Biology subjects

May, E.

Publications and source records attributed to May, E..

7 recordsLinked to original sources

bioRxiv2026

Understanding Iron and Oxidative Stress Response in Escherichia coli Using Multi-phenotype and Ensemble Models

Developing effective antimicrobial strategies requires a predictive understanding of bacterial responses to multiple stress conditions which often result in multiple phenotypes. A microbes survival and proliferation depend on its ability to manage concurrent, dynamically varying stressors within its microenvironment. However, time-resolved predictive models that capture multi-phenotype responses are lacking, and single-phenotype models often fail to accurately replicate a microbes reaction to mixed stress conditions. In this work, we develop a mechanistic in-silico model of multi-stress response in Escherichia coli K12 and use it to characterize phenotype dynamics in iron-limited and hydrogen peroxide containing environments. Specifically, we replicate the iron and oxidative stress response networks in E. coli using a system of ordinary differential equations and applied a multi-phenotype parameterization scheme that leverages multi-measure empirical data, augmented metric-based sensitivity analysis, sequential parameter estimation, and ensemble modeling. Our approach resulted in robust models with a 93% accuracy when compared to experimental datasets across 20 stress-response categories, outperforming traditional single-phenotype approaches (80-87%). Analysis of posterior parameter distributions revealed that multi-phenotype optimization eliminates heavy-tailed distributions characteristic of poorly constrained fits and shifts parameter posteriors from boundary-concentrated to centrally localized forms, indicating improved identifiability. Simulation outcomes confirmed key features of E. colis iron metabolism, showing that moderate peroxide stress in an iron-rich environment creates significant adaptation challenges, leading to a bacteriostatic phenotype. The model provides insights into biochemical mechanisms important to E. colis temporal response to varying iron availability, with implications for ecological dynamics and pathogenesis. Our parameterization approach highlights the effectiveness of a combination of optimization methods and ensemble modeling in developing predictive models that are robust across multiple phenotypes. Results demonstrate that data structure, specifically the integration of multiple phenotypes and response outputs, proves to be as critical if not more critical than data volume for achieving well-constrained parameter estimates and robust predictions across experimental conditions. Author SummaryUnderstanding bacterial stress response is crucial for developing strategies to control bacterial populations, particularly as antibiotic resistance poses a growing threat, with "superbugs" potentially triggering a global health crisis. While mathematical models offer powerful tools to study biological systems, many struggle to predict cellular behavior across multiple phenotypes due to the complexity of responses. Iron metabolism is vital for bacterial survival, particularly under oxidative stress, leading to various bacterial growth dynamics. This work uses mathematical modeling to explore how E. coli manages multiple stressors, focusing on iron metabolism and oxidative stress. By applying a novel combination of optimization and ensemble modeling methods, we improved model accuracy by nearly 16%, enabling predictions of E. colis varied response to single, dual, and dynamic stress environments. Our approach offers a valuable tool for understanding and combating bacterial persistence, with future studies able to expand its use to determine how bacterial communities respond to multiple stressors.

systems biology↗

bioRxiv2025

Using SED-ML for reproducible curation: Verifying BioModels across multiple simulation engines

