PC-based differential model fitting as a support for clinical research

Geltrude Mingrone, Gabriele Sganga, Andrea De Gaetano, Marco Castagneto, Wp Coleman, Pa Tataranni, G Gangeri, Av Greco

Risultato della ricerca: Contributo in rivistaArticolo in rivista

3 Citazioni (Scopus)

Abstract

A PC-based minimisation software written in C-language is described, which solves numerically both simple non-linear regression problems and problems expressed as systems of (unsolved) initial-value ordinary or partial differential equations. The software uses second-order iterated Runge-Kutta algorithm to approximate numerically the solution curves. It uses a quasi-Newton algorithm to minimize either sums of squares (weighted or unweighted) or NONMEM loss functions. Inverse Hessian approximation to the parameter dispersion and Monte Carlo generation of artificial samples are offered to test the robustness of the parameter values obtained. A real test problem is described, involving the hydrolysation of plasma Medium Chain Triglycerides to Free Fatty Acids and the uptake of these from plasma. Two competing models were evaluated, one involving linear terms for each transfer and one involving carrier-mediated, rate-limited hydrolysis and tissue absorption steps. The simpler linear model was found to be more robust and eventually used to describe the experimental data.
Lingua originaleEnglish
pagine (da-a)35-41
Numero di pagine7
RivistaInternational Journal of Clinical Monitoring and Computing
Volume11
Stato di pubblicazionePubblicato - 1994

Keywords

  • Algorithms
  • Fatty Acids, Nonesterified
  • Humans
  • Hydrolysis
  • Linear Models
  • Microcomputers
  • Models, Biological
  • Models, Statistical
  • Monte Carlo Method
  • Problem Solving
  • Research
  • Software
  • Stress, Physiological
  • Surgical Procedures, Operative
  • Triglycerides

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