755 research outputs found
Bayesian inference of biochemical kinetic parameters using the linear noise approximation
Background
Fluorescent and luminescent gene reporters allow us to dynamically quantify changes in molecular species concentration over time on the single cell level. The mathematical modeling of their interaction through multivariate dynamical models requires the deveopment of effective statistical methods to calibrate such models against available data. Given the prevalence of stochasticity and noise in biochemical systems inference for stochastic models is of special interest. In this paper we present a simple and computationally efficient algorithm for the estimation of biochemical kinetic parameters from gene reporter data.
Results
We use the linear noise approximation to model biochemical reactions through a stochastic dynamic model which essentially approximates a diffusion model by an ordinary differential equation model with an appropriately defined noise process. An explicit formula for the likelihood function can be derived allowing for computationally efficient parameter estimation. The proposed algorithm is embedded in a Bayesian framework and inference is performed using Markov chain Monte Carlo.
Conclusion
The major advantage of the method is that in contrast to the more established diffusion approximation based methods the computationally costly methods of data augmentation are not necessary. Our approach also allows for unobserved variables and measurement error. The application of the method to both simulated and experimental data shows that the proposed methodology provides a useful alternative to diffusion approximation based methods
Quantitative model for inferring dynamic regulation of the tumour suppressor gene p53
Background: The availability of various "omics" datasets creates a prospect of performing the study of genome-wide genetic regulatory networks. However, one of the major challenges of using mathematical models to infer genetic regulation from microarray datasets is the lack of information for protein concentrations and activities. Most of the previous researches were based on an assumption that the mRNA levels of a gene are consistent with its protein activities, though it is not always the case. Therefore, a more sophisticated modelling framework together with the corresponding inference methods is needed to accurately estimate genetic regulation from "omics" datasets.
Results: This work developed a novel approach, which is based on a nonlinear mathematical model, to infer genetic regulation from microarray gene expression data. By using the p53 network as a test system, we used the nonlinear model to estimate the activities of transcription factor (TF) p53 from the expression levels of its target genes, and to identify the activation/inhibition status of p53 to its target genes. The predicted top 317 putative p53 target genes were supported by DNA sequence analysis. A comparison between our prediction and the other published predictions of p53 targets suggests that most of putative p53 targets may share a common depleted or enriched sequence signal on their upstream non-coding region.
Conclusions: The proposed quantitative model can not only be used to infer the regulatory relationship between TF and its down-stream genes, but also be applied to estimate the protein activities of TF from the expression levels of its target genes
Unscented Kalman filter with parameter identifiability analysis for the estimation of multiple parameters in kinetic models
In systems biology, experimentally measured parameters are not always available, necessitating the use of computationally based parameter estimation. In order to rely on estimated parameters, it is critical to first determine which parameters can be estimated for a given model and measurement set. This is done with parameter identifiability analysis. A kinetic model of the sucrose accumulation in the sugar cane culm tissue developed by Rohwer et al. was taken as a test case model. What differentiates this approach is the integration of an orthogonal-based local identifiability method into the unscented Kalman filter (UKF), rather than using the more common observability-based method which has inherent limitations. It also introduces a variable step size based on the system uncertainty of the UKF during the sensitivity calculation. This method identified 10 out of 12 parameters as identifiable. These ten parameters were estimated using the UKF, which was run 97 times. Throughout the repetitions the UKF proved to be more consistent than the estimation algorithms used for comparison
Designing attractive models via automated identification of chaotic and oscillatory dynamical regimes
Chaos and oscillations continue to capture the interest of both the scientific and public domains. Yet despite the importance of these qualitative features, most attempts at constructing mathematical models of such phenomena have taken an indirect, quantitative approach, for example, by fitting models to a finite number of data points. Here we develop a qualitative inference framework that allows us to both reverse-engineer and design systems exhibiting these and other dynamical behaviours by directly specifying the desired characteristics of the underlying dynamical attractor. This change in perspective from quantitative to qualitative dynamics, provides fundamental and new insights into the properties of dynamical systems
TRY plant trait database - enhanced coverage and open access
Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives
Unstable Maternal Environment, Separation Anxiety, and Heightened CO2 Sensitivity Induced by Gene-by-Environment Interplay
Background: In man, many different events implying childhood separation from caregivers/unstable parental environment
are associated with heightened risk for panic disorder in adulthood. Twin data show that the occurrence of such events in
childhood contributes to explaining the covariation between separation anxiety disorder, panic, and the related
psychobiological trait of CO2 hypersensitivity. We hypothesized that early interference with infant-mother interaction could
moderate the interspecific trait of response to CO2 through genetic control of sensitivity to the environment.
