Discovering gene networks with a neural-genetic hybrid

Recent advances in biology (namely, DNA arrays) allow an unprecedented view of the biochemical mechanisms contained within a cell. However, this technology raises new challenges for computer scientists and biologists alike, as the data created by these arrays is often highly complex. One of the challenges is the elucidation of the regulatory connections and interactions between genes, proteins and other gene products. In this paper, a novel method is described for determining gene interactions in temporal gene expression data using genetic algorithms combined with a neural network component. Experiments conducted on real-world temporal gene expression data sets confirm that the approach is capable of finding gene networks that fit the data. A further repeated approach shows that those genes significantly involved in interaction with other genes can be highlighted and hypothetical gene networks and circuits proposed for further laboratory testing.     

Large-Scale Functional Networks Identified from Resting-State EEG Using Spatial ICA

Several methods have been applied to EEG or MEG signals to detect functional networks. In recent works using MEG/EEG and fMRI data, temporal ICA analysis has been used to extract spatial maps of resting-state networks with or without an atlas-based parcellation of the cortex. Since the links between the fMRI signal and the electromagnetic signals are not fully established, and to avoid any bias, we examined whether EEG alone was able to derive the spatial distribution and temporal characteristics of functional networks. To do so, we propose a two-step original method: 1) An individual multi-frequency data analysis including EEG-based source localisation and spatial independent component analysis, which allowed us to characterize the resting-state networks. 2) A group-level analysis involving a hierarchical clustering procedure to identify reproducible large-scale networks across the population. Compared with large-scale resting-state networks obtained with fMRI, the proposed EEG-based analysis revealed smaller independent networks thanks to the high temporal resolution of EEG, hence hierarchical organization of networks. The comparison showed a substantial overlap between EEG and fMRI networks in motor, premotor, sensory, frontal, and parietal areas. However, there were mismatches between EEG-based and fMRI-based networks in temporal areas, presumably resulting from a poor sensitivity of fMRI in these regions or artefacts in the EEG signals. The proposed method opens the way for studying the high temporal dynamics of networks at the source level thanks to the high temporal resolution of EEG. It would then become possible to study detailed measures of the dynamics of connectivity.     

Mapping behaviorally relevant neural circuits with immediate-early gene expression

Immediate-early genes have gained widespread popularity as activity markers for mapping neuronal circuits involved in specific behaviors in many different species. In situ immediate early gene detection methods provide cellular level resolution, a major benefit for mapping neuronal networks. Recent advances using fluorescence in situ hybridization also afford temporal resolution, enabling within-animal activity maps for two distinct behaviors. Moreover, use of transgenic mice with fluorescent reporter proteins driven by immediate early gene promoters is enabling repeated measurements, over long time scales, of cortical activity within the same animal. These methodological innovations, coupled with recent advances in fluorescence imaging and probe development, will enable large scale mapping of behaviorally relevant circuits with temporal and three-dimensional spatial resolution in experimental animals.     

Quasi-3D cytoskeletal dynamics of osteocytes under fluid flow

Osteocytes respond to dynamic fluid shear loading by activating various biochemical pathways, mediating a dynamic process of bone formation and resorption. Whole-cell deformation and regional deformation of the cytoskeleton may be able to directly regulate this process. Attempts to image cellular deformation by conventional microscopy techniques have been hindered by low temporal or spatial resolution. In this study, we developed a quasi-three-dimensional microscopy technique that enabled us to simultaneously visualize an osteocyte's traditional bottom-view profile and a side-view profile at high temporal resolution. Quantitative analysis of the plasma membrane and either the intracellular actin or microtubule (MT) cytoskeletal networks provided characterization of their deformations over time. Although no volumetric dilatation of the whole cell was observed under flow, both the actin and MT networks experienced primarily tensile strains in all measured strain components. Regional heterogeneity in the strain field of normal strains was observed in the actin networks, especially in the leading edge to flow, but not in the MT networks. In contrast, side-view shear strains exhibited similar subcellular distribution patterns in both networks. Disruption of MT networks caused actin normal strains to decrease, whereas actin disruption had little effect on the MT network strains, highlighting the networks' mechanical interactions in osteocytes.     

Protein structure protection commits gene expression patterns

Background  

Gene co-expressions often determine module-defining spatial and temporal concurrences of proteins. Yet, little effort has been devoted to tracing coordinating signals for expression correlations to the three-dimensional structures of gene products.     

