Spatial omics-informed graph neural networks for metabolic flux estimation in microbial communities
*V. Uday KumarCorresponding authoruday009u@gmail.comDepartment of Data ScienceGokaraju Rangaraju Institute of Engineering and TechnologyHyderabad, Telangana, 500090, IndiaView full profile → , N. K. Manikandanmanikandan.nk@veltech.edu.inDepartment of Computer Science and EngineeringVel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and TechnologyAvadi, Chennai, Tamil Nadu, 600062, IndiaView full profile → , C. Kaushikkaushik_c@vnrvjiet.inDepartment of Electronics and Communication EngineeringVNR Vignana Jyothi Institute of Engineering & TechnologyHyderabad, Telangana, 500118, IndiaView full profile → , P. Anushapeyyalaanusha@rcee.ac.inDepartment of Computer Science and EngineeringRamachandra College of EngineeringEluru, Andhra Pradesh, 534007, IndiaView full profile → , Parameshwari M. Goudpmgoud21@gmail.comDepartment of MathematicsVardhaman College of EngineeringHyderabad, Telangana, 501218, IndiaView full profile → , L. Pallavipallavi503@gmail.comDepartment of Computer Science and EngineeringB. V. Raju Institute of TechnologyTelangana, Hyderabad, 502313, IndiaView full profile → , K. Saranyadrsaranya@kluniversity.inDepartment of Computer Science and EngineeringKoneru Lakshmaiah Education FoundationVaddeswaram, Andhra Pradesh, 522302, IndiaView full profile → , Sreelatha Malempatilathamoturicse@gmail.comDepartment of Computer Science & EngineeringR. V. R & J. C. College of EngineeringGuntur, Andhra Pradesh, 522019, IndiaView full profile →
* Corresponding author · click or hover a name for details
- Received:
- 01 Oct 2025
- Published Online:
- 30 Sep 2026
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JDMSC-2781
- Pages:
- 3533–3542
Abstract
This research showed a spatial omics integrated Graph Neural Network (GNN) framework for high, resolution metabolic flux estimation across heterogeneous microbial communities using spatially resolved transcriptomic, proteomic, and metabolomic signals combined with microenvironmental interaction graphs. The research is geared towards creating a computational method that can predict cell, specific metabolic fluxes accurately while reflecting metabolic heterogeneity, microbial interactions, and spatially constrained biochemical dynamics. The method creates multi, layer spatial graphs of which nodes are microbial taxa or single cells, edges depict spatial proximity and biochemical interaction strength, and node features incorporate multi, omics signals normalized through spatial, aware preprocessing. A physics, guided flux inference component directs the GNN by stoichiometric and thermodynamic feasibility constraints taken from microbial metabolic models. Full training running spatiotemporal mini, batching as a tool to optimize allows the model to capture non, linear relationships between the local omics states and the emergent flux distributions under nutrient gradients, stress conditions, and community, level cross, feeding. The performance evaluation is based on accuracy, F1, MAE of flux magnitudes, training dynamics, latency profiling, and model stability illustrate that spatial, omics guided GNNs outperform non, spatial baselines by 1832% and at the same time allow sub, second flux inference which is perfect for real, time microbial ecosystem monitoring.
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References
[1] I. Thiele and B. Palsson, “A protocol for generating a high-quality genome-scale metabolic reconstruction,” Nature Protocols, vol. 5, pp. 93–121 (2010), doi: 10.1038/nprot.2009.203.
[2] J. Monk, J. Nogales, and B. O. Palsson, “Optimizing genome-scale network reconstructions,” Nature Biotechnology, vol. 32, pp. 447–452 (2014), doi: 10.1038/nbt.2870.
[3] E. O’Brien, J. Monk, and B. O. Palsson, “Using genome-scale models to predict biological capabilities,” Cell, vol. 161, pp. 971–987 (2015), doi: 10.1016/j.cell.2015.05.019.
[4] J. D. Orth, I. Thiele, and B. Palsson, “What is flux balance analysis?,” Nature Biotechnology, vol. 28, no. 3, pp. 245–248 (2010), doi: 10.1038/nbt.1614.
[5] F. J. Bruggeman, R. Planqué, D. Molenaar, and B. Teusink, “Searching for principles of microbial physiology,” FEMS Microbiology Reviews, vol. 44, pp. 821–844 (2020), doi: 10.1093/femsre/fuaa034.
[6] M. R. Antoniewicz, “A guide to metabolic flux analysis in metabolic engineering: Methods, tools and applications,” Metabolic Engineering, vol. 63, pp. 2–12 (2021), doi: 10.1016/j.ymben.2020.11.002.
[7] G. Tazza, F. Moro, D. Ruggeri, B. Teusink, and L. Vidács, “MINN: A metabolic-informed neural network for integrating omics data into genome-scale metabolic modeling,” bioRxiv (May 2025).
[8] C. D. Nadell, K. Drescher, and K. R. Foster, “Spatial structure, cooperation and competition in biofilms,” Nature Reviews Microbiology, vol. 14, no. 9, pp. 589–600 (2016), doi: 10.1038/nrmicro.2016.84.
[9] S. Salunkhe, A. O. Mulani, D. Shahane, M. Rana, S. M. Shukla, and M. M. Jadhav, “Secure image transmission using chaotic encryption and DWT watermarking on reconfigurable platform,” J. Discrete Math. Sci. Cryptogr., vol. 29, no. 1, pp. 349–361 (2026).
[10] K. R. Chaudhari, A. O. Mulani, M. P. Gajare, V. Jadhav, P. Yawle, and A. V. Bang, “Bit error rate analysis of various error correction codes with concatenated RS-convolutional codes,” J. Discrete Math. Sci. Cryptogr., vol. 29, no. 1, pp. 199–214 (2026).
