Curriculum

Our curriculum provides students with comprehensive training in genetics, genomics, and computational biology. Our curriculum begins with flexible course options that give first year students the opportunity to gain knowledge and skills along their scientific interests. This includes the opportunity to take advanced elective courses in numerous areas including Biomedical informatics, Biostatistics, Cancer Biology, Development, Immunology, Microbiology, Neuroscience, Pharmacology and Therapeutics Design, and Regeneration & Stem Cells. During their first year, students simultaneously do short rotation projects in 2-3 laboratories to find a host laboratory for their thesis. After choosing a dissertation lab at the end of Year 1, in Year 2, students take additional elective coursework, participate in our weekly seminars and journal clubs, propose a thesis project, and assemble a thesis committee of 4-5 faculty members. After completing a thesis proposal exam in Year 2, in Year 3 and beyond, students focus on their research, while continuing to participate in our weekly seminars and journal clubs. During these years, students meeting with their thesis committees regularly to get feedback and advice. To complete the program, each student must complete a thesis exam.

Requirements:

  • Coursework in biomedical science, genetics, and data science
  • Participation in our weekly seminars and journal clubs
  • 2-3 8-10 week laboratory rotations
  • Thesis proposal
  • Thesis defense

Course Requirements:

  • Introduction to Biomedical Science (6 credits). Two have the option of two tracks:
    • Descriptive track – BSR 1012: Biomedical Sciences I and BSR 1013: Biomedical Sciences II
    • Quantitative track – BSR 1800: Systems Biomedicine and BSR 1802: Quantitative Graduate Physiology
  • Introduction to statistics (3 credits): Students can choose from several classes including
    • BIO 6400: Biostatistics for Biomedical Research
    • BSR 1026: Applied Biostatistics for Biomedical Research
    • MPH 0300: Introduction to Biostatistics.
  • Elective courses in genetics, genomics, or data science (3 credits). Numerous options are available including the courses listed below, as well as courses offered by the CUNY Graduate Center, CUNY Grove School of Engineering, and Cooper Union.
    • Genetics and genomics
      • BSR 2301: Embryos Genes and Development
      • BSR 2400: Translational Genomics
      • BSR 2401: Intro to human genome sequencing
      • BSR 6203: Practical Cancer Genomics
      • BSR 6302: Systems & Developmental Biology & Birth Defects
      • BSR 6402: Practical Analysis of a Personal Genome
      • CLR 0007: Methods in Clinical Research: Genetics/Genomics
      • CLR 0801: Fundamentals of Molecular Biology and Genetics
      • CLR 0810: Genetic Epidemiology
      • MGC 1400: Medical Genetics
    • Data science
      • BIO 6300: Introduction to R
      • BIO 8200: Analysis of Categorical Data
      • BIO 8700: Theory of Linear & Generalized Linear Models
      • BMI 1007: Computer Systems - Python
      • BMI 2002: Biomedical Software Engineering I
      • BMI 2005: Introduction to algorithms
      • BMI 3001: Data analytics and mining
      • BMI 3002: Machine Learning
      • BSR 1800: MATLAB Bootmap
      • BSR 1803: Systems Biology - Biomedical Modeling
      • MPH 0812: Applied Linear Models
      • MPH 0821: Analysis of Longitudinal Data
      • MPH 0822: Applied Linear Models II
  • Additional elective coursework (3 credits) in any area of biomedical or data science. Numerous options are available, including the courses listed above.
  • Research ethics (1 credit). All students must take these two classes:
    • BSR 1021: Responsible Conduct of Research
    • BSR 1022: Rigor and Reproducibility

Typical Course Schedule

Year 1- Fall

Course Number Course Title Credits
BSR 1012 or BSR 1800 Biomedical Sciences I or Systems Biomedicine 3
  Elective course in genetics, genomics, or data science 3
BMI 1007, BSR 1800, or BIO 6300 Python, MATLAB Bootcamp, or Introduction to R 1
BSR 1021 Responsible Conduct of Research 0.5
BSR 1006 Lab Rotation 4

Year 1- Spring

Course Number Course Title Credits
BSR 1013 or BSR 1802 Biomedical Sciences II or Quantitative Graduate Physiology 3
BSR 1026 Applied Biostatistics for Biomedical Research 3
BSR1022 Rigor and Reproducibility 0.5
BSR 1007 Lab Rotation 4

Year 2- Fall

Course Number Course Title Credits
  Elective course 3
BSR 4401 Journal Club in Genetics and Data Science 1
BSR 5401 Seminars in Genetics and Data Science 1
BSR 5402 Works in Progress: Genetics and Data Science 1
BSR 8000 Independent Research 10

 Year 2- Spring

Course Number Course Title Credits
BSR 4401 Journal Club in Genetics and Data Science 1
BSR 5401 Seminars in Genetics and Data Science 1
BSR 5402 Works in Progress: Genetics and Data Science 1
BSR 8000 Independent Research 10

Year 3 and Beyond

Course Number Course Title Credits
BSR 9000 Doctoral Dissertation Research 8
BSR 4401 Journal Club in Genetics and Data Science 1
BSR 5401 Seminars in Genetics and Data Science 1
BSR 5402 Works in Progress: Genetics and Data Science 1

Journal clubs and seminars

Beginning in their second year, students are required to participate in GDS’ weekly seminar, work in progress meeting, and journal club.

