Perturbation Detection Through Modeling of Gene Expression on a Latent Biological Pathway Network: A Bayesian hierarchical approach

Cellular response to a perturbation is the result of a dynamic system of biological variables linked in a complex network. A major challenge in drug and disease studies is identifying the key factors of a biological network that are essential in determining the cell's fate. Here our goal is the identification of perturbed pathways from high-throughput gene expression data. We develop a three-level hierarchical model, where (i) the first level captures the relationship between gene expression and biological pathways using confirmatory factor analysis, (ii) the second level models the behavior within an underlying network of pathways induced by an unknown perturbation using a conditional autoregressive model, and (iii) the third level is a spike-and-slab prior on the perturbations. We then identify perturbations through posterior-based variable selection. We illustrate our approach using gene transcription drug perturbation profiles from the DREAM7 drug sensitivity predication challenge data set. Our proposed method identified regulatory pathways that are known to play a causative role and that were not readily resolved using gene set enrichment analysis or exploratory factor models. Simulation results are presented assessing the performance of this model relative to a network-free variant and its robustness to inaccuracies in biological databases

Improvement of experimental testing and network training conditions with genome-wide microarrays for more accurate predictions of drug gene targets

BACKGROUND: Genome-wide microarrays have been useful for predicting chemical-genetic interactions at the gene level. However, interpreting genome-wide microarray results can be overwhelming due to the vast output of gene expression data combined with off-target transcriptional responses many times induced by a drug treatment. This study demonstrates how experimental and computational methods can interact with each other, to arrive at more accurate predictions of drug-induced perturbations. We present a two-stage strategy that links microarray experimental testing and network training conditions to predict gene perturbations for a drug with a known mechanism of action in a well-studied organism. RESULTS: S. cerevisiae cells were treated with the antifungal, fluconazole, and expression profiling was conducted under different biological conditions using Affymetrix genome-wide microarrays. Transcripts were filtered with a formal network-based method, sparse simultaneous equation models and Lasso regression (SSEM-Lasso), under different network training conditions. Gene expression results were evaluated using both gene set and single gene target analyses, and the drug’s transcriptional effects were narrowed first by pathway and then by individual genes. Variables included: (i) Testing conditions – exposure time and concentration and (ii) Network training conditions – training compendium modifications. Two analyses of SSEM-Lasso output – gene set and single gene – were conducted to gain a better understanding of how SSEM-Lasso predicts perturbation targets. CONCLUSIONS: This study demonstrates that genome-wide microarrays can be optimized using a two-stage strategy for a more in-depth understanding of how a cell manifests biological reactions to a drug treatment at the transcription level. Additionally, a more detailed understanding of how the statistical model, SSEM-Lasso, propagates perturbations through a network of gene regulatory interactions is achieved.Published versio

Ranging Behavior of Marsh Rice Rats in a Southern Illinois Wetland Complex

The marsh rice rat (Oryzomys palustris) inhabits wetlands that are often fragmented and isolated by upland cover types. Persistence of marsh rice rat populations and metapopulations likely depends on their ability to enter and traverse the upland matrix, yet basic information, such as home-range size and landcover use patterns, is lacking. Our goal was to quantify home-range size and habitat selection by marsh rice rats in southern Illinois. Between March and November 2011, we radiocollared 21 male rice rats (8 subadults and 13 adults) that were each located 7 to 24 times each via triangulation and homing. We estimated home-range size, compared landcover composition within kernel home ranges to what was available in the surrounding landscape, and quantified daily movement distances. Mean (±SE) home ranges were 3.53 ± 0.66 ha based on 95% kernel isopleths and 1.85 ± 0.49 ha based on minimum convex polygons. Home ranges were largest for individuals followed in early summer, but home-range sizes were similar for adults and subadults. Rice rats’ use of emergent wetland vegetation was greater than availability, indicating they preferred emergent wetlands habitat at the home-range level. However, upland cover types made up \u3e40% of each home range, on average. Daily movements averaged 46.6 ± 3.4 m (maximum: 396 m), and rice rats were located up to 464 m from the nearest wetland. Based on by far the largest sample size (in individuals and locations per individual) available for space use of the marsh rice rat, our findings support the characterization of male rice rats as highly vagile and suggest that rice rats move through upland cover more frequently than previously described

