Professional and student expertise in biotechnology and biomedical data science. Learn methodologies in systems biology including: bioinformatics, dynamic modeling, genomics, network and statistical modeling, proteomics, omics technologies, and single-cell research resources.
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The Systems Biology specialization covers the concepts and methods used in systems-level analysis of biomedical systems.
In the final project, students will apply the methods they learned in five courses of the specialization to a research project.
This course will introduce students to modern systems biology, which focuses on mammalian cells, their components, and their functions. Biology is moving from the molecular to the modular. As our knowledge of our genome and gene expression increases, and the repertoire of molecules (proteins, lipids, ions) involved in cellular processes develops, we will need to understand how these molecules interact with each other to form modules that act as separate functional states. These systems form the basis for key subcellular processes such as signal transduction, transcription, movement, and electrical excitation. These processes come together to exhibit cellular behaviors such as secretion, proliferation, and action potentials.
What are the properties of such subcellular and cellular systems? What are the mechanisms by which new behaviors in the systems evolve? What types of experiments encourage systems-level thinking? Why do we need computation and simulations to understand these systems?
The course will develop several lines of thought to answer the above questions. Two central lines of thought are: the design, execution, and interpretation of multivariate experiments that generate large data sets; quantitative thinking, modeling, and simulations. We will discuss examples to demonstrate “how” cellular-level functions evolve and “why” mechanistic knowledge allows us to predict cellular behaviors that can lead to disease states and drug responses.
Learn about the technologies driving experimentation in systems biology, with an emphasis on RNA sequencing, mass-based proteomics, flow/mass cytometry, and live cell imaging.
A key driver of the field of systems biology is the technology that allows us to gain ever greater insight into how cells respond to experimental perturbations. This allows us to build more detailed quantitative models of cellular function, which can provide valuable insights into applications ranging from biotechnology to human disease. This course provides a broad overview of a variety of modern experimental techniques in systems biology, with an emphasis on obtaining the quantitative data required for computational modeling purposes in later analyses.
We deal with four main technologies:
These techniques are frequently used in systems biology and are performed over a wide genomic range down to single molecule coverage, from millions of cells to a single cell, and between single time points and high-frequency measured trajectories. We present not only the theoretical background on which these technologies operate, but also enter wet labs to demonstrate how these techniques are performed in practice, and how the resulting 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 for processing raw data from genome-wide mRNA expression experiments (microarray and RNA sequencing) including data normalization, clustering, dimensionality reduction, differential expression, enrichment analysis, and network construction.
The course includes practical documents on using several bioinformatic tools and setting up data analysis pipelines, as well as mathematical processes behind the methods applied to these tools and workflows. The course is mainly suitable for advanced undergraduate and graduate students in fields such as biology, statistics, physics, chemistry, computer science, biomedical engineering, and electrical engineering.
The course should be useful for wet and dry lab researchers who deal with large data sets in their research. It introduces software tools developed by the Maayan Lab ( http://labs.icahn.mssm.edu/maayanlab/ ) at the Icahn School of Medicine in Mount Sinai, New York, but also other freely available data analysis and visualization tools. The overall goal of the course is to enable students to use the methods presented in it to analyze their own data for personal projects. For students who are not working in the field, the course introduces the research challenges in the field of systems biology and systems pharmacology.
An introduction to dynamic modeling methods used in modern systems biology research. We take a case-based approach to teach modern mathematical modeling techniques. The course is suitable for advanced undergraduate and beginning graduate students.
The lectures provide a biological background and explain the development of classical mathematical models and more recent representations of biological processes. The course will be useful for students who plan to use experimental techniques in their laboratory approach and to apply computational modeling as a tool for understanding experiments in depth. The course will also be valuable as an introduction for students who plan to conduct original research in modeling biological systems.
This course focuses on dynamic modeling techniques used in systems biology research. These techniques are based on biological mechanisms, and simulations with these models produce predictions that can be tested experimentally. These testable predictions often provide new insights into biological processes.
The approaches studied here can be divided into the following categories:
This course will focus on developing integrative skills through guided reading and analysis of current primary literature, to enable the student to develop the final project as the overall final examination for specialization in systems biology.
Please note: To take this course, the following courses must be completed in the Mark track: Introduction to Systems Biology, Network Analysis in Systems Biology, Dynamic Modeling Methods for Systems Biology, Experimental Methods in Systems Biology, and Integrated Analysis in Systems Biology.
