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Computational biology

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https://www.readbyqxmd.com/read/28231282/mindboggling-morphometry-of-human-brains
#1
Arno Klein, Satrajit S Ghosh, Forrest S Bao, Joachim Giard, Yrjö Häme, Eliezer Stavsky, Noah Lee, Brian Rossa, Martin Reuter, Elias Chaibub Neto, Anisha Keshavan
Mindboggle (http://mindboggle.info) is an open source brain morphometry platform that takes in preprocessed T1-weighted MRI data and outputs volume, surface, and tabular data containing label, feature, and shape information for further analysis. In this article, we document the software and demonstrate its use in studies of shape variation in healthy and diseased humans. The number of different shape measures and the size of the populations make this the largest and most detailed shape analysis of human brains ever conducted...
February 2017: PLoS Computational Biology
https://www.readbyqxmd.com/read/28230923/predicting-phenotype-from-genotype-improving-accuracy-through-more-robust-experimental-and-computational-modeling
#2
Jonathan Gallion, Amanda Koire, Panagiotis Katsonis, Anne-Marie Schoenegge, Michel Bouvier, Olivier Lichtarge
Computational prediction yields efficient and scalable initial assessments of how variants of unknown significance (VUS) may affect human health. However, when discrepancies between these predictions and direct experimental measurements of functional impact arise, inaccurate computational predictions are frequently assumed as the source. Here we present a methodological analysis indicating that shortcomings in both computational and biological data can contribute to these disagreements. We demonstrate that incomplete assaying of multifunctional proteins can affect the strength of correlations between prediction and experiments; a variant's full impact on function is better quantified by considering multiple assays that probe an ensemble of protein functions...
February 23, 2017: Human Mutation
https://www.readbyqxmd.com/read/28230915/a-singlet-oxygen-generating-agent-by-chirality-dependent-plasmonic-shell-satellite-nanoassembly
#3
Fengli Gao, Maozhong Sun, Wei Ma, Xiaoling Wu, Liqiang Liu, Hua Kuang, Chuanlai Xu
Photodynamic therapy (PDT) agent, which generates singlet oxygen ((1) O2 ) under light, has attracted significant attention for its broad biological and medical applications. Here, DNA-driven shell-satellite (SS) gold assemblies as chiral photosensitizers are first fabricated. The chiral plasmonic nanostructure, coupling with cysteine enantiomers on its surface, exhibits intense chiroplasmonic activities (-40.2 ± 2.6 mdeg) in the visible region. These chiral SS nanoassemblies have high reactive oxygen species generating efficiency under circular polarized light illumination, resulting in a (1) O2 quantum yield of 1...
February 23, 2017: Advanced Materials
https://www.readbyqxmd.com/read/28230815/conceptual-foundations-of-systems-biology-explaining-complex-cardiac-diseases
#4
REVIEW
George E Louridas, Katerina G Lourida
Systems biology is an important concept that connects molecular biology and genomics with computing science, mathematics and engineering. An endeavor is made in this paper to associate basic conceptual ideas of systems biology with clinical medicine. Complex cardiac diseases are clinical phenotypes generated by integration of genetic, molecular and environmental factors. Basic concepts of systems biology like network construction, modular thinking, biological constraints (downward biological direction) and emergence (upward biological direction) could be applied to clinical medicine...
February 21, 2017: Healthcare (Basel, Switzerland)
https://www.readbyqxmd.com/read/28230528/biologically-plausible-learning-in-recurrent-neural-networks-reproduces-neural-dynamics-observed-during-cognitive-tasks
#5
Thomas Miconi
Neural activity during cognitive tasks exhibits complex dynamics that flexibly encode task-relevant variables. Chaotic recurrent networks, which spontaneously generate rich dynamics, have been proposed as a model of cortical computation during cognitive tasks. However, existing methods for training these networks are either biologically implausible, and/or require a continuous, real-time error signal to guide learning. Here we show that a biologically plausible learning rule can train such recurrent networks, guided solely by delayed, phasic rewards at the end of each trial...
