AMATH 410:

Introduction to Computational Biology and Chemistry


WINTER 2009

MW 4:30-6:20
4 credits, enroll. limit 28

InstructorTATA
Professor Eric Shea-BrownMotoki Wu Joy Zhou
Guggenheim 415F apewu@u.washington.edu yzhou@amath.washington.edu
Course assistance: W after class - 6:40, in course room Course assistance: M 3:30-4:30, Tu 11-12, in B027, Comm. Bldg. Course assistance: M 9:00-10:00AM, in B022, Comm. Bldg.; Tues. 6-7 PM, in MGH 058; W 9-10AM in B022, Comm. Bldg.

Description and Objectives Textbook and Notes Syllabus Schedule and Homework

Description and Objectives

In AMATH 410, you will learn about models that arise in biology in chemistry and how they're analyzed using modern mathematical and computational techniques. We will cover statistical models, discrete- and continuous- time dynamical models, and stochastic models. Applications will sample a wide range of scales, from biomolecules to population dynamics, with an emphasis on common mathematical concepts and computational techniques. Throughout, our themes will include interpretation of existing data and predictions for new experiments.

MATLAB and R (see more below) will be used for numerical computation, visualization, and data analysis -- and mathematical tools taught in parallel with their computational implementation. No prior programming experience is assumed.

This course is designed for students in a wide variety of departments and with backgrounds across the sciences. A working knowledge of calculus is assumed, together with a desire to learn more about the underlying science, mathematics, or both.

A guide to the course

There's a lot to this course, but if you dig in I think you'll find it rewarding and enjoyable. Here's what you need to keep track of: (1) Reading, listed under syllabus below. (2) Assignments, homeworks, case study presentation and project, and exam, listed under schedule below. (3) Programming assignments, listed under programming skills below. All of these are required parts of the course, and all of these have associated dates given below. Please check this website frequently for updates and postings.

Textbooks, Notes, and Course Resources

The required text for this course is "Dynamic Models in Biology," by Stephen Ellner and John Guckenhiemer (called EG below). A few chapters (including CHAPTER 1) are available free online,

***here***.

The text should be in the bookstore, is available on Amazon, and is on reserve in the Odegaard library.

The "Lab Manual" for this course is also required reading. Freely available from the webpage

***here***,

this manual introduces, from scratch, the basics of scientific programming and computational methods -- and how to use them to solve and analyze the models and problems in the main text. There is both a MATLAB and a R version. The MATLAB and R codes that accompany the lab manual are also available

***here***.


OTHER COURSE RESOURCES -- Mathematical models in biology

There are several useful texts on mathematical modeling in the life sciences on course reserve in the library. Two of special note are:

A course in Mathematical Biology, by de Vries, Hillen, Lewis, Muller, Schonfisch

Mathematical Models in Biology, by Leah Edelstein-Keshet

OTHER COURSE RESOURCES -- Computational methods, R, and MATLAB

The notes of Prof. Nathan Kutz for AMATH 301 are a valuable reference. Prof. Kutz has provided them online here: (****.pdf****)

There are a variety of MATLAB and R resource books available at the library.

For other MATLAB and R resources, including online tutorials, see below.

Programming languages

MATLAB and R are two high-level (i.e., relatively easy to use, with many built in features) programming languages that are in wide use in biological modeling. The lab manual for the course is available in both MATLAB and R, as are example scripts for the course (all freely downloadable from the links above). Students are welcome to follow either lab manual in the lab sessions, gaining practice with either language. Moreover, students are welcome to turn in all assignments, exams, and the course project in either language as well.

MATLAB and R -- access, manuals, and tutorials

In this course, we will make extensive use of Matlab ( The MathWorks, Inc) and R, both technical computing environments for numerical computation and visualization.

There is access to both MATLAB and R at the ICL labs on campus. A Matlab manual is available in the ICL Lab.

Additionally, you can access MATLAB (from a windows machine) remotely by following the links to "terminal server". Another option is to purchase the student version of MATLAB for your personal computer -- this is available for a very heavily discounted price.

R can be downloaded free of charge for mac, pc, and linux variety, via this link.

Many Matlab and R resources are available on the net, such as:

  • Matlab Hypertext Reference, Portland State University
  • R tutorial ``Intro to R" under `manuals' link, accessed from http://www.r-project.org/
  • R_tutorial_from_UW_QERM.txt

    The following useful document provides side-by-side comparisions of MATLAB and R commands: matlabR.pdf

    Step-by-step instructions for accessing MATLAB from a MAC (many of these settings may be already in place):

  • Go to applications folder in MAC, click on remote desktop connection
  • For "computer," type login.aslab.washington.edu
  • Click on the options arrow
  • Go to display tab
  • Choose size of remote desktop to be 1280X1024 or another "big enough" size
  • Choose millions of colors
  • IF you want to save documents on the MAC, or on a flash drive, go to Local Resources Tab, click the Disk Drives checkbox
  • Click connect.
  • If you need the logon and password from here, go to http://depts.washington.edu/aslab/ and click on ``terminal server" link.

