. This is accomplished with a combination of required courses and appropriate electives approved by the student’s advisor(s) and dissertation committee.

Course Requirements

Plans of Study for Bioinformatics Ph.D. students are developed on an individual basis, by the student and his or her advisory committee. The required coursework is organized in three tiers of formal training: Gateway courses, Core courses, and Electives. The Gateway courses will differ according to the student’s background. A student with a strong computer science background would likely take BINF 8100 and BINF 8101, but not BINF 8111 and BINF 8112. A student with a strong background in the life sciences would take BINF 8111 and BINF 8112, but not BINF 8100 and BINF 8101. This determination is made by Program Director when the student enters the Program. All students, regardless of their background, must take the Core courses, BINF 8200, BINF 8201, and BINF 8202 and nine credit hours of Electives. Students may register for Elective courses, or for courses outside of the Bioinformatics and Genomics Department with the permission of the Program Director.

Gateway Courses:

  • BINF 8100 Biological Basis of Bioinformatics

  • BINF 8101 Energy and Interaction in Biological Modeling

  • BINF 8111 Bioinformatics Programming I

  • BINF 8112 Bioinformatics Programming II

Core Courses:

  • BINF 8200 Statistics for Bioinformatics

  • BINF 8201 Molecular Sequence Analysis

  • BINF 8202 Computational Structural Biology

Elective Courses:

  • BINF 8203 Genomics

  • BINF 8204 Mathematical Systems Biology

  • BINF 8205 Computational Molecular Evolution

  • BINF 8210 Numerical Methods and Machine Learning in Bioinformatics

  • BINF 8211 Design and Implementation of Biological Databases

  • BINF 8311 Biophysical Modeling

  • BINF 8312 Computational Comparative Genomics

  • BINF 8313 Structure, Function, and Modeling of Nucleic Acids

  • BINF 8601 Journal Club

  • BINF 8151 Professional Communications

  • BINF 8171 Business of Biotechnology

  • BINF 8310 Advanced Statistics for Genomics

  • BINF 8350 Biotechnology and Genomics Laboratory

  • BINF 8380 Bioinformatics Programming III

Research Rotations

Each Ph.D. student must complete two research rotations, BINF 8911, and 8912, in their first year. These rotations provide two semesters of faculty supervised research experience to supplement regular course offerings. The purpose of these courses is to broaden students’ exposure to state-of-the-art technologies currently being utilized within the field of bioinformatics, to guide them towards recognizing important, outstanding questions in specific scientific domains, and to give them hands-on training within those domains. Students select their rotation projects in consultation with the Program Director and selected faculty members. At the end of each rotation, students must prepare a formal presentation on their findings for the faculty and their peers.

  • BINF 8911 Research Rotation I

  • BINF 8912 Research Rotation II

Seminar

BINF 8600 is taken every semester until advancing to Ph.D. candidacy. The Bioinformatics Seminar has been required of the current IT Bioinformatics track Ph.D. students and Bioinformatics Professional Science Master’s students and will continue to be a part of the BCB Ph.D. program. During the fall and spring semesters, the seminar hosts guest speakers to present talks that focus on bioinformatics, genomics, and computational biology related research. There are also designated seminar slots for the Ph.D. students to present their research rotation work.

  • BINF 8600 Graduate Research Seminar

Qualifying Examination

Students are required to take a qualifying exam to demonstrate proficiency in the fundamentals of bioinformatics and computational biology, as well as competence in statistics, molecular biology, biochemistry, and genetics. The qualifying exam must be passed prior to 5th semester of residence. The qualifying exam for the Bioinformatics and Computational Biology Ph.D. is composed of both a written and oral examination. The qualifying exam committee will have the same members in any given semester. The written component will have three sections that emphasize (a.) molecular sequence analysis, (b.) computational structural bioinformatics, and (c.) statistics and research methods. The qualifying exam is based largely on material covered in the Core courses listed above. The written sections are graded numerically, and the examinations and grades are kept by the Program to assess student outcomes. Each student must pass all sections in order to advance; failure to pass requires that the student attempt the failed sections the following semester. Passed sections carry forward from one exam to the next. Two attempts are permitted. After passing the three written sections, students must pass an oral exam over the same and related topics, for which three attempts are also permitted. Students who do not pass both sections of the qualifying exam will be dismissed from the program.

Research

Students become engaged in research immediately upon entering the Program through two mandatory research rotations in the first year. A student is expected to identify a research mentor by the beginning of the second year, and take pre-dissertation research credits. Once the qualifying examination is passed (i.e., by the 5th semester), the student should have formed a dissertation committee in consultation with the mentor and the Program Director. UNC Charlotte Graduate School rules specify that the committee consist of at least five members, four of whom must be BCB faculty members (Core or Participating) and one appointed by the Graduate School. The committee chair must be a BCB faculty member. Dissertation committee meetings are held once a semester starting in the fall semester of the third year to ensure sufficient progress is being made to complete the dissertation within five years. Students are required to present an oral progress report at each meeting, followed by a discussion of goals for the following semester.

