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Is it possible to predict electromagnetic resonances in proteins, DNA and RNA? Background:
It has been shown that there are electromagnetic resonances in biological molecules (proteins, DNA and RNA) in the wide range of frequencies including THz, GHz, MHz and KHz. These resonances could be important for biological function of macromolecules, as well as could be used in development of devices like molecular computers. As experimental measurements of macromolecular resonances are timely and costly there is a need for computational methods that can reliably predict these resonances.We have previously used the Resonant Recognition Model (RRM) to predict electromagnetic resonances in tubulin and microtubules. Consequently, these predictions were confirmed experimentally.
Methods:
The RRM is developed by authors and is based on findings that protein, DNA and RNA electromagnetic resonances are related to the free electron energy distribution along the macromolecule.
Results:
Here, we applied the Resonant Recognition Model (RRM) to predict possible electromagnetic resonances in telomerase as an example of protein, telomere as an example of DNA and TERT mRNA as an example of RNA macromolecules.
Conclusion:
We propose that RRM is a powerful model that can computationally predict protein, DNA and RNA electromagnetic resonances.
http://www.epjnonlinearbiomedphys.com/content/3/1/5
Irena CosicDrasko CosicKatarina LazarEPJ Nonlinear Biomedical Physics 2015, null:52015-05-23T00:00:00Zdoi:10.1140/epjnbp/s40366-015-0020-6/content/figures/s40366-015-0020-6-toc.gifEPJ Nonlinear Biomedical Physics2195-0008${item.volume}52015-05-23T00:00:00ZXML Brain connectivity extended and expanded Background:
The article is focused on the brain connectivity extensions and expansions, with the introductory elements in this section.MethodIn Causality measures and brain connectivity models, the necessary, basic properties demanded in the problem are summerized, which is followed by short introduction to Granger causality, Geweke developments, PDC, DTF measures, and short reflections on computation and comparison of measures.
Results:
Analyzing model semantic stability, certain criteria are mandatory, formulated in preservation/coherence properties. In the sequel, a shorter addition to earlier critical presentation of brain connectivity measures, together with their computation and comparison is given, with special attention to Partial Directed Coherence, PDC and Directed Transfer Function, DTF, complementing earlier exposed errors in the treatment of these highly renowned authors and promoters of these broadly applied connectivity measures. Somewhat more general complementary methods are introduced in brain connectivity modeling in order to reach faithful and more realistic models of brain connectivity; this approach is applicable to the extraction of common information in multiple signals, when those are masked by, or embedded in noise and are elusive for the connectivity measures in current use; the methods applied are: Partial Linear Dependence and the method of recognition of (small) features in images contaminated with noise. Results are well illustrated with earlier published experiments of renowned authors, together with experimental material illustrating method extension and expansion in time.
Conclusion:
Critical findings, mainly addressing the connectivity model stability, together with the positive effects of method extension with weak connectivity are summarized.
http://www.epjnonlinearbiomedphys.com/content/3/1/4
Obrad KasumEdin DolicaninAleksandar PerovicAleksandar JovanovicEPJ Nonlinear Biomedical Physics 2015, null:42015-04-10T12:00:00Zdoi:10.1140/epjnbp/s40366-015-0019-z/content/figures/s40366-015-0019-z-toc.gifEPJ Nonlinear Biomedical Physics2195-0008${item.volume}42015-04-10T12:00:00ZXML Application of texture analysis to muscle MRI: 1-What kind of information should be expected from texture analysis? Several previous clinical or preclinical studies using computerized texture analysis of MR Images have demonstrated much more clinical discrimination than visual image analysis by the radiologist. In muscular dystrophy, a discriminating power has been already demonstrated with various methods of texture analysis of magnetic resonance images (MRI-TA). Unfortunately, a scale gap exists between the spatial resolutions of histological and MR images making a direct correlation impossible. Furthermore, the effect of the various histological modifications on the grey level of each pixel is complex and cannot be easily analyzed. Consequently, clinicians will not accept the use of MRI-TA in routine practice if TA remains a “black box” without clinical correspondence at a tissue level. A goal therefore of the multicenter European COST action MYO-MRI is to optimize MRI-TA methods in muscular dystrophy and to elucidate the histological meaning of MRI textures.
