Rabb School of Continuing Studies
Course Syllabus
I. Course Information
1. RBIF-108
2. Computational Systems Biology
3. May 24 –August 1 Course Week runs from Wednesday to Tuesday.
4. Instructor’s Name and Contact Information
· Name Richard Allen
· Title
· Email – email
· Phone (not required)
· Office Hours/Availability – By Arrangement
You can use the Private Forum in LATTE, or e-mail, for any direct communications with me. Please send me a message to arrange a time to speak with me directly with questions or concerns or if you need help with the course material or assignments.
5. Document Overview
This syllabus contains all relevant information about the course: its objectives and outcomes, the grading criteria, the texts and other materials of instruction, and of weekly topics, outcomes, assignments, and due dates.
Consider this your roadmap for the course. Please read through the syllabus carefully and feel free to share any questions that you may have. Please print a copy of this syllabus for reference.
6. Course Description
Catalog Description and Course Outcomes
Computational systems biology is a field that aims to provide an integrative, system-level understanding of biology through the modeling of experimental data. The course covers the mathematical basics of modeling biological systems, analysis, storage, and sharing of models, and applications to predicting the effect of pharmacological intervention in physiological models.
Upon completion of this course, students will be able to:
- Develop novel mathematical models of biology in MATLAB that match experimental data
- Analyze and evaluate models using computational and analytical approaches
- Apply perturbations to Systems Pharmacology models to assess efficacious therapeutic targets
- Use basic principles of pharmacokinetics and dynamics to predict drug effects over time.
- Communicate and share models (and associated data) effectively.
- Evaluate and apply effective modeling approaches (linear, non-linear ODEs, non-steady state, steady-state) for a given experimental system.
Welcome Note
Welcome to Computational Systems Biology!
In this course we will learn about the basics of applying computational and mathematical methods to understand biological systems. This is an exciting and broad field which is quickly developing and gaining wider adoption in both academia and industry.
We will start with introductions to the field as whole, ordinary differential equations, and MATLAB - which will be the primary software tool we use in this course. We will then move onto building our own models and discuss using these models in the context of disease and treatment with drugs.
A couple of key housekeeping notes about this course.
• You can download a copy of MATLAB from www.mathworks.com by creating an account with your brandeis email address.
• At some point in this course you will need to requeqst get trial licenses from MathWorks for SimBiology and the Global Optimization toolbox. These are two week trials, so wait for the announcement (and link) to go and request these!
• Each week there will be various references. It is not necessary to exhaustively read all of these. Often times these will be published papers describing a novel model. In this case, be selective and focus on the resources that interest you!
If you get stuck with an assignment don't struggle for too long - ask for advice from me or your peers!
Relevant Programs
Bioinformatics
Prerequisites
RBIF 101, RBIF 100 is recommended
7. Materials of Instruction
a. Optional Texts
· Britton, N. Essential Mathematical Biology. Springer-Verlag London. 2003. ISBN 978-1-4471-0049-2
· Edelstein-Keshet, L., Mathematical Models in Biology. Society for Industrial and Applied Mathematics. 2004. ISBN 978-0-89871-554-5
b. Required Software and Other Supplies
· MATLab, available through Brandeis, go to Knowledge Base for details: https://kb.brandeis.edu/display/LTS/MATLAB
c. Recommended Resources
· For each: [Text Name, Author, Publisher, Edition/Year, ISBN]
Hyperlinks, recommended sites
d. Online Course Content
This course will be conducted completely online using Brandeis’ LATTE site, available at http://latte.brandeis.edu. The site contains the course syllabus, assignments, discussion forums, links/resources to course-related professional organizations and sites, and weekly checklists, objectives, outcomes, topic notes, self-tests, and discussion questions. Access information is emailed to enrolled students before the start of the course.
To begin participating in the course, review the Welcome Message and the materials found in the Week 1 block.
8. Course Grading Criteria
Weighted Grading of AssignmentsPercent / Component
30% / Discussions/Online participation:
individual discussions (including original responses and replies) -% per week, 10 weeks
50% / Assignments 1-3, 5-7
20% / Assignment 4
Description of Assignments [The following descriptions may change slightly]
1. Participation – Discussion Questions
Each week, students are required to post original responses to one or two discussion questions by Saturday (by 11:55pm in his/her time zone), and at least two substantive replies to the responses of others by Tuesday (by 11:55pm in his/her time zone).
