Biology subjects

Wells, M. T.

Publications and source records attributed to Wells, M. T..

7 recordsLinked to original sources

bioRxiv2025

Quantum Cognition Machine Learning for Forecasting Chromosomal Instability

The accurate prediction of chromosomal instability from the morphology of circulating tumor cells (CTCs) enables real-time detection of CTCs with high metastatic potential in the context of liquid biopsy diagnostics. However, it presents a significant challenge due to the high dimensionality and complexity of single-cell digital pathology data. Here, we introduce the application of Quantum Cognition Machine Learning (QCML), a quantum-inspired computational framework, to estimate morphology-predicted chromosomal instability in CTCs from patients with metastatic breast cancer. QCML leverages quantum mechanical principles to represent data as state vectors in a Hilbert space, enabling context-aware feature modeling, dimensionality reduction, and enhanced generalization without requiring curated feature selection. QCML outperforms conventional machine learning methods when tested on out of sample verification CTCs, achieving higher accuracy in identifying predicted large-scale state transitions (pLST) status from CTC-derived morphology features. These preliminary findings support the application of QCML as a novel machine learning tool with superior performance in high-dimensional, low-sample-size biomedical contexts. QCML enables the simulation of cognition-like learning for the identification of biologically meaningful prediction of chromosomal instability from CTC morphology, offering a novel tool for CTC classification in liquid biopsy.

cancer biology↗

bioRxiv2025

Bayesian phylodynamic inference of population dynamics with dormancy

Many organisms employ reversible dormancy, or seedbank, in response to environmental fluctuations. This life-history strategy alters fundamental eco-evolutionary forces, leading to distinct patterns of genetic diversity. Two models of dormancy have been proposed based on the average duration of dormancy relative to coalescent timescales: weak seedbank, induced by scheduled seasonality (e.g., plants, invertebrates), and strong seedbank, where individuals stochastically switch between active and dormant states (e.g., bacteria, fungi). The weak seedbank coalescent is statistically equivalent to the Kingman coalescent with a scaled mutation rate, allowing the use of existing inference methods. In contrast, the strong seedbank coalescent differs fundamentally, as only active lineages can coalesce, while dormant lineages cannot. Additionally, dormant individuals typically mutate at a slower rate than active ones. Consequently, despite the significant role of dormancy in the eco-evolutionary dynamics of many organisms, no methods currently exist for inferring population dynamics involving dormancy and associated parameters. We present a Bayesian framework for jointly inferring a latent genealogy, seedbank parameters, and evolutionary parameters from molecular sequence data under the strong seedbank coalescent. We derive the exact probability density of genealogies sampled under the strong seedbank coalescent, characterize the corresponding likelihood function, and present efficient computational algorithms for its evaluation based on our theoretical framework. We develop a tailored Markov chain Monte Carlo sampler and implement our inference framework as a package SeedbankTree within BEAST2. Our work provides both a theoretical foundation and practical inference framework for studying the population genetic and genealogical impacts of dormancy.

evolutionary biology↗

bioRxiv2024

e3SIM: epidemiological-ecological-evolutionary simulation framework for genomic epidemiology

Infectious disease dynamics are driven by the complex interplay of epidemiological, ecological, and evolutionary processes. Accurately modeling these interactions is crucial for understanding pathogen spread and informing public health strategies. However, existing simulators often fail to capture the dynamic interplay between these processes, resulting in oversimplified models that do not fully reflect real-world complexities in which the pathogens genetic evolution dynamically influences disease transmission. We introduce the epidemiological-ecological-evolutionary simulator (e3SIM), an open-source framework that concurrently models the transmission dynamics and molecular evolution of pathogens within a host population while integrating environmental factors. Using an agent-based, discrete-generation, forward-in-time approach, e3SIM incorporates compartmental models, host-population contact networks, and quantitative-trait models for pathogens. This integration allows for realistic simulations of disease spread and pathogen evolution. Key features include a modular and scalable design, flexibility in modeling various epidemiological and population-genetic complexities, incorporation of time-varying environmental factors, and a user-friendly graphical interface. We demonstrate e3SIMs capabilities through simulations of realistic outbreak scenarios with SARS-CoV-2 and Mycobacterium tuberculosis, illustrating its flexibility for studying the genomic epidemiology of diverse pathogen types.

bioinformatics↗

bioRxiv2023

PROLONG: Penalized Regression for Outcome guided Longitudinal Omics analysis with Network and Group constraints

