Search bioRxiv⌕ Search

Biology subjects

Schmidler, S. C.

Publications and source records attributed to Schmidler, S. C..

2 recordsLinked to original sources

Efficient Enumeration and Visualization of Helix-coil Ensembles

Helix-coil models are routinely used to interpret CD data of helical peptides or predict the helicity of naturally-occurring and designed polypeptides. However, a helix-coil model contains significantly more information than mean helicity alone, as it defines the entire ensemble - the equilibrium population of every possible helix-coil configuration - for a given sequence. Many desirable quantities of this ensemble are either not obtained as ensemble averages, or are not available using standard helicity-averaging calculations. Enumeration of the entire ensemble can allow calculation of a wider set of ensemble properties, but the exponential size of the configuration space typically renders this intractable. We present an algorithm that efficiently approximates the helix-coil ensemble to arbitrary accuracy, by sequentially generating a list of the M highest populated configurations in descending order of population. Truncating this list of (configuration, population) pairs at a desired accuracy provides an approximating sub-ensemble. We demonstrate several uses of this approach for providing insight into helix-coil ensembles and folding mechanisms, including landscape visualization. O_TEXTBOXSIGNIFICANCE Helix-coil models define the probability distribution of helix-coil configurations for a polypeptide (a helix-coil ensemble). Each configuration specifies which residues are -helical and which are not. We used an accurate helix-coil model, paired with concepts from the field of probabilistic graphical modeling, to devise an algorithm capable of enumerating helix-coil configurations in order of decreasing probability. By enumerating ensembles for a representative set of peptides we find that helix-coil ensembles tend to be highly concentrated, with the vast majority of probability mass assigned to a relatively small set of configurations from a configuration space that is often astronomical in size. This result facilitates the development of new and accurate methods for analyzing and predicting the behavior of helical polypeptides. C_TEXTBOX

biophysics↗

A Bayesian Non-parametric Mixed-Effects Model of MicrobialPhenotypes

Substantive changes in gene expression, metabolism, and the proteome are manifested in overall changes in microbial population growth. Quantifying how microbes grow is therefore fundamental to areas such as genetics, bioengineering, and food safety. Traditional parametric growth curve models capture the population growth behavior through a set of summarizing parameters. However, estimation of these parameters from data is confounded by random effects such as experimental variability, batch effects or differences in experimental material. A systematic statistical method to identify and correct for such confounding effects in population growth data is not currently available. Further, our previous work has demonstrated that parametric models are insufficient to explain and predict microbial response under non-standard growth conditions. Here we develop a hierarchical Bayesian non-parametric model of population growth that identifies the latent growth behavior and response to perturbation, while simultaneously correcting for random effects in the data. This model enables more accurate estimates of the biological effect of interest, while better accounting for the uncertainty due to technical variation. Additionally, modeling hierarchical variation provides estimates of the relative impact of various confounding effects on measured population growth.

systems biology↗