Biology subjects

Deyle, E. R.

Publications and source records attributed to Deyle, E. R..

4 recordsLinked to original sources

bioRxiv2026

Existence of Causation without Correlation in Transcriptional Networks

It is commonly assumed that lack of correlation is evidence for lack of causal relationship. Here, however we show that in transcriptional networks, causal linkages can exist in the absence of correlation. We find that a substantial proportion of transcribed genes in yeast and in mouse, show evidence of state-dependent (nonlinear) and temporally coordinated dynamics in their expression patterns (65-77%). Using a test that accommodates this fact, we uncover strong causal relationships that are invisible to correlation-based analyses for both yeast and mouse models. Specifically, for yeast we detect uncorrelated causal relationships for the transcriptional regulators WHI5 and YHP1, and can verify these relationships experimentally. These genes reside at important checkpoints in the cell cycle where multiple signals are integrated at single nodes, giving rise to causal relationships, that despite being uncorrelated, can be accurately detected (71-78%) using a nonlinear causality test.

systems biology↗

bioRxiv2025

Extending empirical dynamic modeling to cross-sectional data beyond traditional time series.

The foundation of Empirical dynamic modeling (EDM) is in representing time-series data as the trajectory of a dynamic system in a multidimensional state space rather than as a collection of traces of individual variables changing through time. Takenss theorem provides a rigorous basis for adopting this state-space view of time-series data even from just a single time series, but there is considerable additional value to building out a state space with explicit covariates. Multivariate EDM case studies to-date, however, generally rely on building up understanding first from univariate to multivariate and use lag-coordinate embeddings for critical steps along the path of analysis. Here, we propose an alternative set of steps for multivariate EDM analysis when the traditional roadmap is not practicable. The general approach borrows ideas of random data projection from compressed sensing, but additional justification is described within the framework of Takenss theorem. We then detail algorithms that implement this alternative method and validate through application to simulated model data. The model demonstrations are constructed to explicitly demonstrate the possibility for this approach to extend EDM application from time-series trajectories to effectively realizations of the underlying vector field, i.e. data sets that measure change over time with very short formal time series but are otherwise "big" in terms of number of variables and samples.

bioinformatics↗

bioRxiv2020

Long-term warming weakens stabilizing effects of biodiversity in aquatic ecosystems

Despite the consensus that warming will affect biodiversity, alter physicochemical environments, and disrupt biological interactions, the relative importance of these key processes and how they interact to determine overall ecosystem function is poorly understood. Here, we analyze long-term (16[~]39 years) time series data from ten aquatic ecosystems and use convergent cross mapping (CCM) to quantify the hidden causal network linking species diversity, ecosystem function, and physicochemical factors. We observe that aquatic ecosystems subject to stronger warming exhibit decreased stability (larger fluctuations in phytoplankton biomass). We further show that this effect can be attributed to a weakening of stabilizing causal pathways between biodiversity, nutrient cycling, and phytoplankton biomass. Thus, rather than thinking in terms of separate factors, a more holistic view, that causally links biodiversity and the other ecosystem components, is required to understand and predict climate impacts on the temporal stability of aquatic ecosystems.

ecology↗

bioRxiv2019

Empirical dynamic modeling reveals ecological drivers of dengue dynamics

Experiments and models suggest that climate affects mosquito-borne disease transmission. However, disease transmission involves complex nonlinear interactions between climate and population dynamics, which makes detecting climate drivers at the population level challenging. By analyzing incidence data, estimated susceptible population size, and climate data with methods based on nonlinear time series analysis (collectively referred to as empirical dynamic modeling), we identified drivers and their interactive effects on dengue dynamics in San Juan, Puerto Rico. Climatic forcing arose only when susceptible availability was high: temperature and rainfall had net positive and negative effects, respectively. By capturing mechanistic, nonlinear, and context-dependent effects of population susceptibility, temperature, and rainfall on dengue transmission empirically, our model improves forecast skill over recent, state-of-the-art models for dengue incidence. Together, these results provide empirical evidence that the interdependence of host population susceptibility and climate drive dengue dynamics in a nonlinear and complex, yet predictable way.

ecology↗