SYBB Tracks and Curriculum

Centrifugation of DNA samples prior to high-throughput genotyping and sequencing at the Cancer Genomics Research Laboratory, part of the National Cancer Institute's Division of Cancer Epidemiology and Genetics (DCEG).


Case Western Reserve University's (CWRU) graduate program in Systems Biology and Bioinformatics (SYBB) has two tracks:

Translational Bioinformatics

The SYBB track in Translational Bioinformatics poises students to work at the interface of applied ‘omics research and clinical medicine. From integrating genomic and functional genomic data into electronic medical records, to developing meta-analysis tools for communicating genomic risk to patients to utilizing this data in personalized medicine. Students trained in the Translational Bioinformatics track work to integrate bioinformatics tools and technologies into clinical workflows. Graduates of this training track will find ample opportunities within industry and, as genomics enters the clinical arena, within hospitals, as well.

Molecular and Computational Biology

The SYBB track in Molecular and Computational Biology embraces the pursuit of basic science research, employing the application and development of computational approaches to address difficult questions derived from today’s “Big data” derived from ‘omics approaches. This track equips students in the acquisition of experimental data utilizing approaches including proteomics, metabolomics, genomics and structural biology and extends this work with interpretation provided by computational analysis. Graduates of this training track will find ample opportunities within the pharmaceutical industry, contract research organizations as well as more traditional academic career paths.

Students can choose either track for both the MS and PhD programs.

Core Courses

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 (, 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.

*Registration each semester in SYBB 501 is required for all students in the SYBB graduate program.

Elective Courses

Genes and Proteins Courses

Course Number Course Description Credits
CLBY 555/BIOC 555/PATH 555
Principles of Genetic Epidemiology 3
PHOL/CHEM/PHRM/BIOC/NEUR 475 Protein Biophysics 3
PHOL 456 Conversations on Protein Structure and Function 2
PHOL 480 Physiology of Organ Systems 4
CBIO 453 Cell Biology I 4
CBIO 455 Molecular Biology I 4
BIOC 420 Current Topics in Cancer 3
BIOC 528 Contemporary Approaches to Drug Discovery 3
BETH 412 Ethical Issues in Genetics/Genomics 3


Bioinformatics and Computational Biology Courses

Course Number Course Description Credits
PQHS 415 Statistical Computing and Data Analytics 3
BIOL/EECS 419 Applied Probability and Stochastic Processes for Biology 3
PHRM/PHOL/CHEM/BIOC 430 Advanced Methods in Structural Biology 1-6
EECS 458 Introduction to Bioinformatics 3
MATH 378/BIOL 478/EBME 478
Computational Neuroscience 3
GENE 508 Bioinformatics and Computational Genomics 3
BIOC 430 Advanced Methods in Structural Biology 1-6


Quantitative Analysis and Modeling

Course Number Course Description Credits
PQHS 431 Statistical Methods I 3
PQHS 432 Statistical Methods II 3
PQHS 480 Introduction to Statistical Theory 3
PQHS 481 Theoretical Statistics I 3
PQHS 482 Theoretical Statistics II 3
PQHS 460 Introduction to Health Services Research 3
PQHS 515 Secondary Analysis of Large Health Care Data Bases 3
MPHP 405 Statistical Methods in Public Health 3
EECS 435 Data Mining 3
EECS 440 Machine Learning 3
MATH 441 Mathematical Modeling 3
EBME 300/MATH 449 Dynamics of Biological Systems: A Quantitative Introduction to Biology 3
MIDS 301 Introduction to Information: A Systems and Design Approach 3

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