49 research outputs found
On the criticality of inferred models
Advanced inference techniques allow one to reconstruct the pattern of
interaction from high dimensional data sets. We focus here on the statistical
properties of inferred models and argue that inference procedures are likely to
yield models which are close to a phase transition. On one side, we show that
the reparameterization invariant metrics in the space of probability
distributions of these models (the Fisher Information) is directly related to
the model's susceptibility. As a result, distinguishable models tend to
accumulate close to critical points, where the susceptibility diverges in
infinite systems. On the other, this region is the one where the estimate of
inferred parameters is most stable. In order to illustrate these points, we
discuss inference of interacting point processes with application to financial
data and show that sensible choices of observation time-scales naturally yield
models which are close to criticality.Comment: 6 pages, 2 figures, version to appear in JSTA
Intrinsic limitations of inverse inference in the pairwise Ising spin glass
We analyze the limits inherent to the inverse reconstruction of a pairwise
Ising spin glass based on susceptibility propagation. We establish the
conditions under which the susceptibility propagation algorithm is able to
reconstruct the characteristics of the network given first- and second-order
local observables, evaluate eventual errors due to various types of noise in
the originally observed data, and discuss the scaling of the problem with the
number of degrees of freedom
Maximum entropy models for antibody diversity
Recognition of pathogens relies on families of proteins showing great
diversity. Here we construct maximum entropy models of the sequence repertoire,
building on recent experiments that provide a nearly exhaustive sampling of the
IgM sequences in zebrafish. These models are based solely on pairwise
correlations between residue positions, but correctly capture the higher order
statistical properties of the repertoire. Exploiting the interpretation of
these models as statistical physics problems, we make several predictions for
the collective properties of the sequence ensemble: the distribution of
sequences obeys Zipf's law, the repertoire decomposes into several clusters,
and there is a massive restriction of diversity due to the correlations. These
predictions are completely inconsistent with models in which amino acid
substitutions are made independently at each site, and are in good agreement
with the data. Our results suggest that antibody diversity is not limited by
the sequences encoded in the genome, and may reflect rapid adaptation to
antigenic challenges. This approach should be applicable to the study of the
global properties of other protein families
Statistical mechanics for natural flocks of birds
Interactions among neighboring birds in a flock cause an alignment of their
flight directions. We show that the minimally structured (maximum entropy)
model consistent with these local correlations correctly predicts the
propagation of order throughout entire flocks of starlings, with no free
parameters. These models are mathematically equivalent to the Heisenberg model
of magnetism, and define an "energy" for each configuration of flight
directions in the flock. Comparing flocks of different densities, the range of
interactions that contribute to the energy involves a fixed number of
(topological) neighbors, rather than a fixed (metric) spatial range. Comparing
flocks of different sizes, the model correctly accounts for the observed scale
invariance of long ranged correlations among the fluctuations in flight
direction
Molecular motors robustly drive active gels to a critically connected state
Living systems often exhibit internal driving: active, molecular processes
drive nonequilibrium phenomena such as metabolism or migration. Active gels
constitute a fascinating class of internally driven matter, where molecular
motors exert localized stresses inside polymer networks. There is evidence that
network crosslinking is required to allow motors to induce macroscopic
contraction. Yet a quantitative understanding of how network connectivity
enables contraction is lacking. Here we show experimentally that myosin motors
contract crosslinked actin polymer networks to clusters with a scale-free size
distribution. This critical behavior occurs over an unexpectedly broad range of
crosslink concentrations. To understand this robustness, we develop a
quantitative model of contractile networks that takes into account network
restructuring: motors reduce connectivity by forcing crosslinks to unbind.
Paradoxically, to coordinate global contractions, motor activity should be low.
Otherwise, motors drive initially well-connected networks to a critical state
where ruptures form across the entire network.Comment: Main text: 21 pages, 5 figures. Supplementary Information: 13 pages,
8 figure
Noise Filtering Strategies of Adaptive Signaling Networks: The Case of E. Coli Chemotaxis
Two distinct mechanisms for filtering noise in an input signal are identified
in a class of adaptive sensory networks. We find that the high frequency noise
is filtered by the output degradation process through time-averaging; while the
low frequency noise is damped by adaptation through negative feedback. Both
filtering processes themselves introduce intrinsic noises, which are found to
be unfiltered and can thus amount to a significant internal noise floor even
without signaling. These results are applied to E. coli chemotaxis. We show
unambiguously that the molecular mechanism for the Berg-Purcell time-averaging
scheme is the dephosphorylation of the response regulator CheY-P, not the
receptor adaptation process as previously suggested. The high frequency noise
due to the stochastic ligand binding-unbinding events and the random ligand
molecule diffusion is averaged by the CheY-P dephosphorylation process to a
negligible level in E.coli. We identify a previously unstudied noise source
caused by the random motion of the cell in a ligand gradient. We show that this
random walk induced signal noise has a divergent low frequency component, which
is only rendered finite by the receptor adaptation process. For gradients
within the E. coli sensing range, this dominant external noise can be
comparable to the significant intrinsic noise in the system. The dependence of
the response and its fluctuations on the key time scales of the system are
studied systematically. We show that the chemotaxis pathway may have evolved to
optimize gradient sensing, strong response, and noise control in different time
scalesComment: 15 pages, 4 figure
Are biological systems poised at criticality?
