Quantitative and Computational Biology

Program Offerings

Offering type

The Program in Quantitative and Computational Biology is offered by the Lewis-Sigler Institute for Integrative Genomics and its affiliated departments. The program is designed to instruct students in the theory and practice of using big data sets to achieve a quantitative understanding of complex biological processes. 

Examples of ongoing research include organizational principles of metabolic networks, quantitative modeling of cell biological processes, the genetic basis of complex behavior, comparative genomics of regulatory networks, quantitative analysis of gene-environment interactions, evolution of gene regulation, and circuitry driving aging. 

At the core of the curriculum is independent research initiated in the fall of sophomore or junior year, in which students participate in the design, execution and analysis of experiments in a host laboratory of their choice. The required courses provide a strong background in modern methodologies in data analysis, interpretation and modeling. A certificate in quantitative and computational biology is awarded to students who successfully complete the program requirements.

Goals for Student Learning

The Lewis-Sigler Institute for Integrative Genomics (LSI) awards the certificate in Quantitative and Computational Biology to undergraduate students who satisfactorily complete a multidisciplinary curriculum and relevant independent work. Through its prerequisite courses, students learn fundamental principles in the natural sciences and tools of computer science, mathematics and statistics, with a strong emphasis on creative application of many of those basic concepts to novel questions in the life sciences. Intermediate-level electives taught by LSI faculty introduce students to theories and practices that use big data sets to quantitatively understand complex biological processes. Students are encouraged to dive into interdisciplinary literature on genomics and systems biology, and to engage with expertise and ideas from a variety of backgrounds in order to hone their research questions. At the core of the curriculum is independent research initiated in the fall of sophomore or junior year, in which students participate in the design, execution and analysis of experiments in a host laboratory of their choice. High-level electives expose students to an even greater range of contemporary methodologies in data analysis, interpretation and modeling across biophysics, molecular biology and biochemistry. Successful completion of the certificate requirements prepares students well for careers in industries related to the life sciences, and propels curious minds into relevant academic fields. 


Integrated science or three foundational classes from the lists below.

ISC 231-234 An Integrated, Quantitative Introduction to the Natural Sciences (counts as three foundational classes, not offered 2023–2024)

Or the following:                                                      

Foundation in Computer Science. The following course or approved equivalent:

  • COS 126/EGR 126 Computer Science: An Interdisciplinary Approach

Foundation in Biology. One of the following courses or approved equivalent:

  • MOL 214/EEB 214/CBE 214 Introduction to Cellular and Molecular Biology
  • EEB 211 Life on Earth: Mechanisms of Change in Nature

Foundation in Math or Statistics. One of the following courses or approved equivalent:

  • 200-level math course (or higher)
  • ORF 245/EGR 245 Fundamentals of Statistics

Admission to the Program

Students are admitted to the program after they have chosen a major, joined a research lab and identified a project (with the help of the program committee if need be), and submitted a complete application by September 1 of their junior year. Although students are encouraged to find a lab on their own, the program committee will, if necessary, assist students in selecting a laboratory for their junior independent and senior thesis work. Students must have identified a lab and research project by the first day of their junior year fall semester. Admission requires the completion of prerequisites listed above. Program electives are chosen in consultation with the adviser.

Program of Study

Students must complete two electives from the lists below. Students may be permitted to take a graduate-level course not listed below to fulfill the elective requirement, but only with permission of the program director.

 Computational Methods and Quantitative Modeling

  • COS 343 Algorithms for Computational Biology
  • COS 557 Analysis & Visualization of Large-Scale Genomic Data Sets
  • EEB 325 Mathematical Modeling in Biology and Medicine
  • ENV 302/CEE 302/EEB 302 Practical Models for Environmental Systems
  • MAT 321/APC 321 Numerical Methods
  • MOL 485/QCB 485 Mathematical Models in Biology
  • NEU 314 Mathematical Tools for Neuroscience
  • NEU 437/MOL 437/PSY 437 Computational Neuroscience
  • NEU 499/PSY 499 The Computational Basis of Natural Intelligence in the Human Brain
  • ORF 350 Analysis of Big Data
  • QCB 505/PHY 555 Topics in Biophysics and Quantitative Biology: Statistical Mechanics for Biological Networks
  • CBE 422 Molecular Modeling Methods

Genomics, Chemical, and Systems Biology

  • CBE 433/MSE 424 Introduction to the Mechanics and Dynamics of Soft Living Matter
  • CHM 301 Organic Chemistry I: Biological Emphasis
  • CHM 302 Organic Chemistry II: Biological Emphasis
  • CHM 337 Organic Chemistry: Bioengineering Emphasis
  • CHM 541/QCB 541 Chemical Biology II
  • EEB 309 Evolutionary Biology
  • EEB 324 Theoretical Ecology
  • EEB 388 Genomics in the Wild (Note: This course is offered as part of the semester abroad program in Kenya)
  • MAE 344/MSE 364 Biomechanics and Biomaterials: From Cells to Organisms
  • MOL 415 Modern Biophysics and Systems Biology
  • NEU 427 Systems Neuroscience
  • QCB 302 Research Topics and Analytical Approaches in Quantitative Biology (recommended)
  • QCB 408 Foundations of Statistical Genomics
  • QCB 455/MOL455/COS 551 Introduction to Genomics and Computational Molecular Biology
  • QCB 490/MOL 490 Molecular Mechanisms of Longevity: The Genetics, Genomics, and Cell Biology of Aging
  • QCB 515/PHY 570/EEB 517/CHM 517/MOL 515 Method and Logic in Quantitative Biology

Independent Work

Junior and Senior Independent Work: Junior and senior independent work must show adequate quantitative and computational biology content and expand upon the existing field.

Certificate of Proficiency

Students who fulfill the requirements of the program receive a certificate of proficiency in quantitative and computational biology upon graduation. 

Additional Information

  • Applications for program admission must be submitted by September 1 of junior year and should include the following information: prerequisite courses, plans for courses in the junior and senior years, and independent work plans. 
  • Program courses cannot be taken pass/D/fail.
  • At least two classes taken to meet the requirements of the certificate must not count toward the student’s major requirements.
  • Students who pursue a certificate in quantitative and computational biology may not also receive a certificate in biophysics.


Program Director: Brittany Adamson ([email protected])
Program Administrator: Ben Xinzi Zhang ([email protected])


  • Director

    • Brittany Adamson
  • Director of Undergraduate Program

    • Brittany Adamson
  • Executive Committee

    • Brittany Adamson, Molecular Biology
    • Thomas Gregor, Physics
    • Coleen T. Murphy, Molecular Biology
    • Olga G. Troyanskaya, Computer Science
    • Martin Helmut Wühr, Molecular Biology

For a full list of faculty members and fellows please visit the department or program website.


QCB 455 - Introduction to Genomics and Computational Molecular Biology (also COS 455/MOL 455) Fall QCR

This interdisciplinary course provides a broad overview of computational and experimental approaches to decipher genomes and characterize molecular systems. We focus on methods for analyzing "omics" data, such as genome and protein sequences, gene expression, proteomics and molecular interaction networks. We cover algorithms used in computational biology, key statistical concepts (e.g., basic probability distributions, significance testing, multiple testing correction, performance evaluation), and machine learning methods which have been applied to biological problems (e.g., classification techniques, hidden Markov models, clustering). J. Akey, M. Singh