Kinetic parameter estimation and fluctuation analysis of CO at SnO2 single nanowires.

In this work, we present calculated numerical values for the kinetic parameters governing adsorption/desorption processes of carbon monoxide at tin dioxide single-nanowire gas sensors. The response of such sensors to pulses of 50 ppm carbon monoxide in nitrogen is investigated at different temperatures to extract the desired information. A rate-equation approach is used to model the reaction kinetics, which results in the problem of determining coefficients in a coupled system of nonlinear ordinary differential equations. The numerical values are computed by inverse-modeling techniques and are then used to simulate the sensor response. With our model, the dynamic response of the sensor due to the gas-surface interaction can be studied in order to find the optimal setup for detection, which is an important step towards selectivity of these devices. We additionally investigate the noise in the current through the nanowire and its changes due to the presence of carbon monoxide in the sensor environment. Here, we propose the use of a wavelet transform to decompose the signal and analyze the noise in the experimental data. This method indicates that some fluctuations are specific for the gas species investigated here.

Nonresonant femtosecond laser vaporization with electrospray postionization for ex vivo plant tissue typing using compressive linear classification.

Laser electrospray mass spectrometry (LEMS) with offline classification is used to discriminate plant tissues at atmospheric pressure using an intense (10(13) W cm(-2)), nonresonant (800 nm) femtosecond laser pulse to vaporize cellular content for subsequent mass analysis. The tissue content of the plant within the 0.05 mm(2) laser interaction region is vaporized into the electrospray plume where the molecules are ionized prior to transfer into the mass spectrometer. The measurements for a flower petal, leaf, and stem of an impatiens plant reveal mass spectral signatures that enable discrimination as performed using a compressive linear classifier. The statistical analysis of the plant tissue samples reveals reproducibility of the data for replicate tissue samples and within a single tissue sample. A similar degree of discrimination was achieved for the green and white regions of aphelandra squarrosa (zebra plant) leaves.

Chemical modifier screen identifies HDAC inhibitors as suppressors of PKD models.

Polycystic kidney disease (PKD) is a common human genetic disease with severe medical consequences. Although it is appreciated that the cilium plays a central role in PKD, the underlying mechanism for PKD remains poorly understood and no effective treatment is available. In zebrafish, kidney cyst formation is closely associated with laterality defects and body curvature. To discover potential drug candidates and dissect signaling pathways that interact with ciliary signals, we performed a chemical modifier screen for the two phenotypes using zebrafish pkd2(hi4166) and ift172(hi2211) models. pkd2 is a causal gene for autosomal dominant PKD and ift172 is essential for building and maintaining the cilium. We identified trichostatin A (TSA), a pan-HDAC (histone deacetylase) inhibitor, as a compound that affected both body curvature and laterality. Further analysis verified that TSA inhibited cyst formation in pkd2 knockdown animals. Moreover, we demonstrated that inhibiting class I HDACs, either by valproic acid (VPA), a class I specific HDAC inhibitor structurally unrelated to TSA, or by knocking down hdac1, suppressed kidney cyst formation and body curvature caused by pkd2 deficiency. Finally, we show that VPA was able to reduce the progression of cyst formation and slow the decline of kidney function in a mouse ADPKD model. Together, these data suggest body curvature may be used as a surrogate marker for kidney cyst formation in large-scale high-throughput screens in zebrafish. More importantly, our results also reveal a critical role for HDACs in PKD pathogenesis and point to HDAC inhibitors as drug candidates for PKD treatment.

A mathematical tool for exploring the dynamics of biological networks.

We have developed a mathematical approach to the study of dynamical biological networks, based on combining large-scale numerical simulation with nonlinear "dimensionality reduction" methods. Our work was motivated by an interest in the complex organization of the signaling cascade centered on the neuronal phosphoprotein DARPP-32 (dopamine- and cAMP-regulated phosphoprotein of molecular weight 32,000). Our approach has allowed us to detect robust features of the system in the presence of noise. In particular, the global network topology serves to stabilize the net state of DARPP-32 phosphorylation in response to variation of the input levels of the neurotransmitters dopamine and glutamate, despite significant perturbation to the concentrations and levels of activity of a number of intermediate chemical species. Further, our results suggest that the entire topology of the network is needed to impart this stability to one portion of the network at the expense of the rest. This could have significant implications for systems biology, in that large, complex pathways may have properties that are not easily replicated with simple modules.

