Biology subjects

Irvin, M. W.

Publications and source records attributed to Irvin, M. W..

2 recordsLinked to original sources

bioRxiv2023

Negative Binomial Mixture Model for Identification of Noise in Antigen-Specificity Predictions by LIBRA-seq

Structured AbstractO_ST_ABSMotivationC_ST_ABSLIBRA-seq (linking B cell receptor to antigen specificity by sequencing) provides a powerful tool for interrogating the antigen-specific B cell compartment and identifying antibodies against antigen targets of interest. Identification of noise in LIBRA-seq antigen count data is critical for improving antigen binding predictions for downstream applications including antibody discovery and machine learning technologies. ResultsIn this study, we present a method for denoising LIBRA-seq data by clustering antigen counts into signal and noise components with a negative binomial mixture model. This approach leverages the VRC01 negative control cells included in a recent LIBRA-seq study(Abu-Shmais et al.) to provide a data-driven means for identification of technical noise. We apply this method to a dataset of nine donors representing separate LIBRA-seq experiments and show that our approach provides improved predictions for in vitro antibody-antigen binding when compared to the standard scoring method used in LIBRA-seq, despite variance in data size and noise structure across samples. This development will improve the ability of LIBRA-seq to identify antigen-specific B cells and contribute to providing more reliable datasets for future machine learning based approaches to predicting antibody-antigen binding as the corpus of LIBRA-seq data continues to grow. Availability and ImplementationJupyter notebooks detailing model fitting and figure generation in Python are available at https://github.com/perrywasdin/mixture_model_denoising. ContactEmail: Ivelin.Georgiev@Vanderbilt.edu Supplementary InformationSupplementary figures are provided in the attached PDF.

bioinformatics↗

bioRxiv2021

Probability-based mechanisms in biological networks with parameter uncertainty

Mathematical models of biomolecular networks are commonly used to study cellular processes; however, their usefulness to explain and predict dynamic behaviors is often questioned due to the unclear relationship between parameter uncertainty and network dynamics. In this work, we introduce PyDyNo (Python Dynamic analysis of biochemical NetwOrks), a non-equilibrium reaction-flux based analysis to identify dominant reaction paths within a biochemical reaction network calibrated to experimental data. We first show, in a simplified apoptosis execution model, that Bayesian parameter optimization can yield thousands of parameter vectors with equally good fits to experimental data. Our analysis however enables us to identify the dynamic differences between these parameter sets and identify three dominant execution modes. We further demonstrate that parameter vectors from each execution mode exhibit varying sensitivity to perturbations. We then apply our methodology to JAK2/STAT5 network in colony-forming unit-erythroid (CFU-E) cells to identify its signal execution modes. Our analysis identifies a previously unrecognized mechanistic explanation for the survival responses of the CFU-E cell population that would have been impossible to deduce with traditional protein-concentration based analyses. Impact StatementGiven the mechanistic models of network-driven cellular processes and the associated parameter uncertainty, we present a framework that can identify dominant reaction paths that could in turn lead to unique signal execution modes (i.e., dominant paths of flux propagation), providing a novel statistical and mechanistic insights to explain and predict signal processing and execution.

systems biology↗