Course Schedule and Catalog
Course Schedule - Fall Semester 2017
Meeting location information can now be found on student schedules in ESTHER (for students) or on the Course Roster in ESTHER (for faculty and instructors).Additional information available here.
Meeting location information can now be found on student schedules in ESTHER (for students) or on the Course Roster in ESTHER (for faculty and instructors).Additional information available here.
SSPB 502 001 (CRN: 11907)
INTRO SYSTEMS BIOLOGY MODELING
Long Title: INTRO COMPUTATIONAL SYSTEMS BIOLOGY: MODELING & DESIGN PRINCIPLES OF BIOCHEM NETWORKS
Department: Systems/Synthetic/Phys Biology
Instructor: Igoshin, Oleg
Meeting: 10:50AM - 12:05PM TR (21-AUG-2017 - 1-DEC-2017)
Part of Term: Full Term
Grade Mode: Standard Letter
Course Type: Lecture
Language of Instruction: Taught in English
Method of Instruction: Face to Face
Credit Hours: 3
Course Syllabus:
Course Materials: Rice Campus Store
Restrictions:
Must be enrolled in one of the following Level(s):
Graduate
Section Max Enrollment: 19
Section Enrolled: 7
Total Cross-list Max Enrollment: 34
Total Cross-list Enrolled: 12
Enrollment data as of: 16-APR-2024 5:12PM
Final Exam: GR Course-Dept Schedules Exam
Description: The course summarizes techniques for quantitative analysis and simulations of basic circuits in genetic regulation, signal transduction and metabolism. We discuss engineering approaches adapted to computational systems biology and aim to formulate evolutionary design principles explaining organization of networks in terms of their physiological demands. We discuss biochemical simulation methodology and software as well as recent advances in the field. Topics include end-product inhibition in biosynthesis, optimality and robustness of the signaling networks and kinetic proofreading. More emphasis on recent advances in the field - paper reading and presentations. Cross-list: BIOE 552, BIOE 490. Recommended Prerequisite(s): Basic knowledge of biochemistry, cell biology, linear algebra, and ordinary differential equations is expected. Experience with MATLAB or Python programming is highly recommended.