A stochastic model for heart rate fluctuations

Normal human heart rate shows complex fluctuations in time, which is natural, since heart rate is controlled by a large number of different feedback control loops. These unpredictable fluctuations have been shown to display fractal dynamics, long-term correlations, and 1/f noise. These characterizations are statistical and they have been widely studied and used, but much less is known about the detailed time evolution (dynamics) of the heart rate control mechanism. Here we show that a simple one-dimensional Langevin-type stochastic difference equation can accurately model the heart rate fluctuations in a time scale from minutes to hours. The model consists of a deterministic nonlinear part and a stochastic part typical to Gaussian noise, and both parts can be directly determined from the measured heart rate data. Studies of 27 healthy subjects reveal that in most cases the deterministic part has a form typically seen in bistable systems: there are two stable fixed points and one unstable one.Comment: 8 pages in PDF, Revtex style. Added more dat

GGOPT: an unconstrained non-linear optimizer

Comput Methods Programs Biomed. Author manuscript; available in PMC 2012 Jun 7GGOPT is a derivative-free non-linear optimizer for smooth functions with added noise. If the function values arise from observations or from extensive computations, these errors can be considerable. GGOPT uses an adjustable mesh together with linear least squares to find smoothed values of the function, gradient and Hessian at the center of the mesh. These values drive a descent method that estimates optimal parameters. The smoothed values usually result in increased accuracy.grant RR 01243 and EB08407 from the National Institutes of Healthgrant RR 01243 and EB08407 from the National Institutes of Healt

Computational biology of cardiac myocytes: proposed standards for the physiome

Predicting information about human physiology and pathophysiology from genomic data is a compelling, but unfulfilled goal of post-genomic biology. This is the aim of the so-called Physiome Project and is, undeniably, an ambitious goal. Yet if we can exploit even a small proportion of the rich and varied experimental data currently available, significant insights into clinically important aspects of human physiology will follow. To achieve this requires the integration of data from disparate sources into a common framework. Extrapolation of available data across species, laboratory techniques and conditions requires a quantitative approach. Mathematical models allow us to integrate molecular information into cellular, tissue and organ-level, and ultimately clinically relevant scales. In this paper we argue that biophysically detailed computational modelling provides the essential tool for this process and, furthermore, that an appropriate framework for annotating, databasing and critiquing these models will be essential for the development of integrative computational biology