Synchronization of stochastic genetic oscillator networks with time delays and Markovian jumping parameters

The official published version of the article can be found at the link below.Genetic oscillator networks (GONs) are inherently coupled complex systems where the nodes indicate the biochemicals and the couplings represent the biochemical interactions. This paper is concerned with the synchronization problem of a general class of stochastic GONs with time delays and Markovian jumping parameters, where the GONs are subject to both the stochastic disturbances and the Markovian parameter switching. The regulatory functions of the addressed GONs are described by the sector-like nonlinear functions. By applying up-to-date ‘delay-fractioning’ approach for achieving delay-dependent conditions, we construct novel matrix functional to derive the synchronization criteria for the GONs that are formulated in terms of linear matrix inequalities (LMIs). Note that LMIs are easily solvable by the Matlab toolbox. A simulation example is used to demonstrate the synchronization phenomena within biological organisms of a given GON and therefore shows the applicability of the obtained results.This work was supported in part by the Biotechnology and Biological Sciences Research Council (BBSRC) of the UK under Grants BB/C506264/1 and 100/EGM17735, the Royal Society of the UK, the National Natural Science Foundation of China under Grant 60804028, the Teaching and Research Fund for Excellent Young Teachers at Southeast University of China, the International Science and Technology Cooperation Project of China under Grant 2009DFA32050, and the Alexander von Humboldt Foundation of Germany

Energy-scaling of the product state distribution for three-body recombination of ultracold atoms

Three-body recombination is a chemical reaction where the collision of three atoms leads to the formation of a diatomic molecule. In the ultracold regime it is expected that the production rate of a molecule generally decreases with its binding energy $E_b$, however, its precise dependence and the physics governing it have been left unclear so far. Here, we present a comprehensive experimental and theoretical study of the energy dependency for three-body recombination of ultracold Rb. For this, we determine production rates for molecules in a state-to-state resolved manner, with the binding energies $E_b$ ranging from 0.02 to 77 GHz$\times h$. We find that the formation rate approximately scales as $E_b^{-\alpha}$, where $\alpha$ is in the vicinity of 1. The formation rate typically varies only within a factor of two for different rotational angular momenta of the molecular product, apart from a possible centrifugal barrier suppression for low binding energies. In addition to numerical three-body calculations we present a perturbative model which reveals the physical origin of the energy scaling of the formation rate. Furthermore, we show that the scaling law potentially holds universally for a broad range of interaction potentials.Comment: 15 pages, 13 figure

The Ubiquitous Conserved Glycopeptidase Gcp Prevents Accumulation of Toxic Glycated Proteins

Amadori-modified proteins (AMPs) are the products of nonenzymatic glycation formed by reaction of reducing sugars with primary amine-containing amino acids and can develop into advanced glycated end products (AGEs), highly stable toxic compounds. AGEs are known to participate in many age-related human diseases, including cardiovascular, neurological, and liver diseases. The metabolism of these glycated proteins is not yet understood, and the mechanisms that reduce their accumulation are not known so far. Here, we show for Escherichia coli that a conserved glycopeptidase (Gcp, also called Kae1), which is encoded by nearly every sequenced genome in the three domains of life, prevents the accumulation of Amadori products and AGEs. Using mutants, we show that Gcp depletion results in accumulation of AMPs and eventually leads to the accumulation of AGEs. We demonstrate that Gcp binds to glycated proteins, including pyruvate dehydrogenase, previously shown to be a glycation-prone enzyme. Our experiments also show that the severe phenotype of Gcp depletion can be relieved under conditions of low intracellular glycation. As glycated proteins are ubiquitous, the involvement of Gcp in the metabolism of AMPs and AGEs is likely to have been conserved in evolution, suggesting a universal involvement of Gcp in cellular aging and explaining the essentiality of Gcp in many organisms

Integration of all FSSIM components within SEAMLESS-IF and a stand alone Graphical User Interface for FSSIM

Agricultural and Food Policy, Environmental Economics and Policy, Farm Management, Land Economics/Use,

Changes in Gene Expression During the Formation of Bioengineered Heart Muscle

A three-dimensional bioengineered heart muscle (BEHM) construct model had been previously developed, exhibiting contractile forces up to 800 µN. The interest of this study was to determine gene expression levels of biologic markers involved in calcium-handling between BEHM, cell monolayer, and neonatal heart. Cardiac cells were isolated from one litter of F344 rats and organized into groups ( n  = 5): 4-, 7-, 10-day BEHM and cell monolayer; BEHM was evaluated for cell viability and contractility. Groups were then analyzed for mRNA expression of calcium-handling proteins: myosin heavy chain (MHC) α and β, Sarcoplasmic reticulum Ca++ ATPase (SERCA) 2, phospholamban (PBL), and ryanodine receptor. BEHM exhibited electrically stimulated active force (208 ± 12 µN day 4, 361 ± 22 µN day 7, and 344 ± 29 µN day 10) and no decrease in cell number. Real-time polymerase chain reaction (PCR) showed an increase in gene expression of all calcium-handling proteins in BEHM at 7 and 10 days compared with monolayers, for example, comparing BEHM to monolayer (7 and 10 days, respectively), MHC-α: 2600-fold increase and a 100-fold increase; MHC-β: 70-fold increase at 10 days; ryanodine receptor: 74-fold increase at 10 days; SERCA: 19-fold increase and sixfold increase; PBL: 158-fold increase and 24-fold increase. It was concluded that a three-dimensional environment is a better culturing condition of cardiac cells than a monolayer. Also, BEHM constructs demonstrated a high similarity to a native myocardium, and is, thus, a good starting foundation for engineered heart muscle. Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/72077/1/j.1525-1594.2008.00669.x.pd