Methodology: Having spent the first 24 hours after birth with their biological mother, outbred NMRI mice were crossfostered
to adoptive mothers for the following 4 post-natal days. They were successively compared to normally-reared
individuals for: number of ultrasonic vocalizations during isolation, respiratory physiology responses to normal air (20%O2),
CO2-enriched air (6% CO2), hypoxic air (10%O2), and avoidance of CO2-enriched environments.
Results: Cross-fostered pups showed significantly more ultrasonic vocalizations, more pronounced hyperventilatory
responses (larger tidal volume and minute volume increments) to CO2-enriched air and heightened aversion towards CO2-
enriched environments, than normally-reared individuals. Enhanced tidal volume increment response to 6%CO2 was present
at 16â20, and 75â90 postnatal days, implying the traitâs stability. Quantitative genetic analyses of unrelated individuals, sibs
and half-sibs, showed that the genetic variance for tidal volume increment during 6%CO2 breathing was significantly higher
(Bartlett x = 8.3, p = 0.004) among the cross-fostered than the normally-reared individuals, yielding heritability of 0.37 and
0.21 respectively. These results support a stress-diathesis model whereby the genetic influences underlying the response to
6%CO2 increase their contribution in the presence of an environmental adversity. Maternal grooming/licking behaviour, and
corticosterone basal levels were similar among cross-fostered and normally-reared individuals.
Conclusions: A mechanism of gene-by-environment interplay connects this form of early perturbation of infant-mother
interaction, heightened CO2 sensitivity and anxiety. Some no
Extended Kalman Filter for Estimation of Parameters in Nonlinear State-Space Models of Biochemical Networks
It is system dynamics that determines the function of cells, tissues and organisms. To develop mathematical models and estimate their parameters are an essential issue for studying dynamic behaviors of biological systems which include metabolic networks, genetic regulatory networks and signal transduction pathways, under perturbation of external stimuli. In general, biological dynamic systems are partially observed. Therefore, a natural way to model dynamic biological systems is to employ nonlinear state-space equations. Although statistical methods for parameter estimation of linear models in biological dynamic systems have been developed intensively in the recent years, the estimation of both states and parameters of nonlinear dynamic systems remains a challenging task. In this report, we apply extended Kalman Filter (EKF) to the estimation of both states and parameters of nonlinear state-space models. To evaluate the performance of the EKF for parameter estimation, we apply the EKF to a simulation dataset and two real datasets: JAK-STAT signal transduction pathway and Ras/Raf/MEK/ERK signaling transduction pathways datasets. The preliminary results show that EKF can accurately estimate the parameters and predict states in nonlinear state-space equations for modeling dynamic biochemical networks
Jet energy measurement with the ATLAS detector in proton-proton collisions at root s=7 TeV
The jet energy scale and its systematic uncertainty are determined for jets measured with the ATLAS detector at the LHC in proton-proton collision data at a centre-of-mass energy of âs = 7TeV corresponding to an integrated luminosity of 38 pb-1. Jets are reconstructed with the anti-kt algorithm with distance parameters R=0. 4 or R=0. 6. Jet energy and angle corrections are determined from Monte Carlo simulations to calibrate jets with transverse momenta pTâ„20 GeV and pseudorapidities {pipe}η{pipe}<4. 5. The jet energy systematic uncertainty is estimated using the single isolated hadron response measured in situ and in test-beams, exploiting the transverse momentum balance between central and forward jets in events with dijet topologies and studying systematic variations in Monte Carlo simulations. The jet energy uncertainty is less than 2. 5 % in the central calorimeter region ({pipe}η{pipe}<0. 