Biology subjects

Williams, C. D.

Publications and source records attributed to Williams, C. D..

5 recordsLinked to original sources

bioRxiv2026

Genetically informed distribution models refine predictions for the overwintering range of Helicoverpa armigera in North America

The ecological and economic threat posed by invasive pests demands proactive mitigation. Species distribution models (SDMs) are widely used in efforts to predict where invasive species might spread after introduction, though such models face several limitations. Among these is the unrealistic assumption of niche uniformity throughout a species' range. This has led to interest in developing SDMs that explicitly account for local adaptation, though few methods have achieved this in a way that confidently separates local adaptation from population structure. Here we develop and implement a sequential SDM approach that incorporates experimentally verified associations between genotype, phenotype, and environment to forecast establishment risk in a major agricultural pest. We leverage genomic data from 738 individuals to characterize the geographic distribution of alleles at a major-effect locus for cold tolerance (tret1) in Helicoverpa armigera, an invasive crop pest of major economic concern in North America. We demonstrate that a recently detected North American population carries a cold-adapted tret1 allele, which has likely contributed to its persistence. We quantify the contribution of cold-adapted tret1 to the potential invasive range of H. armigera in North America under current and future climate scenarios. We find that cold-adapted tret1 may dramatically expand the potential range of H. armigera, and that potential future range expansion is likely to be driven primarily by cold-adapted individuals. Our results highlight the importance of accounting for intraspecific variation in invasive species risk assessments and management strategies.

ecology↗

bioRxiv2023

Overlapping and Distinct Mechanisms of Effective Neoantigen Cancer Vaccines and Immune Checkpoint Therapy

The goal of therapeutic cancer vaccines and immune checkpoint therapy (ICT) is to eliminate cancer by expanding and/or sustaining T cells with anti-tumor capabilities. However, whether cancer vaccines and ICT enhance anti-tumor immunity by distinct or overlapping mechanisms remains unclear. Here, we compared effective therapeutic tumor-specific mutant neoantigen (NeoAg) cancer vaccines with anti-CTLA-4 and/or anti-PD-1 ICT in preclinical models. Both NeoAg vaccines and ICT induce expansion of intratumoral NeoAg-specific CD8 T cells, though the degree of expansion and acquisition of effector activity was much more substantial following NeoAg vaccination. Further, we found that NeoAg vaccines are particularly adept at inducing proliferating and stem-like NeoAg-specific CD8 T cells. Single cell T cell receptor (TCR) sequencing revealed that TCR clonotype expansion and diversity of NeoAg-specific CD8 T cells relates to their phenotype and functional state associated with specific immunotherapies employed. Effective NeoAg vaccines and ICT required both CD8 and CD4 T cells. While NeoAg vaccines and anti-PD-1 affected the CD4 T cell compartment, it was to less of an extent than observed with anti-CTLA-4, which notably induced ICOS+Bhlhe40+ Th1-like CD4 T cells and, when combined with anti-PD-1, a small subset of Th2-like CD4 T cells. Although effective NeoAg vaccines or ICT expanded intratumoral M1-like iNOS+ macrophages, NeoAg vaccines expanded rather than suppressed (as observed with ICT) M2-like CX3CR1+CD206+ macrophages, associated with the vaccine adjuvant. Further, combining NeoAg vaccination with ICT induced superior efficacy compared to either therapy in isolation, highlighting the utility of combining these modalities to eliminate cancer. HighlightsO_LINeoAg cancer vaccines utilize distinct mechanisms from CTLA-4 or PD-1 ICT C_LIO_LINeoAg vaccines induce TCF1+ stem-like and proliferating NeoAg-specific CD8 T cells C_LIO_LICD8 TCR clonotype expansion relates to phenotype and functional state associated with immunotherapy C_LIO_LINeoAg vaccines induce partially distinct macrophage remodeling from ICT C_LIO_LINeoAg vaccines synergize with ICT, exceeding combination CTLA-4/PD-1 ICT efficacy C_LI

immunology↗

bioRxiv2020

A Data-driven Surrogate Model for Work Computation of a Periodically Forced Half-Sarcomere