On-Chip Super-Resolution Imaging with Fluorescent Polymer Films

Wide field of view (FOV), label-free super-resolution imaging is demonstrated using a specially designed waveguide chip that can illuminate a sample with multi-colour evanescent waves travelling along different directions. The method is enabled by a polymer fluorescent film which emits over a broad wavelength range. Its polygonal geometry ensures coverage over all illumination directions, enabling high fidelity image reconstruction whilst minimizing distortion and image blurring. By frequency shifting and iterative stitching of different spatial frequencies in Fourier space, the reconstruction of two dimensional samples is achieved without distortion over wide FOVs. The fabrication process is facile and compatible with conventional semiconductor-fabrication methods. The super-resolution chip (SRC) can thus be produced with high yield, offer opportunities for potential conjunction of super-resolution techniques integrated optical circuits or for the development of single-use diagnostic kits

The C-Terminal Domain of the Novel Essential Protein Gcp Is Critical for Interaction with Another Essential Protein YeaZ of Staphylococcus aureus

Previous studies have demonstrated that the novel protein Gcp is essential for the viability of various bacterial species including Staphylococcus aureus; however, the reason why it is required for bacterial growth remains unclear. In order to explore the potential mechanisms of this essentiality, we performed RT-PCR analysis and revealed that the gcp gene (sa1854) was co-transcribed with sa1855, yeaZ (sa1856) and sa1857 genes, indicating these genes are located in the same operon. Furthermore, we demonstrated that Gcp interacts with YeaZ using a yeast two-hybrid (Y2H) system and in vitro pull down assays. To characterize the Gcp-YeaZ interaction, we performed alanine scanning mutagenesis on the residues of C-terminal segment of Gcp. We found that the mutations of the C-terminal Y317-F322 region abolished the interaction of Gcp and YeaZ, and the mutations of the D324-N329 and S332-Y336 regions alleviated Gcp binding to YeaZ. More importantly, we demonstrated that these key regions of Gcp are also necessary for the bacterial survival since these mutated Gcp could not complement the depletion of endogenous Gcp. Taken together, our data suggest that the interaction of Gcp and YeaZ may contribute to the essentiality of Gcp for S. aureus survival. Our findings provide new insights into the potential mechanisms and biological functions of this novel essential protein

PGS: a tool for association study of high-dimensional microRNA expression data with repeated measures

Motivation: MicroRNAs (miRNAs) are short single-stranded non-coding molecules that usually function as negative regulators to silence or suppress gene expression. Due to interested in the dynamic nature of the miRNA and reduced microarray and sequencing costs, a growing number of researchers are now measuring high-dimensional miRNAs expression data using repeated or multiple measures in which each individual has more than one sample collected and measured over time. However, the commonly used site-by-site multiple testing may impair the value of repeated or multiple measures data by ignoring the inherent dependent structure, which lead to problems including underpowered results after multiple comparison correction using false discovery rate (FDR) estimation and less biologically meaningful results. Hence, new methods are needed to tackle these issues. Results: We propose a penalized regression model incorporating grid search method (PGS), for analyzing association study of high-dimensional microRNA expression data with repeated measures. The development of this analytical framework was motivated by a real-world miRNA dataset. Comparisons between PGS and the site-by-site testing revealed that PGS provided smaller phenotype prediction errors and higher enrichment of phenotype-related biological pathways than the site-by-site testing. Simulation study showed that PGS provided more accurate estimates and higher sensitivity than site-by-site testing with comparable specificities. Availability: R source code for PGS algorithm, implementation example, and simulation study are available for download at https://github.com/feizhe/PGS

An Evaluation of Methods for Inferring Boolean Networks from Time-Series Data

Regulatory networks play a central role in cellular behavior and decision making. Learning these regulatory networks is a major task in biology, and devising computational methods and mathematical models for this task is a major endeavor in bioinformatics. Boolean networks have been used extensively for modeling regulatory networks. In this model, the state of each gene can be either ‘on’ or ‘off’ and that next-state of a gene is updated, synchronously or asynchronously, according to a Boolean rule that is applied to the current-state of the entire system. Inferring a Boolean network from a set of experimental data entails two main steps: first, the experimental time-series data are discretized into Boolean trajectories, and then, a Boolean network is learned from these Boolean trajectories. In this paper, we consider three methods for data discretization, including a new one we propose, and three methods for learning Boolean networks, and study the performance of all possible nine combinations on four regulatory systems of varying dynamics complexities. We find that employing the right combination of methods for data discretization and network learning results in Boolean networks that capture the dynamics well and provide predictive power. Our findings are in contrast to a recent survey that placed Boolean networks on the low end of the ‘‘faithfulness to biological reality’’ and ‘‘ability to model dynamics’’ spectra. Further, contrary to the common argument in favor of Boolean networks, we find that a relatively large number of time points in the timeseries data is required to learn good Boolean networks for certain data sets. Last but not least, while methods have been proposed for inferring Boolean networks, as discussed above, missing still are publicly available implementations thereof. Here, we make our implementation of the methods available publicly in open source at http://bioinfo.cs.rice.edu/