Toward automatic phenotyping of developing embryos from videos.

We describe a trainable system for analyzing videos of developing C. elegans embryos. The system automatically detects, segments, and locates cells and nuclei in microscopic images. The system was designed as the central component of a fully automated phenotyping system. The system contains three modules 1) a convolutional network trained to classify each pixel into five categories: cell wall, cytoplasm, nucleus membrane, nucleus, outside medium; 2) an energy-based model, which cleans up the output of the convolutional network by learning local consistency constraints that must be satisfied by label images; 3) a set of elastic models of the embryo at various stages of development that are matched to the label images.

A coherent framework for multiresolution analysis of biological networks with "memory": Ras pathway, cell cycle, and immune system.

Various biological processes exhibit characteristics that vary dramatically in response to different input conditions or changes in the history of the process itself. One of the examples studied here, the Ras-PKC-mitogen-activated protein kinase (MAPK) bistable pathway, follows two distinct dynamics (modes) depending on duration and strength of EGF stimulus. Similar examples are found in the behavior of the cell cycle and the immune system. A classification methodology, based on time-frequency analysis, was developed and tested on these systems to understand global behavior of biological processes. Contrary to most traditionally used statistical and spectral methods, our approach captures complex functional relations between parts of the systems in a simple way. The resulting algorithms are capable of analyzing and classifying sets of time-series data obtained from in vivo or in vitro experiments, or in silico simulation of biological processes. The method was found to be considerably stable under stochastic noise perturbation and, therefore, suitable for the analysis of real experimental data.

A gene expression map for the euchromatic genome of Drosophila melanogaster.

We used a maskless photolithography method to produce DNA oligonucleotide microarrays with unique probe sequences tiled throughout the genome of Drosophila melanogaster and across predicted splice junctions. RNA expression of protein coding and nonprotein coding sequences was determined for each major stage of the life cycle, including adult males and females. We detected transcriptional activity for 93% of annotated genes and RNA expression for 41% of the probes in intronic and intergenic sequences. Comparison to genome-wide RNA interference data and to gene annotations revealed distinguishable levels of expression for different classes of genes and higher levels of expression for genes with essential cellular functions. Differential splicing was observed in about 40% of predicted genes, and 5440 previously unknown splice forms were detected. Genes within conserved regions of synteny with D. pseudoobscura had highly correlated expression; these regions ranged in length from 10 to 900 kilobase pairs. The expressed intergenic and intronic sequences are more likely to be evolutionarily conserved than nonexpressed ones, and about 15% of them appear to be developmentally regulated. Our results provide a draft expression map for the entire nonrepetitive genome, which reveals a much more extensive and diverse set of expressed sequences than was previously predicted.

A sense of life: computational and experimental investigations with models of biochemical and evolutionary processes.

We collaborate in a research program aimed at creating a rigorous framework, experimental infrastructure, and computational environment for understanding, experimenting with, manipulating, and modifying a diverse set of fundamental biological processes at multiple scales and spatio-temporal modes. The novelty of our research is based on an approach that (i) requires coevolution of experimental science and theoretical techniques and (ii) exploits a certain universality in biology guided by a parsimonious model of evolutionary mechanisms operating at the genomic level and manifesting at the proteomic, transcriptomic, phylogenic, and other higher levels. Our current program in "systems biology" endeavors to marry large-scale biological experiments with the tools to ponder and reason about large, complex, and subtle natural systems. To achieve this ambitious goal, ideas and concepts are combined from many different fields: biological experimentation, applied mathematical modeling, computational reasoning schemes, and large-scale numerical and symbolic simulations. From a biological viewpoint, the basic issues are many: (i) understanding common and shared structural motifs among biological processes; (ii) modeling biological noise due to interactions among a small number of key molecules or loss of synchrony; (iii) explaining the robustness of these systems in spite of such noise; and (iv) cataloging multistatic behavior and adaptation exhibited by many biological processes.