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With the emergence of Systems Biology, there is a greater realization that the whole behavior of a living system may not be simply described as the sum of its elements. To represent a living system using mathematical principles, practical quantities with units are required. Quantities are not only the bridge between mathematical description and biological observations; they often stand as essential elements similar to genome information in genetics. This important realization has greatly rejuvenated research in the area of Quantitative Biology. Because of the increased need for precise quantification, a new era of technological development has opened. For example, spatio-temporal high-resolution imaging enables us to track single molecule behavior in vivo. Clever artificial control of experimental conditions and molecular structures has expanded the variety of quantities that can be directly measured. In addition, improved computational power and novel algorithms for analyzing theoretical models have made it possible to investigate complex biological phenomena. This research topic is organized on two aspects of technological advances which are the backbone of Quantitative Biology: (i) visualization of biomolecules, their dynamics and function, and (ii) generic technologies of model optimization and numeric integration. We have also included articles highlighting the need for new quantitative approaches to solve some of the long-standing cell biology questions. In the first section on visualizing biomolecules, four cutting-edge techniques are presented. Ichimura et al. provide a review of quantum dots including their basic characteristics and their applications (for example, single particle tracking). Horisawa discusses a quick and stable labeling technique using click chemistry with distinct advantages compared to fluorescent protein tags. The relatively small physical size, stability of covalent bond and simple metabolic labeling procedures in living cells provides this type of technology a potential to allow long-term imaging with least interference to protein function. Obien et al. review strategies to control microelectrodes for detecting neuronal activity and discuss techniques for higher resolution and quality of recordings using monolithic integration with on-chip circuitry. Finally, the original research article by Amariei et al. describes the oscillatory behavior of metabolites in bacteria. They describe a new method to visualize the periodic dynamics of metabolites in large scale cultures populations. These four articles contribute to the development of quantitative methods visualizing diverse targets: proteins, electrical signals and metabolites. In the second section of the topic, we have included articles on the development of computational tools to fully harness the potential of quantitative measurements through either calculation based on specific model or validation of the model itself. Kimura et al. introduce optimization procedures to search for parameters in a quantitative model that can reproduce experimental data. They present four examples: transcriptional regulation, bacterial chemotaxis, morphogenesis of tissues and organs, and cell cycle regulation. The original research article by Sumiyoshi et al. presents a general methodology to accelerate stochastic simulation efforts. They introduce a method to achieve 130 times faster computation of stochastic models by applying GPGPU. The strength of such accelerated numerical calculation are sometimes underestimated in biology; faster simulation enables multiple runs and in turn improved accuracy of numerical calculation which may change the final conclusion of modeling study. This also highlights the need to carefully assess simulation results and estimations using computational tools.

fluorescence chemistry --- numerical integration --- molecular crowding --- quantum dot --- cell division --- data visualization --- imaging --- model optimization --- GPGPU

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This volume deals with novel high-quality research results of a wide class of mathematical models with applications in engineering, nature, and social sciences. Analytical and numeric, deterministic and uncertain dimensions are treated. Complex and multidisciplinary models are treated, including novel techniques of obtaining observation data and pattern recognition. Among the examples of treated problems, we encounter problems in engineering, social sciences, physics, biology, and health sciences. The novelty arises with respect to the mathematical treatment of the problem. Mathematical models are built, some of them under a deterministic approach, and other ones taking into account the uncertainty of the data, deriving random models. Several resulting mathematical representations of the models are shown as equations and systems of equations of different types: difference equations, ordinary differential equations, partial differential equations, integral equations, and algebraic equations. Across the chapters of the book, a wide class of approaches can be found to solve the displayed mathematical models, from analytical to numeric techniques, such as finite difference schemes, finite volume methods, iteration schemes, and numerical integration methods.

mathematical modeling --- infiltration well --- differential equations --- porous medium --- fractal conductivity model --- incomplete rankings --- Kendall’s tau --- permutation graph --- competitive balance --- Spotify --- collocation --- volterra integral equation --- highly oscillatory --- convergence --- areal porosity --- volumetric porosity --- fractal area-volume relationship --- tortuosity factor --- joint probability --- corrugated box printing machine --- modified Delphi method --- analytic network process (ANP) --- supplier --- nonlinear system --- iterative method --- divided difference operator --- stability --- parameter plane --- dynamical plane --- random hyperbolic model --- random laplace transform --- numerical integration --- monte carlo method --- numerical simulation --- talbot algorithm --- stochastic perturbations --- random nonlinear oscillator --- maximum entropy principle --- probability density function --- stationary Gaussian noise --- random mean square parabolic model --- finite degree of randomness --- random finite difference scheme --- relativistic harmonic oscillator --- kinematics of a particle --- special relativity --- nonlinear problems in mechanics --- equations of motion in gravitational theory --- virus propagation --- stochastic modeling --- Gillespie algorithm --- conservative formulation --- multidimensional fragmentation equation --- weight functions --- finite volume scheme --- contamination plume --- advection-diffusion --- universal curves --- Dirichlet-to-Neumann map --- Schrödinger operator --- contagion effect --- difference equation --- elections --- labor condition --- mathematical compartmental discrete model --- political corruption --- revolving doors --- sensitivity analysis --- simulation --- numerical methods --- integro-interpolation method --- splitting method --- convergence of models --- standard deviation of the error --- diabetic retinopathy --- ocular fundus --- laser coagulation --- optical coherence tomography --- image processing --- segmentation --- safe treatment --- Hermite interpolation --- nodal systems --- unit circle --- circular membrane --- fluid-structure interaction --- differential-integral equations --- power series method --- closed-form solution --- time series model --- wavelet transform --- ARIMA model --- neural network NARX --- ionospheric parameters --- courtyard --- climate change --- microclimate --- Support Vector Regression (SVR) --- machine learning --- matrix functions --- matrix hyperbolic tangent --- matrix exponential --- Taylor series --- matrix polynomial evaluation --- n/a --- Kendall's tau --- Schrödinger operator