Core Excitations with Excited State Mean Field and Perturbation Theory

We test the efficacy of excited state mean field theory and its excited-state-specific perturbation theory on the prediction of K-edge positions and X-ray peak separations. We find that the mean field theory is surprisingly accurate, even though it contains no accounting of differential electron correlation effects. In the perturbation theory, we test multiple core-valence separation schemes and find that, with the mean field theory already so accurate, electron-counting biases in one popular separation scheme become a dominant error when predicting K-edges. Happily, these appear to be relatively easy to correct for, leading to a perturbation theory for K-edge positions that is lower scaling and more accurate than coupled cluster theory and competitive in accuracy with recent high-accuracy results from restricted open-shell Kohn Sham theory. For peak separations, our preliminary data show excited state mean field theory to be exceptionally accurate, but more extensive testing will be needed to see how it and its perturbation theory compare to coupled cluster peak separations more broadly.Comment: 7 pages, 2 figures, 3 table

A variational Monte Carlo approach for core excitations

We present a systematically-improvable approach to core excitations in variational Monte Carlo. Building on recent work in excited-state-specific Monte Carlo, we show how a straightforward protocol, starting from a quantum chemistry guess, is able to capture core state's strong orbital relaxations, maintain accuracy in the near-nuclear region during these relaxations, and explicitly balance accuracy between ground and core excited states. In water, ammonia, and methane, which serve as prototypical representatives for oxygen, nitrogen, and carbon core states, respectively, this approach delivers accuracies on par with the best available theoretical methods even when using relatively small wave function expansions.Comment: 10 pages, 4 figures, 1 tabl

Comparing Permeability of Matrix Cover Types for the Marsh Rice Rat (Oryzomys palustris)

Context Matrix land cover types differ in permeability to animals moving between habitat patches, and animals may actually move faster across lesssuitable areas. Marsh rice rats are wetland specialists whose dispersal crosses upland matrix. Objectives Our objectives were to (1) compare matrix permeability for the marsh rice rat among upland cover types, (2) compare permeability within versus outside perceptual range of the wetland, and (3) explore intrinsic and extrinsic features influencing matrix use and permeability. Methods We quantified permeability of grassland, crop field, and forest to the marsh rice rat during 2011–2012, by marking rats in wetlands and estimating the slope of capture rate versus distance (0–95 m) into the matrix. We also compared permeability within (0–15 m) and beyond the perceptual range of rice rats, and tested whether age, sex, time, water depth, rice rat abundance, and vegetation density influenced matrix use and permeability. Results Permeability was greater for soybean fields than grassland or forest but did not appear to differ within versus beyond rice rats’ perceptual range. Matrix capture rates were higher early in the study and in times and locations with thick ground vegetation and high rice rat abundance in the wetlands. Rice rats captured in the matrix were younger than those in wetland patches. Conclusions Our findings expand known matrix use by marsh rice rats, and support permeability being high in matrix types dissimilar to suitable habitat. Studying individual movements will help identify mechanisms underlying enhanced permeability in crop fields

Cofinite Induced Subgraphs of Impartial Combinatorial Games: An Analysis of CIS-Nim

Given an impartial combinatorial game G, we create a class of related games (CIS-G) by specifying a finite set of positions in G and forbidding players from moving to those positions (leaving all other game rules unchanged). Such modifications amount to taking cofinite induced subgraphs (CIS) of the original game graph. Some recent numerical/heuristic work has suggested that the underlying structure and behavior of such "CIS-games" can shed new light on, and bears interesting relationships with, the original games from which they are derived. In this paper we present an analytical treatment of the cofinite induced subgraphs associated with the game of (three-heap) Nim. This constitutes one of the simplest nontrivial cases of a CIS game. Our main finding is that although the structure of the winning strategies in games of CIS-Nim can differ greatly from that of Nim, CIS-Nim games inherit a type of period-two scale invariance from the original game of Nim.Comment: 26 pages, 5 figure