39,154 research outputs found
On the Origins and Control of Community Types in the Human Microbiome
Microbiome-based stratification of healthy individuals into compositional
categories, referred to as "community types", holds promise for drastically
improving personalized medicine. Despite this potential, the existence of
community types and the degree of their distinctness have been highly debated.
Here we adopted a dynamic systems approach and found that heterogeneity in the
interspecific interactions or the presence of strongly interacting species is
sufficient to explain community types, independent of the topology of the
underlying ecological network. By controlling the presence or absence of these
strongly interacting species we can steer the microbial ecosystem to any
desired community type. This open-loop control strategy still holds even when
the community types are not distinct but appear as dense regions within a
continuous gradient. This finding can be used to develop viable therapeutic
strategies for shifting the microbial composition to a healthy configurationComment: Main Text, Figures, Methods, Supplementary Figures, and Supplementary
Tex
Categorical Dimensions of Human Odor Descriptor Space Revealed by Non-Negative Matrix Factorization
In contrast to most other sensory modalities, the basic perceptual dimensions of olfaction remain unclear. Here, we use non-negative matrix factorization (NMF) – a dimensionality reduction technique – to uncover structure in a panel of odor profiles, with each odor defined as a point in multi-dimensional descriptor space. The properties of NMF are favorable for the analysis of such lexical and perceptual data, and lead to a high-dimensional account of odor space. We further provide evidence that odor dimensions apply categorically. That is, odor space is not occupied homogenously, but rather in a discrete and intrinsically clustered manner. We discuss the potential implications of these results for the neural coding of odors, as well as for developing classifiers on larger datasets that may be useful for predicting perceptual qualities from chemical structures
Kinetic distance and kinetic maps from molecular dynamics simulation
Characterizing macromolecular kinetics from molecular dynamics (MD)
simulations requires a distance metric that can distinguish
slowly-interconverting states. Here we build upon diffusion map theory and
define a kinetic distance for irreducible Markov processes that quantifies how
slowly molecular conformations interconvert. The kinetic distance can be
computed given a model that approximates the eigenvalues and eigenvectors
(reaction coordinates) of the MD Markov operator. Here we employ the
time-lagged independent component analysis (TICA). The TICA components can be
scaled to provide a kinetic map in which the Euclidean distance corresponds to
the kinetic distance. As a result, the question of how many TICA dimensions
should be kept in a dimensionality reduction approach becomes obsolete, and one
parameter less needs to be specified in the kinetic model construction. We
demonstrate the approach using TICA and Markov state model (MSM) analyses for
illustrative models, protein conformation dynamics in bovine pancreatic trypsin
inhibitor and protein-inhibitor association in trypsin and benzamidine
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