Nanoliter scale microbioreactor array for quantitative cell biology

A nanoliter scale microbioreactor array was designed for multiplexed quantitative cell biology. An addressable 8 x 8 array of three nanoliter chambers was demonstrated for observing the serum response of HeLa human cancer cells in 64 parallel cultures. The individual culture unit was designed with a "C" shaped ring that effectively decoupled the central cell growth regions from the outer fluid transport channels. The chamber layout mimics physiological tissue conditions by implementing an outer channel for convective "blood" flow that feeds cells through diffusion into the low shear "interstitial" space. The 2 microm opening at the base of the "C" ring established a differential fluidic resistance up to 3 orders of magnitude greater than the fluid transport channel within a single mold microfluidic device. Three-dimensional (3D) finite element simulation were used to predict fluid transport properties based on chamber dimensions and verified experimentally. The microbioreactor array provided a continuous flow culture environment with a Peclet number (0.02) and shear stress (0.01 Pa) that approximated in vivo tissue conditions without limiting mass transport (10 s nutrient turnover). This microfluidic design overcomes the major problems encountered in multiplexing nanoliter culture environments by enabling uniform cell loading, eliminating shear, and pressure stresses on cultured cells, providing stable control of fluidic addressing, and permitting continuous on-chip optical monitoring.     

Isolation by resistance

Despite growing interest in the effects of landscape heterogeneity on genetic structuring, few tools are available to incorporate data on landscape composition into population genetic studies. Analyses of isolation by distance have typically either assumed spatial homogeneity for convenience or applied theoretically unjustified distance metrics to compensate for heterogeneity. Here I propose the isolation-by-resistance (IBR) model as an alternative for predicting equilibrium genetic structuring in complex landscapes. The model predicts a positive relationship between genetic differentiation and the resistance distance, a distance metric that exploits precise relationships between random walk times and effective resistances in electronic networks. As a predictor of genetic differentiation, the resistance distance is both more theoretically justified and more robust to spatial heterogeneity than Euclidean or least cost path-based distance measures. Moreover, the metric can be applied with a wide range of data inputs, including coarse-scale range maps, simple maps of habitat and nonhabitat within a species' range, or complex spatial datasets with habitats and barriers of differing qualities. The IBR model thus provides a flexible and efficient tool to account for habitat heterogeneity in studies of isolation by distance, improve understanding of how landscape characteristics affect genetic structuring, and predict genetic and evolutionary consequences of landscape change.     

Formulation of competition between sexual and selfing forms

In a recent issue, Rausch and Morgan (2005) analyzed the role of selfing in the autopolyploidization process theoretically based on the formulation of Felber (1991), although they did not explicitly model a pollen pool. This paper reformulates the male contribution to outcrossing in terms of gamete frequencies within a pollen pool. This is a general issue when considering competition between sexual and selfing or asexual forms in a single population, which must be considered in theoretical treatments.     

Assessing latitudinal gradients in speciation rates and biodiversity at the global scale

The mechanisms responsible for latitudinal biodiversity gradients have fascinated and perplexed biologists since the time of Darwin. Ecological theory has yielded two general classes of mechanisms to account for variation in biodiversity: dispersal–assembly mechanisms that invoke differences in stochastic rates of speciation, extinction and dispersal; and niche–assembly mechanisms that invoke species differences, species interactions and environmental heterogeneity. Distinguishing between these two classes of mechanisms requires explicit consideration of macroevolutionary dynamics. Here, we assess the importance of dispersal–assembly mechanisms in the origin and maintenance of biodiversity using fossil data that encompass 30 million years of macroevolution for three distinct groups of ocean plankton: foraminifera, nannoplankton and radiolaria. Applying new methods of analysis to these fossil data, we show here for the first time that latitudinal biodiversity gradients exhibit strong positive correlations with speciation rates even after explicitly controlling for variation in sampling effort and for increases in habitat area towards the equator. These findings provide compelling evidence that geographical variation in macroevolutionary dynamics is a primary determinant of contemporary biodiversity gradients, as predicted by dispersal–assembly theory.     

Spatial segregation of specialists and generalists in bird communities

Each species generally has a close relationship with one or more habitats and can therefore be classified as either specialist or generalist. We studied whether specialist and generalist species are spatially distributed independently of each other. Repeating the analysis for 100 of the most frequent terrestrial bird species recorded over the 10 000 sampled sites of the French Breeding Bird survey, we found that specialists were more abundant if the rest of the community was specialized, and that the inverse was also true. This pattern was far subtler than just a simple dichotomy: most species actually presented a maximum abundance at a value of community specialization similar to their own level of specialization. Bird communities appear very well defined along a specialist–generalist gradient. We believe this pattern becomes more apparent with habitat degradation. The consequences on both ecological services and community resilience may well be considerable.     