Representativity of a mesoscale network for weather-related factors governing pollen dispersal

The cultivation of transgenic crops, such as maize, requires successful gene isolation in field environments. Five spatial statistical techniques are used to evaluate the use of a regional mesoscale observation network (Iowa Environmental Mesonet) as a means to drive field-scale pollen dispersion modeling. The Nearest Neighbor Index, Fractal Dimension, Morisita Index, Thiessen Polygons, and Coefficient of Representativity are computed showing the positive and negative impacts of sequential addition of observation networks into a mesonet framework (a collection of pre-existing networks). While it is shown that the arbitrary combination of disparate observing networks increases spatial resolution, this improvement is often at the expense of increased clustering due to co-location of observation sites near urban areas. Network composition in terms of density and degree of clustering was evaluated with a grid analysis using the Barnes scheme as a means to mitigate clustering and improve prediction accuracies when mesonet data are applied to modeling. This paper shows the importance of understanding and accounting for the spatial characteristics of an observational network before applying it to a modeling effort such as field scale pollen dispersion.     

Consistency of network modules in resting-state FMRI connectome data

At rest, spontaneous brain activity measured by fMRI is summarized by a number of distinct resting state networks (RSNs) following similar temporal time courses. Such networks have been consistently identified across subjects using spatial ICA (independent component analysis). Moreover, graph theory-based network analyses have also been applied to resting-state fMRI data, identifying similar RSNs, although typically at a coarser spatial resolution. In this work, we examined resting-state fMRI networks from 194 subjects at a voxel-level resolution, and examined the consistency of RSNs across subjects using a metric called scaled inclusivity (SI), which summarizes consistency of modular partitions across networks. Our SI analyses indicated that some RSNs are robust across subjects, comparable to the corresponding RSNs identified by ICA. We also found that some commonly reported RSNs are less consistent across subjects. This is the first direct comparison of RSNs between ICAs and graph-based network analyses at a comparable resolution.     

Computer-vision analysis of seedling responses to light and gravity

Measuring the effects of mutation, natural variation or treatment on the development of plant form is often complicated by the shapes, dynamics or small size of the organismal structures under study. This limits accuracy and throughput of measurement and thereby limits progress toward understanding the underlying gene networks and signaling systems. A computer-vision platform based on electronic image capture and shape-analysis algorithms was developed as an alternative to the mostly manual methods of measuring seedling development currently in use. The spatial and temporal resolution of the method is in the range of microns and minutes, respectively. The algorithm simultaneously quantifies apical hook opening and inhibition of hypocotyl elongation during photomorphogenesis of Arabidopsis thaliana seedlings. It can determine when and where gravitropic curvature develops along the root axis in A. thaliana and Medicago truncatula seedlings. Novel features of gravitropic curvature development were discovered as a result of the high resolution. The computer-vision algorithms developed and demonstrated here could be used to study mutant phenotypes in detail, to form the basis of a high-throughput screening platform, or to quantify natural variation in a population of plants.     

All systems go: launching cell simulation fueled by integrated experimental biology data

Biological simulation serves to unify the basic elements of systems biology, namely, model selection, experimentation and model refinement. To select biochemical models for simulation, metabolome analysis can be performed using capillary electrophoresis or liquid chromatography coupled with mass spectrometry. In this manner, selected models can be elaborated with temporal/spatial gene and protein expression data obtained from model organisms such as Escherichia coli. The E. coli single gene deletion mutant library (KO collection) and His-tag/GFP-fusion single open reading frame clone expression library (ASKA) are powerful resources for this task. The integration of parallel experimental datasets into dynamic simulation tools forms the remaining challenge for the systematic analysis and elucidation of biological networks and holds promise for biotechnological applications.     

Signalling specificity in GPCR-dependent Ca2+ signalling

Cells use signalling networks to translate with high fidelity extracellular signals into specific cellular functions. Signalling networks are often composed of multiple signalling pathways that act in concert to regulate a particular cellular function. In the centre of the networks are the receptors that receive and transduce the signals. A versatile family of receptors that detect a remarkable variety of signals are the G protein-coupled receptors (GPCRs). Virtually all cells express several GPCRs that use the same biochemical machinery to transduce their signals. Considering the specificity and fidelity of signal transduction, a central question in cell signalling is how signalling specificity is achieved, in particular among GPCRs that use the same biochemical machinery. Ca(2+) signalling is particularly suitable to address such questions, since [Ca(2+)](i) can be recorded with excellent spatial and temporal resolutions in living cells and tissues and now in living animals. Ca(2+) is a unique second messenger in that both biochemical and biophysical components form the Ca(2+) signalling complex to regulate its concentration. Both components act in concert to generate repetitive [Ca(2+)](i) oscillations that can be either localized or in the form of global, propagating Ca(2+) waves. Most of the key proteins that form Ca(2+) signalling complexes are known and their activities are reasonably well understood on the biochemical and biophysical levels. We review here the information gained from studying Ca(2+) signalling by GPCRs to gain further understanding of the mechanisms used to generate cellular signalling specificity.     

