The development of a portable optical system for telemonitoring of skin blood oxygen level

Oxygen is one of the keys parameters required for tissues metabolism to ensure life sustainability. Without it, human’s health would suffer and eventually result in fatal. Cells consume oxygen to break down sugar to produce adenosine triphosphate (ATP) during cellular respiration [1]. ATPs are the main source of energy for metabolic functions [2] and every cell in the body, especially muscles cell, for its ability to store and use energy; muscle would not contract or relax without ATP. Cell is not able to function well under the condition of low oxygen level, thus it would lead to hypoxemia. If left untreated, severe hypoxemia can be fatal [3]

Probabilistic sensitivity analysis of biochemical reaction systems

Sensitivity analysis is an indispensable tool for studying the robustness and fragility properties of biochemical reaction systems as well as for designing optimal approaches for selective perturbation and intervention. Deterministic sensitivity analysis techniques, using derivatives of the system response, have been extensively used in the literature. However, these techniques suffer from several drawbacks, which must be carefully considered before using them in problems of systems biology. We develop here a probabilistic approach to sensitivity analysis of biochemical reaction systems. The proposed technique employs a biophysically derived model for parameter fluctuations and, by using a recently suggested variance-based approach to sensitivity analysis [Saltelli et al., Chem. Rev. (Washington, D.C.) 105, 2811 (2005)], it leads to a powerful sensitivity analysis methodology for biochemical reaction systems. The approach presented in this paper addresses many problems associated with derivative-based sensitivity analysis techniques. Most importantly, it produces thermodynamically consistent sensitivity analysis results, can easily accommodate appreciable parameter variations, and allows for systematic investigation of high-order interaction effects. By employing a computational model of the mitogen-activated protein kinase signaling cascade, we demonstrate that our approach is well suited for sensitivity analysis of biochemical reaction systems and can produce a wealth of information about the sensitivity properties of such systems. The price to be paid, however, is a substantial increase in computational complexity over derivative-based techniques, which must be effectively addressed in order to make the proposed approach to sensitivity analysis more practical