February 23, 2017: ELife
https://www.readbyqxmd.com/read/28228927/computational-approaches-for-revealing-the-structure-of-membrane-transporters-case-study-on-bilitranslocase
#6
REVIEW
Katja Venko, A Roy Choudhury, Marjana Novič
The structural and functional details of transmembrane proteins are vastly underexplored, mostly due to experimental difficulties regarding their solubility and stability. Currently, the majority of transmembrane protein structures are still unknown and this present a huge experimental and computational challenge. Nowadays, thanks to X-ray crystallography or NMR spectroscopy over 3000 structures of membrane proteins have been solved, among them only a few hundred unique ones. Due to the vast biological and pharmaceutical interest in the elucidation of the structure and the functional mechanisms of transmembrane proteins, several computational methods have been developed to overcome the experimental gap...
2017: Computational and Structural Biotechnology Journal
https://www.readbyqxmd.com/read/28228542/accelerated-simulation-of-evolutionary-trajectories-in-origin-fixation-models
#7
Ashley I Teufel, Claus O Wilke
We present an accelerated algorithm to forward-simulate origin-fixation models. Our algorithm requires, on average, only about two fitness evaluations per fixed mutation, whereas traditional algorithms require, per one fixed mutation, a number of fitness evaluations of the order of the effective population size, Ne Our accelerated algorithm yields the exact same steady state as the original algorithm but produces a different order of fixed mutations. By comparing several relevant evolutionary metrics, such as the distribution of fixed selection coefficients and the probability of reversion, we find that the two algorithms behave equivalently in many respects...
February 2017: Journal of the Royal Society, Interface
https://www.readbyqxmd.com/read/28228094/sfreemap-a-simulation-free-tool-for-stochastic-mapping
#8
Diego Pasqualin, Marcos Barbeitos, Fabiano Silva
BACKGROUND: Stochastic mapping is frequently used in comparative biology to simulate character evolution, enabling the probabilistic computation of statistics such as number of state transitions along a tree and distribution of states in its internal nodes. Common implementations rely on Continuous-time Markov Chain simulations whose parameters are difficult to adjust and subjected to inherent inaccuracy. Thus, researchers must run a large number of simulations in order to obtain adequate estimates...
February 22, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/28228028/characterization-of-the-electronic-states-of-the-biological-relevant-ssno-molecule
#9
Tarek Ayari, Majdi Hochlaf, Muneerah Mogren Al-Mogren, Joseph S Francisco
Using configuration interaction ab initio methods, we investigate the lowest electronic states of doublet and quartet spin multiplicities of SSNO where the one-dimensional cuts of the six-dimensional potential energy surfaces of these electronic states along the stretching and bending coordinates are computed. Mainly, these electronic states are found to be repulsive along the central SN distance. A high density of electronic states is computed even at low excitation energies that may favor their couplings...
February 21, 2017: Journal of Chemical Physics
https://www.readbyqxmd.com/read/28227778/hybrid-automata-models-of-cardiac-ventricular-electrophysiology-for-real-time-computational-applications
#10
Sidharta Andalam, Harshavardhan Ramanna, Avinash Malik, Parthasarathi Roop, Nitish Patel, Mark L Trew, Sidharta Andalam, Harshavardhan Ramanna, Avinash Malik, Parthasarathi Roop, Nitish Patel, Mark L Trew, Nitish Patel, Avinash Malik, Parthasarathi Roop, Harshavardhan Ramanna, Mark L Trew, Sidharta Andalam
Virtual heart models have been proposed for closed loop validation of safety-critical embedded medical devices, such as pacemakers. These models must react in real-time to off-the-shelf medical devices. Real-time performance can be obtained by implementing models in computer hardware, and methods of compiling classes of Hybrid Automata (HA) onto FPGA have been developed. Models of ventricular cardiac cell electrophysiology have been described using HA which capture the complex nonlinear behavior of biological systems...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227677/multipoint-temperature-monitoring-in-liver-undergoing-computed-tomography-guided-radiofrequency-ablation-with-fiber-bragg-grating-probes
