Search bioRxiv⌕ Search

bioRxiv · 10.64898/2026.01.21.700759

Understanding Coral Health from Reactor Engineering Perspective: Multiphysics Modeling of Coral - Environment Interactions

Abstract

Coral, as a bioreactor, has to continuously interact with surrounding environment to maintain a healthy state. A multi-physics reaction engineering model has been developed to capture this interaction. The coral interior is modeled as interconnected reaction units respectively for photosynthesis, respiration, and calcification, whose reaction kinetics are influenced by environmental fluctuations. Coupling between coral and environment is realized by bi-directional mass transfer at the coral-seawater interface, with consideration of the unique flow fields induced by ciliary beating. By resorting to this comprehensive model, we discover that ciliary beating demonstrates distinctively different diurnal and nocturnal functions. During daytime, beating can help reduce photosynthetic oxygen accumulation to prevent hyperoxia-induced mortality, while enhancing carbon dioxide uptake efficiency to promote nutrient production. At night, however, beating promotes oxygen acquisition for adequate respiration, while expelling carbon dioxide to inhibit symbiotic destruction under acidic stress. The model further enables mechanistic analysis of the detrimental impact of climate change on coral health, where the influences from two key factors (i.e., temperature and CO2 level) can be decoupled. Its interesting to find out that the elevated temperature plays a dominant role during daytime, while at night the coral is dominantly influenced by rising CO2 level.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Zhuo, H., Xiao, F. L., Chen, X. D., Xiao, J.. 2026-01-23. Understanding Coral Health from Reactor Engineering Perspective: Multiphysics Modeling of Coral - Environment Interactions. https://doi.org/10.64898/2026.01.21.700759

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

Concentration limits and localization of hydrogen peroxide in the extracellular space of solid tissues

H2O2 released to the extracellular space (ECS) regulates diverse physiological processes, yet its concentrations and spatial distribution in tissues remain poorly defined. This uncertainty hampers mechanistic understanding of redox signaling. Here, we used reaction-diffusion modeling to estimate extracellular H2O2 concentrations and transport ranges in various scenarios. Idealized analytical models were combined with numerical models incorporating localized NADPH oxidase (NOX) clusters, ECS microstructure, membrane permeability, and the thioredoxin- and GSH-dependent clearance systems. Using maximal neutrophil and NOX superoxide/H2O2 release rates, we obtained upper bounds for extracellular H2O2. Adjacent to isolated average-sized, fully active NOX2 clusters H2O2 peaked at ~540 nM at adhesion cell-cell separations, and decreased radially over ~50-100 nm. At the receptor cell surface, peak concentration decreased inversely with intercellular separation, to <5 nM at 1 m separation. Radial decrease here, for this wide separation, was over ~2.5 m. Even the former maximal extracellular concentrations induce just a minimal, highly localized oxidation of the intracellular Prdx, Trx and GSH pools. In turn, maximally activated neutrophils carry ~2000 such NOX2 clusters, inducing 10s of M peak ECS H2O2 concentrations. These cause extensive Prdx and Trx oxidation near the exposed membranes. However, the GSH-dependent system still sustains a strong transmembrane gradient if the permeation barrier remains intact, and ECS H2O2 concentrations decay to sub-M within a few m of the source cell. Extracellular H2O2 concentrations scaled linearly with source flux in all the examined conditions. These results establish stringent constraints on autocrine, juxtacrine and next-cell paracrine H2O2 signaling.

systems biology↗

LSD-pipeline: Causal Inference of miRNA Network Effects in Alzheimer's Disease

MicroRNAs (miRNAs) are implicated in Alzheimer's disease (AD), but research has focused on individual miRNAs and direct targets. Existing approaches to miRNA regulation in AD identify associations rather than causal effects, and few methods estimate multi-stage chains from miRNAs through target genes to target transcription factor (TF) cascades. We developed the LSD pipeline (LASSO-SEM-DoWhy), integrating LASSO feature selection, multi-stage structural equation modeling, and DoWhy causal inference to identify and validate miRNA causal pathways in AD. Applying LSD to six blood miRNA and brain mRNA datasets, we identified four LSD-validated miRNAs (miR-30d-5p, miR-92a-3p, miR-296-5p, miR-193a-5p) as AD biomarkers, achieving >86% ROC accuracy in an independent validation cohort. Several miRNAs with no significant direct association with AD showed significant effects when estimated through their target networks, while others significant in direct analysis were not supported at the network level, underscoring the value of network-level analysis. Extending to the TF layer revealed complete miRNA [->] targets [->] TF cascades [->] AD causal chains, with HMGA1, NKX2-3, and PRRX2 as key intermediaries. Confirmed classic pathways converge primarily on tau pathology and synaptic dysfunction. miRNA effects were largely age-independent, suggesting miRNAs act as early initiators of AD pathogenesis. Beyond AD, the LSD pipeline provides a generalizable framework for uncovering causal regulatory mechanisms in other diseases.

systems biology↗

PyKappa: Rule-based modeling in Python

Rule-based languages have proven effective for modeling systems of interacting structured entities as typically encountered in chemistry and molecular biology. We present PyKappa, a rule-based modeling package written in Python whose interpreted nature enables interactive simulation and analysis, including by agentic AI. The package seeks to broaden the base of developers by utilizing a widely known programming language and serves as an easy-to-deploy teaching tool. Using PyKappa, we conduct a case study of phase separation.

systems biology↗