308 research outputs found
Automated smoother for the numerical decoupling of dynamics models
<p>Abstract</p> <p>Background</p> <p>Structure identification of dynamic models for complex biological systems is the cornerstone of their reverse engineering. Biochemical Systems Theory (BST) offers a particularly convenient solution because its parameters are kinetic-order coefficients which directly identify the topology of the underlying network of processes. We have previously proposed a numerical decoupling procedure that allows the identification of multivariate dynamic models of complex biological processes. While described here within the context of BST, this procedure has a general applicability to signal extraction. Our original implementation relied on artificial neural networks (ANN), which caused slight, undesirable bias during the smoothing of the time courses. As an alternative, we propose here an adaptation of the Whittaker's smoother and demonstrate its role within a robust, fully automated structure identification procedure.</p> <p>Results</p> <p>In this report we propose a robust, fully automated solution for signal extraction from time series, which is the prerequisite for the efficient reverse engineering of biological systems models. The Whittaker's smoother is reformulated within the context of information theory and extended by the development of adaptive signal segmentation to account for heterogeneous noise structures. The resulting procedure can be used on arbitrary time series with a nonstationary noise process; it is illustrated here with metabolic profiles obtained from <it>in-vivo </it>NMR experiments. The smoothed solution that is free of parametric bias permits differentiation, which is crucial for the numerical decoupling of systems of differential equations.</p> <p>Conclusion</p> <p>The method is applicable in signal extraction from time series with nonstationary noise structure and can be applied in the numerical decoupling of system of differential equations into algebraic equations, and thus constitutes a rather general tool for the reverse engineering of mechanistic model descriptions from multivariate experimental time series.</p
Identification of neutral biochemical network models from time series data
<p>Abstract</p> <p>Background</p> <p>The major difficulty in modeling biological systems from multivariate time series is the identification of parameter sets that endow a model with dynamical behaviors sufficiently similar to the experimental data. Directly related to this parameter estimation issue is the task of identifying the structure and regulation of ill-characterized systems. Both tasks are simplified if the mathematical model is canonical, <it>i.e</it>., if it is constructed according to strict guidelines.</p> <p>Results</p> <p>In this report, we propose a method for the identification of admissible parameter sets of canonical S-systems from biological time series. The method is based on a Monte Carlo process that is combined with an improved version of our previous parameter optimization algorithm. The method maps the parameter space into the network space, which characterizes the connectivity among components, by creating an ensemble of decoupled S-system models that imitate the dynamical behavior of the time series with sufficient accuracy. The concept of sloppiness is revisited in the context of these S-system models with an exploration not only of different parameter sets that produce similar dynamical behaviors but also different network topologies that yield dynamical similarity.</p> <p>Conclusion</p> <p>The proposed parameter estimation methodology was applied to actual time series data from the glycolytic pathway of the bacterium <it>Lactococcus lactis </it>and led to ensembles of models with different network topologies. In parallel, the parameter optimization algorithm was applied to the same dynamical data upon imposing a pre-specified network topology derived from prior biological knowledge, and the results from both strategies were compared. The results suggest that the proposed method may serve as a powerful exploration tool for testing hypotheses and the design of new experiments.</p
Matched sizes of activating and inhibitory receptor/ligand pairs are required for optimal signal integration by human Natural Killer cells
It has been suggested that receptor-ligand complexes segregate or co-localise within immune synapses according to their size, and this is important for receptor signaling. Here, we set out to test the importance of receptor-ligand complex dimensions for immune surveillance of target cells by human Natural Killer (NK) cells. NK cell activation is regulated by integrating signals from activating receptors, such as NKG2D, and inhibitory receptors, such as KIR2DL1. Elongating the NKG2D ligand MICA reduced its ability to trigger NK cell activation. Conversely, elongation of KIR2DL1 ligand HLA-C reduced its ability to inhibit NK cells. Whereas normal-sized HLA-C was most effective at inhibiting activation by normal-length MICA, only elongated HLA-C could inhibit activation by elongated MICA. Moreover, HLA-C and MICA that were matched in size co-localised, whereas HLA-C and MICA that were different in size were segregated. These results demonstrate that receptor-ligand dimensions are important in NK cell recognition, and suggest that optimal integration of activating and inhibitory receptor signals requires the receptor-ligand complexes to have similar dimensions
