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Computational Biomedicine & Biotechnology M.S. Program:

Computational Biomedicine & Biotechnology M.S. Program:

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Computational Biomedicine & Biotechnology M.S. Program

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Innovative Learning That Builds The Future

Program Overview

Program duration is from 12 to 20 months, depending on the needs and goals of the student, as well as their background and preparedness. To complete the degree in one year, students must demonstrate proficiency in programming, calculus and linear algebra. 

The minimum requirements for graduation are at least 30 credit hours of graduate-level training, including:   

  • At least four research credits of independent study with a research mentor 
  • A summer internship or additional independent study, worth three credits 
  • A Professional Development course and a Faculty Seminar course 
  • One course from each of the six core areas of study 
  1. Data Science, Programming, Probability, and Statistics 
  2. Artificial Intelligence, Machine Learning and Bioimage Analysis
  3. Genomics and Precision Medicine
  4. Molecular and Cell Systems Modeling 
  5. Drug Discovery and Quantitative Pharmacology 
  6. Specialized Courses, based on the student’s interests, can be selected from a wide range of offerings at the University of Pittsburgh, including the Schools of Medicine, Engineering and Health Sciences, among others.

Directed Study – Gaining hands-on experience in solving problems in computational biology is an essential part of CoBB training. Each student is therefore required to take a minimum of 4 credits of Directed Study with a University of Pittsburgh faculty member. Students select a research mentor for the Directed Study in their first semester, and complete the research requirement in the second and/or third semesters.

 Internship – To gain experience in the professional application of computational biology, students are encouraged to participate in a 2- to 3-month summer internship at a company of their choosing. Acceptable internship sites include industrial labs, biotech/pharma companies, and governmental organizations. CoBB will assist students with identifying corporate partners and potential internship sites, but it is the student’s responsibility to contact the company and secure the internship. Previous internship sites for CoBB students include UPMC Enterprises and Janssen Pharmaceuticals.

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Detailed Course Listing

The CoBB curriculum was designed to provide students with a strong foundation in essential areas of computational biology, including genomics, structural biology, systems biology, and machine learning.  Descriptions of courses that fulfill CoBB core requirements are below.

Data Science, Programming, Probability and Statistics

CoBB 2010 | (3 credits, Fall)
This course introduces masters-level students to the essential concepts, tools and techniques of modern computational biology. The course focuses on mathematical concepts, such as linear algebra, differential equations and statistics, that are central to modeling biological systems. Students will learn the basic theory behind widely-used techniques like automated clustering, parameter estimation, sampling, and numerical integration. Project-based assignments will center around real-world computational biology problems from genomics, structural biology, and systems modeling.

CoBB 2025
(3 credits, Fall)

The course will introduce students to a variety of computational tools for solving common problems in biological research. Students will be taught the Python programming language through hands on exercises and assignments. Students will acquire knowledge and programming skills that will increase their productivity as researchers.”

Artificial Intelligence, Machine Learning and Bioimage Analysis

COBB 2066 |  (4 credits, Spring)
Machine learning (ML) has become an integral part of computational thinking in the era of big data biology. This course focuses on understanding the statistical structure of large-scale biological datasets using ML algorithms. We cover the basics of ML and study their scalable versions for implementation on a distributed computing framework. We pursue distributed ML algorithms for matrix factorization, convex optimization, dimensional reduction, clustering, classification, graph analytics and deep learning, among others. This course is project driven (3 to 4 small projects) with source material from genomic sciences, structural biology, drug discovery, systems modeling and biological imaging. Students are expected to design, implement and test their ML solutions in Apache Spark.

BIOINF 2105 | (3 credits, Fall)

This course provides an introduction to artificial intelligence (AI) in Biomedical Informatics, offering a rigorous and practical education on fundamental AI topics. While the lessons focus on AI subjects not specific to the biomedical domain, the course will direct students toward problems and applications from biomedicine relevant to each AI topic. The course is practical in the sense that the homework assignments will provide students with hands-on experience in applying the AI methods covered throughout the course.

MSCBIO 2027  | (3 credits, Spring)
This course will introduce students to micron, nanometer, and sub-nanometer scale imaging-based approaches in experimental, computational and systems biology. Emphasis will be placed on understanding the fundamentals of image formation, processing, and analysis of brightfield, phase-contrast, highly multiplexed, super-resolution, live-cell, cryo-EM, and computational imaging, along with imaging-based spatial transcriptomic methods. Applications of these methods in studying biological phenomena in space and time will also be discussed. Python will be the programming language in which image processing, machine learning, and system biology methods introduced in the course will be implemented. Students are expected to know Python basics. It is also expected that students have a basic understanding of linear algebra and calculus.

