Computational Biology

STAT/BIOC 442/842, Fall 2016
Instructor: Steve (Istvan) Ladunga, Ph.D.
Professor of Computational Biology
Department of Statistics
also affiliated with the Department of Biochemistry and
the School of Biological Sciences
346C Hardin Hall NORTH WING, Phone: (402) 472-6074,

Term Offered Every Fall Semester.
Credit hours 3
Lectures Tuesdays, 12:30-1:45, Room N177, Beadle Center
Computer Lab Thursdays, 2:30-4:15 PM, 214 Keim Hall (the Dean's Classroom)
Office Hours Wednesdays, 10 AM - 12 PM 346C Hardin Hall North Wing
Final exam 7:30 - 9:30 AM, Friday, December 16. See: Registrar's Office

Course Prerequisites:  
Any introductory course in biology or statistics:

Necessary background

The scope of the course Both the US and China will sequence the genomes of a million of their citizens by 2020. Precision medicine, plant and animal breeding, molecular biology are being transformed by big data (exabytes, 1018 bytes) and computational biology (bioinformatics). How can one make sense out of that without computers and computational literacy, and not even understanding the concepts?

Learning objectives:

Who would benefit from taking this course?
This course is designed first of all for biology, agronomy, premed, and statistics students. However, computer science, mathematics, physics and chemistry majors may also find it beneficial.  This course is designed to benefit computational and experimental biologists as well as biostatisticians to understand the principles of analyzing biological data, building models and testing hypotheses using computer science paradigms. This Course does not depend on any graduate course.

Assessment Plan: 
I believe that one of the most critical but somewhat overlooked skill is reading, understanding and presenting scientific publications that are reasonably challenging and matching to your background (e.g., biology, statistics, or computer science). Each student will be assigned a book chapter and/or a journal article to present during the computer labs using PowerPoint.
Your final grade will be based on the following scale:
97% - A+, 93% - A, 90% - A-, 87% - B+, 83% - B, 80% - B-, 77% - C+, 73% -C, 70% - C-, less than 70% - F.

Methods: lecture (75 minutes) and computer laboratory (105 minutes).

Most computations will rely on either web services or the servers and node computers of the Holland Computer Center of NU.


Please note that computational biology is one of the fastest evolving areas of science. No wonder, there is NO up-to-date and comprehensive textbook on bioinformatics/computational biology. By the time a book leaves the press, it is already outdated. Therefore most of the Course will be taught on the basis of my PowerPoint slides (quite a number of them) soon available from Blackboard. Please note that I re-write my presentations every year. We will also use recent scientific reviews.

Recommended but optional textbook: Zvelebil and Baum (2008): Understanding Bioinformatics
Garland Science, ISBN-13: 978-0-8153-4024-9, 772 pages. Note: Amazon displays a date of 2012, but this refers to the Kindle edition with identical text.

Optionally, we will also use selected chapters from the series: Current Protocols in Bioinformatics (Wiley Interscience).  This series provides both theoretical foundations and practical instructions to the most important bioinformatics algorithms and tools. 

A preliminary schedule of classes
The order and subject of the classes may change, partly based on student requests.

Aug. 23 How has the Information Age been changing biology and biologists? Roadmap to the Course.
Aug. 30
How to use and search biological databases professionally? Finding out the sense of your research from 27 million abstracts on PUBMED, Scopus, Web of Science and a lot more on Google Scholar.
Sep. 6
Inferring from sequence to function: aligning a pair or nucleic acid or protein sequences.
Sep. 13 Inferring from sequence to function on a grand scale: searching sequence databases using BLAST.
Sep. 20 Universal features of the domain architecture of proteins. Search domain models using hidden Markov models.
Sept. 27
Precision medicine: some three millions of differences between two humans: sequence polymorphisms
Oct. 4
Analysis of gene expression, Part I.
Oct. 11 Analysis of gene expression, Part II.
Oct. 18 Fall Break, no class
Oct 25 Making sense out of gene expression: Gene Ontology, metabolic pathways, and gene set enrichment analysis.
Nov. 1
Transcriptomics for 2016: RNA sequencing
Nov. 8 Proteomics.
Nov. 15 Biological Networks: Protein-protein interactions
Nov. 23 Metabolomics or Machine Learning, students choose
Nov. 29
Systems biology.
Dec. 6
Preparation for the final exam.
7:30 - 9:30 AM Dec. 16 Final Exam, last day of the Course.