http://www.epjnonlinearbiomedphys.com/content/3/1/3
Jacques De CertainesThibaut LarcherDorota DudaNoura AzzabouPierre-Antoine EliatLuis EscuderoAntonio PinheiroGuanyu YangJean-Louis CoatrieuxEduard SnezkhoAlexey ShukelovichManuela PereiraRichard LerskiEPJ Nonlinear Biomedical Physics 2015, null:32015-03-19T12:00:00Zdoi:10.1140/epjnbp/s40366-015-0017-1/content/figures/s40366-015-0017-1-toc.gifEPJ Nonlinear Biomedical Physics2195-0008${item.volume}32015-03-19T12:00:00ZXML Application of texture analysis to muscle MRI: 2 – technical recommendations A goal of the multicenter European Cooperation in Science and Technology (COST) action MYO-MRI is to optimize Magnetic Resonance Imaging Texture Analysis (MRI-TA) methods for application in the study of muscle disease. This paper deals with recommendations on the optimal methodology to collect the MRI data, to analyse it via texture analysis and to make conclusions from the resultant texture parameter data. A full and detailed description is provided with respect to MR image quality control, sequence choice, image pre-processing, region of interest selection, texture analysis methods and data analysis. A series of conclusions are presented.
http://www.epjnonlinearbiomedphys.com/content/3/1/2
Richard LerskiJacques de CertainesDorota DudaWlodzimierz KlonowskiGuanyu YangJean CoatrieuxNoura AzzabouPierre-Antoine EliatEPJ Nonlinear Biomedical Physics 2015, null:22015-03-19T00:00:00Zdoi:10.1140/epjnbp/s40366-015-0018-0/content/figures/s40366-015-0018-0-toc.gifEPJ Nonlinear Biomedical Physics2195-0008${item.volume}22015-03-19T00:00:00ZXML Nonlinear analysis of EEG in chess players Background:
The chess game is a good example of cognitive task which needs a lot of training and experience. The aim of this work is to compare applicability of two nonlinear methods - Higuchi Fractal Dimension and Empirical Mode Decomposition - in analysis of EEG data recorded during chess match. We analyzed data of three master chess players registered during their matches with computer program.
Methods:
We used two nonlinear methods: Higuchi Fractal Dimension that is a good and fast tool for analyzing signal complexity and modification of Empirical Mode Decomposition, called Sliding Window Empirical Mode Decomposition, that breaks down a signal into its monocomponents. Obtained results are compared with the resting state i.e. EEG during relax witch closed eyes.
Results:
The analysis shows higher values of Higuchi Fractal Dimension during the thinking over chess moves than in the players’ rest state. There are no statistically significant differences in contribution of EEG bands to total power of EEG calculated with Sliding Window Empirical Mode Decomposition.
Conclusions:
Our results show beter applicability of Higuchi Fractal Dimension method for analysis of EEG signals related to chess tasks than that of Sliding Window Empirical Mode Decomposition.
http://www.epjnonlinearbiomedphys.com/content/3/1/1
Pawel StepienWlodzimierz KlonowskiNikolay SuvorovEPJ Nonlinear Biomedical Physics 2015, null:12015-03-12T12:00:00Zdoi:10.1140/epjnbp/s40366-015-0016-2/content/figures/s40366-015-0016-2-toc.gifEPJ Nonlinear Biomedical Physics2195-0008${item.volume}12015-03-12T12:00:00ZXMLIntrinsic synergistic-topological mechanism versus synergistic-topological matrix in microtubule self-organizationBackground:
In this body of work we investigate the synergistic-topological relationship during self-organization of the microtubule fiber in vitro, which is composed of straight, axially shifted and non-shifted, acentrosomal microtubules under crowded conditions.
Methods:
We used electron microscopy to observe morphological details of ordered straight microtubules. This included the observation of the differences in length distribution between microtubules in ordered and non-ordered phases followed by the observation of the formation of interface gaps between axially shifted and ordered microtubules. We performed calculations to confirm that the principle of summation of pairwise electrostatic forces act between neighboring microtubules all their entire length.