Genomics and Precision Medicine

CoBB 2020 | (3 credits, Spring)
This course introduces students to genomic data and basic analytical principles pertaining  them. Students  will  learn  about  high-throughput  sequencing  methods and applications, genomic variation, transcriptomics and epigenomic data. At the end of the course, the students will be able to analyze efficiently these types of data sets using existing algorithms or algorithms they will develop.

CoBB 2070 | (3 credits, Spring)

Dramatic advances in experimental technology and computational analysis are fundamentally transforming the basic nature and goal of biological research. The emergence of new frontiers in biology, such as evolutionary genomics and systems biology is demanding new methodologies that can confront quantitative issues of substantial computational and mathematical sophistication. This course introduces classical approaches and the latest methodological advances in the context of the following biological problems: 1) Computational genomics, focusing on gene finding, motifs detection and sequence evolution. 2) Analysis of high throughput biological data, such as gene expression data, focusing on issues ranging from data acquisition to pattern recognition and classification. 3) Molecular and regulatory evolution, focusing on phylogenetic inference and regulatory network evolution, and 4) Systems biology, concerning how to combine sequence, expression and other biological data sources to infer the structure and function of different systems in the cell. From the computational side this course focuses on modern machine learning methodologies for computational problems in molecular biology and genetics, including probabilistic modeling, inference and learning algorithms, pattern recognition, data integration, time series analysis, active learning, etc.

Molecular & Cellular Systems Modeling

CoBB 2041 | (4 credits, Fall)
This course introduces students to the theory and practice of modeling biological systems from the molecular to the population level with an emphasis on intracellular processes. Topics covered include kinetic and equilibrium descriptions of biological processes, systematic approaches to model building and parameter estimation, analysis of biochemical circuits modeled as differential equations, modeling the effects of noise using stochastic methods. A range of biological models and applications are considered, including gene regulatory networks, cell signaling, molecular motors, and developmental biology.

CoBB 2030 | (4 credits, Spring)

Topics covered include: applying computational and statistical methods to the analysis of DNA and protein structures representing protein, DNA and RNA structure; homology modeling and protein structure prediction; theoretical description of basic interactions, along with computational methods to estimate them; statistical mechanical theory of molecules; molecular dynamics and other sampling methods; modeling protein flexibility, from side chains to loops to slow modes; reaction paths and basics of path sampling; protein-protein and protein-small molecule docking; supramolecular assembly; introduction to Quantitative Structure Activity Relationship (QSAR) in drug design.

 

Drug Discovery and Quantitative Pharmacology

CoBB 2030 | (4 credits, Spring)
Topics covered include: applying computational and statistical methods to the analysis of DNA and protein structures representing protein, DNA and RNA structure; homology modeling and protein structure prediction; theoretical description of basic interactions, along with computational methods to estimate them; statistical mechanical theory of molecules; molecular dynamics and other sampling methods; modeling protein flexibility, from side chains to loops to slow modes; reaction paths and basics of path sampling; protein-protein and protein-small molecule docking; supramolecular assembly; introduction to Quantitative Structure Activity Relationship (QSAR) in drug design.

CoBB 2051 | (4 credits, Spring)

This course provides an introduction to the concepts and tools of computational drug discovery, from small molecules to modeling clinical trials. Covered topics include small molecule structural similarity, molecular dynamics and virtual screening, pharmacophore modeling, disease pathway inference and modeling, pharmacokinetics and pharmacodynamics (PK/PD) modeling. The emphasis is on practical application of computational tools and techniques used throughout the drug discovery process.

Specialized Courses

All specialized courses are listed below. If you’d like more information on these classes, click the “Begin Your Journey – Apply Now” button to receive more information. 

Begin Your Journey - Apply Now
  • CS 2056: Introduction to Data Science
  • BIOENG 2340: Introduction to Medical Imaging and Image Analysis
  • BIOENG 2383: Biomedical Optical Microscopy
  • BIOENG 2505: Multi Modal Biomedical Imaging Technologies: Functional, Molecular and Hybrid Imaging Techniques
  • BIOST 2079: Introductory Statistical Learning for Health Sciences
  • BIOST 2069: Statistical Methods for OMICS Data
  • BIOST 2080: Advanced Statistical Learning
  • MSCMP3790: Basics of Personalized Medicine
  • MSMPHL3360: Molecular Pharmacology
  • MATH3370: Mathematical Neuroscience
  • MATH3375: Computational Neuroscience Methods
  • MATH3380: Mathematical Biology
  • MSCMP3780: Systems Approach Inflammation
  • BIOINF2118: Probability and Statistics in Biomedical Informatics
  • BIOINF2018: Introduction to R Programming for Scientific Research
  • HUGEN2022: Human Population Genetics
  • BIOSC2545: The Mathematics of Biology
  • NROSCI2012: Neurophysiology
  • BIOSC2810: Macromolecular Structure and Function
  • MSCBIO2074: Evolutionary Biology of Human Disease
  • COBB2100: Computational Systems and Biology Seminar

Frequently Asked Questions

What is the tuition?
Graduate Tuition and Mandatory Fees can be found here.
Is financial assistance available?

