305 research outputs found
Detection of hepatitis B virus X product using an open reading frame Escherichia coli expression vector.
The Nondeterministic Waiting Time Algorithm: A Review
We present briefly the Nondeterministic Waiting Time algorithm. Our technique
for the simulation of biochemical reaction networks has the ability to mimic
the Gillespie Algorithm for some networks and solutions to ordinary
differential equations for other networks, depending on the rules of the
system, the kinetic rates and numbers of molecules. We provide a full
description of the algorithm as well as specifics on its implementation. Some
results for two well-known models are reported. We have used the algorithm to
explore Fas-mediated apoptosis models in cancerous and HIV-1 infected T cells
Simple, Fast and Accurate Implementation of the Diffusion Approximation Algorithm for Stochastic Ion Channels with Multiple States
The phenomena that emerge from the interaction of the stochastic opening and
closing of ion channels (channel noise) with the non-linear neural dynamics are
essential to our understanding of the operation of the nervous system. The
effects that channel noise can have on neural dynamics are generally studied
using numerical simulations of stochastic models. Algorithms based on discrete
Markov Chains (MC) seem to be the most reliable and trustworthy, but even
optimized algorithms come with a non-negligible computational cost. Diffusion
Approximation (DA) methods use Stochastic Differential Equations (SDE) to
approximate the behavior of a number of MCs, considerably speeding up
simulation times. However, model comparisons have suggested that DA methods did
not lead to the same results as in MC modeling in terms of channel noise
statistics and effects on excitability. Recently, it was shown that the
difference arose because MCs were modeled with coupled activation subunits,
while the DA was modeled using uncoupled activation subunits. Implementations
of DA with coupled subunits, in the context of a specific kinetic scheme,
yielded similar results to MC. However, it remained unclear how to generalize
these implementations to different kinetic schemes, or whether they were faster
than MC algorithms. Additionally, a steady state approximation was used for the
stochastic terms, which, as we show here, can introduce significant
inaccuracies. We derived the SDE explicitly for any given ion channel kinetic
scheme. The resulting generic equations were surprisingly simple and
interpretable - allowing an easy and efficient DA implementation. The algorithm
was tested in a voltage clamp simulation and in two different current clamp
simulations, yielding the same results as MC modeling. Also, the simulation
efficiency of this DA method demonstrated considerable superiority over MC
methods.Comment: 32 text pages, 10 figures, 1 supplementary text + figur
Statistical abstraction for multi-scale spatio-temporal systems
Spatio-temporal systems exhibiting multi-scale behaviour are common in
applications ranging from cyber-physical systems to systems biology, yet they
present formidable challenges for computational modelling and analysis. Here we
consider a prototypic scenario where spatially distributed agents decide their
movement based on external inputs and a fast-equilibrating internal
computation. We propose a generally applicable strategy based on statistically
abstracting the internal system using Gaussian Processes, a powerful class of
non-parametric regression techniques from Bayesian Machine Learning. We show on
a running example of bacterial chemotaxis that this approach leads to accurate
and much faster simulations in a variety of scenarios.Comment: 14th International Conference on Quantitative Evaluation of SysTems
(QEST 2017
An Environment-Sensitive Synthetic Microbial Ecosystem
Microbial ecosystems have been widely used in industrial production, but the inter-relationships of organisms within them haven't been completely clarified due to complex composition and structure of natural microbial ecosystems. So it is challenging for ecologists to get deep insights on how ecosystems function and interplay with surrounding environments. But the recent progresses in synthetic biology show that construction of artificial ecosystems where relationships of species are comparatively clear could help us further uncover the meadow of those tiny societies. By using two quorum-sensing signal transduction circuits, this research designed, simulated and constructed a synthetic ecosystem where various population dynamics formed by changing environmental factors. Coherent experimental data and mathematical simulation in our study show that different antibiotics levels and initial cell densities can result in correlated population dynamics such as extinction, obligatory mutualism, facultative mutualism and commensalism. This synthetic ecosystem provides valuable information for addressing questions in ecology and may act as a chassis for construction of more complex microbial ecosystems
