Curriculum

Description

This is a unique graduate course, which addresses fundamental data structures and algorithms that are being applied in modern computational biology. The students will focus on algorithmic problem solving and learn several algorithmic techniques. Students will also learn how to design and apply data structures and algorithms to state-of-the-art biology problems such as large-scale genome sequence analysis.

The course is scheduled for the Fall semester and meets on Tuesdays and Thursdays from 9:30 am to 11:00 am.

Course Director: Trine Krogh-Madsen, Ph.D.

Objective

Upon completion of this course, students will be able to formulate, implement, and analyze different types of mathematical models used to simulate a variety of biological systems.

Description

This course covers fundamental concepts and techniques used for mathematical modeling of biological systems. Course topics include theoretical analysis of nonlinear ordinary differential equations as well as numerical simulation of different classes of models, including stochastic and spatial models. Students will also learn approaches for model development, model validation, parameter identification, and determining parameter sensitivity. Techniques and methods covered in lectures will be implemented and demonstrated in computer labs. Models demonstrated in class will originate from various biological systems, including electrophysiology, gene networks, and immunology. 

The course consists of twice-weekly lectures and weekly computer labs, held during the Fall semester on Wednesdays from 11:00 am to 12:15 pm and Fridays from 11:00 am to 1:15 pm.

Course Director: Trine Krogh-Madsen, PhD

Objective

An overview of modern cellular and molecular biology for computational biologists.

Description

The course presents a review of essential cellular and molecular processes aimed at students with limited educational background in biology. Topics include cellular structure and function, genetics and genomics, transcriptomics, proteins and proteomics, post-translational regulation, and cell signaling. The course will have a special focus on experimental techniques and data quantification, including methods pertaining to computational biology. Methodologies and quantification include: microscopies, PCR, blots, antibodies, immunoprecipitation, fluorescence, mass spectroscopy, and flow cytometry. The course will also cover latest genetic engineering techniques and showcase visualization of 3D protein structures. 

The course, held in the Fall semester, will consist of twice-weekly lectures within five subject modules on Mondays and Wednesdays from 2:00 pm to 3:30 pm.

Course Director:  Nicholas Brady, PhD

Description

The objective of this course is to introduce MS-CB faculty and their research programs to MS-CB students. Each week will feature one or two faculty members, presenting their research area and possible topics for MS thesis research projects.

The course is scheduled on Fridays from 2:00 pm to 3:00 pm.

Course Director: Trine Krogh-Madsen, PhD

Course Objectives

After completing this course, students will be able to:

• Have a deep appreciation of current DNA sequencing technologies, and an awareness of pitfalls, caveats, and confounding factors.
• Understand which technologies are appropriate for which use cases.
• Be aware of the details in deriving insights from raw data.
• Be able to critically assess next generation sequencing data and analyses, and be aware of common biases.

Course Description

Next generation DNA sequencing technology has revolutionized our ability to ask almost any question of our genome, epigenome or transcriptome. In Part I of the course, we focus on the principles of the dominant technology: the Illumina short read sequencing by synthesis platform. The complete analysis pipeline is examined in detail, from the generation of raw reads, through alignment to the genome (Part II), and up to gene-centric analyses in Part III. At each step, there will be a strong emphasis on quality control, highlighting limitations and common pitfalls of the most commonly used tools, as well as ways to deal with them. In Part IV, alternate DNA sequencing technologies are surveyed, showcasing their applications.

Students will use the knowledge gained throughout this course to apply to a practical project which will focus on the analysis of one or more NGS data types to address a biomedically relevant question.

The course is scheduled for the Spring semester and meets on Wednesdays from 1:30-3:00pm and Thursdays from 2:00-3:30pm. 

Course Requirements and Grading

70% of the grade will be assessed by an individual project, using techniques learned in class to explore a meaningful biological question. The project will be developed throughout the course, with opportunities every week to refine and get feedback. 30% of the grade will be assessed via weekly short programming exercises.

