Curriculum

Description

This graduate course delves into the core principles of data structures and algorithms with a focus on their application in computational biology. Students will explore foundational topics such as graph algorithms, dynamic programming, string matching, and sorting algorithms, alongside advanced techniques tailored to biological data analysis. The course emphasizes the design and implementation of algorithms for solving contemporary challenges, such as genome sequencing, phylogenetics, and machine learning. Through a combination of lectures and hands-on assignments, students will develop algorithmic problem-solving skills, preparing them for research and innovation in computational biology.

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 techniques in mathematical modeling of biological systems, bridging theory and application. Students will study nonlinear dynamics, ordinary and partial differential equations, and stochastic processes to understand biological phenomena. The course teaches model development, parameter estimation, sensitivity analysis, and model validation. Computational tools and numerical simulations are integral to the curriculum, providing students hands-on experience in implementing and analyzing models, through models in systems such as gene regulation, disease modeling, and neural activity.

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

This course presents a review of essential cellular and molecular processes with a focus on experimental techniques, data quantification, and analysis of recently-published primary research publications. Topics include cellular structure and function, genetics and genomics, transcriptomics, proteins and proteomics, post-translational regulation, cell signaling, and cancer. Methodologies and quantification include: microscopy, PCR, blots, antibodies, immunoprecipitation, fluorescence, mass spectroscopy, flow cytometry, genome-wide sequencing approaches, and single cell-based strategies. We will also cover the latest genetic engineering techniques and discuss how researchers are applying these to their own research. 

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. Faculty members will introduce their research area and discuss possible 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

Advances in high-throughput technologies have transformed biomedical research, generating vast amounts of genomic, transcriptomic, proteomic, imaging, and other large-scale biological data. Extracting meaningful biological and clinical insights from these complex datasets requires sophisticated computational and statistical approaches. For example, identifying disease-associated genetic variants, characterizing cellular heterogeneity from single-cell data, and integrating multimodal datasets remain major challenges.

In this course, students will explore applications of high-throughput technologies across basic, translational, and clinical research. They will learn how computational methods are used to analyze and integrate diverse biological datasets and to generate insights into biological function and disease mechanisms.

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

Course Director: 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 serves as the MS thesis component for students in the MS-CB program, focusing on foundational research skills in computational biology. Guided by a faculty mentor, students will learn to critically assess research articles, identify problems, formulate research questions, and design methodologies. They will conduct thesis research involving data collection/generation, analysis, and interpretation, developing a comprehensive understanding of scientific inquiry.

Throughout the course, students will refine their science communication skills through active engagement in their mentor's research group and presentations to peers and faculty. The course culminates in the creation of an original thesis document and an oral defense presented to the 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 also required to take at least three electives among program-approved WCGS and Cornell Tech offerings. At least one elective must cover statistical or machine learning. Possibilities include courses in statistical learning, deep learning, natural language processing, computer vision, and data science. Other electives include courses on biostatistics, health informatics, biomedical entrepreneurship, etc.