Teaching and Professional Development

Students are required to serve as teaching assistants for at least one Bioinformatics and Computational Biology course after they have passed their qualifying exam. Faculty members supervising teaching assistants will specify their duties, which may include: attending classes, assisting with grading, preparing and delivering lectures, and/or proctoring exams. The Professional Communications course is highly recommended but not required. This course will be useful for Ph.D. students with their oral presentations, poster presentations, scientific writing, use of references and avoiding plagiarism. Students will also learn how to properly organize and a run a meeting. Lastly, students will prepare a CV, job application letter with supporting documents, and job talk.

Dissertation Proposal, Oral Defense, and Publication(s)

Each student must present and defend a Ph.D. dissertation proposal no more than three semesters after passing the qualifying exam. The proposal defense will be conducted by the student’s Dissertation Committee and will be open to faculty and students. At the discretion of the Dissertation Committee, the defense may include questions that cover the student’s program of study and background knowledge in the area of the proposal. A student can retake the proposal defense if he/she does not pass it the first time. The second failed defense of a dissertation proposal will result in the termination of the student’s enrollment in the Ph.D. program. A doctoral student advances to Ph.D. candidacy after the dissertation proposal has been successfully defended. Each student must complete a research program approved by the student’s Dissertation Advisor(s) that yields a high quality, original and substantial piece of research. The Ph.D. dissertation describes this research and its results. The dissertation defense is a public presentation. A written copy of the dissertation must be made available to each member of the student Ph.D. Dissertation Committee, to the Program Director, and to the UNC Charlotte Library at least three weeks before the public defense. The date of the defense must be publicly announced at least three weeks prior to the defense. The student must present the dissertation and defend it in a manner accepted by the Dissertation Committee. The dissertation will be graded as pass/fail by the Dissertation Committee and must be approved by the Dean of the Graduate School. A student who fails the defense of a dissertation twice will be terminated from the Ph.D. program.

COURSES IN BIOINFORMATICS

BINF 8100. Biological Basis of Bioinformatics. (3)

Prerequisites: Admission to graduate standing in Bioinformatics and undergraduate training in Computer Science or other non-biological discipline. This course provides a foundation in molecular genetics and cell biology focusing on foundation topics for graduate training in bioinformatics and genomics. (Fall)

BINF 8101. Energy and Interaction in Biological Modeling. (3)

Prerequisites: Admission to graduate standing in Bioinformatics. This course covers: (i.) the major organic and inorganic chemical features of biological macromolecules; (ii.) the physical forces that shape biological molecules, assemblies and cells; (iii.) the chemical driving forces that govern living systems; (iv.) the molecular roles of biological macromolecules and common metabolites; (v.) and the pathways of energy generation and storage. Each section of the course builds upon the relevant principles in biology and chemistry to explain the most common mathematical and physical abstractions used in modeling in the relevant context. (Spring)

BINF 8111. Bioinformatics Programming I. (3)

Prerequisite: Admission to graduate standing in Bioinformatics. This course introduces fundamentals of programming for bioinformatics using a high-level object-oriented language such as python. The first weeks cover core data types, syntax, and functional programming, focusing on construction of programs from small, testable parts. Students will learn productive use of the Unix environment, focusing on Unix utilities that are particularly useful in bioinformatics. The course will cover object-oriented programming, introduce analysis of algorithms and sequence alignment methods, and introduce computational environments that are particularly useful in bioinformatics analyses such as R, BioPython, and Web services in bioinformatics. By the end of the class, students will have gained the ability to analyze data within the python interpreter (for example) and write well-documented, well-organized programs. (Fall)

BINF 8112. Bioinformatics Programming II. (3)

Prerequisite: BINF 8111*/ITSC 8111. This is a continuation of Bioinformatics Programming I (BINF 8111). This course is the second semester of Introduction to Bioinformatics Programming I. In this semester, students will practice and refine skills learned in the first semester. New topics introduced will include: programming as part of a team, using sequence analysis algorithms in realistic settings; writing maintainable and re-usable code; Web programming; and graphical user interface development. At the end of the semester, students will be able to evaluate and deploy computer languages, tools, and software engineering techniques in bioinformatics research. (Spring)

BINF 8151/GRAD 8151. Professional Communications. (1) This course covers: Principles and useful techniques for effective oral presentations, poster presentations, scientific writing, use of references and avoiding plagiarism. Students in the class will critique and help revise each other’s presentations and learn how to avoid common pitfalls. In addition, students will learn how to properly organize and run a meeting. Students will prepare a CV, job application letter and job talk. (Fall).

