Integrated Calendar

We consider both time series as well as spatial distributions (in 1-4 dimensions).
In the first, we observe that time series for individual and independently deviating random variables can manifest pattern through the emergence of peak-to-peak sequences that are visible to the eye yet fail all Fourier analysis schemes and reveal a seeming periodicity of 3-events per cycle. We note that this can explain observations of apparent cycles in mammalian animal populations. We consider models, as well, based on the Langevin equation of kinetic theory and the Smolouchowski relation that present circumstances where the apparent period can vary from 3-4 and, for a special subclass of problems, to periods between 2 and 3. We explore how cataloged observational data from global earthquake catalogues, magnetospheric AL index observations, Old Faithful Geyser eruption data, and the performance of the Standard & Poor's 500 index (percent daily variation) manifest different degrees of statistical agreement with the theory we derived. We present a simple model for many mammalian population cycles whose underlying phenomenological basis has strong biological implications.
We then employ directed graphs to explore nearest-neighbor relationships and isolate the character of spatial clustering in 1-4 dimension. We observe that the one-dimensional problem is formally equivalent to that presented by peak-to-peak sequences in time series and also demonstrates a mean number of points per cluster of 3 in one dimension. We then take the first moment of each of the clusters formed, and observed that they too form clusters.
We observe the emergence of a hierarchy of clusters and the emergence of universal cluster numbers, analogous to branching ratios and, possibly, Feigenbaum numbers. These, in turn, are related to fractals as well as succularity and lacunarity, although the exact nature of this connection has not been identified. Finally, we show how hierarchical clustering emerging from random distributions may help provide an explanation for observations of hierarchical clustering in cosmology via the virial theorem and simulation results relating to the gravitational stabilization in a self-similar way of very large self-gravitating ensembles.

Sleep may be universal in the animal kingdom. Yet, the roles of sleep in organizing living mat-ter and the underlying reason for this universality remain controversial. Fundamental ques-tions under debate include the boundaries of this universality (do all animals sleep?), natural history (when did sleep evolve?), and core function (what for, originally?). The roundworm C. elegans is the simplest model system in which these questions can be addressed.

A key feature distinguishing sleep from other states of decreased activity is its intricate home-ostatic regulation: following disruptions, ‘restoring forces’ extend or modify sleep to compen-sate for the loss. This talk will describe measurements of three regimes of perturbations to worm sleep. We have shown that weak and intermediate perturbations reveal distinct man-ners in which small losses of worm sleep are compensated for. In addition, we have shown that stronger perturbations, causing substantial but nonlethal loss to worms sleep, inflicts long-term deficits. These deficits and the protective mechanisms that mitigate them are ex-pected to be directly linked to functions of sleep in this (phylogenetically) ancient model.

These findings add to the list of similarities between worm and vertebrate sleep and open the door to a better understanding of sleep’s core functions.

In this talk I will describe a simple model for the growth of a bacterial population under the challenge of ribosome-targeting antibiotics. The model is statistical physics-like in that it makes a coarse-grained description of the growth process, reduced to three variables within the bacterial cell - the antibiotic concentration, the concentration of ribosomes bound to antibiotics and the concentration of unbound ribosomes. Furthermore, there is biological input from empirically established physiological constraints which relate the three variables. Remarkably the model can explain several observations concerning antibiotic action and bacterial growth rate. In particular the growth-dependent bacterial susceptibility is controlled by a single, `universal' parameter and the extreme behaviours correspond to the phenomenological classification into bactericidal and bacteriostatic antibiotics. If time allows I will describe how the predictions of the model are backed up by experimental studies.
Reference:
Growth-dependent bacterial susceptibility to ribosome-targeting antibiotics Philip Greulich, Matthew Scott, Martin R. Evans, Rosalind J. Allen Molecular Systems Biology 11:796 (2015)