Biology subjects

Vaughn, M.

Publications and source records attributed to Vaughn, M..

7 recordsLinked to original sources

bioRxiv2023

CyVerse: Cyberinfrastructure for Open Science

CyVerse, the largest publicly-funded open-source research cyberinfrastructure for life sciences, has played a crucial role in advancing data-driven research since the 2010s. As the technology landscape evolved with the emergence of cloud computing platforms, machine learning and artificial intelligence (AI) applications, CyVerse has enabled access by providing interfaces, Software as a Service (SaaS), and cloud-native Infrastructure as Code (IaC) to leverage new technologies. CyVerse services enable researchers to integrate institutional and private computational resources, custom software, perform analyses, and publish data in accordance with open science principles. Over the past 13 years, CyVerse has registered more than 110,000 verified accounts from 160 countries and was used for over 1,600 peer-reviewed publications. Since 2011, 45,000 students and researchers have been trained to use CyVerse. The platform has been replicated and deployed in two countries outside the US, with additional private deployments on commercial clouds for US government agencies and multinational corporations. In this manuscript, we present a strategic blueprint for creating and managing SaaS cyberinfrastructure and IaC as free and open-source software.

scientific communication and education↗

bioRxiv2022

Computational Prediction of Synthetic Circuit Function Across Growth Conditions

A challenge in the design and construction of synthetic genetic circuits is that they will operate within biological systems that have noisy and changing parameter regimes that are largely unmeasurable. The outcome is that these circuits do not operate within design specifications or have a narrow operational envelope in which they can function. This behavior is often observed as a lack of reproducibility in function from day to day or lab to lab. Moreover, this narrow range of operating conditions does not promote reproducible circuit function in deployments where environmental conditions for the chassis are changing, as environmental changes can affect the parameter space in which the circuit is operating. Here we describe a computational method for assessing the robustness of circuit function across broad parameter regions. Previously designed circuits are assessed by this computational method and then circuit performance is measured across multiple growth conditions in budding yeast. The computational predictions are correlated with experimental findings, suggesting that the approach has predictive value for assessing the robustness of a circuit design.

synthetic biology↗

bioRxiv2021

Prediction of Whole-Cell Transcriptional Response with Machine Learning

Applications in synthetic and systems biology can benefit from measuring whole-cell response to biochemical perturbations. Execution of experiments to cover all possible combinations of perturbations is infeasible. In this paper, we present the host response model (HRM), a machine learning approach that takes the cell response to single perturbations as the input and predicts the whole cell transcriptional response to the combination of inducers. We find that the HRM is able to qualitatively predict the directionality of dysregulation to a combination of inducers with an accuracy of >90% using data from single inducers. We further find that the use of known prior, known cell regulatory networks doubles the predictive performance of the HRM (an R2 from 0.3 to 0.65). This tool will significantly reduce the number of high-throughput sequencing experiments that need to be run to characterize the transcriptional impact of the combination of perturbations on the host.

bioinformatics↗

bioRxiv2021

Large-scale design and refinement of stable proteins using sequence-only models

Engineered proteins generally must possess a stable structure in order to achieve their designed function. Stable designs, however, are astronomically rare within the space of all possible amino acid sequences. As a consequence, many designs must be tested computationally and experimentally in order to find stable ones, which is expensive in terms of time and resources. Here we report a neural network model that predicts protein stability based only on sequences of amino acids, and demonstrate its performance by evaluating the stability of almost 200,000 novel proteins. These include a wide range of sequence perturbations, providing a baseline for future work in the field. We also report a second neural network model that is able to generate novel stable proteins. Finally, we show that the predictive model can be used to substantially increase the stability of both expert-designed and model-generated proteins.

biophysics↗

bioRxiv2017

Subtle perturbations of the maize methylome reveal genes and transposons silenced by DNA methylation

DNA methylation is a chromatin modification that can provide epigenetic regulation of gene and transposon expression. Plants utilize several pathways to establish and maintain DNA methylation in specific sequence contexts. The chromomethylase (CMT) genes maintain CHG (where H = A, C or T) methylation. The RNA-directed DNA methylation (RdDM) pathway is important for CHH methylation. Transcriptome analysis was performed in a collection of Zea mays lines carrying mutant alleles for CMT or RdDM-associated genes. While the majority of the transcriptome was not affected, we identified sets of genes and transposon families sensitive to context-specific decreases in DNA methylation in mutant lines. Many of the genes that are up-regulated in CMT mutant lines have high levels of CHG methylation, while genes that are differentially expressed in RdDM mutants are enriched for having nearby mCHH islands, providing evidence that context-specific DNA methylation directly regulates expression of a small number of genes. The analysis of a diverse set of inbred lines revealed that many genes regulated by CMTs exhibit natural variation for DNA methylation and gene expression. Transposon families with differential expression in the mutant genotypes show few defining features, though several families up-regulated in RdDM mutants show enriched expression in endosperm, highlighting the importance for this pathway during reproduction. Taken together, our findings suggest that while the number of genes and transposon families whose expression is reproducibly affected by mild perturbations in context-specific methylation is small, there are distinct patterns for loci impacted by RdDM and CMT mutants.

plant biology↗

bioRxiv2017

The Association of Externalizing and Internalizing Problems with Indicators of Intelligence in a Sample of At-Risk Children

To date, a substantial body of research exists suggesting an association between indicators of intelligence and various deleterious outcomes, including externalizing and internalizing behavioral problems. Much of this research, however, has focused on samples drawn from the general population, thus it remains less clear how (and if) intelligence relates to problem behaviors in samples of highly at-risk individuals. The current study seeks to contribute to this knowledge base by examining the associations between intelligence and internalizing, externalizing, and total scores on the Child Behavioral Checklist in a sample of approximately 2,500 highly disadvantaged respondents considered by Child Protective Services as at-risk for abuse or neglect. While the two measures of intelligence performed differently, there emerged some association between overall lower IQ and higher total behavioral problem scores. There was some evidence that low IQ also predicted higher internalizing scores, but this relationship varied greatly by measure and model. Results, limitations, and implications of the current study are discussed.

neuroscience↗

bioRxiv2017

Resting heart rate and psychopathy: Findings from the Add Health Survey

Despite the prior linkages of low resting heart rate to antisocial behavior broadly defined, less work has been done examining possible associations between heart rate to psychopathic traits. The small body of research on the topic that has been conducted so far seems to suggest an inverse relationship between the two constructs. A smaller number of studies have found the opposite result, however, and some of the previous studies have been limited by small sample sizes and unrepresentative samples. The current study attempts to help clarify the relationship between resting heart rate and psychopathic traits in a large, nationally representative sample (analytical N ranged from 14,173-14,220) using an alternative measure of psychopathic traits that is less focused on antisocial processes, and rooted in personality traits. No significant relationship between heart rate and psychopathic traits, or heart rate and a measure of cold heartedness, was found after controlling for age, sex, and race. Implications of the findings, study limitations, and directions for future research are discussed.

physiology↗