title: A parametrically constrained optimization method for fitting sedimentation velocity experiments.
abstract: A method for fitting sedimentation velocity experiments using whole boundary Lamm equation solutions is presented. The method, termed parametrically constrained spectrum analysis (PCSA), provides an optimized approach for simultaneously modeling heterogeneity in size and anisotropy of macromolecular mixtures. The solutions produced by PCSA are particularly useful for modeling polymerizing systems, where a single-valued relationship exists between the molar mass of the growing polymer chain and its corresponding anisotropy. The PCSA uses functional constraints to identify this relationship, and unlike other multidimensional grid methods, assures that only a single molar mass can be associated with a given anisotropy measurement. A description of the PCSA algorithm is presented, as well as several experimental and simulated examples that illustrate its utility and capabilities. The performance advantages of the PCSA method in comparison to other methods are documented. The method has been added to the UltraScan-III software suite, which is available for free download from http://www.ultrascan.uthscsa.edu.
authors.affiliation: The University of Texas Health Science Center at San Antonio, Department of Biochemistry, San Antonio, Texas.
authors.forename: Gary
authors.initials: G
authors.lastname: Gorbet
authors.affiliation: The University of Texas Health Science Center at San Antonio, Department of Biochemistry, San Antonio, Texas.
authors.forename: Taylor
authors.initials: T
authors.lastname: Devlin
authors.affiliation: The University of Texas Health Science Center at San Antonio, Department of Biochemistry, San Antonio, Texas.
authors.forename: Blanca I
authors.initials: BI
authors.lastname: Hernandez Uribe
authors.affiliation: The University of Texas Health Science Center at San Antonio, Department of Biochemistry, San Antonio, Texas.
authors.forename: Aysha K
authors.initials: AK
authors.lastname: Demeler
authors.affiliation: Texas A&M University, Department of Mechanical Engineering, College Station, Texas.
authors.forename: Zachary L
authors.initials: ZL
authors.lastname: Lindsey
authors.affiliation: The University of Texas Health Science Center at San Antonio, Department of Biochemistry, San Antonio, Texas.
authors.forename: Suma
authors.initials: S
authors.lastname: Ganji
authors.affiliation: The University of Texas Health Science Center at San Antonio, Department of Biochemistry, San Antonio, Texas.
authors.forename: Sabrah
authors.initials: S
authors.lastname: Breton
authors.affiliation: University of North Carolina at Chapel Hill, Department of Pathology and Laboratory Medicine, Chapel Hill, North Carolina.
authors.forename: Laura
authors.initials: L
authors.lastname: Weise-Cross
authors.affiliation: The University of Texas Health Science Center at San Antonio, Department of Biochemistry, San Antonio, Texas.
authors.forename: Eileen M
authors.initials: EM
authors.lastname: Lafer
authors.affiliation: The University of Texas Health Science Center at San Antonio, Department of Biochemistry, San Antonio, Texas.
authors.forename: Emre H
authors.initials: EH
authors.lastname: Brookes
authors.affiliation: The University of Texas Health Science Center at San Antonio, Department of Biochemistry, San Antonio, Texas. Electronic address: demeler@biochem.uthscsa.edu.
authors.forename: Borries
authors.initials: B
authors.lastname: Demeler
mesh_terms: D000465:Algorithms; D000818:Animals; D002417:Cattle; D002966:Clathrin / Q000737:chemistry / Q000378:metabolism; D004247:DNA / Q000737:chemistry; D009010:Monte Carlo Method; D058105:Polymerization; D014461:Ultracentrifugation / Q000379:methods*
chemical_list: D002966:Clathrin; D004247:DNA
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