This course provides participants with an introduction to systems biology along with insights into the methods used to generate large datasets using ‘omics’ technologies, and the bioinformatics tools required for analysis. Despite the power of genomic, transcriptomic, and proteomic technologies, most researchers do not have the necessary computational expertise to analyze or interpret high content datasets unaided. Furthermore, many investigators are unaware of the unique challenges involved in experimental design as it pertains to systems-wide analyses. This new course addresses the above challenges through five modules:

• Module 1 provides an overview of basic concepts related to systems biology, the impact of technological advances and examples of experiments enabled with ‘omics’ approaches.
• Module 2 discusses experimental design challenges of ‘omics’ analyses, including a discussion of working with high dimensional datasets. The use of ‘omics’ analyses for biomarker discovery and application in medicine, along with statistical considerations required in biomarker science are also discussed.
• Module 3 provides practical information on the execution of the most common types of ‘omics’ experiments. Experimental goals, sample types and availability, analytes and analytical strategies are discussed. Basic strategies for the use of publicly available data are also presented.
• Module 4 discusses mass spectrometry-based techniques for generating large datasets and the unique analytical approaches required for data manipulation and interpretation. Practical information on the different types of MS-based protein analysis is presented, including quantitative approaches, along with some newer applications of MS.
• Module 5 provides an introduction to data analysis specifically tailored to high dimensional data. A brief introduction to artificial intelligence and machine learning approaches is provided along with different kinds of clustering methods used to classify samples based on expression.

A tutorial in the use of the R software for data analysis and statistics is also included.

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