Results:
We have shown that the self-organization of a microtubule fiber imposes a variety of topological restrictions onto its constituting components: (a) tips of axially shifted neighboring microtubules are not in direct contact but rather create an ‘interface gap’; (b) fibers are always composed of a restricted number of microtubules at given solution conditions; (c) the average length of microtubules that constitute a fiber is always shorter than that of microtubules outside a fiber; (d) the length distribution of microtubules that constitute a fiber is narrower than that of microtubules outside a fiber and this effect is more pronounced at higher GTP-tubulin concentrations; (e) a cooperative motion of fiber microtubules due to actualization of the summation principle of pairwise electrostatic forces; (f) appearance of local GTP-tubulin depletion immediately in front of the tips of fiber microtubules.
Conclusion:
Overall our data indicate that under crowded conditions in vitro, the self-organization of a microtubule fiber is governed by an intrinsic synergistic-topological mechanism, which in conjunction with the topological changes, GTP-tubulin depletion, and cooperative motion of fiber constituting microtubules, may generate and maintain a ‘synergistic-topological matrix’. Failure of the mechanism to form biologically feasible microtubule synergistic-topological matrix may, per se, precondition tumorigenesis.
http://www.epjnonlinearbiomedphys.com/content/2/1/15
Vlado BuljanR HolsingerBrett HamblyVangelis KanellisElie MatarXanthe LarkinGuo LiuJohn Bohorquez-FlorezRichard BanatiEPJ Nonlinear Biomedical Physics 2014, null:152014-12-04T12:00:00Zdoi:10.1140/epjnbp/s40366-014-0015-8/content/figures/s40366-014-0015-8-toc.gifEPJ Nonlinear Biomedical Physics2195-0008${item.volume}152014-12-04T12:00:00ZXMLSliding Window Empirical Mode Decomposition -its performance and qualityBackground:
In analysis of nonstationary nonlinear signals the classical notion of frequency is meaningless. Instead one may use Instantaneous Frequency (IF) that can be interpreted as the frequency of a sine wave which locally fits the signal. IF is meaningful for monocomponent nonstationary signals and may be calculated by Hilbert transform (HT).
Methods:
A multicomponent signal may be decomposed into its monocomponents. Empirical Mode Decomposition (EMD), developed by Norden E. Huang, is a new method of such breaking down of a signal into its monocomponents. EMD combined with HT (called Hilbert-Huang Transform) is a good tool for analyzing nonstationary signals, but unfortunately the traditional EMD algorithm consumes a lot of time and computer resources. I propose a modified EMD algorithm - Sliding Window EMD, SWEMD.
Results:
Proposed algorithm speeds up (about 10 times) the computation with acceptable quality of decomposition.
Conclusions:
Sliding Window EMD algorithm is suitable for decomposition of long signals with high sampling frequency.
http://www.epjnonlinearbiomedphys.com/content/2/1/14
Pawel StepienEPJ Nonlinear Biomedical Physics 2014, null:142014-11-22T00:00:00Zdoi:10.1140/epjnbp/s40366-014-0014-9/content/figures/s40366-014-0014-9-toc.gifEPJ Nonlinear Biomedical Physics2195-0008${item.volume}142014-11-22T00:00:00ZXMLA multi-compartment pharmacokinetic model of the interaction between paclitaxel and doxorubicinBackground:
In this paper the interactions between paclitaxel, doxorubicin and the metabolic enzyme CYP3A4 are studied using computational models. The obtained results are compared with those of available clinical data sets. Analysis of the drug-enzyme interactions leads to a recommendation of an optimized paclitaxel-doxorubicin drug regime for chemotherapy treatment.
Methods:
A saturable multi-compartment pharmacokinetic model for the multidrug treatment of cancer using paclitaxel and doxorubicin in a combination is developed. The model’s kinetic equations are then solved using standard numerical methods for solving systems of nonlinear differential equations. The parameters were adjusted by fitting to available clinical data. In addition, we studied the interaction of each drug with the metabolic enzyme CYP3A4 through blind docking simulations to demonstrate that these drugs compete for the same metabolic enzyme and to show their molecular mode of binding. This provides a molecular-level justification for the introduction of interaction terms in the kinetic model.
