SBCNY Fellow Spotlights
, currently enrolled in the PhD program at Icahn School of Medicine at Mount Sinai, is the first author of a study published in the journal PNAS, titled "ERK Regulation of Phosphodiesterase 4 Enhances Dopamine-stimulated AMPA Receptor Membrane Insertion".
, currently enrolled in the Accelerated Physician-Scientist Program (BS/MD) at Rensselaer Polytechnic Institute and Albany Medical College, is a recipient of a 2011 Davidson Scholarship. Jay, who conducted research under the mentorship of SBCNY Investigator Avi Ma'ayan, was the winner in the Engineering category at the Westchester Science and Engineering Fair (WESEF). His project was selected as one of the top eight overall projects which earned him a trip to the Intel International Science and Engineering Fair (ISEF).
] [Davidson Fellow Bio
] [Putnam Examiner Article
, City College of New York, Biomedical Engineering major, received the 2011 Barry M. Goldwater Scholarship and was selected as the 2011-2012 Valedictorian of the Grove School of Engineering. Johnson was a summer 2009 SBCNY Fellow who worked under the mentorship of Kevin Costa (MSSM, Department of Medicine) on a project titled: Post-Infarction Left Ventricular Remodeling and the Law of Laplace.
] [CCNY News
from Queens College (Class of 2010), was one of the recipients of the 2010 Jonas E. Salk Scholarship. She is currently a medical student at the Albert Einstein College of Medicine. Under the mentorship of SBCNY Investigator Eric Sobie during the summer of 2009, Sara's project was titled: Computational modeling of 'leaky' ryanodine receptors and triggered arrhythmias in heart cells.
] [2010 Salk Scholars]
Graduate Education in Systems Biology
Courses on Coursera
Ravi Iyengar, Avi Ma'ayan and Eric Sobie are offering three free systems biology massive open online courses (MOOCs) on Coursera. The courses, Introduction to Systems Biology (Iyengar), Network Analysis in Systems Biology (Ma’ayan), and Dynamical Modeling Methods for Systems Biology (Sobie) focus on training students to use computation to convert the information in large and small data sets in biomedicine into conceptual knowledge.
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.
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).
Extramural Teaching Activities
- 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.