Participation Evaluation Criteria:
Question Responses / 60% of weekly participation grade / Max. Points per criteriaIncludes your own insights into the topics, sharing your professional experiences as appropriate and your own conclusions / 16
Includes references to weekly required readings and/or other external sources, cited appropriately. All original responses must draw on external references / 16
Answers the question posed completely; poses questions or points of consideration to elicit responses from classmates / 16
Consists of at least 250-300 words / 6
Well written, with no spelling or grammatical errors, and with the care normally exercised for the student’s professional communications / 6
One day late: -15 out of 30 possible raw points; more than one day late: no credit
Discussion Replies / 30% of Weekly Participation Grade / Max. Points per criteria
Substantive (beyond an "I agree" or complimentary post) with:
o Follow-on points from your related experiences and/or from the readings
o Consists of at least 200 words
o Follow-up questions of others to extend the conversation (encouraged, but not required) / 24
Grammar/spelling/format/sources noted as appropriate / 6
Posting Activity / 10% of Participation Grade / Max. Points
Post the minimum number of required posts on three or more days of the course week
Post the minimum number of required posts on two days of the course week
Post any number of posts on one day of the course week / 10
5
1
Thoughts on Discussions
Keep in mind that these postings to the forums will be as rich as we make them; not having a traditional classroom in which to discuss topics, we can have some interesting discussions and share our experiences during the 10 weeks. They are required to encourage you to share your knowledge and ideas while gaining from the experiences of your peers as well. You will quickly adjust to the weekly requirements and become familiar with the review criteria, and I look forward to some rich discussions.
2. Assignments
Assignment 1 / Identify a biological system that would be suited to being described mathematically with a systems biology model. Why is this good choice? What data is available to validate this model? Draw a diagram of the key interactions, and document the key data for this model to match.Assignment 2 / For the system you identified in Assessment 1 write down the simplest linear ODE model (hint: use the diagram you drew) possible. Derive a mathematical condition for your system to be stable.
Assignment 3 / Comment on the provided code (MyModel.m and RunMyModel.m) using best practices and consistent style. Modify the parameters and plot the results.
Using similar code, implement and simulate the model you wrote down in Week 2. Is the expression that you derived for steady-state correct?
Assignment 4 / Build and simulate a model in MATLAB for an enzyme-substrate system using mass-action kinetics. Simplify your system using QSS assumptions, and check the validty of your assumption through simulation
Assignment 5 / Extend the model from Week 3 to be more realistic. Progress summaries due Weeks 5-7, Assignment due Week 8.
Assignment 6 / Construct a Systems Pharmacolgy model in MATLAB by adding your PK model to your existing model.
Assignment 7 / Reflection
II. Weekly Information
Learning Objectives / At the end of week 1, students will be able to:
· Recognize the value of systems biology modeling
· Explain what constitutes a systems biology model
Readings / · Topic Notes
· Studies referenced in Topic Notes
Discussions / · Introduce Yourself Forum
· Week 1 Discussion: original responses no later than Saturday, replies no later than Tuesday
Assignments/ Assessments / · Assignment 1 - Identify a biological system…
Week 2 / Mathematics for Modeling Primer (I): ODEs, Linear Systems
Learning Objectives / At the end of week 2, students will be able to:
· Analyze linear systems of ODEs
· Interpret the basic features of an ODE system
Readings / · Topic Notes
· Studies referenced in Topic Notes
Discussions / · Week 2 Discussion: original responses no later than Saturday, replies no later than Tuesday
Assignments/ Assessments / · Assignment 2 – Write down the simplest linear ODE model…
Week 3 / Introduction to MATLAB coding: Building and simulating ODEs
Learning Objectives / At the end of week 4, students will be able to:
· Prepare and simulate a linear system of ODEs in MATLAB
· Organize MATLAB code for readability
Readings / · Topic Notes
· Studies referenced in Topic Notes
Discussions / · Week 3 Discussion: original responses no later than Saturday, replies no later than Tuesday
Assignments/ Assessments / · Assignment 3 - Comment on the provided code….
