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Find us
help@cbd.cmu.edu
Computational Biology Department Computational Biology Department
  • About Us
    • Leadership
    • What is Computational Biology?
    • What is Automated Science?
    • Computational Biology Careers Website
    • List of Educational Programs in Computational Biology
    • The Hillman Center
    • History
    • CBD at a Glance
    • News and Events
      • News
      • Calendar
      • Departmental Seminars
      • Meetings
    • Department Resources
  • Education
    • Ph.D. in Computational Biology
    • M.S. in Automated Science
    • M.S. in Computational Biology
    • Undergraduate Program in Computational Biology
      • Major in Computational Biology
        • Why Major in Computational Biology?
        • Degree Requirements
        • The Undergraduate Research Pledge
        • Sample Course Sequence for Computational Biology Majors
        • Guidelines for Transfer to Major in Computational Biology
        • Suggested Courses for “Pre-Med” Computational Biology Majors
        • Additional Major in Computational Biology
          • Double-Counting Suggestions for Additional Majors in SCS
      • Minor in Computational Biology
      • Concentration in Computational Biology
      • Visit Us
    • Pre-College Program in Computational Biology
    • Courses Offered
      • Undergraduate Courses Offered
      • Graduate Courses Offered
      • Course Profiles
        • 02-201/601 Programming for Scientists
        • 02-223 Personalized Medicine: Understanding your Own Genome
        • 02-250 Introduction to Computational Biology
        • 02-251 Great Ideas in Computational Biology
        • 02-261 Quantitative Cell and Molecular Biology Laboratory
        • 02-319/719 Genomics and Epigenetics of the Brain
        • 02-331/731 Modeling Evolution
        • 02-402/702 Computational Biology Seminar
        • 02-414/614 String Algorithms
        • 02-425/725 Computational Methods for Proteogenomics and Metabolomics
        • 02-450/750 Automation of Scientific Research
        • 02-500 Undergraduate Research in Computational Biology
        • 02-510/710 Computational Genomics
        • 02-512/02-712 Computational Methods for Biological Modeling and Simulation
        • 02-518/718 Computational Medicine
        • 02-530/730 Cell and Systems Modeling
        • 02-602 Professional Issues in Computational Biology
        • 02-604 Fundamentals of Bioinformatics
        • 02-605 Professional Issues in Automated Science
        • 02-613 Algorithms and Advanced Data Structures
        • 02-620 Machine Learning for Scientists
        • 02-700 M.S. Research
        • 02-701 CPCB Course / Current Topics in Computational Biology
        • 02-715 Advanced Topics in Computational Genomics
        • 02-740 Bioimage Informatics
        • 02-750 Automation of Scientific Research
        • 02-760 Laboratory Methods for Computational Biologists
        • 02-761 Laboratory Methods for Automated Biology I
        • 02-762 Laboratory Methods for Automated Biology II
        • 02-801 Computational Biology Internship
        • 02-900 Ph.D. Thesis Research
  • Research
    • Software
    • Faculty Research Pages
    • Computational Biology Technical Reports
    • White Papers
  • People
    • Faculty
      • Voting Faculty
      • Affiliated Faculty
      • Visiting Faculty
      • Adjunct Faculty
      • Visiting Interns
    • Staff
      • Department Staff
      • Research Staff
    • Fellows and Special Faculty
      • The Lane Fellows Program
      • Current Lane Fellows
      • Past Lane Fellows
      • Postdoctoral Fellows
      • Past Postdoctoral Fellows
      • Special Faculty
    • Alumni
      • Ph.D. Graduates
      • Alumni Profiles
  • Join Us!
    • Life in Pittsburgh
      • Neighborhoods Near Carnegie Mellon University
      • Things to Do in Pittsburgh
    • Positions Available
    • Apply to Ph.D. Program
    • Apply to MSAS
    • Apply to MSCB
    • Apply to Undergraduate Program
    • Apply to be a Lane Fellow
  • Donate!

02-512/02-712 Computational Methods for Biological Modeling and Simulation

02-512/02-712 COURSE PROFILE

Return to Courses Offered

Course Number 02-512 02-712
Course Level

Undergraduate & Graduate

Units 9 Units 12 Units
Special Permission Required?

No

Frequency Offered

Fall

Course Relevance (Who should take this course?) This is a required class for Bachelors in Computational Biology students and available as an elective for advanced undergraduates. This is a required class for Masters in Computational Biology students and available as an elective for graduate students and advanced undergraduates.
Key Topics The general topics covered will be:

[4 weeks] Models for optimization problems
[6 weeks] Simulation and sampling
[4 weeks] Model inference
Parameter tuning

 

The course will emphasize:

Practical algorithms
Algorithm design methods drawn from various disciplines of computer science and applied mathematics that are useful in biological applications

 

Coursework will include:

Problem sets with significant programming components
[1 week] Independent or group final projects

Background Knowledge It is intended for graduates and advanced undergraduates with either biological or computational backgrounds who are interested in developing computer models and simulations of biological systems. It is intended for graduates and advanced undergraduates with either biological or computational backgrounds who are interested in developing computer models and simulations of biological systems.
Assessment Structure Your grade in this class will be based on six problem sets, each of which you will have two weeks to complete and a final project.

75% of your grade on problems sets

25% of your grade on final project

Those taking the graduate version of this class [03-712/02-712] will be required to do additional problems on the problem sets and will be expected to choose more ambitious final projects. The class will be graded on separate curves for the 03-512/02-512 and 03-712/02-712 students.

Most Recent Syllabus  02512-A  02712-A
Sample class notes This course has a textbook that was developed based on the class lecture notes:

Russell Schwartz. Biological Modeling and Simulation. MIT Press: Cambridge, MA, 2008.

Copies are available through the CMU Book Store and various online retailers. The References section of each chapter will recommend additional background readings. You may find these helpful if you want to know more about any of the topics we cover, but they are optional and you should not need them to complete any of the assignments. A few additional readings will be provided on Blackboard for topics not covered in the text.

There are also recorded videos of most lectures from a previous year. The quality is poor and the content will change somewhat each year, but you may find them useful. You can view them here.

Course Goals/Objectives
  1. Learn how to formally define and reason about mathematical models of biological systems.
  2. Be familiar with a collection of commonly encountered discrete optimization problems, both tractable and intractable, and be able to reason about adapting them to related problems and solving them in practice.
  3. Be familiar with continuous optimization problems and know general techniques for solving them in constrained and unconstrained variants.
  4. Understand Markov models, how to pose them, how to analyze their mixing times, and how to use both discrete and continuous time variants.
  5. Know how to sample from a probability density.
  6. Know how to pose and numerically integrate ordinary, partial, and stochastic differential equations.
  7. Know how to pose parameter inference problems as optimization or sampling problems and be familiar with a set of general techniques for solving them.
  8. Understand basic principles behind validating models and quantifying uncertainty in those models.
Course Website
Pre-reqs, Cross List, Related

Cross listed with 03-512 and 02-712

Pre-reqs: 15-110 or 02-201 or 15-112

Cross-listed with 03-712

Pre-reqs: [15-110 or 15-112] and [02-613 or 02-201]

Department Website http://www.cbd.cmu.edu/ http://www.cbd.cmu.edu/
College Website https://www.cs.cmu.edu/ https://www.cs.cmu.edu/

 

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