Deconvolving mutational patterns of poliovirus outbreaks reveals its intrinsic fitness landscape.

Vaccination has essentially eradicated poliovirus. Yet, its mutation rate is higher than that of viruses like HIV, for which no effective vaccine exists. To investigate this, we infer a fitness model for the poliovirus viral protein 1 (vp1), which successfully predicts in vitro fitness measurements. This is achieved by first developing a probabilistic model for the prevalence of vp1 sequences that enables us to isolate and remove data that are subject to strong vaccine-derived biases. The intrinsic fitness constraints derived for vp1, a capsid protein subject to antibody responses, are compared with those of analogous HIV proteins. We find that vp1 evolution is subject to tighter constraints, limiting its ability to evade vaccine-induced immune responses. Our analysis also indicates that circulating poliovirus strains in unimmunized populations serve as a reservoir that can seed outbreaks in spatio-temporally localized sub-optimally immunized populations

Birth, death and diffusion of interacting particles

Individual-based models of chemical or biological dynamics usually consider individual entities diffusing in space and performing a birth-death type dynamics. In this work we study the properties of a model in this class where the birth dynamics is mediated by the local, within a given distance, density of particles. Groups of individuals are formed in the system and in this paper we concentrate on the study of the properties of these clusters (lifetime, size, and collective diffusion). In particular, in the limit of the interaction distance approaching the system size, a unique cluster appears which helps to understand and characterize the clustering dynamics of the model.Comment: 15 pages, 6 figures, Iop style. To appear in Journal of Physics A: Condensed matte

Estimation of the mixing kernel and the disturbance covariance in IDE-based spatiotemporal systems

The integro-difference equation (IDE) is an increasingly popular mathematical model of spatiotemporal processes, such as brain dynamics, weather systems, and disease spread. We present an efficient approach for system identification based on correlation techniques for linear temporal systems that extended to spatiotemporal IDE-based models. The method is derived from the average (over time) spatial correlations of observations to calculate closed-form estimates of the spatial mixing kernel and the disturbance covariance function. Synthetic data are used to demonstrate the performance of the estimation algorithm

A computationally engineered RAS rheostat reveals RAS-ERK signaling dynamics.

Synthetic protein switches controlled with user-defined inputs are powerful tools for studying and controlling dynamic cellular processes. To date, these approaches have relied primarily on intermolecular regulation. Here we report a computationally guided framework for engineering intramolecular regulation of protein function. We utilize this framework to develop chemically inducible activator of RAS (CIAR), a single-component RAS rheostat that directly activates endogenous RAS in response to a small molecule. Using CIAR, we show that direct RAS activation elicits markedly different RAS-ERK signaling dynamics from growth factor stimulation, and that these dynamics differ among cell types. We also found that the clinically approved RAF inhibitor vemurafenib potently primes cells to respond to direct wild-type RAS activation. These results demonstrate the utility of CIAR for quantitatively interrogating RAS signaling. Finally, we demonstrate the general utility of our approach in design of intramolecularly regulated protein tools by applying it to the Rho family of guanine nucleotide exchange factors

Temporal Tracking of Microglia Activation in Neurodegeneration at Single-Cell Resolution

Microglia, the tissue-resident macrophages in the brain, are damage sensors that react to nearly any perturbation, including neurodegenerative diseases such as Alzheimer's disease (AD). Here, using single-cell RNA sequencing, we determined the transcriptome of more than 1,600 individual microglia cells isolated from the hippocampus of a mouse model of severe neurodegeneration with AD-like phenotypes and of control mice at multiple time points during progression of neurodegeneration. In this neurodegeneration model, we discovered two molecularly distinct reactive microglia phenotypes that are typified by modules of co-regulated type I and type II interferon response genes, respectively. Furthermore, our work identified previously unobserved heterogeneity in the response of microglia to neurodegeneration, discovered disease stage-specific microglia cell states, revealed the trajectory of cellular reprogramming of microglia in response to neurodegeneration, and uncovered the underlying transcriptional programs. Mathys et al. use single-cell RNA sequencing to determine the phenotypic heterogeneity of microglia during the progression of neurodegeneration. They identify multiple disease stage-specific cell states, including two molecularly distinct reactive microglia phenotypes that are typified by modules of co-regulated type I and type II interferon response genes, respectively.National Institutes of Health (U.S.) (Grant RF1 AG054321

Global analyses of human immune variation reveal baseline predictors of postvaccination responses.

A major goal of systems biology is the development of models that accurately predict responses to perturbation. Constructing such models requires the collection of dense measurements of system states, yet transformation of data into predictive constructs remains a challenge. To begin to model human immunity, we analyzed immune parameters in depth both at baseline and in response to influenza vaccination. Peripheral blood mononuclear cell transcriptomes, serum titers, cell subpopulation frequencies, and B cell responses were assessed in 63 individuals before and after vaccination and were used to develop a systematic framework to dissect inter- and intra-individual variation and build predictive models of postvaccination antibody responses. Strikingly, independent of age and pre-existing antibody titers, accurate models could be constructed using pre-perturbation cell populations alone, which were validated using independent baseline time points. Most of the parameters contributing to prediction delineated temporally stable baseline differences across individuals, raising the prospect of immune monitoring before intervention

Exploring the Spatiotemporal Dynamics of Cooperative Members' Switching Decisions

This article analyses the spatiotemporal dynamics of the actual switching behaviour of farmers’ in a dairy cooperative’s membership base. Space-time permutation scan statistic is used to identify clusters of switching decisions in space and time, while objective and publicly available indicators are related to the occurrence of these clusters. The analysis reveals two classes of clustered switching decisions: Clusters in which many farmers switch on a particular day and clusters covering longer periods of time with farmers switching in a herd-like pattern. Additionally, the relationship between farm sizes as well as price incentives and clustered switching decisions is observed. [EconLit citations: Q13; C23; L25]