Carnegie Mellon University

Intro to Computational Biology

Course Number: 02-250

This 12-unit class provides a general introduction to computational tools for biology. The course is divided into two modules, which may be taken individually as courses 02-251 and 02-252. Module 1 covers computational molecular biology/genomics. It examines important sources of biological data, how they are archived and made available to researchers, and what computational tools are available to use them effectively in research. In the process, it covers basic concepts in statistics, mathematics, and computer science needed to effectively use these resources and understand their results. Specific topics covered include sequence data, searching and alignment, structural data, genome sequencing, genome analysis, genetic variation, gene and protein expression, and biological networks and pathways. Module 2 covers computational cell biology, including biological modeling and image analysis. It includes homeworks requiring modification of scripts to perform computational analyses. The modeling component includes computer models of population dynamics, biochemical kinetics, cell pathways, neuron behavior, and stochastic simulations. The imaging component includes basics of machine vision, morphological image analysis, image classification and image-derived models. Lectures and examinations are joint with 03-250 but recitations are separate.  Recitations for this course are intended primarily for computer science, statistics or engineering majors at the undergraduate or graduate level who have had significant prior experience with computer science or programming. Students may not take both 02-250/03-250 and either 02-251/03-251 or 02-252/03-252 for credit.

Academic Year: 2019-2020
Semester(s): Spring
Units: 12
Location(s): Pittsburgh

Format

Lecture

Learning Objectives

  1. Learn major biological data types, the methods by which they are produced, and their uses.
  2. Learn to critically assess the reliability of biological data sources.
  3. Learn essential concepts of statistics and algorithms needed to productively use database search and inference tools and interpret their results.
  4. Learn to synthesize results from different data sources and select sources appropriate to a given problem.
  5. Learn about of the major repositories of biological data and the tools to access them.
  6. Learn to independently research a biological question using online resources.
  7. Learn how to pose biological questions through mathematical models and reason about the assumptions and limitations of those models.
  8. Learn to simulate the behavior of simple biological models.
  9. Learn basic image processing methods and concepts of biological image analysis.
  10. Learn concepts behind supervised and unsupervised machine learning as applied to biological problems.