Week 4 / Mass Action Kinetics and Approximations: Michaelis Menten Kinetics
Learning Objectives / At the end of week 5, students will be able to:
· Interpret when Michaelis-Menten kinetics and QSS assumptions are valid
Readings / · Topic Notes
· Studies referenced in Topic Notes
Discussions / · Week 4 Discussion: original responses no later than Saturday, replies no later than Tuesday
Assignments/ Assessments / · Assignment 4 – Build and simulate a model…
Week 5 / Mathematics for Modeling Primer (II): Global Optimization, Sensitivity Analysis
Learning Objectives / At the end of week 3, students will be able to:
· Apply global optimization techniques to fit a model
· Analyze a model parameters to assess sensitivity
Readings / · Topic Notes
· Studies referenced in Topic Notes
Discussions / · Week 5 Discussion: original responses no later than Saturday, replies no later than Tuesday
Assignments/ Assessments / · Assignment 5 – Progress summary
Week 6 / Alternative Modeling Approaches
Learning Objectives / At the end of week 6, students will be able to:
· Summarize key concepts for other modeling approaches beyond dyncamic ODEs (PDEs, agent based modeling, FBA/MFA)
· Suggest when is a appropriate to apply these approaches
Readings / · Topic Notes
· Studies referenced in Topic Notes
Discussions / · Week 6 Discussion: original responses no later than Saturday, replies no later than Tuesday
Assignments/ Assessments / · Assignment 5 – Progress summary
Week 7 / Phamacokinetic and Pharmacodynamic Modeling
Learning Objectives / At the end of week 7, students will be able to:
· Implement a simple model describing the pharmacokinetics of a drug
· Identify how to extend a PK model to include pharmacodynamic effects
Readings / · Topic Notes
· Studies referenced in Topic Notes
Discussions / · Week 7 Discussion: original responses no later than Saturday, replies no later than Tuesday
Assignments/ Assessments / Assignment 5 – Progress summary
Week 8 / Building Models in SimBiology & exporting to SBML: Exporting from SimBiology; Modifying .xml file; Ontologies
Learning Objectives / At the end of week 8, students will be able to:
· Use SimBiology to build and share models
· Export models to different xml based formats
Readings / · Topic Notes
· Studies referenced in Topic Notes
Discussions / · Week 8 Discussion: original responses no later than Saturday, replies no later than Tuesday
Assignments/ Assessments / · Assignment 5 – Report due.
Week 9 / Systems Pharmacology Modeling and Virtual Populations
Learning Objectives / At the end of week 9, students will be able to:
· Explain the key industry applications of Systems Pharmacology Models and Virtual Populations
· Construct a virtual population for a model
Readings / · Topic Notes
· Studies referenced in Topic Notes
Discussions / · Week 9 Discussion: original responses no later than Saturday, replies no later than Tuesday
Assignments/ Assessments / Assignment 6 - Construct a Systems Pharmacology model...
Week 10 / Course Overview
Learning Objectives / At the end of week 10, students will be able to:
· Apply key concepts from entirety of RBIF108 Computational Systems Biology.
Readings / · Topic Notes (briefly review weeks 1-10)
· Studies referenced in Topic Notes
Discussions / · Week 10 Discussion: original responses no later than Saturday, replies no later than Tuesday
Assignments/ Assessments / Assignment 7 - Reflection
III. Course Policies and Procedures
[These are policies and procedures determined by the instructor. The following sections are recommended. Add sections as needed for your course.]
1. Late Policies
2. Grading Standards
· Work expectations – how much time can the student expect to spend per week on various course activities throughout the term
· For example:
Students are responsible to explore each week's materials and submit required work by their due dates. On average, a student can expect to spend approximately 3-5 hours per week reading and approximately 5-8 hours per week completing assignments and posting to discussions.The calendar of assignments and due dates is located at the end of this syllabus, and all assignments are due by the close of the associated week (Tuesday evenings, midnight EST).
· How points and percentages equate to grades
100-94 / A / 76-73 / C93-90 / A- / 72-70 / C-
89-87 / B+ / 69-67 / D+
86-83 / B / 66-63 / D
82-80 / B- / 62-60 / D-
79-77 / C+ / 59 or < / F
3. Feedback [How and when feedback on course assignments will be provided]
4. Confidentiality
· For example:
· We can draw on the wealth of examples from our organizations in class discussions and in our written work. However, it is imperative that we not share information that is confidential, privileged, or proprietary in nature. We must be mindful of any contracts we have agreed to with our companies. In addition, we should respect our fellow classmates and work under the assumption that what is discussed here (as it pertains to the workings of particular organizations) stays within the confines of the classroom.