Course Director: Luce Skrabanek, PhD

Course Description

Vast amounts of biological and medical data are generated by high-throughput techniques, but these data sets require careful analysis and interpretation. In this course, students will learn applications of high-throughput screens across different disciplines, in both basic and clinical research. The course consists of 3 modules: 

1. Precision Medicine, 
2. Networks & Pathways, and 
3. Epigenetics and Single Cell Analyses. 

During the course, students will learn how to apply computational tools to data to guide illumination of biological function. 

Course Directors: Trine Krogh-Madsen, PhD

Course Description

The objective of this course is to introduce MS-CB faculty and their research programs to MS-CB students. Each week will feature one or two faculty members, presenting their research area and possible topics for MS thesis research projects.

The course is scheduled on Fridays from 2:00 pm to 3:00 pm.

Course Director: Trine Krogh-Madsen, PhD

Course Description

Memorial Sloan Kettering (MSK), Weill Cornell Medicine (WCM), The Rockefeller University (RU), and the Hospital for Special Surgery (HSS) collaborate closely to advance medical, educational, and research missions. Together, we offer a biannual Responsible Conduct of Research (RCR) course aimed at research trainees and others interested in ethical research practices. This course fulfills mandated RCR instruction requirements by major funding agencies. Learn more about the course here.

Course Description

This course forms the MS thesis component for students in the MS-CB program. 

Prior to enrollment, students must have a faculty mentor assigned. Students will first learn basic concepts of research in computational biology. Working with their faculty mentor, each student will learn how to plan and perform scientific research, including critical reading of published research articles, problem finding, formulation of a research question, research methodology and design. Under the continued guidance of their faculty mentor, students will carry out their thesis research, including data collection and/or generation, data analysis, and data interpretation. 

Students will practice science communication throughout their research experience. This includes active participation in general communications within the mentor’s research group and presentations to faculty and fellow students. 

Students will write an original thesis document and present an oral defense of it to their Thesis Committee. 

Course Director: Trine Krogh-Madsen, PhD

Course Description

This course forms the MS thesis component for students in the MS-CB program. 

Prior to enrollment, students must have a faculty mentor assigned. Students will first learn basic concepts of research in computational biology. Working with their faculty mentor, each student will learn how to plan and perform scientific research, including critical reading of published research articles, problem finding, formulation of a research question, research methodology and design. Under the continued guidance of their faculty mentor, students will carry out their thesis research, including data collection and/or generation, data analysis, and data interpretation. 

Students will practice science communication throughout their research experience. This includes active participation in general communications within the mentor’s research group and presentations to faculty and fellow students. 

Students will write an original thesis document and present an oral defense of it to their Thesis Committee. 

Course Director: Trine Krogh-Madsen, PhD

Course Description

This course prepares students in computational biology for the processes of initiating a professional career. Workshop sessions will train practical skills in writing resumes and cover letters and more generally in science communication. Course topics also include job searching tools, online profiles, job interview preparation, and skills assessment. Additionally, the course will introduce students to the computational biology profession outside of academia. Invited professionals from different occupational venues (including pharmaceutical and biotech companies) will discuss their work and career pathways.

The course is scheduled for the Spring semester on Fridays from 11:00 am to 12:00 pm.

Course Directors: Aubrey DeCarlo, PhD, and  Trine Krogh-Madsen, PhD

Course Description

This course forms the MS thesis component for students in the MS-CB program. 

Prior to enrollment, students must have a faculty mentor assigned. Students will first learn basic concepts of research in computational biology. Working with their faculty mentor, each student will learn how to plan and perform scientific research, including critical reading of published research articles, problem finding, formulation of a research question, research methodology and design. Under the continued guidance of their faculty mentor, students will carry out their thesis research, including data collection and/or generation, data analysis, and data interpretation. 

Students will practice science communication throughout their research experience. This includes active participation in general communications within the mentor’s research group and presentations to faculty and fellow students. 

Students will write an original thesis document and present an oral defense of it to their Thesis Committee. 

Course Director: Trine Krogh-Madsen, PhD

Students are required to take at least two electives among program-approved WCGS and Cornell Tech offerings. At least one elective must cover statistical or machine learning. Possibilities include courses on applied machine learning, deep learning, natural language processing, computer vision, AI, and statistical learning. Other electives include courses on biostatistics, health informatics, biomedical entrepreneurship, genomic innovation, etc.