02701nas a2200241   4500008004100000022001400041245014500055210006900200260000900269300001200278490000800290520183600298653002102134653002102155653002202176653002202198653002002220653002502240100002602265700001602291700002102307856013102328       2015                        eng d  a0009-250900aEmbedded multiple shooting methodology in a genetic algorithm framework for parameter estimation and state identification of complex systems0 aEmbedded multiple shooting methodology in a genetic algorithm fr  cSEP   a605-6180 v1343 a<p>A novel parameter estimation and state identification algorithm for nonlinear dynamical systems from data by embedding a multiple shooting methodology in the framework of a genetic algorithm (EMSGA) is described. The advantages of EMSGA are brought out by studies with two highly nonlinear examples, viz., the chaotic dynamics of a non-isothermal CSTR and the glycolysis regulation in Lactococcus lactis for production of lactic acid. For the chaotic dynamics with extremely high sensitivity to parameter values and initial conditions, EMSGA accurately estimates all process parameters while at the same time recovering the true dynamics of monitored and unmonitored variables from limited extents of noisy dynamic data. The superiority in accuracy and computational time of EMSGA over standalone genetic or multiple shooting algorithms for parameter estimation is shown for comparison purposes. In fact, EMSGA adapts well to the use of generalized canonical models, e.g., the S-system, where use of the fundamental Taylor series method of any order for integration becomes possible. This makes EMSGA highly efficient and exemplified here by estimating all the parameters of the derived higher dimensional 5-system model for the CSTR. Comparative studies with a well-known global-local enhanced scatter search method corroborate the suitability and advantages of the EMSGA methodology. For the glycolysis regulation example the numerical robustness of EMSGA is brought out by estimating a large number of model parameters when a majority of them are raised to the power of the state variables. Interestingly, we show that from noisy in vivo dynamic data it becomes possible to completely recover the noise-free dynamical behavior of unmonitored species concentrations using EMSGA. (C) 2015 Elsevier Ltd. All rights reserved.</p>10aCanonical models10aChaotic dynamics10aGenetic algorithm10aMultiple shooting10aNoise reduction10aparameter estimation1 aSarode, Ketan, Dinkar1 aKumar, Ravi1 aKulkarni, B., D.  uhttps://library.ncl.res.in/content/embedded-multiple-shooting-methodology-genetic-algorithm-framework-parameter-estimation-and02450nas a2200157   4500008004100000245014900041210006900190260000800259300001100267490000800278520181500286100002602101700001602127700002102143856012802164       2016                        eng d00aInverse problem studies of biochemical systems with structure identification of S-systems by embedding training functions in a genetic algorithm0 aInverse problem studies of biochemical systems with structure id  cMAY  a93-1060 v2753 aAn efficient inverse problem approach for parameter estimation, state and structure identification from dynamic data by embedding training functions in a genetic algorithm methodology (ETFGA) is proposed for nonlinear dynamical biosystems using S-system canonical models. Use of multiple shooting and decomposition approach as training functions has been shown for handling of noisy datasets and computational efficiency in studying the inverse problem. The advantages of the methodology are brought out systematically by studying it for three biochemical model systems of interest. By studying a small-scale gene regulatory system described by a S-system model, the first example demonstrates the use of ETFGA for the multifold aims of the inverse problem. The estimation of a large number of parameters with simultaneous state and network identification is shown by training a generalized S-system canonical model with noisy datasets. The results of this study bring out the superior performance of ETFGA on comparison with other metaheuristic approaches. The second example studies the regulation of cAMP oscillations in Dictyostelium cells now assuming limited availability of noisy data. Here, flexibility of the approach to incorporate partial system information in the identification process is shown and its effect on accuracy and predictive ability of the estimated model are studied. The third example studies the phenomenological toy model of the regulation of circadian oscillations in Drosophila that follows rate laws different from S-system power-law. For the limited noisy data, using a priori information about properties of the system, we could estimate an alternate S-system model that showed robust oscillatory behavior with predictive abilities. (C) 2016 Elsevier Inc. All rights reserved.1 aSarode, Ketan, Dinkar1 aKumar, Ravi1 aKulkarni, B., D.  uhttps://library.ncl.res.in/content/inverse-problem-studies-biochemical-systems-structure-identification-s-systems-embedding02769nas a2200217   4500008004100000022001200041245011000053210006900163260000800232300001600240490000700256520201100263100002102274700001902295700002102314700002602335700001602361700002302377700002702400856012402427       2018                        eng d  a743746300aSingle-particle tracking to probe the local environment in ice-templated crosslinked colloidal assemblies0 aSingleparticle tracking to probe the local environment in icetem  cAPR  a4603–46130 v343 a<p>We use single-particle tracking to investigate colloidal dynamics in hybrid assemblies comprising colloids enmeshed in a crosslinked polymer network. These assemblies are prepared using ice templating and are macroporous monolithic structures. We investigate microstructure-property relations in assemblies that appear chemically identical but show qualitatively different mechanical response. Specifically, we contrast elastic assemblies that can recover from large compressive deformations with plastic assemblies that fail on being compressed. Particle tracking provides insights into the microstructural differences that underlie the different mechanical response of elastic and plastic assemblies. Since colloidal motions in these assemblies are sluggish, particle tracking is especially sensitive to imaging artifacts such as stage drift. We demonstrate that the use of wavelet transforms applied to trajectories of probe particles from fluorescence microscopy eliminates stage drift, allowing a spatial resolution of about 2 nm. In elastic and plastic scaffolds, probe particles are surrounded by other particles—thus, their motion is caged. We present mean square displacement and van Hove distributions for particle motions and demonstrate that plastic assemblies are characterized by significantly larger spatial heterogeneity when compared with the elastic sponges. In elastic assemblies, particle diffusivities are peaked around a mean value, whereas in plastic assemblies, there is a wide distribution of diffusivities with no clear peak. Both elastic and plastic assemblies show a frequency independent solid modulus from particle tracking microrheology. Here too, there is a much wider distribution of modulus values for plastic scaffolds as compared to elastic, in contrast to bulk rheological measurements where both assemblies exhibit a similar response. We interpret our results in terms of the spatial distribution of crosslinks in the polymer mesh in the colloidal assemblies.</p>1 aSuresh, Karthika1 aSharma, D., K.1 aChulliyil, Ramya1 aSarode, Ketan, Dinkar1 aKumar, Ravi1 aChoudhary, Arindam1 aKumaraswamy, Guruswamy  uhttps://library.ncl.res.in/content/single-particle-tracking-probe-local-environment-ice-templated-crosslinked-colloidal