    Review and overview materials

  • matlab_programming_review.pdf
  • R_programming_review.pdf
  • cell_reproduction_review_code_1.m
  • cell_reproduction_review_code_1.R
  • helpful_programming_ techniques_in_R.pdf
  • helpful_programming_ techniques_in_MATLAB.pdf
  • note_on_eigenvalues_and_eigenvectors.pdf
  • tutorial_on_odes_in_MATLAB.pdf LINK TO MATLAB TUTORIAL ON ODEs

    • Optional background tutorials

  • lec2_vectors_matrices_review_410_W09.pdf
  • tutorial_on_summation_convention.pdf
  • tutorial_on_for_loops.pdf

    Syllabus

    (1) Course overview and introduction to mathematical models in the life sciences (0.5 weeks).
    Reading: EG Chapter 1, EG lab manual sections 1-7.
    • Modeling objectives: prediction and theory development
    • Introduction to programming: vectors, matrices, loops, logic, plotting

    Lecture materials -- notes, codes, and sampling of papers referred to in class:
  • noteset_11_ODEs.pdf
  • solving_ODEs_in_R.txt
  • direction_field_plotter.m
  • my_odefun.m
  • noteset_12_ODEs_2.pdf
  • More MATLAB and R codes from lectures on ODEs
  • noteset_13_ODEs_3.pdf
  • noteset_14_systems_bio.pdf

    • (5) More on fitting and testing models (1 weeks)

    Reading: EG Chapter 9

    Schedule and Homework

    Follow links in the table below to obtain a copy of the homework in Adobe Acrobat (.pdf) format. You may also obtain here solutions to some of the homework and exam problems. For additional information regarding viewing and printing the homework and solution sets, click here.

    Homework, Exams, and Events Date Problem sets, etc.
    Homework#1 Due Thu. 1/22, 4:30 PM, in Shea-Brown mailbox, room 414 Guggenheim Hall Homework #1 (.pdf) Homework #1 solutions ( MATLAB.pdf OR R.pdf) MATLAB and R SOLUTION CODES (link)
    Homework#2 Due Thu. 1/29, 4:30 PM, in Shea-Brown mailbox, room 414 Guggenheim Hall Homework #2(.pdf) Homework #2 solutions ( .pdf) MATLAB and R SOLUTION CODES (link)
    Homework#3 Due Thu. 2/12, 4:30 PM, in Shea-Brown mailbox, room 414 Guggenheim Hall Homework #3(.pdf) SequenceOfCurrentsDatamatrix.dat Homework #3 solutions ( .pdf) MATLAB and R SOLUTION CODES (link)
    Information about course projects and case study presentation FOLLOW THIS LINK TO PROJECT AND CASE STUDY DETAILS.
    Midterm 2/18, in class PLEASE NOTE: one question on the midterm will be to describe your course project plans, so please think about them starting soon!
    Student case study presentations 2/25, in class
    Homework#3 Due Thu. 2/12, 4:30 PM, in Shea-Brown mailbox, room 414 Guggenheim Hall Homework #3(.pdf) SequenceOfCurrentsDatamatrix.dat Homework #3 solutions ( .pdf) MATLAB and R SOLUTION CODES (link)
    Homework#4 Due Fri. 3/6 4:30 PM, in Shea-Brown mailbox, room 414 Guggenheim Hall Homework #4 (.pdf)
    FINAL PROJECTS DUE (HAND IN PAPER), COURSE PRESENTATIONS Weds. 3/11, in class 4:30 PM

    Programming skills and lab sessions

    It's essential and a course requirement to keep up with the below lab assignments and (accumulating) list of programming skills. Please use our book's lab manual (link to free .pdf above), and other MATLAB and R resources as mentioned above.
    Week(s) of Lab session assignment
    Weeks of Jan. 5 - Jan 12 Work through EG Lab manual sections 1-7. Do all exercises in sec. 1-7. Skip sec, 8 for now. Work through sec. 9, but do not do exercises in sec. 9 yet. Also work through programming review given in ``overview" section at top of website.
    Weeks of Jan. 19-26 Work through EG Lab manual sections 8 (MATLAB) or 8.1 and 8.2 only (R) and the exercises in those sections. Also work through note_on_eigenvalues_and_eigenvectors.pdf in ``overview" section at top of website.
    Week of Feb. 2 Work through EG Lab manual section 11.
    Week of Feb. 23 Work through EG Lab manual section 13 (MATLAB) and 13, 13.1 (R).

    Homework submission and course policies

    Homework is due on the dates above. Please take careful note of these dates, as they are somewhat irregularly spaced. Please also note that these dates might change somewhat as the course progresses -- webpage and email updates will be provided.

     

    Grading

    Your course grade will be calculated via the following weights:

    Homework 40%

    Midterm 20%

    Case study presentation 5%

    Course project 35%

    The test schedule is in the table above.

    Case study and final project presentation -- follow link to more details.

    Each student group will give a brief in-class presentation of a paper that applies the modeling and computational techniques we have learned in the course (case study). These studies will be developed into course projects, WITH PAPERS DUE ON THE LAST DAY OF CLASS. See link above for project and case study details.