Coarse climate change projections for species living in a fine‐scaled world

Accurately predicting biological impacts of climate change is necessary to guide policy. However, the resolution of climate data could be affecting the accuracy of climate change impact assessments. Here, we review the spatial and temporal resolution of climate data used in impact assessments and demonstrate that these resolutions are often too coarse relative to biologically relevant scales. We then develop a framework that partitions climate into three important components: trend, variance, and autocorrelation. We apply this framework to map different global climate regimes and identify where coarse climate data is most and least likely to reduce the accuracy of impact assessments. We show that impact assessments for many large mammals and birds use climate data with a spatial resolution similar to the biologically relevant area encompassing population dynamics. Conversely, impact assessments for many small mammals, herpetofauna, and plants use climate data with a spatial resolution that is orders of magnitude larger than the area encompassing population dynamics. Most impact assessments also use climate data with a coarse temporal resolution. We suggest that climate data with a coarse spatial resolution is likely to reduce the accuracy of impact assessments the most in climates with high spatial trend and variance (e.g., much of western North and South America) and the least in climates with low spatial trend and variance (e.g., the Great Plains of the USA). Climate data with a coarse temporal resolution is likely to reduce the accuracy of impact assessments the most in the northern half of the northern hemisphere where temporal climatic variance is high. Our framework provides one way to identify where improving the resolution of climate data will have the largest impact on the accuracy of biological predictions under climate change.     

Aging process modulates nonlinear dynamics in liver cell metabolism

Regulatory dynamics of energy metabolism in living cells entails a coordinated response of multiple enzyme networks that operate under non-equilibrium conditions. Here we show that mitochondrial dysfunctions associated with the aging process significantly modify nonlinear dynamical signatures in free radical generation/removal, thereby altering energy metabolism in liver cells. We support our data with a plausible biochemical mechanism for modified bioenergetics that involves uncoupling protein-2 that is up-regulated in aged cells as an adaptive response to mitigate increased oxidative stress. Combining high spatial and temporal resolution imaging and bio-energetic measurements, our work provides experimental support to the hypothesis that mitochondria manifest nonlinear dynamical behavior for efficiently regulating energy metabolism in intact cells, and any partial or complete reduction in this behavior would contribute to organ dysfunctions including the aging process and other disease processes.     

Genetic Variation in a Heterogeneous Environment. I. Temporal Heterogeneity and the Absolute Dominance Model

The conditions for a stable polymorphism and the equilibrium gene frequency in an infinite population are compared when there is spatial or temporal environmental heterogeneity for the absolute dominance model. For temporal variation the conditions for stability are more restrictive and the equilibrium gene frequency is often at a low gene frequency. In a finite population, temporal environmental heterogeneity for the absolute dominance model was found to be quite ineffective in maintaining genetic variation and is often less effective than no selection at all. For comparison, the maximum maintenance for temporal variation is related to the overdominant model. In general, cyclic environmental variation was found to be more effective at maintaining genetic variation than where the environment varies stochastically. The importance of temporal environmental variation and the maintenance of genetic variation is discussed.     

Contrasting spatial and temporal global change impacts on butterfly species richness during the 20th century

Regional patterns of species richness are often explained by models using temperature or measures habitat suitability. Generally, species richness is positively associated with temperature, and negatively associated with habitat degradation. While these models have been well tested across spatial scales, they have rarely been tested on a temporal scale – in part due to the difficulty in ascertaining accurate historical data at an appropriate resolution. In this study, we compared the results of temporal and spatial models, each incorporating two predictors of species richness: temperature, and human population density (as a surrogate of human-related habitat impacts). We found that the change in species richness from the early to late part of the 20th century was positively correlated with temperature change, and negatively correlated with human population density change. When we compared these results to two spatial models using contemporary and historic data, the spatial effects of temperature on butterfly richness were similar to its temporal effects, while the effect of human population density through time is the opposite of its spatial effect. More generally, the assumption that spatial patterns are equivalent to temporal ones when applying macroecological data to global change is clearly unreliable.     

Three-dimensional representation of chemical gradients

Perspective display techniques are applied to chemical and biochemical data sets. These represent spatially distributed gradients of reactive compounds that participate in pattern-formation processes due to reaction-diffusion or reaction-convection coupling. The patterns form in thin solution layers and are observed as chemical waves in the Belousov-Zhabotinskii reaction, as convection-induced stationary structures during oscillating glycolysis in yeast cytoplasm, and as the diffusive spreading of enzyme-catalyzed metabolic turnover in a substrate layer. The digital data are measured with a two-dimensional spectrophotometer based on a computerized video equipment with high spatial, temporal and intensity resolution. By application of three-dimensional procedures detailed structural properties of chemical and biochemical model systems will be presented yielding localization of reaction and transport events.