#11
P Saccomandi, E Schena, M Diana, F M Di Matteo, G Costamagna, J Marescaux, P Saccomandi, E Schena, M Diana, F M Di Matteo, G Costamagna, J Marescaux, F M Di Matteo, J Marescaux, E Schena, M Diana, G Costamagna, P Saccomandi
In this work, we investigated the temperature increment experienced by biological tissue during radiofrequency ablation (RFA). The measurements were performed by using two custom-made thermal probes based on fiber optic sensors (fiber Bragg gratings, FBGs). The two probes embed a total of 9 FBGs. Experiments were performed during RFA of an ex vivo healthy porcine liver. The RFA heating module was equipped with 5 thermocouples. Results show that the temperature increment close to the applicator (i.e., 0.6 cm-0...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227256/binding-affinity-prediction-of-s-cerevisiae-14-3-3-and-gyf-peptide-recognition-domains-using-support-vector-regression
#12
Volkan Uslan, Huseyin Seker, Volkan Uslan, Huseyin Seker, Huseyin Seker, Volkan Uslan
Proteins interact with other proteins and bio-molecules to carry out biological processes in a cell. Computational models help understanding complex biochemical processes that happens throughout the life of a cell. Domain-mediated protein interaction to peptides one such complex problem in bioinformatics that requires computational predictive models to identify meaningful bindings. In this study, domain-peptide binding affinity prediction models are proposed based on support vector regression. Proposed models are applied to yeast bmh 14-3-3 and syh GYF peptide-recognition domains...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227230/a-biologically-inspired-image-classifier-adaptive-feature-detection
#13
Jeffrey C Ames, Konstantinos P Michmizos, Jeffrey C Ames, Konstantinos P Michmizos, Jeffrey C Ames, Konstantinos P Michmizos
Today's artificial neural networks use computational models and algorithms inspired by the knowledge of the brain in the '90s. Powerful as they are, artificial networks are impressive but their domain specificity and reliance on vast numbers of labeled examples are obvious limitations. About a decade ago, spiking neural networks (SNNs) emerged as a new formalism that takes advantage of the spike timing and are particularly versatile when depicting spatio-temporal representations. The challenge now is to design rules for SNNs that can help them interact with their environment just like humans do...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227226/learning-approaches-to-improve-prediction-of-drug-sensitivity-in-breast-cancer-patients
#14
Turki Turki, Zhi Wei, Turki Turki, Zhi Wei, Turki Turki, Zhi Wei
Predicting drug response to cancer disease is an important problem in modern clinical oncology that attracted increasing recent attention from various domains such as computational biology, machine learning, and data mining. Cancer patients respond differently to each cancer therapy owing to disease diversity, genetic factors, and environmental causes. Thus, oncologists aim to identify the effective therapies for cancer patients and avoid adverse drug reactions in patients. By predicting the drug response to cancer, oncologists gain full understanding of the effective treatments on each patient, which leads to better personalized treatment...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227099/a-large-scale-detailed-neuronal-model-of-electrical-stimulation-of-the-dentate-gyrus-and-perforant-path-as-a-platform-for-electrode-design-and-optimization
#15
Clayton S Bingham, Kyle Loizos, Gene Yu, Andrew Gilbert, Jean-Marie Bouteiller, Dong Song, Gianluca Lazzi, Theodore W Berger, Clayton S Bingham, Kyle Loizos, Gene Yu, Andrew Gilbert, Jean-Marie Bouteiller, Dong Song, Gianluca Lazzi, Theodore W Berger, Kyle Loizos, Gene Yu, Theodore W Berger, Gianluca Lazzi, Jean-Marie Bouteiller, Clayton S Bingham, Dong Song, Andrew Gilbert
Owing to the dramatic rise in treatment of neurological disorders with electrical micro-stimulation it has become apparent that the major technological limitation in deploying effective devices lies in the process of designing efficient, safe, and outcome specific electrode arrays. The time-consuming and low-fidelity nature of gathering test data using experimental means and the immense control and flexibility of computational models, has prompted us and others to build models of electrical stimulation of neural networks that can be simulated in a computer...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28226785/mechanisms-of-stochastic-focusing-and-defocusing-in-biological-reaction-networks-insight-from-accurate-chemical-master-equation-acme-solutions