BINF 8171. Business of Biotechnology. (3) This course introduces students to the field of biotechnology and how biotech businesses are created and managed. The students should be able to define biotechnology and understand the difference between a biotech company and a pharmaceutical company. Additional concepts covered will include platform technology, biotechnology’s history, biotechnology products and development processes, current technologies used by biotech companies today, biotechnology business fundamentals, research and development within biotech companies, exit strategies, and careers in the biotech field. (Summer)

BINF 8200. Statistics for Bioinformatics. (3)

This course aims to introduce statistical methods commonly used in bioinformatics. Basic concepts from probability, stochastic processes, information theory, and other statistical methods will be introduced and illustrated by examples from molecular biology, genomics and population genetics with an outline of algorithms and software. R is introduced as the programming language for homework. (Fall)

BINF 8201. Molecular Sequence Analysis. (3)

Prerequisite: BINF 8100*/ITSC 8100 or equivalent. BINF 8100 or equivalent. Introduction to bioinformatics methods that apply to molecular sequence. Intro to biological databases online. Sequence databases, molecular sequence data formats, sequence data preparation and database submission. Local and global sequence alignment, multiple alignment, alignment scoring and alignment algorithms for protein and nucleic acids, gene finding and feature finding in sequence, models of molecular evolution, phylogenetic analysis, comparative modeling. (Fall)

BINF 8202. Computational Structural Biology. (3)

Prerequisite: BINF 8101*, 8201*/ITSC 8101, 8201 or equivalents. This course covers: (a) the fundamental concepts of structural biology (chemical building blocks, structure, superstructure, folding, etc.); (b) structural databases and software for structure visualization; (c) structure determination and quality assessment; (d) protein structure comparison and the hierarchical nature of biomacromolecular structure classification; (e) protein structure prediction and assessment; and (f) sequence- and structure-based functional site prediction. (Fall)

BINF 8203. Genomics. (3)

Prerequisite: BINF 8100*/ITSC 8100 or equivalent. This course surveys the application of high-throughput molecular biology and analytical biochemistry methods and data interpretation for those kinds of high volume biological data most commonly encountered by bioinformaticians. The relationship between significant biological questions, modern genomics technology methods, and the bioinformatics solutions that enable interpretation of complex data is emphasized. Topics include: Genome sequencing and assembly, annotation, and comparison. Genome evolution and individual variation. Function prediction. Gene ontologies. Transcription assay design, data acquisition, and data analysis. Proteomics methods. Methods for identification of molecular interactions. Metabolic databases, pathways and models. (Spring)

BINF 8204. Mathematical Systems Biology. (3)

Prerequisites: BINF 8200* and 8210*/ITSC 8200 AND 8210 or equivalents. This course introduces basic concepts, principles and common methods used in systems biology. The class emphasizes on molecular networks, models and applications, and covers the following topics: the structure of molecular networks; network motifs, their system properties and the roles they play in biological processes; complexity and robustness of molecular networks; hierarchy and modularity of molecular interaction networks; kinetic proofreading; optimal gene circuit design; the rules for gene regulation. (Spring)

BINF 8205. Computational Molecular Evolution. (3)

Pre-requisites: BINF 8201*/ITSC 8201 (Molecular Sequence Analysis) and BINF 8200*/ITSC 8200 Statistics for Bioinformatics (or permission of the instructor). This course will cover major aspects of molecular evolution and phylogenetics with an emphasis on the modeling and computational aspects of the fields. Topics will include: models of nucleotide substitution, models of amino acid and codon substitution, phylogenetic reconstruction, maximum likelihood methods, Bayesian methods, comparison of phylogenetic methods and tests on trees, neutral and adaptive evolution and simulating molecular evolution. Students will obtain an in-depth knowledge of the various models of evolutionary processes, a conceptual understanding of the methods associated with phylogenetic reconstruction and testing of those methods and develop an ability to take a data-set and address fundamental questions with respect to genome evolution. (On demand)

BINF 8210. Numerical Methods and Machine Learning in Bioinformatics. (3)

Prerequisites: Ability to program in a high-level language (Perl, Java, C#, Python, Ruby, C/C++), Calculus. This course focuses on commonly used numerical methods and machine learning techniques. Topics will include: solutions to linear systems, curve fitting, numerical differentiation and integration, PCA, SVD, ICA, SVM, PLS. Time permitting, hidden markov chains and Monte Carlo simulations will be covered as well. Students will learn both the underlying theory and how to apply the theory to solve problems. (Fall)

BINF 8211. Design and Implementation of Bioinformatics Databases. (3)