In addition, students have opportunities to participate in many additional seminars and journal clubs organized by specific institutes and centers at Sinai. Below are some of the most popular seminars among our students.

Laboratory Rotations

Students complete 2-3 8-10 week laboratory rotations, working an average of 20 hours/week in the laboratory. The aim of this rotations is to give students opportunities to explore their scientific interests, gain new skills and knowledge, and explore potential host labs for their dissertation.

Thesis Proposal Exam

Students are required to pass a Thesis Proposal exam in their second year. The exam consists of a written proposal in the style of a 6-page NIH F30 proposal, a 20-minute oral presentation, and an oral exam. The exam is administered by the student’s research advisor and 2-3 additional faculty with relevant knowledge on their thesis topic.

Thesis Committee Meetings

Starting their second year, students meet with their thesis committee 1-2 times per year to solicit feedback and advice.

Thesis defense

Students are required to give a public presentation of their thesis, and must also pass a closed oral defense exam immediately following their thesis presentation. The exam is administrated by the student’s thesis committee, consisting of their advisor and 3-4 additional faculty.

More Information about Some of Our Courses

Course Directors:
Anthony Costa, PhD
Assistant Professor
Director of the Neurosurgery Simulation Core

Course Description:
This course provides an introduction to computer systems and scientific computing environments to enable effective use of computational and data resources. The course assumes no prior computing experience and is broken into 3 component modules. These are:

  1. UNIX/Linux fundamentals
  2. Computer system architectures and applications in scientific computing
  3. Introduction to scientific programming in Python 3

Course Directors:
Anthony Costa, PhD
Assistant Professor
Director of the Neurosurgery Simulation Core

Course Description:
This course is a computer-science intensive program intended as a survey of algorithms – that is, computational methods used to solve appropriately defined problems, and their implementation on modern scientific computing hardware. Core to any modern discussion of algorithms is competency in one or more object-oriented programming languages, in addition to a deep dive into data structures, without which the discussion of practical algorithm implementation is not useful. We complete the course with a survey of mathematical optimization techniques typically not encountered in an ordinary course on algorithms, but which form the mathematical basis for many problems in computational biology, biochemistry, genomics, and data science. In this course, we use Python 3 as the core programming tool. The class is structured as 1.5 hours of lecture each week with a 1.5 hour lab component, for 12 total weeks. The course can be logically broken up into 3 modular topics, with the bulk of the time discussing fundamental algorithms and data structures; however, each module builds on the previous and therefore the course should be taken as a whole.

Course Directors:
Gaurav Pandey, PhD
Assistant Professor, Department of Genetics and Genomic Sciences

Course Description:
Data are becoming all the more important in today’s world to discover reliable understanding of complex processes and actionable hypothesis on ways to productively perturb these processes. Biology and medicine have been witnessing a data revolution driven by rapid progress in a variety of biotechnologies and an increased emphasis on personalized medicine. This course is designed to train students, staff and faculty in commonly used methods to organize, mine and learn from data sets, especially those that are complex and large (big data). These methods include basic data concepts, classification, clustering, network inference and analysis and outlier/anomaly detection. Note that the teaching of these methods will focus on the abstract and mathematical concepts involved in learning from general data sets. Relatable examples of the application of these methods to biomedical datasets will also be provided. Students in teams will also be expected to conceive a relevant project at the beginning of the course and present their approach and results at the end. The overall goal of this course is to teach the attendees how to apply the methods above to complex biomedical data sets to extract actionable knowledge that may not be obtainable from other methods.

Course directors:
Andrew Sharp
Stuart Scott 

Course description:
Genetics and genomics are rapidly evolving fields. This course aims to provide an overview of the state-of-the-art in these fields with a special focus on translation to human disease. As such the course addresses technical aspects of genetics and genomics research as well as their practical applications. Some of the topics include the analysis the human genome in psychiatric disease and cancer, population genomics and pharmacogenomics.

Course directors:
Eimear Kenny
Sabrina Suckiel 

Course description:
Students in this course will analyze a human genome sequence starting with raw sequence reads through identifying a list of sequence variants. Using public databases, literature and other resources students will formulate hypotheses about the phenotypic significance of these variants. This is a hands-on, laboratory course in which students will choose to analyze either their own genome or a reference genome after lectures and counseling to make the consequences of personal genome analysis clear.