#16
Gamze Giirsoy, Anna Terebus, Youfang Cao, Jie Liang, Gamze Gursoy, Anna Terebus, Youfang Cao, Jie Liang, Gamze Gursoy, Youfang Cao, Anna Terebus, Jie Liang
Stochasticity plays important roles in regulation of biochemical reaction networks when the copy numbers of molecular species are small. Studies based on Stochastic Simulation Algorithm (SSA) has shown that a basic reaction system can display stochastic focusing (SF) by increasing the sensitivity of the network as a result of the signal noise. Although SSA has been widely used to study stochastic networks, it is ineffective in examining rare events and this becomes a significant issue when the tails of probability distributions are relevant as is the case of SF...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28226784/parameter-estimation-for-gene-regulatory-networks-a-two-stage-mcmc-bayesian-approach
#17
Niannan Xue, Wei Pan, Yike Guo, Niannan Xue, Wei Pan, Yike Guo, Wei Pan, Yike Guo, Niannan Xue
Genetic regulatory networks have emerged as a useful way to elucidate the biochemical pathways for biological functions. Yet, determination of the exact parametric forms for these models remain a major challenge. In this paper, we present a novel computational approach implemented in C++ to solve this inverse problem. This takes the form of an optimization stage first after which Bayesian filtering takes place. The key advantage of such a flexible, general and robust approach is that it provides us with a joint probability distribution of the model parameters instead of single estimates, which we can propagate to final predictions...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28226779/biologically-inspired-design-of-feedback-control-systems-implemented-using-dna-strand-displacement-reactions
#18
Mathias Foo, Rucha Sawlekar, Vishwesh V Kulkarni, Declan G Bates, Mathias Foo, Rucha Sawlekar, Vishwesh V Kulkarni, Declan G Bates, Vishwesh V Kulkarni, Rucha Sawlekar, Declan G Bates, Mathias Foo
The use of abstract chemical reaction networks (CRNs) as a modelling and design framework for the implementation of computing and control circuits using enzyme-free, entropy driven DNA strand displacement (DSD) reactions is starting to garner widespread attention in the area of synthetic biology. Previous work in this area has demonstrated the theoretical plausibility of using this approach to design biomolecular feedback control systems based on classical proportional-integral (PI) controllers, which may be constructed from CRNs implementing gain, summation and integrator operators...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28226772/computational-analysis-of-androgen-receptor-dependent-radiosensitivity-in-prostate-cancer
#19
Mengdi Qian, Alexandru Almasan, Evren Gurkan-Cavusoglu, Mengdi Qian, Alexandru Almasan, Evren Gurkan-Cavusoglu, Mengdi Qian, Evren Gurkan-Cavusoglu, Alexandru Almasan
In this study, we quantitatively analyze the mechanism by which androgen deprivation therapy (ADT) is enhancing radiosensitivity in prostate cancer (PCa) patients. It has been shown in laboratory experiments, as well as in patient data in the literature, that the androgen receptor (AR) reduces the effectiveness of ionizing radiation treatment by enhancing the non-homologous end joining (NHEJ) repair of radiation damage. The suppression of AR by ADT suppresses the activity of NHEJ that leads to radiosensitivity in PCa patients...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28226701/an-image-resolution-perspective-on-functional-activity-mapping
#20
Keith Dillon, Yu-Ping Wang, Keith Dillon, Yu-Ping Wang, Yu-Ping Wang, Keith Dillon
In this paper we apply techniques for numerical estimation of system resolution from imaging, to the regression problem of relating biological data to phenotypes. Our approach can be viewed as an extension of Backus-Gilbert theory, which attempts to find the most concentrated estimator that may be reliably computed in an inverse problem. Applied to a regression model, we estimate a minimal combination of collinear variables that may be found in a predictor, which gives a robust multivariable estimate of the network relationships in the data...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
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