Course Information
Course Meeting Time and Place:
Tuesday/Thursday: 12:40-2 PM in MBB 1424
Text: I will provide handouts on various topics as the course proceeds.
Instructor/TA Information
Instructor: Professor Julie A Dickerson, Electrical and Computer Engineering
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Office(s): 3123 Coover Hall and 2624 Howe Hall
Office Hours: TBD
Email: julied@iastate.edu
Instructor: Associate Professor Carolyn Lawrence, GDCB
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Office: 0077 Carver Co-Lab
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Office Hours: Thursdays 10-11 AM
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Email: triffid@iastate.edu
TA: Mr. Kannan Sankar
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Office: 108 Office and Lab
Office Hours: Wed 2-3 PM
Email: ksankar@iastate.edu
Course Description
Text: I will provide handouts on various topics as the course proceeds.
Course Description
Algorithmic and statistical approaches in computational functional genomics and systems biology;
Biological Information Integration – Knowledge (ontology) driven and statistical approaches;
Qualitative, probabilistic, and dynamic network models; Modeling, analysis, simulation and inference
of transcriptional regulatory modules and networks, protein-protein interaction networks; metabolic
networks; cells and systems.
Syllabus
Introduction:
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What is systems biology? From parts to interactions to wholes;
Data integration, predictive model construction, simulation and
model-based prediction, model-driven experimentation, bridging
levels of abstraction.
What is a (mathematical or computational) model? What are
models good for? How can we construct models? How can we
evaluate models?
Networks
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Introduction to networks and network types
Finding structure in networks
Clustering in networks
Association networks, correlation networks, hypergraph models
Analysis – module identification (spectral clustering),
comparative analysis
Inferring or building networks
Network comparison
Network Visualization
Modeling Gene Expression and Gene expression data analysis:
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Gene expression data acquisition Microarrays and RNAseq
Data and Standards
Tests for differential expression, multiple testing
Cluster analysis – hierarchical clustering, SOM, k-means, PCA,
how many clusters?
Modules of gene expression (network motifs)
Classification based on gene expression
Models of genetic networks
Modeling and analysis of protein-protein networks
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Protein-protein interaction data acquisition
Data and Standards
Metabolic Networks and Pathways
Integrating networks
Modeling metabolism:
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Metabolomics, metabolic flux
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Data and standards
Differential, difference, and stochastic equations
Enzyme Kinetics and thermodynamics
Metabolic networks
Metabolic control analysis
Steady-state models
Whole genome models
Modeling Signal Transduction:
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Intracellular communication
Receptor-ligand interaction
Structural components of signaling pathways
Example pathways – MAP-Kinase, JAK-Stat
Dynamic regulatory features
Data and Standards
Pathway Databases and Pathway Models
Modeling and analysis.
Integrative and multi-scale modelling
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What and why?
Data integration
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Sources
Model (ontology)-driven integration – ontologies, mappings,
database federation
Graph-theoretic methods
Probabilistic methods
Fuzzy methods
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Multi-scale modeling
Grading and Dead Week
Grading: (Preliminary)
Homework Assignments
40%
Projects on data integration and large-scale network analysis (40%)
Case Studies from literature and in-class discussion (20%)
Modeling/Computational/Visualization Tools:
You are expected to download and install these packages onto your laptops so that we may run
simulations and models in class.
Matlab: ISU student download
R: www.r-project.org
Cytoscape: Cytoscape.org
Dead Week: This class follows the Iowa State University Dead Week policy as noted in section
10.6.4 of the Faculty Handbook http://www.provost.iastate.edu/resources/facultyhandbook. There will be a homework assignment and student presentations due during Dead
Week.
Course Policies
Disability Accomodation: Iowa State University complies with the Americans with Disabilities Act
and Sect 504 of the Rehabilitation Act. If you have a documented disability and anticipate needing
accommodations in this course, please make arrangements to meet with Professor Dickerson as
soon as you become aware of your need. Retroactive request for accommodations will not be
honored. Please have the Disability Resources (Disability Resources Office) complete a SAAR form
verifying your disability and specifying the accommodations you will need for this course. You will
need to present this form to Professor Dickerson.
Harassment and Discrimination: Iowa State University strives to maintain our campus as a place of
work and study for faculty, staff, and students that is free of all forms of prohibited discrimination and
harassment based upon race, ethnicity, sex (including sexual assault), pregnancy, color, religion,
national origin, physical or mental disability, age, marital status, sexual orientation, gender identity,
genetic information, or status as a U.S. veteran. Any student who has concerns about such behavior
should contact his/her instructor, Student Assistance at 515-294-1020 or email dso-sas@iastate.edu,
or the Office of Equal Opportunity and Compliance at 515-294-7612.
Religious Accommodation: If an academic or work requirement conflicts with your religious
practices and/or observances, you may request reasonable accommodations. Your request must be
in writing, and your instructor or supervisor will review the request. You or your instructor may also
seek assistance from the Dean of Students Office or the Office of Equal Opportunity and Compliance.
Academic Misconduct & Integrity: Academic Misconduct in any form is in violation of Iowa
State University Student Disciplinary Regulations and will not be tolerated. This includes, but is
not limited to: copying or sharing answers on tests or assignments, plagiarism, and having
someone else do your academic work. Depending on the act, a student could receive an F grade
on the test/assignment, F grade for the course, and could be suspended or expelled from the
University. You are expected to practice academic honesty in every aspect of this course and all
other courses. Students who engage in academic misconduct are subject to university
disciplinary procedures, as well as consequences with regard to this course. See the Conduct
Code at www.dso.iastate.edu/ja for more details and a full explanation of the Academic
Misconduct policies.
Forms of academic dishonesty:
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Obtaining unauthorized information. Information is obtained
dishonestly, for example, by copying graded homework assignments
from another student, by working with another student on a take-
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home test or homework when not specifically permitted to do so by
the instructor, or by looking at your notes or other written work
during an examination when not specifically permitted to do so.
Tendering of information. Students may not give or sell their work
to another person who plans to submit it as his or her own. This
includes giving their work to another student to be copied, giving
someone answers to exam questions during the exam, taking an
exam and discussing its contents with students who will be taking
the same exam, or giving or selling a term paper to another student.
Misrepresentation. Students misrepresent their work by handing in
the work of someone else. The following are examples: purchasing
a paper from a term paper service; reproducing another person’s
paper (even with modifications) and submitting it as their own;
having another student do their computer program or having
someone else take their exam.
Plagiarism and Academic Dishonesty
Plagiarism is the act of representing directly or indirectly another
person’s work as your own. It can involve presenting someone’s
speech, wholly or partially, as yours; quoting without acknowledging
the true source of the quoted material; copying and handing in
another person’s work with your name on it; and similar infractions.
Even indirect quotations, paraphrasing, etc., can be considered
plagiarism unless sources are properly cited. Plagiarism will not be
tolerated, and students could receive an F grade on the
test/assignment or an F grade for the course. A useful link to
understanding plagiarism, the consequences of plagiarism, and best
practices for avoiding plagiarism is available at:
http://instr.iastate.libguides.com/content.php?pid=10314.
Contact Information: If you are experiencing, or have experienced, a problem with any of the above
issues, email academicissues@iastate.edu.