All students are responsible for their own tuition.

CoBB does not offer grants or scholarships. Most students finance their education by utilizing Federal Direct loans (unsubsidized and Graduate PLUS), and/or private education loans.
Information on Federal options can be found at studentloans.gov. Private education loans are available through the websites of individual lenders. Private loans are typically issued in the student’s name and require a credit check. A credit worthy cosigner, who is a U.S. citizen or permanent resident, may be needed if the student’s credit is not sufficient. Students should compare interest rates, fees, and repayment options such as deferment and forbearance. Maximum loan amounts, loan terms, borrower qualification, repayment schedules, and interest rates vary among lenders. Some private education loans may require interest payments while the student is enrolled in school.

If you are going to apply for financial aid, use this link:
https://oafa.pitt.edu/financialaid/applying-for-aid/graduate-school-instructions/

However, there are alternative options to help support you financially during your academic journey, such as paid Research Positions at Pitt and Internships in the Industry. Our university is deeply invested in cutting-edge research across various departments and disciplines. As an MS student, you have the unique opportunity to participate in this research as a paid researcher. These positions are available throughout the year, and not only provide financial remuneration but also offer valuable hands-on experience that can greatly benefit your academic and professional development.

How to Apply for Paid Research Positions

  1. University Job Portal: Visit [Careers at Pitt](https://www.hr.pitt.edu/careers-pitt) to search for available research positions. The portal is updated regularly with new opportunities.
  2. Approach Faculty Directly: If there’s a specific area or project you’re interested in, feel free to approach faculty members directly to inquire about research opportunities. Faculty are often looking for motivated students to assist in their research projects.
When is the deadline to submit my CoBB application?

CoBB enrolls students in the Spring and Fall terms. We encourage to you to submit your application as possible, as the Admissions Committee will begin reviewing files as they are received – it is beneficial to apply and interview early as we accept students until we reach capacity. Applications received after we are at capacity or after the term deadlines will be considered for admission the following term. All required materials must be received by the deadline. Please specify which semester you are applying for in your cover letter.

Fall 2025

Application Open: September 1st, 2024

Deadline to apply: June 1st, 2025

What is the GPA requirement?
Although we suggest a minimum GPA of 3.0 for a competitive application, all applications will given full consideration.
What materials are required for application?

To apply to CoBB, you will need to submit:

  • Transcripts from all colleges and universities at which you have studied
  • A Personal Statement of roughly 2 pages
  • 3 letters of recommendation
  • Some international applicants will also need to submit IELTS or TOEFL scores
  • CoBB Student Handbook can be found here – CoBB Student Handbook
Do I have to submit MCAT, GRE, or DAT scores?
No, CoBB does not require MCAT, GRE or DAT scores
Do I need to take TOEFL or IELTS?

Applicants who are citizens of countries where English is not the official language (and the Province of Quebec in Canada) are required to submit evidence of English language proficiency by submitting the results of the Test of English as a Foreign Language (TOEFL), International English Language Testing System (IELTS), or Duolingo English Test minimum result of 600 paper test, 100 iBT on the TOEFL or a minimum result of Band 7.0 on the IELTS, or 120 on the Duolingo English Test is required for admission to the program. Scores must be less than two years old. Applicants who have earned a bachelor’s degree or a higher degree from a regionally accredited institution in the United States are exempt from submitting TOEFL, IELTS, and Duolingo scores.

 

A current list of exempt countries can be found at
https://oafa.pitt.edu/apply/admissions-process/international-students/toefl-exceptions/

Do I need health insurance?
Students enrolled in the CoBB Program must have health insurance coverage. You have the option to obtain your own or of enrolling in the University sponsored programs. Review detailed information on the health insurance policies and plans. 
Do I need to upload my official transcripts in my application?
During the application process, we only require unofficial transcripts for all post-secondary institutions attended, whether or not a degree is granted.  Official transcripts for all schools listed on the application are required for all applicants who are offered admission and accept the offer. Official transcripts are required to be submitted prior to orientation in August.
How long does it take to complete the CoBB Master’s Program?
Students enrolled full-time can expect to complete the degree in 12-16 months. Part-time students should complete the program in about 2 years. 
Is there an application fee?
The current application fee is $50.
Is this only a full-time program?
CoBB does not require students to be enrolled full-time, and we are continuously expanding our asynchronous and remote offerings; however, courses such as electives may still be offered only during weekdays.