Muscle force generation follows from molecular scale interactions that drive macroscopic behaviors and macroscopic processes that influence those at the molecular scale. A particuarly challenging issue is that models at the molecular level of organization are often quite difficult to apply to larger spatial scales. This is particularly true of moleuclar models driven by Monte-Carlo simulations. This challenge of multiscale dynamics requires methods to extract reduced order behaviors from detailed high-dimensional simulations. In this work we present a novel deterministic simulation method yielding accurate predictions of force-length behaviors of contracting muscle sarcomeres undergoing periodic length changes (work loops). The model maintains interpretability by tracking macroscopic state variables throughout the simulation while using data-driven representations of dynamics. Parameters of the data-driven dynamics are learned from trajectories from Monte-Carlo simulations of a half-sarcomere. Our method significantly reduces computational cost by tracking the state of the sarcomere in a course grained set of variables while maintaining accurate prediction of macroscopic level observables and time series for course grained variables. This allows for rapid sampling of the models output and builds towards the ability to scale to multiple-sarcomere simulations. Author SummaryWe develop a data-driven surrogate model for the dynamics of the half-sarcomere. This model achieves the same behavior with respect to force traces as more sophisticated Monte Carlo models at a substantially lower computational cost. The model is built by finding a course grained description of the full state space of the Monte Carlo simulation and learning dynamical models on the course grained space. Data-driven representations of the dynamics in the course grained space are trained using data from the full model. Data-driven models for forcing are also learned, and the result fed back into the dynamics. In doing so, the model seeks to replicate the effects of filament compliance on macro scale dynamics without explicitly tracking micro scale features. We withhold some input parameter regimes and demonstrate accurate reconstruction of course grained state and force traces using the data-driven model and given only knowledge of the initial condition and input. This work allows for faster computation of the forcing behavior of the half-sarcomere, as well as consistent representations of the course grained state variables. It is therefore promising as a step towards multi-sarcomere or even tissue scale models of skeletal muscle.

biophysics↗

bioRxiv2019

A Bayesian framework for the detection of diffusive heterogeneity

Cells are crowded and spatially heterogeneous, complicating the transport of organelles, proteins and other substrates. One aspect of this complex physical environment, the mobility of passively transported substrates, can be quantitatively characterized by the diffusion coefficient: a descriptor of how rapidly substrates will diffuse in the cell, dependent on their size and effective local viscosity. The spatial dependence of diffusivity is challenging to quantitatively characterize, because temporally and spatially finite observations offer limited information about a spatially varying stochastic process. We present a Bayesian framework that estimates diffusion coefficients from single particle trajectories, and predicts our ability to distinguish differences in diffusion coefficient estimates, conditional on how much they differ and the amount of data collected. This framework is packaged into a public software repository, including a tutorial Jupyter notebook demonstrating implementation of our method for diffusivity estimation, analysis of sources of uncertainty estimation, and visualization of all results. This estimation and uncertainty analysis allows our framework to be used as a guide in experimental design of diffusivity assays.

biophysics↗

bioRxiv2019

DLITE: Dynamic Local Intercellular Tension Estimation

The shape of cell-cell interfaces and the forces resulting from actomyosin interactions, mem-brane tension, or cell-cell adhesion are closely coupled. For example, the tight junction protein, ZO-1, forms a link between the force-bearing actin cortex and the rest of the tight junction protein (TJP) complex, regulating epithelial cell differentiation and the flux of solutes across epithelia. Here we introduce a method for Dynamic Local Intercellular Tension Estimation (DLITE) to computationally infer the evolution of cell-cell forces from a mechanical model of collective cell behaviour. This builds upon prior work in the field (CellFIT, Brodland et al., PloS one 9.6 (2014): e99116). We validate our estimated forces against those predicted by Surface Evolver simulations. Inferred tensions of a cell colony rearranging over time correlate better with the ground truth for our method (DLITE) than for prior methods intended for single time-points. DLITE is robust to both skeletonization errors and topological changes. Finally, we used DLITE in WTC-11 human induced pluripotent stem (hIPS) cells endogenously expressing ZO-1 GFP to find that major topo-logical changes in cell connectivity, e.g. mitosis, can result in an increase in tension. This suggests a correlation between the dynamics of cell-cell forces and colony rearrangement.

biophysics↗