The BioModels Repository contains over 1000 manually curated mechanistic models from published literature, most often encoded in the Systems Biology Markup Language (SBML). This community-based standard formally specifies each model, but does not describe the computational experimental conditions to run a simulation. Therefore, it can be challenging to reproduce any figure or result from a publication with an SBML model alone. The Simulation Experiment Description Markup Language (SED-ML) provides a solution: a standard way to specify exactly how to run an experiment corresponding to a specific figure or result. BioModels was established years before SED-ML, and both systems evolved over time, both in content and acceptance. Hence, only about half of the entries in BioModels contained SED-ML files, and these files reflected the version of SED-ML that was available at the time. Additionally, almost all of these SED-ML files had at least one minor mistake that made them invalid. To make these models and their results more reproducible, we report here on our work updating, correcting and generating new SED-ML files for 1055 curated mechanistic models in BioModels. In addition, because SED-ML is implementation-independent, it can be used for verification, demonstrating that results hold across multiple simulation engines. We tested, corrected, and improved over 450 existing SED-ML files in the BioModels database, and created basic files for the rest of the entries. Then, we used a wrapper architecture for interpreting SED-ML, and report verification results across five different ODE-based biosimulation engines, after further improving the models, the wrappers, and the engines themselves. Our work with SED-ML and the BioModels collection aims to improve the utility of these models by making them more reproducible and credible. Improved reproducibility means these models are now even more fit for re-use, such as in new investigations and as components of multiscale models. Author summaryReproducing computationally-derived scientific results seems like it should be straightforward, but is often elusive. Code is lost, file formats change, and knowledge of what was done is only partially recorded and/or forgotten. Model repositories such as BioModels address this failing in the Systems Biology domain by encoding models in a standard format that can reproduce a figure from the paper from which it was drawn. Here, we delved into the BioModels repository to create and correct the instructions on what to do with every curated model, and then tested those instructions on a variety of simulation platforms, allowing us to find and correct issues with the platforms and the simulators themselves. Not only did this improve the BioModels repository, but also improved the infrastructure necessary to run these verification comparisons in the future, and improved the fitness of the models for re-use by other researchers.

systems biology↗

bioRxiv2022

Agricultural margins could enhance landscape connectivity for pollinating insects across the Central Valley of California, U.S.A.

One of the defining features of the Anthropocene is eroding ecosystem services as a function of decreases in biodiversity and overall reductions in the abundance of once-common organisms, including many insects that play innumerable roles in natural communities and agricultural systems that support human society. It is now clear that the preservation of insects cannot rely solely on the legal protection of natural areas far removed from the densest areas of human habitation. Instead, a critical challenge moving forward is to intelligently manage areas that include intensively farmed landscapes, such as the Central Valley of California. Here we attempt to meet this challenge with a tool for modeling landscape connectivity for insects (with pollinators in particular in mind) that builds on available information including lethality of pesticides and expert opinion on insect movement. Despite the massive fragmentation of the Central Valley, we find that connectivity is possible, especially utilizing the restoration or improvement of agricultural margins which (in their summed area) exceed natural areas. Finally, we highlight steps moving forward and the great many knowledge gaps that could be addressed in the field to improve future iterations of our modeling approach.

ecology↗

bioRxiv2021

Temporal-spectral signaling of sensory information and expectations in the cerebral processing of pain

The perception of pain is shaped by somatosensory information about threat. However, pain is also influenced by an individuals expectations. Such expectations can result in clinically relevant modulations and abnormalities of pain. In the brain, sensory information, expectations (predictions), and discrepancies thereof (prediction errors) are signaled by an extended network of brain areas. These brain areas generate evoked potentials and oscillatory responses at different latencies and frequencies. Recent evidence has provided first insights into how oscillatory responses at different frequencies signal predictions and prediction errors. However, a comprehensive picture of how evoked and oscillatory brain responses signal sensory information, predictions, and prediction errors in the processing of pain is lacking so far. We therefore built upon a neuroimaging study which investigated the spatial signalling of sensory information, predictions and predictions errors in the processing of pain (Geuter et al., 2017). To complement and extend this study, we applied brief painful stimuli to 48 healthy human participants and independently modulated sensory information (stimulus intensity) and expectations of pain intensity while measuring brain activity using electroencephalography (EEG). Pain ratings confirmed that pain intensity was shaped by both sensory information and expectations. In contrast, Bayesian analyses revealed that stimulus-induced EEG responses at different latencies (the N1, N2, and P2 components) and frequencies (alpha, beta, and gamma oscillations) were shaped by sensory information but not by expectations. Expectations, however, shaped alpha and beta oscillations before the painful stimuli. These findings indicate that commonly analyzed EEG responses to painful stimuli are more involved in signaling sensory information than in signaling expectations or mismatches of sensory information and expectations. Moreover, they indicate that the effects of expectations on pain are served by brain mechanisms which differ from those conveying effects of sensory information on pain.

