SYBB Tracks and Curriculum

The purpose of this journal club is to provide an opportunity for students to critically discuss a wide variety of informatics and systems biology topics and to present their works in progress. A wide range of informatics and systems theory approaches to conducting biomedical research will be accomplished through the guided selection of articles to be discussed during the club. Potential articles will be chosen from scientific journals including: Nature, Science, BMC Bioinformatics, BMC Systems Biology, the Journal of Bioinformatics and Computational Biology, and the Journal for Biomedical Informatics. During journal presentations, trainees will be expected to lead a discussion of the article that leads to the critical evaluation of the merit of the article and its implication for biomedical informatics and systems biology. The Journal Club will also provide a forum for trainees to present proposed, on-going, and completed research. Trainees will attend and participate in the Journal Club throughout their tenure in the program. The Journal Club will meet twice a month and each trainee will be required to present one journal article and one research in progress presentation yearly. The Journal Club will also include sessions where issues related to the responsible conduct of research are reviewed and extended.

SYBB 412 is a 3 credit-course that will introduce students to bioinformatics analysis and basic programming. This course is designed for those with little or no prior programming experience. However, advanced programmers can still learn bioinformatics pipelines and software packages to conduct research. Students will gain hands-on experience working with bioinformatics software, R packages and functions designed for bioinformatics applications. Programming for Bioinformatics course mainly focuses on R (rproject.org), and introduces students to basic programming in R, what packages are available, and teaches an introductory hands-on experience working with R by walking through the students in analyzing large -omics datasets. At the end of the class, the students are assessed with a small-scale project, where they analyze a publicly available dataset and produce a short report. This is an active learning class where adaptive learning and active learning teaching practices are used. Adaptive learning provide personalized learning, where efficient, effective, and customized learning paths to engage each student is offered. Recommended Preparation: BIOL 326 (Genetics) or equivalent Prereq: (SYBB 411A and Graduate Standing) or Requisites Not Met Permission.

Description of omic data (biological sequences, gene expression, protein-protein interactions, protein-DNA interactions, protein expression, metabolomics, biological ontologies), regulatory network inference, topology of regulatory networks, computational inference of protein-protein interactions, protein interaction databases, topology of protein interaction networks, module and protein complex discovery, network alignment and mining, computational models for network evolution, network-based functional inference, metabolic pathway databases, topology of metabolic pathways, flux models for analysis of metabolic networks, network integration, inference of domain-domain interactions, signaling pathway inference from protein interaction networks, network models and algorithms for disease gene identification, identification of dysregulated subnetworks network-based disease classification.

This course is designed for graduate students across the university who wish to acquire a better understanding of fundamental concepts of proteomics and related bioinformatics as well as hands-on experience with techniques used in current proteomics. Lectures will cover protein/peptide separation techniques, protein mass spectrometry, and biological applications which include quantitative proteomics, protein modification proteomics, interaction proteomics, structural genomics and structural proteomics. Also, it will cover experimental design, basic statistical concept and issues related to high-dimensional data from high-throughput technologies. Laboratory portion will involve practice on the separation of proteins by two-dimensional gel electrophoresis, molecular weight measurement of proteins by mass spectrometry, peptide structural characterization by tandem mass spectrometry. It will also include bioinformatics tools for protein identification and protein-protein interaction networks. The instructors' research topics will also be discussed. Recommended preparation: CBIO 453, CBIO 455, and PQHS 431.