Data-Driven Robust Control for Type 1 Diabetes Under Meal and Exercise Uncertainties
We present a fully closed-loop design for an artificial pancreas (AP) which
regulates the delivery of insulin for the control of Type I diabetes. Our AP
controller operates in a fully automated fashion, without requiring any manual
interaction (e.g. in the form of meal announcements) with the patient. A major
obstacle to achieving closed-loop insulin control is the uncertainty in those
aspects of a patient's daily behavior that significantly affect blood glucose,
especially in relation to meals and physical activity. To handle such
uncertainties, we develop a data-driven robust model-predictive control
framework, where we capture a wide range of individual meal and exercise
patterns using uncertainty sets learned from historical data. These sets are
then used in the controller and state estimator to achieve automated, precise,
and personalized insulin therapy. We provide an extensive in silico evaluation
of our robust AP design, demonstrating the potential of this approach, without
explicit meal announcements, to support high carbohydrate disturbances and to
regulate glucose levels in large clusters of virtual patients learned from
population-wide survey data.Comment: Extended version of paper accepted at the 15th International
Conference on Computational Methods in Systems Biolog
Study of photo-proton reactions driven by bremsstrahlung radiation of high-intensity laser generated electrons
Photo-nuclear reactions were investigated using a high power table-top laser. The laser system at the University of Jena ( I similar to 3-5 x 10(19) W cm(-2)) produced hard bremsstrahlung photons ( kT similar to 2(9 MeV) via a laser-gas interaction which served to induce ( gamma, p) and ( gamma, n) reactions in Mg, Ti, Zn and Mo isotopes. Several ( gamma, p) decay channels were identified using nuclear activation analysis to determine their integral reaction yields
Prediction by Promoter Logic in Bacterial Quorum Sensing
Quorum-sensing systems mediate chemical communication between bacterial cells, coordinating cell-density-dependent processes like biofilm formation and virulence-factor expression. In the proteobacterial LuxI/LuxR quorum sensing paradigm, a signaling molecule generated by an enzyme (LuxI) diffuses between cells and allosterically stimulates a transcriptional regulator (LuxR) to activate its cognate promoter (pR). By expressing either LuxI or LuxR in positive feedback from pR, these versatile systems can generate smooth (monostable) or abrupt (bistable) density-dependent responses to suit the ecological context. Here we combine theory and experiment to demonstrate that the promoter logic of pR – its measured activity as a function of LuxI and LuxR levels – contains all the biochemical information required to quantitatively predict the responses of such feedback loops. The interplay of promoter logic with feedback topology underlies the versatility of the LuxI/LuxR paradigm: LuxR and LuxI positive-feedback systems show dramatically different responses, while a dual positive/negative-feedback system displays synchronized oscillations. These results highlight the dual utility of promoter logic: to probe microscopic parameters and predict macroscopic phenotype
Comparative study of the extracellular proteome of Sulfolobus species reveals limited secretion
Although a large number of potentially secreted proteins can be predicted on the basis of genomic distribution of signal sequence-bearing proteins, protein secretion in Archaea has barely been studied. A proteomic inventory and comparison of the growth medium proteins in three hyperthermoacidophiles, i.e., Sulfolobus solfataricus, S. acidocaldarius and S. tokodaii, indicates that only few proteins are freely secreted into the growth medium and that the majority originates from cell envelope bound forms. In S. acidocaldarius both cell-associated and secreted α-amylase activities are detected. Inactivation of the amyA gene resulted in a complete loss of activity, suggesting that the same protein is responsible for the a-amylase activity at both locations. It is concluded that protein secretion in Sulfolobus is a limited process, and it is suggested that the S-layer may act as a barrier for the free diffusion of folded proteins into the medium
- …