neuroscience↗

bioRxiv2020

The choice of plant provenance for ecological restoration affects pollinator network

The selection of plant provenance for ecological restoration is an intensively debated topic. Throughout this debate, arguments mostly focus on plant performance, but little attention is paid to the effects of provenance on other members of the restored ecosystem. On the other hand, in restoration projects that focus specifically on supporting interacting biota, for example flower stripes among fields to support pollinators, the provenance choice is often not considered, partly because the effect of provenance on pollinators is unknown. In this pioneering case study, we tested whether pollinators differentiate between experimental plant communities of different provenances. We established experimental plant communities with the same species composition but with plants originating from three different provenances. We then recorded plant phenology and observed pollinators and flower visitors interacting with these experimental communities and related the pollinator visitation to the provenance identity. The provenances of the experimental plant communities had a strong and significant effect on the diversity and abundance of flower-pollinator interactions, with one provenance interacting twice as often as the other two provenances. The effect was driven by the differences in flowering phenology among provenances. Synthesis and applicationsPlant provenances substantially differ in their interactions with local pollinators. Therefore, the selection of plant provenance should be considered when planning restoration projects for the support of pollinators.

ecology↗

bioRxiv2020

Dynamics of brain function in chronic pain patients assessed by microstate analysis of resting-state electroencephalography

Chronic pain is a highly prevalent and severely disabling disease, which is associated with substantial changes of brain function. Such changes have mostly been observed when analyzing static measures of brain activity during the resting-state. However, brain activity varies over time and it is increasingly recognized that the temporal dynamics of brain activity provide behaviorally relevant information in different neuropsychiatric disorders. Here, we therefore investigated whether the temporal dynamics of brain function are altered in chronic pain. To this end, we applied microstate analysis to eyes-open and eyes-closed resting-state electroencephalography (EEG) data of 101 patients suffering from chronic pain and 88 age- and gender-matched healthy controls. Microstate analysis describes EEG activity as a sequence of a limited number of topographies termed microstates, which remain stable for tens of milliseconds. Our results revealed that sequences of 5 microstates, labelled with the letters A to E, described resting-state brain activity in both groups and conditions. Bayesian analysis of the temporal characteristics of microstates revealed that microstate D has a less predominant role in patients than in healthy participants. This difference was consistently found in eyes-open and eyes-closed EEG recordings. No evidence for differences in other microstates was found. As microstate D has been previously related to attentional networks and functions, abnormalities of microstate D might relate to dysfunctional attentional processes in chronic pain. These findings add to the understanding of the pathophysiology of chronic pain and might eventually contribute to the development of an EEG-based biomarker of chronic pain.

neuroscience↗

bioRxiv2018

Interaction specificity of clustered protocadherins inferred from sequence covariation and structural analysis

Clustered protocadherins are a large family of paralogous proteins that play important roles in neuronal development. The more than 50 clustered protocadherin isoforms have remarkable homophilic specificity for interactions between cellular surfaces that is controlled by a large antiparallel dimer interface formed by the first four extracellular cadherin (EC) domains. To understand how specificity is achieved between the numerous paralogs, we used a combination of structural and computational approaches. Molecular dynamics simulations revealed that individual EC interactions are weak and go through binding and unbinding events, but together they form a stable complex through polyvalency. Using sequence coevolution, we generated a statistical model of interaction energy for the clustered protocadherin family that measures the contributions of all amino acid pairs in the interface. Our interaction energy model assesses specificity for all possible pairs of isoforms, recapitulating known pairings and predicting the effects of experimental changes in isoform specificity that are consistent with literature results. Our results show that sequence coevolution can be used to understand specificity determinants in a protein family and prioritize interface amino acid substitutions to reprogram specific protein-protein interactions.

biophysics↗