Courses at Mount Sinai School of Medicine
We developed two new graduate level core courses (Systems Biology: Biomedical Modeling and Systems Biomedicine) focused on integration across biomedical disciplines. The underlying philosophy of both courses is that quantitative reasoning serves as the glue to integrate across disciplines (e.g. biochemistry, cell and molecular biology, pharmacology and physiology) as well as across scales of biological organization.
- Core Courses
Systems Biology: Biomedical Modeling [Course Description, Syllabus and Lectures]
The course takes a case-based approach to teach current mathematical modeling techniques to graduate students. The course is aimed at students with no prior computational modeling experience. Students are taught how to develop and analyze mathematical models and how to use computation to generate predictions that may be experimentally tested. The course has four sections to cover different modeling approaches that are currently used in biomedical research 1) graph theory and network analysis, 2) statistical models and principal component analysis, 3) ordinary differential equation & partial differential equation-based models and 4) stochastic & hybrid models. MatLab is the major modeling software used for the course.
Systems Biomedicine: Molecules, Cells and Networks [Course Description, Syllabus and Lectures]
The Systems Biomedicine course is sponsored by the Center and is a core course in the Systems Biology of Disease and Therapeutics Multidisciplinary Training Area of the Mount Sinai School of Medicine's PhD program. The course introduces core biochemical, cell biological and molecular mechanisms together with basic bioinformatics and systems biology concepts and applications in the context of human biomedical research. The approach is on 'top-down', beginning with a pathophysiological condition studied from a clinical perspective and moving towards explication of the molecular and metabolic logic, regulatory circuits and cell and tissue specific properties that distinguish the disease from normal state. The course provides students with an appreciation of the complexity of biological systems across scales, introduces them to multiscale modeling and gives insights into pathophysiology as a basis for scientific inquiry and development of new therapeutic strategies.
[Schedule Fall 2011] [Poster Fall 2011][Schedule Fall 2012] [Poster Fall 2012]
CMCs (customized mini-courses)
Cell Signaling Networks and Systems Pharmacology [Course Description]
This mini-course uses a discussion-forum-type format. Students read and discuss a set of papers with the preceptor and participate in a web-based discussion forum. The focus of this course is how networks can be analyzed for combinatorial drug targets and distal signal propagation leading to adverse effects.
Computer Programming in Systems Biology and Bioinformatics [Course Description and Syllabus]
This customized mini-course covers computer programming methodologies applied to processing data and analysis of data in the broad fields of Systems Biology and Bioinformatics. Students are required to complete small programming assignments throughout the course and assigned a final project.
Data Mining and Network Analysis [Course Description and Syllabus]
This mini-course is sponsored by the Center and is offered as an elective during the Fall semesters. In this course, methods to analyze the topology of biological regulatory networks are discussed. Students are introduced to several machine learning approaches and how they can be applied to study biological molecular networks. During the course, students study papers that combine computational predictions with experimental validation; and use software tools to analyze proteomics and genomics experimental expression data.
Pharmacogenomics [Course Description, Syllabus and Lectures]
This course taught jointly by the faculty of Genetics and Genomic Sciences and Pharmacology and Systems Therapeutics, as well as invited faculty from other departments introduces students to the tools, methodologies, and goals of genomic medicine. We cover advances in the fields of pharmacogenomics (i.e., drug effects based on polymorphisms in the human genome).
Short Course in Computational Systems Biology In the Spring 2012, Neil Clark PhD (Postdoctoral Fellow in the Ma'ayan Laboratory) delivered a two-day short course on the following topics at the University of Sao Paulo, Brazil:
- Mathematics: Linear Algebra, Calculus and Ordinary Differential Equations, Graph Theory and Network Biology
- Statistics: Fundamental Principles, Descriptive Statistics, Hypothesis Testing
- High-throughput Data Analysis: Principal Component Analysis, Gene Set Enrichment Analysis, Network Inference
- Computing and Tools: The R Environment, Expression2Kinases
Cold Spring Harbor Laboratory Computational Cell Biology Course
SBCNY Investigators teach in the annual Cold Spring Harbor Laboratory Computational Cell Biology course (2008, 2009, 2010, 2011, 2012). The three week course includes lectures by visiting faculty on various topics in Computational Biology.
Applied Mathematics for Biologists [Course Description, Syllabus and Lectures]
The purpose of this course is to present an extensive, integrated treatment of applied mathematics useful to science students who wish to include mathematical modeling and simulation in their future research. The topics that are covered are: Linear Algebra with Applications to Data Analysis and Modeling; Complex Analysis; Fourier Methods; Probabilistic & Stochastic Modeling; Dynamical Systems and Applications to Chemical Kinetics with Applications to Biochemical Systems; Dimension Reduction & Low Dimensional Systems.
Cold Spring Harbor Asia Summer School Computational & Cognitive Neurobiology
Upinder Bhalla PhD (SBCNY Investigator) gave a lecture during this summer course. This summer course provided an introduction to theoretical concepts and computational methods in neuroscience.