BIOMEDICAL SCIENCES CORE COURSE

IDPT 7811, 7812, 7813, 7814 & 7815

This is an interdiciplinary course required for first year graduate students enrolled in basic science programs. The objective of the course is to provide basic science information and intoduce the skills required for a successful research career in all disciplines of modern biomedical sciences.  Topics cover the fundamentals of biochemistry, molecular biology, cell biology, developmental biology, molecular genetics and biomolecular structure.

OTHER REQUIRED COURSES

CPBS 7605 Ethics in Bioinformatics
Credits: 1 semester Hours.

Status: required.

Previous Course #:  BIOI 7605
Discussion of professional conduct, social implications fo research and questions raised by biomedical research with an emphasis on topics relevent to computational biologists. Active student participation in required.
 

CPBS 7606 Statistics for the Basic Sciences
Credits: 3 semester Hours.

Status: required.

Previous Course #:  BIOI 7606
This course provides an overview of fundamental concepts in statistics such as hypothesis testing and estimation and it provides an overview of statistical methods that apply to many areas of science.

CBPS-7711 Bioinformatics I.
Credits: 4 semester hours.
Prerequisite: permission of instructor.
Status: required.

Previous Course #:  BIOI 7711
What is bioinformatics, and why study it? How is large scale molecular biology data generated, how can researchers gain access to it, and what is the quality of the data? Topics discussed include nucleotide sequence data such as: genomic sequencing, expressed sequence tags, gene expression, transcription factor binding sites and single nucleotide polymorphisms.
Metadata: summary and reference systems, finding new types of data online, likely growth areas. Private and future data sources. Computational representations of molecular biological data, data storage techniques: databases (flat, relational and object oriented), and controlled vocabularies.
General data retrieval techniques: indices, Boolean search, fuzzy search and neighboring. Biological data types and their special requirements: sequences, macromolecular structures, chemical compounds, genetic variability, and connections to clinical data. Representations of patterns and relationships: alignments, regular expressions, hierarchies, and graphical models (including Markov chains and Bayes nets).
Visualization: methods for presenting large quantities of biological data, particularly sequence viewers, 3D structure viewers, anatomical visualization, and database-driven web sites.
Interoperability: the challenges of data exchange and integration, including ontologies, interchange languages and standardization efforts. XML, UMLS, CORBA and OMG/Life Sciences.
Inference problems and techniques for molecular biology with an overview of key inference problems in biology, including: homology identification, genomic sequence annotation, protein structure prediction, protein function prediction, gene expression characterization, network identification, and drug discovery.
 
CBPS-7712 Bioinformatics II.
Credits: 4 semester hours.
Prerequisite: CBPS 7711.
Status: required.

Previous Course #:  BIOI 7712
This course continues to define and discuss inference problems and techniques for molecular biology. Overview of key inference problems in biology: homology identification, genomic sequence annotation, protein structure prediction, protein function prediction, gene expression characterization, network identification, and drug discovery. Machine learning: neural networks, genetic algorithms, simulated annealing. Evaluation of prediction methods: parametric tests, cross-validation and empirical significance testing. Sequence alignment methods: dot plots, dynamic programming, hidden Markov models. Current alignment methods: PSI-BLAST, Needleman–Wunsch, Smith–Waterman. Protein structure predictions: secondary structure, fold recognition, new fold methods. Computer simulation methods: molecular dynamics, Monte Carlo.

Additionally, this course addresses recent developments in bioinformatics and focus on advanced issues in specific areas including (but not limited to) information extraction from biomedical literature, inference of biochemical networks from high–throughput data, and prediction of protein function.
 

CBPS-8990 Doctoral Thesis.
Credits: Minimum 30 semester hours.
Prerequisite: successful completion of required bioinformatics courses.
Status: required

Previous Course #:  BIOI 8990
Doctoral study for the Ph.D. degree by students in the AHS/Bioinformatics program only.