Results:
Using docking simulations we compared the relative binding affinities for the metabolic enzyme of the two chemotherapy drugs. Since paclitaxel binds more strongly to CYP3A4 than doxorubicin, an explanation is given why doxorubicin has no apparent influence upon paclitaxel, while paclitaxel has a profound effect upon doxorubicin. Finally, we studied different time sequences of paclitaxel and doxorubicin concentrations and calculated their AUCs.
Conclusions:
We have found excellent agreement between our model and available empirical clinical data for the drug combination studied here. To support the kinetic model at a molecular level, we built an atomistic three-dimensional model of the ligands interacting with the metabolic enzyme and elucidated the binding modes of paclitaxel and doxorubicin within CYP3A4. Blind docking simulations provided estimates of the corresponding binding energies. The paper is concluded with clinical implications for the administration of the two drugs in combination.
http://www.epjnonlinearbiomedphys.com/content/2/1/13
Kenneth VosAngela MartinMaxine TrimboliLindsay ForestellKhaled BarakatJack TuszynskiEPJ Nonlinear Biomedical Physics 2014, null:132014-11-05T00:00:00Zdoi:10.1140/epjnbp/s40366-014-0013-x/content/figures/s40366-014-0013-x-toc.gifEPJ Nonlinear Biomedical Physics2195-0008${item.volume}132014-11-05T00:00:00ZXMLRealistic human muscle pressure for driving a mechanical lungBackground:
An important issue in noninvasive mechanical ventilation consists in understanding the origins of patient-ventilator asynchrony for reducing their incidence by adjusting ventilator settings to the intrinsic ventilatory dynamics of each patient. One of the possible ways for doing this is to evaluate the performances of the domiciliary mechanical ventilators using a test bench. Such a procedure requires to model the evolution of the pressure imposed by respiratory muscles, but for which there is no standard recommendations.
Methods:
In this paper we propose a mathematical model for simulating the muscular pressure developed by the inspiratory muscles and corresponding to different patient ventilatory dynamics to drive the ASL 5000 mechanical lung. Our model is based on the charge and discharge of a capacitor through a resistor, simulating contraction and relaxation phases of the inspiratory muscles.
Results:
Our resulting equations were used to produce 420 time series of the muscle pressure with various contraction velocities, amplitudes and shapes, in order to represent the inter-patient variability clinically observed. All these dynamics depend on two parameters, the ventilatory frequency and the mouth occlusion pressure.
Conclusion:
Based on the equation of the respiratory movement and its electrical analogy, the respiratory muscle pressure was simulated with more consistency in regards of physiological evidences than those provided by the ASL 5000 software. The great variability in the so-produced inspiratory efforts can cover most of realistic patho-physiological conditions.
http://www.epjnonlinearbiomedphys.com/content/2/1/7
Emeline FresnelJean-François MuirChristophe LetellierEPJ Nonlinear Biomedical Physics 2014, null:72014-08-19T12:00:00Zdoi:10.1140/epjnbp/s40366-014-0007-8/content/figures/s40366-014-0007-8-toc.gifEPJ Nonlinear Biomedical Physics2195-0008${item.volume}72014-08-19T12:00:00ZXMLLumping Izhikevich neuronsWe present the construction of a planar vector field that yields the firing rate of a bursting Izhikevich neuron can be read out, while leaving the sub-threshold behavior intact. This planar vector field is used to derive lumped formulations of two complex heterogeneous networks of bursting Izhikevich neurons. In both cases, the lumped model is compared with the spiking network. There is excellent agreement in terms of duration and number of action potentials within the bursts, but there is a slight mismatch of the burst frequency. The lumped model accurately accounts for both intrinsic bursting and post inhibitory rebound potentials in the neuron model, features which are absent in prevalent neural mass models.
http://www.epjnonlinearbiomedphys.com/content/2/1/6
Sid VisserStephan Van GilsEPJ Nonlinear Biomedical Physics 2014, null:62014-05-12T00:00:00Zdoi:10.1140/epjnbp19/content/figures/epjnbp19-toc.gifEPJ Nonlinear Biomedical Physics2195-0008${item.volume}62014-05-12T00:00:00ZXML