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Computational Biology


Professor Lei Xie


This course introduces the fundamental algorithms and applications of data mining, computational, and modeling techniques in biology and medicine. The focus will be on capability of formulating biological questions into computational problems, and hand-on skills in making use of software and databases to solve real-world problems. The goal is to provide students a holistic, quantitative, and multi-scale view of biological systems.

List of topics

  • Fundamental of systems biology

  • Application of data mining techniques to biology and biochemistry

  • Prediction, modeling, and simulation of biomolecular interactions

  • Protein function prediction and annotation

  • Genomics data analysis

  • Omics data integration

  • Biological network reconstruction, modeling, and simulation

  • Genome-wide association studies

  • Predictive modeling for precision medicine


No textbook is required. Reading materials will be the latest research papers.


  • Basic knowledge of Unix OS, statistics, and linear algebra.

  • Programming skills are desired but not required.

Learning objectives

The student must demonstrate working knowledge of foundation and software of computational biology represented by topic of

  • Data processing

  • Data integration

  • Predictive modeling

  • Data visualization and interpretation


Students need to complete an independent project targeting a conference or journal publication. The project will at least include the following steps: data preparation for the computational modeling, construction of a predictive model, and the generation of a testable biological hypothesis for further experimental validation.