Biology subjects

Moses, M. E.

Publications and source records attributed to Moses, M. E..

4 recordsLinked to original sources

bioRxiv2026

Collective learning and manifold behaviors in predator groups

Collective foraging in animal groups often relies on behavioral diversity, with individuals adopting different, sometimes complementary roles during shared tasks. However, most theoretical models predict that individuals responding to similar information cues in a shared environment should converge on a single optimal behavioral strategy. Using a spatially explicit multi-agent deep reinforcement learning model embedded in a three-species food chain, we show that stable behavioral diversity can emerge spontaneously among initially naive agents. Rather than converging on a single optimum, agents differentiate along a low-dimensional manifold of sensorimotor control, reflecting tradeoffs in speed regulation, spatial exploration, and deterministic turning rules. While multiple strategies yield comparable individual energetic returns, they are not interchangeable; group performance depends on how specific strategies combine to produce spatial resource partitioning and distributed directional influence. Replacing co-learned individuals with similarly competent agents trained in other groups disrupts these interaction structures and strongly reduces total energy acquisition. These results demonstrate that coordinated collective behavior and diverse, compatible strategies can arise endogenously from shared learning histories, but that this form of collective performance is path dependent and may be fragile to changes in group composition.

ecology↗

bioRxiv2024

Multi-scale drivers of daily flow intermittency in a regulated desert river

Fluvial ecosystems are vital for biodiversity and human welfare but face increasing threats from flow intermittency caused by climate change and other human activities. To better understand drivers of flow intermittency, we analyzed long-term and spatially explicit river drying data from the Rio Grande, a regulated river in the North American desert southwest that was historically perennial but is now persistently intermittent. We examined the spatial structure and influences of precipitation, temperature, in-channel infrastructure, and river discharge on flow intermittency using multivariate autoregressive state space (MARSS) models and 12 years of daily data. Our findings indicate that river diversion rates at dams and irrigation return flows significantly structure the spatial occurrence of flow intermittency, but factors (possibly geologic) at distances [≤] 7 kilometers (km) are more influential as predictors of drying. Controlling influences of temperature and precipitation were not detected at the reach level ([~]154 km) but were significant at each of the subreach scales (n = 3) investigated. At all subreach scales, temperatures effect size exceeds precipitation by 2.5 times and is the strongest predictor of drying. Overall, process variance decreased by 98% between our reach- and all subreach models, suggesting that scale-sensitive models have great potential to accurately inform environmental flow management strategies aimed at mitigating negative effects of climate change and water extraction.

ecology↗

bioRxiv2021

Spatially distributed infection increases viral load in a computational model of SARS-CoV-2 lung infection

A key question in SARS-CoV-2 infection is why viral loads and patient outcomes vary dramatically across individuals. Because spatial-temporal dynamics of viral spread and immune response are challenging to study in vivo, we developed Spatial Immune Model of Coronavirus (SIMCoV), a scalable computational model that simulates hundreds of millions of lung cells, including respiratory epithelial cells and T cells. SIMCoV replicates viral growth dynamics observed in patients and shows how spatially dispersed infections can lead to increased viral loads. The model also shows how the timing and strength of the T cell response can affect viral persistence, oscillations, and control. By incorporating spatial interactions, SIMCoV provides a parsimonious explanation for the dramatically different viral load trajectories among patients by varying only the number of initial sites of infection, and the magnitude and timing of the T cell immune response. When the branching airway structure of the lung is explicitly represented, we find that virus spreads faster than in a 2D layer of epithelial cells, but much more slowly than in an undifferentiated 3D grid or in a well-mixed ODE model. These results illustrate how realistic spatially explicit computational models can improve understanding of within-host dynamics of SARS-CoV-2 infection. SummaryA key question in SARS-CoV-2 infection is why viral loads and patient outcomes are so different across individuals. Because its difficult to see how the virus spreads in the lungs of infected people, we developed Spatial Immune Model of Coronavirus (SIMCoV), a computational model that simulates hundreds of millions of cells, including lung cells and immune cells. SIMCoV simulates how virus grows and then declines, and the simulations match data observed in patients. SIMCoV shows that when there are more initial infection sites, the virus grows to a higher peak. The model also shows how the timing of the immune response, particularly the T cell response, can affect how long the virus persists and whether it is ultimately cleared from the lungs. SIMCoV shows that the different viral loads in different patients can be explained by how many different places the virus is initially seeded inside their lungs. We explicitly add the branching airway structure of the lung into the model and show that virus spreads slightly faster than it would in a two-dimensional layer of lung cells, but much slower than traditional mathematical models based on differential equations. These results illustrate how realistic spatial computational models can improve understanding of how SARS-CoV-2 infection spreads in the lung.

immunology↗

bioRxiv2020

Across kingdom biased CYP-mediated metabolism via small-molecule ligands docking on P450 oxidoreductase

Metabolic control is mediated by the dynamic assemblies and function of multiple redox enzymes. A key element in these assemblies, the P450 oxidoreductase (POR), donates electrons and selectively activates numerous (>50 in humans and >300 in plants) cytochromes P450 (CYPs) controlling metabolism of drugs, steroids and xenobiotics in humans and natural product biosynthesis in plants. The mechanisms underlying POR-mediated CYP metabolism remain poorly understood and to date no ligand binding has been described to regulate the specificity of POR. Here, using a combination of computational modeling and functional assays, we identified ligands that dock on POR and bias its specificity towards CYP redox partners. Single molecule FRET studies revealed ligand docking to alter POR conformational sampling, which resulted in biased activation of metabolic cascades in whole cell assays. We propose the model of biased metabolism, a mechanism akin to biased signaling of GPCRs, where ligand docking on POR stabilizes different conformational states that are linked to distinct metabolic outcomes. Biased metabolism may allow designing pathway-specific therapeutics or personalized food suppressing undesired, disease related, metabolic pathways.

biophysics↗