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Learn about the technologies underlying experimentation used in systems biology, with particular focus on RNA sequencing, mass spec-based proteomics, flow/mass cytometry and live-cell imaging.

A key driver of the systems biology field is the technology allowing us to delve deeper and wider into how cells respond to experimental perturbations. This in turns allows us to build more detailed quantitative models of cellular function, which can give important insight into applications ranging from biotechnology to human disease. This course gives a broad overview of a variety of current experimental techniques used in modern systems biology, with focus on obtaining the quantitative data needed for computational modeling purposes in downstream analyses. We dive deeply into four technologies in particular, mRNA sequencing, mass spectrometry-based proteomics, flow/mass cytometry, and live-cell imaging. These techniques are often used in systems biology and range from genome-wide coverage to single molecule coverage, millions of cells to single cells, and single time points to frequently sampled time courses. We present not only the theoretical background upon which these technologies work, but also enter real wet lab environments to provide instruction on how these techniques are performed in practice, and how resultant data are analyzed for quality and content.

This course introduces data analysis methods used in systems biology, bioinformatics, and systems pharmacology research. The course covers methods to process raw data from genome-wide mRNA expression studies (microarrays and RNA-seq) including data normalization, clustering, dimensionality reduction, differential expression, enrichment analysis, and network construction. The course contains practical tutorials for using several bioinformatics tools and setting up data analysis pipelines, also covering the mathematics behind the methods applied by these tools and workflows. The course is mostly appropriate for beginning graduate students and advanced undergraduates majoring in fields such as biology, statistics, physics, chemistry, computer science, biomedical and electrical engineering. The course should be useful for wet- and dry-lab researchers who encounter large datasets in their own research. The course presents software tools developed by the Ma鈥檃yan Laboratory (http://labs.icahn.mssm.edu/maayanlab/) from the Icahn School of Medicine at Mount Sinai in New York City, but also other freely available data analysis and visualization tools. The overarching goal of the course is to enable students to utilize the methods presented in this course for analyzing their own data for their own projects. For those students that do not work in the field, the course introduces research challenges faced in the fields of computational systems biology and systems pharmacology.

An introduction to dynamical modeling techniques used in contemporary Systems Biology research.

We take a case-based approach to teach contemporary mathematical modeling techniques. The course is appropriate for advanced undergraduates and beginning graduate students. Lectures provide biological background and describe the development of both classical mathematical models and more recent representations of biological processes. The course will be useful for students who plan to use experimental techniques as their approach in the laboratory and employ computational modeling as a tool to draw deeper understanding of experiments. The course should also be valuable as an introductory overview for students planning to conduct original research in modeling biological systems. This course focuses on dynamical modeling techniques used in Systems Biology research. These techniques are based on biological mechanisms, and simulations with these models generate predictions that can subsequently be tested experimentally. These testable predictions frequently provide novel insight into biological processes. The approaches taught here can be grouped into the following categories: 1) ordinary differential equation-based models, 2) partial differential equation-based models, and 3) stochastic models.

This course will focus on developing integrative skills through directed reading and analysis of the current primary literature to enable the student to develop the capstone project as the overall final exam for the specialization in systems biology.

NOTE: In order to take this course you should have taken and complete the following courses in the Signature Track: Introduction to Systems Biology, Network Analysis in Systems Biology, Dynamical Modeling Methods for Systems Biology, Experimental Methods in SB and Integrated Analysis In Systems Biology