Dynamic modelling and analysis of biochemical networks: mechanism-based models and model-based experiments

Systems biology applies quantitative, mechanistic modelling to study genetic networks, signal transduction pathways and metabolic networks. Mathematical models of biochemical networks can look very different. An important reason is that the purpose and application of a model are essential for the selection of the best mathematical framework. Fundamental aspects of selecting an appropriate modelling framework and a strategy for model building are discussed. Concepts and methods from system and control theory provide a sound basis for the further development of improved and dedicated computational tools for systems biology. Identification of the network components and rate constants that are most critical to the output behaviour of the system is one of the major problems raised in systems biology. Current approaches and methods of parameter sensitivity analysis and parameter estimation are reviewed. It is shown how these methods can be applied in the design of model-based experiments which iteratively yield models that are decreasingly wrong and increasingly gain predictive power.     

From components to regulatory motifs in signalling networks.

The developments in biochemistry and molecular biology over the past 30 years have produced an impressive parts list of cellular components. It has become increasingly clear that we need to understand how components come together to form systems. One area where this approach has been growing is cell signalling research. Here, instead of focusing on individual or small groups of signalling proteins, researchers are now using a more holistic perspective. This approach attempts to view how many components are working together in concert to process information and to orchestrate cellular phenotypic changes. Additionally, the advancements in experimental techniques to measure and visualize many cellular components at once gradually grow in diversity and accuracy. The multivariate data, produced by experiments, introduce new and exciting challenges for computational biologists, who develop models of cellular systems made up of interacting cellular components. The integration of high-throughput experimental results and information from legacy literature is expected to produce computational models that would rapidly enhance our understanding of the detail workings of mammalian cells.     

Cropping systems for phytoremediation of phosphorus-enriched soils

Eutrophication of freshwater bodies is frequently attributed to elevated phosphorus (P) concentrations in surface runoff from P-enriched agricultural soils. Forage and grain-cropping systems were compared for their effectiveness at remediating P-enriched soils. At each of four locations, one of three forage systems (Forage I = cereal rye silage and corn silage annually; Forage II = alfalfa; Forage III = annual ryegrass and corn silage annually) and the grain system (corn, small grain, and soybean rotation) were maintained for 3 yr on soils with five distinct initial soil P concentrations that were established by using four annual applications (1994-1997) of five different rates (0, 100, 200, 300, and 400 kg total P ha(-1) y(-1)) of poultry manure, dairy manure, or commercial fertilizer. Across all manure P treatments at all locations, the forage systems had greater removal of P than the grain system. Soil P concentration changes (2001-2004) did not reflect differences in crop P removal. Few significant reductions in soil P concentration were observed for either crop system. When reductions did occur, they were for the more highly enriched soil P treatments. No significant reductions in soil P concentration have occurred for the lowest manure P treatments. Considerable variability in crop P concentrations was observed among species at locations and among years produced. However, crop P concentrations did increase uniformly as soil P concentration increased, indicating that luxury consumption of P does occur in agronomic species produced on P-enriched soils.     

A dynamical model for the basal ganglia-thalamo-cortical oscillatory activity and its implications in Parkinson’s disease

We propose to investigate brain electrophysiological alterations associated with Parkinson’s disease through a novel adaptive dynamical model of the network of the basal ganglia, the cortex and the thalamus. The model uniquely unifies the influence of dopamine in the regulation of the activity of all basal ganglia nuclei, the self-organised neuronal interdependent activity of basal ganglia-thalamo-cortical circuits and the generation of subcortical background oscillations. Variations in the amount of dopamine produced in the neurons of the substantia nigra pars compacta are key both in the onset of Parkinson’s disease and in the basal ganglia action selection. We model these dopamine-induced relationships, and Parkinsonian states are interpreted as spontaneous emergent behaviours associated with different rhythms of oscillatory activity patterns of the basal ganglia-thalamo-cortical network. These results are significant because: (1) the neural populations are built upon single-neuron models that have been robustly designed to have eletrophysiologically-realistic responses, and (2) our model distinctively links changes in the oscillatory activity in subcortical structures, dopamine levels in the basal ganglia and pathological synchronisation neuronal patterns compatible with Parkinsonian states, this still remains an open problem and is crucial to better understand the progression of the disease.Electronic supplementary materialThe online version of this article (10.1007/s11571-020-09653-y) contains supplementary material, which is available to authorized users.