Biology subjects

Gilpin, W.

Publications and source records attributed to Gilpin, W..

4 recordsLinked to original sources

bioRxiv2026

Inference and validation of causal genetic interactions in aging human fibroblasts

In the past hundred years, the fraction of fatalities due to age-related disease has risen from an envied minority of cases to the leading cause of death and economic morbidity in the developed world. Despite notable advances in understanding aging, its intrinsic complexity has prevented major therapeutic breakthroughs. This complexity extends across all systems and scales in an organism, leaving a "fingerprint of aging" across every aspect of cell and tissue behavior whose interrelationships are difficult to untangle. Compounding this problem, human aging is observed largely through cross-sectional rather than longitudinal data. Whether such data retain sufficient dynamical information to reveal causal interactions is unclear. Here we show that they can. Using convergent cross mapping (CCM), a method for identifying causal interactions in nonlinear dynamical systems, we reconstruct directed genetic interactions from age-ordered, cross-sectional transcriptomic data - recovering dynamics from populations rather than individuals. Applied to dermal fibroblasts from 490 GTEx donors, CCM identifies a genome-scale causal network whose topology, together with age-dependent changes in gene expression, predicts candidate drivers of aging. We tested these predictions experimentally by single-gene overexpression in fibroblasts from 55-year-old donors. Single-cell RNA sequencing confirmed 53.8% of testable CCM-predicted causal interactions, showing that dynamics reconstructed from cross-sectional populations contain experimentally recoverable information about gene-gene interactions. This 53.8% additionally exceeded that produced by multiple comparanda methods and a random null. We then asked whether this information could identify interventions that reverse cellular aging. Independent transcriptomic and high-dimensional morphological assays show that CCM-selected perturbations shift old fibroblasts toward states characteristic of young cells. These two measures of rejuvenation are strongly correlated, and the strongest interventions cause a majority of 55-year-old cells to resemble cells from 25-year-old donors. Thus, causal dynamics can be recovered from cross-sectional population data and used to identify interventions in aging. More generally, the results suggest a way to distinguish genes that change with age from genes that drive those changes, providing experimentally testable targets for altering the aging phenotype.

systems biology↗

bioRxiv2025

Interpretable gene network inference with nonlinear causality

Interactions among genes orchestrate the growth and survival of all living systems. Such interactions are mediated by vast networks of time-dependent couplings, yet existing strategies for inferring gene networks from omics data do not account for the strong causal constraints acting on nonlinear dynamical systems. As a result, we show here that current gene network inference approaches fail when applied to time series from physical systems. We thus introduce RiCE, a physics-based algorithm for inferring causal interaction graphs using geometric information embedded within time series. We benchmark RiCE against 30 other gene network inference methods, including recent probabilistic machine learning methods, and obtain leading results across 15 datasets spanning diverse experimental modalities and tissue types. RiCE learns physically-interpretable parameters of complex biological systems, requiring low overhead to achieve leading performance. We show diverse applications of RiCE: identifying transition states during the epithelial-mesenchymal transition, classifying cell subtypes during immune cell activation, and determining transcriptional dynamics during endocrinogenesis. Our work demonstrates nonlinearity as a critical inductive bias to accurately infer gene-gene interactions, as well as a key quantitative metric for understanding the dynamic biology of the cell.

systems biology↗

bioRxiv2018

Cryptic selection forces and dynamic heritability in generalized phenotypic evolution

Individuals with different phenotypes can have widely-varying responses to natural selection, yet many classical approaches to evolutionary dynamics emphasize how a populations average phenotype increases in fitness over time. However, recent experimental results have produced examples of populations that have multiple fitness peaks, or that experience frequency-dependence that affects the direction and strength of selection on certain individuals. Here, we extend classical fitness gradient formulations of natural selection in order to describe the dynamics of a phenotype distribution in terms of its moments--such as the mean, variance, skewness, etc. The number of governing equations in our model can be adjusted in order to capture different degrees of detail about the population. We compare our simplified model to direct Wright-Fisher simulations of evolution in several canonical fitness landscapes, we find that our model provides a low-dimensional description of complex dynamics not typically explained by classical theory, such as cryptic selection forces due to selection on trait ranges, time-variation of the heritability, and nonlinear responses to stabilizing or disruptive selection due to asymmetric trait distributions. In addition to providing a framework for extending general understanding of common qualitative concepts in phenotypic evolution--such as fitness gradients, selection pressures, and heritability--our approach has practical importance for studying evolution in contexts in which genetic analysis is infeasible.

evolutionary biology↗

bioRxiv2016

Flowtrace: simple visualization of coherent structures in biological fluid flows

1We present a simple, intuitive algorithm for visualizing time-varying flow fields that can reveal complex flow structures with minimal user intervention. We apply this technique to a variety of biological systems, including the swimming currents of invertebrates and the collective motion of swarms of insects. We compare our results to more experimentally-diffcult and mathematically-sophisticated techniques for identifying patterns in fluid flows, and suggest that our tool represents an essential \"middle ground\" allowing experimentalists to easily determine whether a system exhibits interesting flow patterns and coherent structures without the need to resort to more intensive techniques. In addition to being informative, the visualizations generated by our tool are often striking and elegant, illustrating coherent structures directly from videos without the need for computational overlays. Our tool is available as fully-documented open-source code available for MATLAB, Python, or ImageJ at www.flowtrace.org.

biophysics↗