8,547 research outputs found
Maximal information component analysis: a novel non-linear network analysis method.
BackgroundNetwork construction and analysis algorithms provide scientists with the ability to sift through high-throughput biological outputs, such as transcription microarrays, for small groups of genes (modules) that are relevant for further research. Most of these algorithms ignore the important role of non-linear interactions in the data, and the ability for genes to operate in multiple functional groups at once, despite clear evidence for both of these phenomena in observed biological systems.ResultsWe have created a novel co-expression network analysis algorithm that incorporates both of these principles by combining the information-theoretic association measure of the maximal information coefficient (MIC) with an Interaction Component Model. We evaluate the performance of this approach on two datasets collected from a large panel of mice, one from macrophages and the other from liver by comparing the two measures based on a measure of module entropy, Gene Ontology (GO) enrichment, and scale-free topology (SFT) fit. Our algorithm outperforms a widely used co-expression analysis method, weighted gene co-expression network analysis (WGCNA), in the macrophage data, while returning comparable results in the liver dataset when using these criteria. We demonstrate that the macrophage data has more non-linear interactions than the liver dataset, which may explain the increased performance of our method, termed Maximal Information Component Analysis (MICA) in that case.ConclusionsIn making our network algorithm more accurately reflect known biological principles, we are able to generate modules with improved relevance, particularly in networks with confounding factors such as gene by environment interactions
Minimizing the number of optimizations for efficient community dynamic flux balance analysis
Dynamic flux balance analysis uses a quasi-steady state assumption to
calculate an organism's metabolic activity at each time-step of a dynamic
simulation, using the well-known technique of flux balance analysis. For
microbial communities, this calculation is especially costly and involves
solving a linear constrained optimization problem for each member of the
community at each time step. However, this is unnecessary and inefficient, as
prior solutions can be used to inform future time steps. Here, we show that a
basis for the space of internal fluxes can be chosen for each microbe in a
community and this basis can be used to simulate forward by solving a
relatively inexpensive system of linear equations at most time steps. We can
use this solution as long as the resulting metabolic activity remains within
the optimization problem's constraints (i.e. the solution to the linear system
of equations remains a feasible to the linear program). As the solution becomes
infeasible, it first becomes a feasible but degenerate solution to the
optimization problem, and we can solve a different but related optimization
problem to choose an appropriate basis to continue forward simulation. We
demonstrate the efficiency and robustness of our method by comparing with
currently used methods on a four species community, and show that our method
requires at least fewer optimizations to be solved. For reproducibility,
we prototyped the method using Python. Source code is available at
\verb|https://github.com/jdbrunner/surfin_fba|.Comment: 9 figure
Metabolic Model-based Ecological Modeling for Probiotic Design
The microbial community composition in the human gut has a profound effect on
human health. This observation has lead to extensive use of microbiome
therapies, including over-the-counter ``probiotic" treatments intended to alter
the composition of the microbiome. Despite so much promise and commercial
interest, the factors that contribute to the success or failure of
microbiome-targeted treatments remain unclear. We investigate the biotic
interactions that lead to successful engraftment of a novel bacterial strain
introduced to the microbiome as in probiotic treatments. We use pairwise
genome-scale metabolic modeling with a generalized resource allocation
constraint to build a network of interactions between 818 species with well
developed models available in the AGORA database. We create induced sub-graphs
using the taxa present in samples from three experimental engraftment studies
and assess the likelihood of invader engraftment based on network structure. To
do so, we use a set of dynamical models designed to reflect connect network
topology to growth dynamics. We show that a generalized Lotka-Volterra model
has strong ability to predict if a particular invader or probiotic will
successfully engraft into an individual's microbiome. Furthermore, we show that
the mechanistic nature of the model is useful for revealing which
microbe-microbe interactions potentially drive engraftment.Comment: 18 pages, 6 figure
Tuning Interparticle Hydrogen Bonding in Shear-Jamming Suspensions: Kinetic Effects and Consequences for Tribology and Rheology
The shear-jamming of dense suspensions can be strongly affected by
molecular-scale interactions between particles, e.g. by chemically controlling
their propensity for hydrogen bonding. However, hydrogen bonding not only
enhances interparticle friction, a critical parameter for shear jamming, but
also introduces (reversible) adhesion, whose interplay with friction in
shear-jamming systems has so far remained unclear. Here, we present atomic
force microscopy studies to assess interparticle adhesion, its relationship to
friction, and how these attributes are influenced by urea, a molecule that
interferes with hydrogen bonding. We characterize the kinetics of this process
with nuclear magnetic resonance, relating it to the time dependence of the
macroscopic flow behavior with rheological measurements. We find that
time-dependent urea sorption reduces friction and adhesion, causing a shift in
the shear-jamming onset. These results extend our mechanistic understanding of
chemical effects on the nature of shear jamming, promising new avenues for
fundamental studies and applications alike
Manufacture of Regularly Shaped Sol-Gel Pellets
An extrusion batch process for manufacturing regularly shaped sol-gel pellets has been devised as an improved alternative to a spray process that yields irregularly shaped pellets. The aspect ratio of regularly shaped pellets can be controlled more easily, while regularly shaped pellets pack more efficiently. In the extrusion process, a wet gel is pushed out of a mold and chopped repetitively into short, cylindrical pieces as it emerges from the mold. The pieces are collected and can be either (1) dried at ambient pressure to xerogel, (2) solvent exchanged and dried under ambient pressure to ambigels, or (3) supercritically dried to aerogel. Advantageously, the extruded pellets can be dropped directly in a cross-linking bath, where they develop a conformal polymer coating around the skeletal framework of the wet gel via reaction with the cross linker. These pellets can be dried to mechanically robust X-Aerogel
Discrimination of prostate cancer cells and non-malignant cells using secondary ion mass spectrometry
This communication utilises Time-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS) combined with multivariate analysis to obtain spectra from the surfaces of three closely related cell lines allowing their discrimination based upon mass spectral ions
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