MotivationThere is a growing interest in longitudinal omics data, but there are gaps in existing methodology in the high-dimensional setting. This paper focuses on selecting metabolites that co-vary with Tuberculosis mycobacterial load. The proposed method is applied to general continuous longitudinal outcomes with continuous longitudinal omics predictors. Simple longitudinal models examining a single omic predictor at a time do not leverage the correlation across predictors, thus losing power. We propose a penalized regression approach on the first differences of the data that extends the lasso + Laplacian method (Li and Li 2008) to a longitudinal group lasso + Laplacian approach. Our method, PROLONG, leverages the first differences of the data to address the piecewise linear structure and the observed time dependence. The Laplacian network constraint incorporates the dependence structure of the predictors, and the group lasso constraint induces sparsity while grouping metabolites across their first differenced observations. ResultsWith an automated selection of model hyper-parameters, PROLONG correctly selects target metabolites with high specificity and sensitivity across simulation scenarios and sizes. PROLONG selects a set of metabolites from the real data that includes interesting targets identified during EDA. AvailabilityR package prolong is in development. ConclusionsPROLONG is a powerful method for selecting interesting features in high dimensional longitudinal omics data that co-vary with some continuous clinical outcome. Contactsb2643@cornell.edu

bioinformatics↗

bioRxiv2022

Time series transcriptome analysis uncovers regulatory networks and a role for the circadian clock in the Drosophila melanogaster female's response to Sex Peptide

Sex Peptide, a seminal fluid protein of D. melanogaster males, has been described as driving a virgin-to-mated switch in females, through eliciting an array of responses, including increased egg laying, activity and food intake and a decreased re-mating rate. While it is known that Sex Peptide achieves this, at least in part, by altering neuronal signaling in females, the identity of key molecular regulators that act downstream of Sex Peptide is not known. Here, we used a high-resolution time series RNA-sequencing dataset of female heads at 10 time points within the first 24 hours after mating to investigate the genetic architecture, at the gene- and exon-level, of the females response to Sex Peptide. We find that Sex Peptide is not essential to trigger a virgin-to-mated transcriptional switch, which involves changes in a metabolic gene regulatory network. However, Sex Peptide is needed to maintain and diversify metabolic changes and to trigger changes in a neuronal gene regulatory network. We further find that Sex Peptide might interact with the females circadian clock to orchestrate transcriptional changes across different regulatory networks. That a male seminal fluid protein can alter a females rhythmic gene expression has implications for our understanding of both reproductive and circadian behaviors.

genomics↗

bioRxiv2021

An Empirical Bayes Approach to Estimating Dynamic Models of Co-regulated Gene Expression

Time-course gene expression datasets provide insight into the dynamics of complex biological processes, such as immune response and organ development. It is of interest to identify genes with similar temporal expression patterns because such genes are often biologically related. However, this task is challenging due to the high dimensionality of these datasets and the nonlinearity of gene expression time dynamics. We propose an empirical Bayes approach to estimating ordinary differential equation (ODE) models of gene expression, from which we derive a similarity metric between genes called the Bayesian lead-lag R2 (LLR2). Importantly, the calculation of the LLR2 leverages biological databases that document known interactions amongst genes; this information is automatically used to define informative prior distributions on the ODE models parameters. As a result, the LLR2 is a biologically-informed metric that can be used to identify clusters or networks of functionally-related genes with co-moving or time-delayed expression patterns. We then derive data-driven shrinkage parameters from Steins unbiased risk estimate that optimally balance the ODE models fit to both data and external biological information. Using real gene expression data, we demonstrate that our methodology allows us to recover interpretable gene clusters and sparse networks. These results reveal new insights about the dynamics of biological systems.

genomics↗

bioRxiv2020

Dense time-course gene expression profiling of the Drosophila melanogaster innate immune response

BackgroundImmune responses need to be initiated rapidly, and maintained as needed, to prevent establishment and growth of infections. At the same time, resources need to be balanced with other physiological processes. On the level of transcription, studies have shown that this balancing act is reflected in tight control of the initiation kinetics and shutdown dynamics of specific immune genes. ResultsTo investigate genome-wide expression dynamics and trade-offs after infection at a high temporal resolution, we performed an RNA-seq time course on D. melanogaster with 20 time points post Imd stimulation. A combination of methods, including spline fitting, cluster analysis, and Granger causality inference, allowed detailed dissection of expression profiles, lead-lag interactions, and functional annotation of genes through guilt-by-association. We identified Imd-responsive genes and co-expressed, less well characterized genes, with an immediate-early response and sustained up-regulation up to five days after stimulation. In contrast, stress response and Toll-responsive genes, among which were Bomanins, demonstrated early and transient responses. We further observed a strong trade-off with metabolic genes, which strikingly recovered to pre-infection levels before the immune response was fully resolved. ConclusionsThis high-dimensional dataset enabled the comprehensive study of immune response dynamics through the parallel application of multiple temporal data analysis methods. The well annotated data set should also serve as a useful resource for further investigation of the D. melanogaster innate immune response, and for the development of methods for analysis of a post-stress transcriptional response time-series at whole-genome scale.

systems biology↗