In this course students will acquire skills needed to access and utilize public biomedical data repositories, and will be expected to design, instantiate, populate, query and maintain a personal database to support research in an assigned domain of bioinformatics. The course content includes common data models and representation styles, use of open-source relational DBMS, and basic and advanced SQL. The course focuses on how data integration is achieved, including the use of standardized schemas, exchange formats and ontologies. We will examine large public biomedical data repositories such as GenBank and PDB, learn how to locate and assess the quality of data in Web-accessible databases, and look at representation, standards and access methods for such databases. (Spring)

BINF 8310. Advanced Statistics for Genomics. (3)

Prerequisite: BINF 8200*/ITSC 8200 or equivalent. The first half of this course emphasizes canonical linear statistics (t-test, ANOVA, PCA) and their non-parametric equivalents. The second half of the course emphasized Bayesian statistics and the application of Hidden Markov Models to problems in bioinformatics. Students should have fluency in a high-level programming language (PERL, Java, C# or equivalent) and will be expected in assignments to manipulate and analyze large public data sets. The course will utilize the R statistical package with the bioconductor extension. (Spring)

BINF 8311. Biophysical Modeling. (3)

This course covers: (a) an overview of mechanical force fields; (b) energy minimization; (c) dynamics simulations (molecular and coarse-grained); (d) Monte-Carlo methods; (e) systematic conformational analysis (grid searches); (f) classical representations of electrostatics (Poisson-Boltzmann, Generalized Born and Coulombic); (g) free energy decomposition schemes; and (h) hybrid quantum/classical (QM/MM) methods. (On demand)

BINF 8312. Computational Comparative Genomics. (3)

Prerequisite: BINF 8201*/ITSC 8201 or equivalent. This course introduces computational methods for comparative genomics analyses. The course covers the following topics: the architecture of prokaryotic and eukaryotic genomes; the evolutionary concept in genomics; databases and resources for comparative genomics; principles and methods for sequence analysis; evolution of genomes; comparative gene function annotation; evolution of the central metabolic pathways and regulatory networks; genomes and the protein universe; cis-regulatory binding site prediction; operon and regulon predictions in prokaryotes; regulatory network mapping and prediction. (On demand)

BINF 8313. Structure, Function, and Modeling of Nucleic Acids. (3)

Prerequisite: BINF 8100-8101 or equivalent. The course covers the following topics: atomic structure,

macromolecular structure-forming tendencies and dynamics of nucleic acids; identification of genes which code for functional nucleic acid molecules, cellular roles and metabolism of nucleic acids; 2D and 3D abstractions of nucleic acid macromolecules and methods for structural modeling and prediction; modeling of hybridization kinetics and equilibria; hybridization-based molecular biology protocols, detection methods and molecular genetic methods, and the role of modeling in designing these experiments and predicting their outcome. (On demand)

BINF 8350. Biotechnology and Genomics Laboratory. (3) Prerequisite: none. This course teaches basic wet-lab techniques commonly used in biotechnology to generate genomics data. Lectures will cover methods for sample isolation, cell disruption, nucleic acid and protein purification, nucleic acid amplification, protein isolation and characterization, molecular labeling methods and commonly used platforms for characterizing genome-wide molecular profiles. In particular we will discuss and learn to perform: tissue culture and LCM isolation of cells, DNA sequencing methods, DNA fingerprinting methods, RT-qPCR and microarrays of cDNA, 1D and 2D gels for protein separation, protein activity assays, and proteomics platforms. Lectures will describe emerging methodologies and platforms, and will discuss the ways in which the wet-lab techniques inform the design and use of bioinformatics tools, and how the tools carry out the processing and filtering that leads to reliable data. The course will also discuss the commercial products beginning to emerge from genomics platforms. (Spring)

BINF 8380. Programming III. (3)

Prerequisite: BINF 8112 or equivalent. This course emphasizes implementation of bioinformatics algorithms in the context of parallel processing. Topics covered depend on instructor expertise and student interest but may include development of multi-threaded applications, developing for multi-core processors and utilization of large clusters and “cloud” supercomputers. Students will be expected to complete a significant independent project (Fall).

BINF 8600. Seminar. (1)

Prerequisites: Admission to graduate standing in Bioinformatics. Departmental seminar. Weekly seminars will be given by bioinformatics researchers from within the university and across the world. (Fall, Spring)

BINF 8601. Journal Club. (1)

Prerequisites: Admission to graduate standing in Bioinformatics. Each week, a student in the class is assigned to choose and present a paper from the primary bioinformatics literature. (Fall, Spring)

BINF 8911 Research Rotation I (2), BINF 8912 Research Rotation II (2).

Faculty supervised research experience in bioinformatics to supplement regular course offerings.