Stochastic Gravity: Theory and Applications
Whereas semiclassical gravity is based on the semiclassical Einstein equation
with sources given by the expectation value of the stress-energy tensor of
quantum fields, stochastic semiclassical gravity is based on the
Einstein-Langevin equation, which has in addition sources due to the noise
kernel. In the first part, we describe the fundamentals of this new theory via
two approaches: the axiomatic and the functional. In the second part, we
describe three applications of stochastic gravity theory. First, we consider
metric perturbations in a Minkowski spacetime, compute the two-point
correlation functions of these perturbations and prove that Minkowski spacetime
is a stable solution of semiclassical gravity. Second, we discuss structure
formation from the stochastic gravity viewpoint. Third, we discuss the
backreaction of Hawking radiation in the gravitational background of a black
hole and describe the metric fluctuations near the event horizon of an
evaporating black holeComment: 100 pages, no figures; an update of the 2003 review in Living Reviews
in Relativity gr-qc/0307032 ; it includes new sections on the Validity of
Semiclassical Gravity, the Stability of Minkowski Spacetime, and the Metric
Fluctuations of an Evaporating Black Hol
Study of the reaction e^{+}e^{-} -->J/psi\pi^{+}\pi^{-} via initial-state radiation at BaBar
We study the process with
initial-state-radiation events produced at the PEP-II asymmetric-energy
collider. The data were recorded with the BaBar detector at center-of-mass
energies 10.58 and 10.54 GeV, and correspond to an integrated luminosity of 454
. We investigate the mass
distribution in the region from 3.5 to 5.5 . Below 3.7
the signal dominates, and above 4
there is a significant peak due to the Y(4260). A fit to
the data in the range 3.74 -- 5.50 yields a mass value
(stat) (syst) and a width value (stat)(syst) for this state. We do not
confirm the report from the Belle collaboration of a broad structure at 4.01
. In addition, we investigate the system
which results from Y(4260) decay
Flavonoids in Kidney Health and Disease
This review summarizes the latest advances in knowledge on the effects of flavonoids on
renal function in health and disease. Flavonoids have antihypertensive, antidiabetic, and
antiinflammatory effects, among other therapeutic activities. Many of them also exert
renoprotective actions that may be of interest in diseases such as glomerulonephritis,
diabetic nephropathy, and chemically-induced kidney insufficiency. They affect several
renal factors that promote diuresis and natriuresis, which may contribute to their
well-known antihypertensive effect. Flavonoids prevent or attenuate the renal injury
associated with arterial hypertension, both by decreasing blood pressure and by acting
directly on the renal parenchyma. These outcomes derive from their interference with
multiple signaling pathways known to produce renal injury and are independent of their
blood pressure-lowering effects. Oral administration of flavonoids prevents or ameliorates
adverse effects on the kidney of elevated fructose consumption, high fat diet, and
types I and 2 diabetes. These compounds attenuate the hyperglycemia-disrupted renal
endothelial barrier function, urinarymicroalbumin excretion, and glomerular hyperfiltration
that results from a reduction of podocyte injury, a determinant factor for albuminuria
in diabetic nephropathy. Several flavonoids have shown renal protective effects against
many nephrotoxic agents that frequently cause acute kidney injury (AKI) or chronic kidney
disease (CKD), such as LPS, gentamycin, alcohol, nicotine, lead or cadmium. Flavonoids
also improve cisplatin- or methotrexate-induced renal damage, demonstrating important
actions in chemotherapy, anticancer and renoprotective effects. A beneficial prophylactic
effect of flavonoids has been also observed against AKI induced by surgical procedures
such as ischemia/reperfusion (I/R) or cardiopulmonary bypass. In several murine models
of CKD, impaired kidney function was significantly improved by the administration of
flavonoids from different sources, alone or in combination with stem cells. In humans,
cocoa flavanols were found to have vasculoprotective effects in patients on hemodialysis.
Moreover, flavonoids develop antitumor activity against renal carcinoma cells with no
toxic effects on normal cells, suggesting a potential therapeutic role in patients with renal
carcinoma.This study was supported by grants from the Carlos III Health
Institute of Spain, and the Red de Investigación Renal REDinREN
number 5 012/0021. FEDER una manera de hacer Europa
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