c o m p u t e r m e t h o d s a n d p r o g r a m s i n b i o m e d i c i n e 1 0 2 ( 2 0 1 1 ) [295][296][297][298][299][300][301][302][303][304] j o u r n a l h o m e p a g e : w w w . i n t l . e l s e v i e r h e a l t h . IntroductionAt the time of its conception (1972), the integrated physiology model developed by Guyton [1] was a breakthrough in many respects:(1) It assembled the available knowledge on the dynamics of the body's circulatory components and how they interacted with each other, (2) It presented a diagrammatic form, which allowed the totality of the model to be viewed and interactions examined, and (3) The model was specified using the basic components of integrators, summers and (sometimes nonlinear) gains, the fundamental building blocks of analogue computers, facilitating computation. * Corresponding author. Tel.: +353 879886557.E-mail address: violeta.i.mangourova@nuim.ie (V. Mangourova).Clearly, the longevity of this model is testament to its quality and usefulness. Unfortunately, developments in integrative physiological modelling have not kept pace with further discoveries in physiological science and developments in computing. In particular, the absence of an agreed computing platform, which can serve current teaching and research purposes, and be added to and updated by the physiology community at large, is disappointing. Recently, a number of large-scale initiatives have emerged, such as the IUPS (International Union of Physiological Sciences) Physiome Project [2], which attempt to directly address this issue. However, there is the issue of the universal adoption of a single standard in a situation where a number of competing standards exist and the associated question as to whether a single computing platform can cater for needs at all levels of detail and timescale. Furthermore, in the multi-disciplinary world of physiological modelling, where mathematicians, engineers, scientists 0169-2607/$ -see front matter

The control of blood pressure is a complex mixture of neural, hormonal and intrinsic interactions at the level of the heart, kidney and blood vessels. While experimental approaches to understanding these interactions remain useful, it remains difficult to conduct experiments to quantify these interactions as the number of parameters increases. Thus modelling approaches can offer considerable assistance. Typical mathematical models which describe the ability of the blood vessels to change their diameter (vasoconstriction) assume linearity of operation. However, due to the interaction of multiple vasocontrictive and vasodilative effectors, there is a significant nonlinear response to the influence of neural factors, particularly at higher levels of nerve activity (often seen in subjects with high blood pressure) which leads to low blood flow rates. This paper proposes a nonlinear mathematical model for the relationship between neural influences (sympathetic nerve activity (SNA) and blood flow, using a feedback path to model the predominently nonlinear effect of local vasoactive modulators such as Nitric Oxide, which oppose the action of SNA. The model, the structure of which is motivated by basic physiological principles, is parameterised using a numerical optimisation method using open-loop data collected from rabbits. The model responses are shown to be in good agreement with the experimental data.

_______________________________________________________________________________Abstract-Since the brain has no constant energy reserves, a continuous supply of energy substrates is central to all processes that maintain the functionality of the neuronal cells. EEG has been found to be tightly related to variations in the concentration of the energy substrates such as oxygen and glucose. Prediction of neural activation is particularly useful as it could contribute significantly in the prevention, stabilization, or treatment of diseases such as Alzheimer's disease, migraine headache, and ischemic stroke, in which signaling between neurons and brain vessels is threatened because of dysfunctions that affect the neuronal, astroglial, and/or vascular components of the neurovascular unit. This work deals with investigation of events in the EEG signal correlated with changes in both oxygen and glucose signals in the brain. The topic is to implement a model that through measures of oxygen and glucose in the brain of rats allow to achieve a good estimation of the neural signals, which reflecting the simultaneous metabolic changes, during spontaneous oscillation and electrical stimulation.

Gray box modelling of physiological systems involves constructing a model structure based on physical knowledge of the system and model parameterisation using numerical techniques. This paper presents a gray box model of arterial vasoaction (the process of constricting and dilating blood vessels in order to maintain an appropriate level of blood pressure and blood flow). The model structure is built in accordance with the physical system. The initial parameterisation was manual, with the model consequently optimised using gradient techniques and genetic algorithms. The model was validated by demonstrating good correlation between experimental results and model output.

The control of blood pressure is a complex mixture of neural, hormonal and intrinsic interactions at the level of the heart, kidney and blood vessels. While experimental approaches to understanding these interactions are useful, it remains difficult to conduct experiments to quantify these interactions as the number of parameters increases. Thus, modelling of such physiological systems can offer considerable assistance. Typical mathematical models which describe the ability of the blood vessels to change their diameter (vasoconstriction) assume linearity of operation. However, due to the interaction of multiple vasocontrictive and vasodilative effectors, there is a significant nonlinear response to the influence of neural factors, particularly at higher levels of nerve activity (often seen in subjects with high blood pressure) which leads to low blood flow rates. This paper proposes a number of nonlinear mathematical models for the relationship between neural influences (sympathetic nerve activity (SNA)) and renal blood flow, using a feedback path to model the predominantly nonlinear effect of local vasoactive modulators such as nitric oxide, which oppose the action of SNA. The model structures are motivated by basic physiological principles, while the model parameters are determined using numerical optimisation techniques using open-loop data collected from rabbits. The models were verified by demonstrating correlation between experimental results and model outputs. #