Many of life's most fascinating phenomena emerge from interactions among many
elements--many amino acids determine the structure of a single protein, many
genes determine the fate of a cell, many neurons are involved in shaping our
thoughts and memories. Physicists have long hoped that these collective
behaviors could be described using the ideas and methods of statistical
mechanics. In the past few years, new, larger scale experiments have made it
possible to construct statistical mechanics models of biological systems
directly from real data. We review the surprising successes of this "inverse"
approach, using examples form families of proteins, networks of neurons, and
flocks of birds. Remarkably, in all these cases the models that emerge from the
data are poised at a very special point in their parameter space--a critical
point. This suggests there may be some deeper theoretical principle behind the
behavior of these diverse systems.Comment: 21 page
Characterizing the pathotype of neonatal meningitis causing <i>Escherichia coli</i> (NMEC)
Background
Neonatal meningitis-causing Escherichia coli (NMEC) is the predominant Gram-negative bacterial pathogen associated with meningitis in newborn infants. High levels of heterogeneity and diversity have been observed in the repertoire of virulence traits and other characteristics among strains of NMEC making it difficult to define the NMEC pathotype. The objective of the present study was to identify genotypic and phenotypic characteristics of NMEC that can be used to distinguish them from commensal E. coli.
Methods
A total of 53 isolates of NMEC obtained from neonates with meningitis and 48 isolates of fecal E. coli obtained from healthy individuals (HFEC) were comparatively evaluated using five phenotypic (serotyping, serum bactericidal assay, biofilm assay, antimicorbial susceptibility testing, and in vitro cell invasion assay) and three genotypic (phylogrouping, virulence genotyping, and pulsed-field gel electrophoresis) methods.
Results
A majority (67.92 %) of NMEC belonged to B2 phylogenetic group whereas 59 % of HFEC belonged to groups A and D. Serotyping revealed that the most common O and H types present in NMEC tested were O1 (15 %), O8 (11.3 %), O18 (13.2 %), and H7 (25.3 %). In contrast, none of the HFEC tested belonged to O1 or O18 serogroups. The most common serogroup identified in HFEC was O8 (6.25 %). The virulence genotyping reflected that more than 70 % of NMEC carried kpsII, K1, neuC, iucC, sitA, and vat genes with only less than 27 % of HFEC possessing these genes. All NMEC and 79 % of HFEC tested were able to invade human cerebral microvascular endothelial cells. No statistically significant difference was observed in the serum resistance phenotype between NMEC and HFEC. The NMEC strains demonstrated a greater ability to form biofilms in Luria Bertani broth medium than did HFEC (79.2 % vs 39.9 %).
Conclusion
The results of our study demonstrated that virulence genotyping and phylogrouping may assist in defining the potential NMEC pathotype
The Case for Absolute Ligand Discrimination: Modeling Information Processing and Decision by Immune T Cells
Is diet partly responsible for differences in COVID-19 death rates between and within countries?
Correction: Volume: 10 Issue: 1 Article Number: 44 DOI: 10.1186/s13601-020-00351-w Published: OCT 26 2020Reported COVID-19 deaths in Germany are relatively low as compared to many European countries. Among the several explanations proposed, an early and large testing of the population was put forward. Most current debates on COVID-19 focus on the differences among countries, but little attention has been given to regional differences and diet. The low-death rate European countries (e.g. Austria, Baltic States, Czech Republic, Finland, Norway, Poland, Slovakia) have used different quarantine and/or confinement times and methods and none have performed as many early tests as Germany. Among other factors that may be significant are the dietary habits. It seems that some foods largely used in these countries may reduce angiotensin-converting enzyme activity or are anti-oxidants. Among the many possible areas of research, it might be important to understand diet and angiotensin-converting enzyme-2 (ACE2) levels in populations with different COVID-19 death rates since dietary interventions may be of great benefit.Peer reviewe