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  1. Chew YH, Shia YL, Lee CT, Majid FA, Chua LS, Sarmidi MR, et al.
    Mol Cell Endocrinol, 2009 May 6;303(1-2):13-24.
    PMID: 19428987 DOI: 10.1016/j.mce.2009.01.018
    A model of glucose regulation system was combined with a model of insulin-signaling pathways in this study. A feedback loop was added to link the transportation of glucose into cells (by GLUT4 in the insulin-signaling pathways) and the insulin-dependent glucose uptake in the glucose regulation model using the Michaelis-Menten kinetic model. A value of K(m) for GLUT4 was estimated using Genetic Algorithm. The estimated value was found to be 25.3 mM, which was in the range of K(m) values found experimentally from in vivo and in vitro human studies. Based on the results of this study, the combined model enables us to understand the overall dynamics of glucose at the systemic level, monitor the time profile of components in the insulin-signaling pathways at the cellular level and gives a good estimate of the K(m) value of glucose transportation by GLUT4. In conclusion, metabolic modeling such as displayed in this study provides a good predictive method to study the step-by-step reactions in an organism at different levels and should be used in combination with experimental approach to increase our understanding of metabolic disorders such as type 2 diabetes.
    Matched MeSH terms: Feedback, Physiological
  2. Chang JJ, Syafiie S, Kamil R, Lim TA
    J Clin Monit Comput, 2015 Apr;29(2):231-9.
    PMID: 24961365 DOI: 10.1007/s10877-014-9590-6
    Anaesthesia is a multivariable problem where a combination of drugs are used to induce desired hypnotic, analgesia and immobility states. The automation of anaesthesia may improve the safety and cost-effectiveness of anaesthesia. However, the realization of a safe and reliable multivariable closed-loop control of anaesthesia is yet to be achieved due to a manifold of challenges. In this paper, several significant challenges in automation of anaesthesia are discussed, namely model uncertainty, controlled variables, closed-loop application and dependability. The increasingly reliable measurement device, robust and adaptive controller, and better fault tolerance strategy are paving the way for automation of anaesthesia.
    Matched MeSH terms: Feedback, Physiological/drug effects
  3. Abdullah A, Deris S, Anwar S, Arjunan SN
    PLoS One, 2013;8(3):e56310.
    PMID: 23469172 DOI: 10.1371/journal.pone.0056310
    The development of accurate computational models of biological processes is fundamental to computational systems biology. These models are usually represented by mathematical expressions that rely heavily on the system parameters. The measurement of these parameters is often difficult. Therefore, they are commonly estimated by fitting the predicted model to the experimental data using optimization methods. The complexity and nonlinearity of the biological processes pose a significant challenge, however, to the development of accurate and fast optimization methods. We introduce a new hybrid optimization method incorporating the Firefly Algorithm and the evolutionary operation of the Differential Evolution method. The proposed method improves solutions by neighbourhood search using evolutionary procedures. Testing our method on models for the arginine catabolism and the negative feedback loop of the p53 signalling pathway, we found that it estimated the parameters with high accuracy and within a reasonable computation time compared to well-known approaches, including Particle Swarm Optimization, Nelder-Mead, and Firefly Algorithm. We have also verified the reliability of the parameters estimated by the method using an a posteriori practical identifiability test.
    Matched MeSH terms: Feedback, Physiological
  4. Hasan H, Davids K, Chow JY, Kerr G
    Hum Mov Sci, 2016 Aug;48:102-11.
    PMID: 27155962 DOI: 10.1016/j.humov.2016.04.008
    The purpose of this study was to observe effects of wearing textured insoles and clinical compression socks on organisation of lower limb interceptive actions in developing athletes of different skill levels in association football. Six advanced learners and six completely novice football players (15.4±0.9years) performed 20 instep kicks with maximum velocity, in four randomly organised insoles and socks conditions, (a) Smooth Socks with Smooth Insoles (SSSI); (b) Smooth Socks with Textured Insoles (SSTI); (c) Compression Socks with Smooth Insoles (CSSI) and (d), Compression Socks with Textured Insoles (CSTI). Reflective markers were placed on key anatomical locations and the ball to facilitate three-dimensional (3D) movement recording and analysis. Data on 3D kinematic variables and initial ball velocity were analysed using one-way mixed model ANOVAs. Results revealed that wearing textured and compression materials enhanced performance in key variables, such as the maximum velocity of the instep kick and increased initial ball velocity, among advanced learners compared to the use of non-textured and compression materials. Adding texture to football boot insoles appeared to interact with compression materials to improve kicking performance, captured by these important measures. This improvement in kicking performance is likely to have occurred through enhanced somatosensory system feedback utilised for foot placement and movement organisation of the lower limbs. Data suggested that advanced learners were better at harnessing the augmented feedback information from compression and texture to regulate emerging movement patterns compared to novices.
    Matched MeSH terms: Feedback, Physiological
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