8) for jets with 60â€pT<800 GeV, and is maximally 14 % for pT<30 GeV in the most forward region 3. 2â€{pipe}η{pipe}<4. 5. The jet energy is validated for jet transverse momenta up to 1 TeV to the level of a few percent using several in situ techniques by comparing a well-known reference such as the recoiling photon pT, the sum of the transverse momenta of tracks associated to the jet, or a system of low-pT jets recoiling against a high-pT jet. More sophisticated jet calibration schemes are presented based on calorimeter cell energy density weighting or hadronic properties of jets, aiming for an improved jet energy resolution and a reduced flavour dependence of the jet response. The systematic uncertainty of the jet energy determined from a combination of in situ techniques is consistent with the one derived from single hadron response measurements over a wide kinematic range. The nominal corrections and uncertainties are derived for isolated jets in an inclusive sample of high-pT jets. Special cases such as event topologies with close-by jets, or selections of samples with an enhanced content of jets originating from light quarks, heavy quarks or gluons are also discussed and the corresponding uncertainties are determined. © 2013 CERN for the benefit of the ATLAS collaboration
Measurement of the inclusive and dijet cross-sections of b-jets in pp collisions at sqrt(s) = 7 TeV with the ATLAS detector
The inclusive and dijet production cross-sections have been measured for jets
containing b-hadrons (b-jets) in proton-proton collisions at a centre-of-mass
energy of sqrt(s) = 7 TeV, using the ATLAS detector at the LHC. The
measurements use data corresponding to an integrated luminosity of 34 pb^-1.
The b-jets are identified using either a lifetime-based method, where secondary
decay vertices of b-hadrons in jets are reconstructed using information from
the tracking detectors, or a muon-based method where the presence of a muon is
used to identify semileptonic decays of b-hadrons inside jets. The inclusive
b-jet cross-section is measured as a function of transverse momentum in the
range 20 < pT < 400 GeV and rapidity in the range |y| < 2.1. The bbbar-dijet
cross-section is measured as a function of the dijet invariant mass in the
range 110 < m_jj < 760 GeV, the azimuthal angle difference between the two jets
and the angular variable chi in two dijet mass regions. The results are
compared with next-to-leading-order QCD predictions. Good agreement is observed
between the measured cross-sections and the predictions obtained using POWHEG +
Pythia. MC@NLO + Herwig shows good agreement with the measured bbbar-dijet
cross-section. However, it does not reproduce the measured inclusive
cross-section well, particularly for central b-jets with large transverse
momenta.Comment: 10 pages plus author list (21 pages total), 8 figures, 1 table, final
version published in European Physical Journal
Observation of associated near-side and away-side long-range correlations in âsNN=5.02ââTeV proton-lead collisions with the ATLAS detector
Two-particle correlations in relative azimuthal angle (ÎÏ) and pseudorapidity (Îη) are measured in âsNN=5.02ââTeV p+Pb collisions using the ATLAS detector at the LHC. The measurements are performed using approximately 1ââÎŒb-1 of data as a function of transverse momentum (pT) and the transverse energy (ÎŁETPb) summed over 3.1<η<4.9 in the direction of the Pb beam. The correlation function, constructed from charged particles, exhibits a long-range (2<|Îη|<5) ânear-sideâ (ÎÏâŒ0) correlation that grows rapidly with increasing ÎŁETPb. A long-range âaway-sideâ (ÎÏâŒÏ) correlation, obtained by subtracting the expected contributions from recoiling dijets and other sources estimated using events with small ÎŁETPb, is found to match the near-side correlation in magnitude, shape (in Îη and ÎÏ) and ÎŁETPb dependence. The resultant ÎÏ correlation is approximately symmetric about Ï/2, and is consistent with a dominant cosâĄ2ÎÏ modulation for all ÎŁETPb ranges and particle pT
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