bioRxiv Science⌕ Search

bioRxiv · 10.64898/2026.10.10.758176

A mathematical model of tumor growth driven by oxygen lactate exosome feedback

Abstract

Solid tumors develop within a heterogeneous microenvironment shaped by coupled biochemical and metabolic processes. As a tumor grows, oxygen gradients develop from the periphery towards the core, giving rise to hypoxic regions. Hypoxia induces a metabolic shift from oxidative phosphorylation to glycolysis, leading to elevated lactate production. Experimental studies further indicate that increased lactate levels enhance the secretion of tumor-derived exosomes carrying proteins, mRNA, and lipids, which in turn promote tumor cell proliferation. Together, these interactions form a feedback loop linking oxygen availability, lactate accumulation, and exosome-mediated signaling. Despite growing experimental evidence for each component, the impact of this coupled oxygen lactate exosome axis on tumor growth dynamics has not yet been systematically explored from a mathematical perspective. It remains unclear how the oxygen lactate exosome axis affects tumor growth - impact of nonlocal effects. In this paper, we develop a mechanistic mathematical model to investigate how this feedback mechanisms affects tumor growth. We first consider an in vitro setting oxygen levels are spatially homogeneous and vary periodically in time between normoxic and hypoxic states, capturing key features of cyclic hypoxia observed in vivo. Numerical simulations reveal that exosome mediated feedback has a pronounced effect on tumor volume under chronic hypoxia. In addition, we observe that the tumor volume decreases as the period of cyclic hypoxia increases. We then extend the model to an in vivo framework by incorporating oxygen diffusion and consumption, allowing spatial heterogeneity to emerge. Simulations reveal that tumor cells in normoxic regions benefit indirectly from enhanced exosome secretion driven by lactate produced in hypoxic regions. Overall, the results show how spatially mediated metabolic signaling can amplify tumor growth and aggressiveness, and illustrate how mathematical modeling can increase understanding of non local feedback mechanisms within the tumor microenvironment.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Sadhu, G., Dalal, D., Byrne, H.. 2026-10-13. A mathematical model of tumor growth driven by oxygen lactate exosome feedback. https://doi.org/10.64898/2026.10.10.758176

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

KEEP EXPLORING

Related preprints

Gut microbially produced phenolic acids have differential and sex-dependent effects on host metabolic health

Gut microbiota-derived metabolites are increasingly recognized as important regulators of host metabolism, but the specific functions of individual metabolites remain poorly understood. We therefore investigated the metabolic effects of two structurally related, phenylalanine-derived microbial metabolites phenylacetic acid (PAA) and phenylpropionic acid (PPA) in a mouse model of diet-induced obesity. Although clinical studies have linked PAA and its downstream product phenylacetylglutamine to metabolic disease, PAA did not alter body mass or adiposity in this model. Instead, it improved glucose tolerance selectively in male mice and markedly remodeled lipid metabolism in both sexes, reducing circulating triglycerides and acylcarnitines and activating hepatic PPAR--associated transcriptional programs associated with fatty acid {omega}-hydroxylation and peroxisomal lipid metabolism. Despite this extensive metabolic remodeling, PAA did not worsen hepatic steatosis, fibrosis, or markers of liver injury. By contrast, PPA had minimal effects on hepatic metabolic pathways but produced pronounced sex-dependent physiological responses, increasing adiposity in female mice and improving glucose tolerance in male mice. These findings show that structurally related microbial metabolites can have distinct, sex-dependent metabolic effects and highlight the need to define the actions of individual microbiome-derived metabolites when developing targeted therapies for metabolic disease.

systems biology↗

Polarization of regulatory inputs reveals structural constraints in reconstructed pathway-associated regulatory networks

Activating and inhibitory interactions jointly shape cellular regulation, yet it remains unclear whether their allocation among individual targets exhibits systematic organization beyond basic structural constraints. Here we introduce antagonistic--cooperative polarization, a target-wise principle in which the sign of each regulatory interaction is determined by the class of its target. This formulation yields a node-separable graph polarization index and decomposes departures from perfect polarization exactly into localized conflicts at targets receiving both activating and inhibitory inputs. We applied the framework to 98 pathway-associated regulatory subnetworks reconstructed from a unified signed regulatory network. Target-wise sign consistency was generally high, but the key evidence for non-random organization came from constrained null models: mixed-input targets were depleted relative to both global sign shuffling and degree-preserving directed rewiring, with depletion increasing systematically with pathway size. This association was robust to rewiring-mobility and pathway-overlap analyses, and indegree-stratified analysis identified the clearest statistically supported depletion among targets receiving exactly two signed inputs. Mixed-input organization was pathway-specific but recurrent across genes; recurrent mixed-input targets remained enriched for Cancer Gene Census genes after accounting for pathway opportunity. Mixed-input occurrences were also preferentially associated with signed feedback motifs, particularly negative feedback, whereas downstream reach was not increased. Together, these results reveal a prevalent polarized backbone punctuated by localized sites of opposing regulation whose abundance departs from expectations under complementary null models controlling for network topology, sign composition, degree structure, and source-specific regulatory output.

systems biology↗

SHAPPEing agent-based models to spatial data: Sequential Hooke's law Algorithm for Point Pattern reconstruction with Environmental constraints

Agent-based models (ABMs) are widely used to study biological systems consisting of interacting species, such as tumour and immune cells, but their predictions depend on how agents are initially placed. High-resolution imaging techniques can capture the spatial configurations of various biological systems, but their use is costly. Furthermore, they only provide small regions of interest that may be too small to initialise most ABMs directly. Here, we present the Sequential Hooke's law Algorithm for Point Pattern reconstruction with Environmental constraints, or SHAPPE, a multitype point pattern reconstruction algorithm that generates synthetic reconstructions of arbitrary size and shape by moving points under spring-like forces with data-derived target distances. As it enforces a first-order structure through environmental constraints estimated with density grids, SHAPPE can be applied to both homogeneous and inhomogeneous point patterns. Applied to 87 glioblastoma IMC samples, we show that SHAPPE reduces dissimilarity in density distributions by a median of 53% and errors in inter-cell-type exposure levels by 34% relative to a random baseline. We then initialise a glioblastoma ABM with SHAPPE reconstructions instead of random configurations and find reduced tumour-CD8+ T cell interaction frequency with SHAPPE. This decrease translates into an over 20% decrease in the average number of tumour cells killed by CD8+ T cells over 50 days. Altogether, SHAPPE shows the potential to ease the integration of high-resolution imaging techniques into computational biology ABMs and ground initial conditions in data.

systems biology↗