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Curriculum - Core Course
02-730 Cellular and Systems Modeling
2006-07 course description
This class covers biological background, core computational methods and illustrative real-world applications in the broad areas of cell and systems modeling. The course is organized into three modules covering the biological background, the computational background and extended illustrative examples of interdisciplinary modeling in cell and systems biology.
General Course Information
Lecturers: Ivan Maly, Russell Schwartz, Joel Stiles
Lecture Times: Tuesdays and Thursdays 2:30-3:50 (Fall 2007)
Texts: Principles of Human Physiology, Germann and Stanfield (Second Edition); Computational Cell Biology, eds. Fall, Marland, Wagner, and Tyson; and class notes prepared by the lecturers; optional text Computational Physiology, Keener and Sneyd
Exams: There will be one exam per module. There will be no comprehensive final exam.
Grading: Grades will be based on homework assignments (40%) and the three exams (20% each).
Syllabus (tentative)
Module 1: Physiology and Systems Biology (Stiles; Aug. 28 - Sept. 27)
Cellular and system homeostasis and energy balance
Membrane potential and cellular excitation
Overview of organ system physiology
Overview of intra- and extracellular signaling
Computational approaches to cellular and systems physiology
Module 2: Algorithms and Numerical Methods (Schwartz; Oct. 2 - Oct. 25)
Numerical integration: ordinary differential equations, partial differential equations, and stochastic differential equations
Markov models: definitions, basic theory, continuous-time variants
Applications to reaction chemistry: mass action, stochastic simulation, hybrid methods
Optimization: general single and multi-variable optimization, constrained optimization
Parameter tuning: fitting by continuous optimization, expectation-maximization, application to problems in network inference
Module 3: Applications (Maly, Stiles, others; Oct. 30 - Dec. 6)
The kinetic origin of the cell structure: the cytoskeleton, organelle
transport, cell shape, and locomotion
Linear aggregation theory, velocity-jump stochastic processes, reaction-diffusion-advection models for the structural dynamics of the cell
Deriving the microtubule cytoskeleton structure and dynamics from the kinetics of tubulin aggregation constrained by the cell
Deriving organelle distributions in the cell from the kinetics of motor-driven transport along microtubules
Deriving the shape of motile cells from the kinetics of the actin cytoskeleton assembly constrained by the cell
Microphysiology: continuous versus stochastic approaches to reaction-diffusion simulations
Microphysiology: calcium dynamics and synaptic transmission in spatially realistic stochastic models
Other applications of modeling in biomedical sciences: topics vary, but are likely to include regulatory networks, immunological models, and modeling in the clinic
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