A generalizable framework for unlocking missing reactions in genome-scale metabolic networks using deep learning

• URL: http://arxiv.org/abs/2409.13259v1
• Date: Fri, 20 Sep 2024 06:47:44 GMT
• Title: A generalizable framework for unlocking missing reactions in genome-scale metabolic networks using deep learning
• Authors: Xiaoyi Liu, Hongpeng Yang, Chengwei Ai, Ruihan Dong, Yijie Ding, Qianqian Yuan, Jijun Tang, Fei Guo,
• Abstract summary: CLOSEgaps is a deep learning tool that maps metabolic networks as hypergraphs and learns their hyper-topology features to identify missing reactions and gaps. Results demonstrate that CLOSEgaps accurately gap-filling over 96% of artificially introduced gaps for various GEMs.
• Score: 3.765163284974983
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Incomplete knowledge of metabolic processes hinders the accuracy of GEnome-scale Metabolic models (GEMs), which in turn impedes advancements in systems biology and metabolic engineering. Existing gap-filling methods typically rely on phenotypic data to minimize the disparity between computational predictions and experimental results. However, there is still a lack of an automatic and precise gap-filling method for initial state GEMs before experimental data and annotated genomes become available. In this study, we introduce CLOSEgaps, a deep learning-driven tool that addresses the gap-filling issue by modeling it as a hyperedge prediction problem within GEMs. Specifically, CLOSEgaps maps metabolic networks as hypergraphs and learns their hyper-topology features to identify missing reactions and gaps by leveraging hypothetical reactions. This innovative approach allows for the characterization and curation of both known and hypothetical reactions within metabolic networks. Extensive results demonstrate that CLOSEgaps accurately gap-filling over 96% of artificially introduced gaps for various GEMs. Furthermore, CLOSEgaps enhances phenotypic predictions for 24 GEMs and also finds a notable improvement in producing four crucial metabolites (Lactate, Ethanol, Propionate, and Succinate) in two organisms. As a broadly applicable solution for any GEM, CLOSEgaps represents a promising model to automate the gap-filling process and uncover missing connections between reactions and observed metabolic phenotypes.

Related papers

• Generative Intervention Models for Causal Perturbation Modeling [80.72074987374141]
  In many applications, it is a priori unknown which mechanisms of a system are modified by an external perturbation. We propose a generative intervention model (GIM) that learns to map these perturbation features to distributions over atomic interventions.
  arXiv  Detail & Related papers  (2024-11-21T10:37:57Z)
• Gene-Metabolite Association Prediction with Interactive Knowledge Transfer Enhanced Graph for Metabolite Production [49.814615043389864]
  We propose a new task, Gene-Metabolite Association Prediction based on metabolic graphs. We present the first benchmark containing 2474 metabolites and 1947 genes of two commonly used microorganisms. Our proposed methodology outperforms baselines by up to 12.3% across various link prediction frameworks.
  arXiv  Detail & Related papers  (2024-10-24T06:54:27Z)
• Stacked ensemble\-based mutagenicity prediction model using multiple modalities with graph attention network [0.9736758288065405]
  Mutagenicity is a concern due to its association with genetic mutations which can result in a variety of negative consequences. In this work, we introduce a novel stacked ensemble based mutagenicity prediction model.
  arXiv  Detail & Related papers  (2024-09-03T09:14:21Z)
• Protein binding affinity prediction under multiple substitutions applying eGNNs on Residue and Atomic graphs combined with Language model information: eGRAL [1.840390797252648]
  Deep learning is increasingly recognized as a powerful tool capable of bridging the gap between in-silico predictions and in-vitro observations. We propose eGRAL, a novel graph neural network architecture designed for predicting binding affinity changes from amino acid substitutions in protein complexes. eGRAL leverages residue, atomic and evolutionary scales, thanks to features extracted from protein large language models.
  arXiv  Detail & Related papers  (2024-05-03T10:33:19Z)
• Efficiently Predicting Protein Stability Changes Upon Single-point Mutation with Large Language Models [51.57843608615827]
  The ability to precisely predict protein thermostability is pivotal for various subfields and applications in biochemistry. We introduce an ESM-assisted efficient approach that integrates protein sequence and structural features to predict the thermostability changes in protein upon single-point mutations.
  arXiv  Detail & Related papers  (2023-12-07T03:25:49Z)
• ODBAE: a high-performance model identifying complex phenotypes in high-dimensional biological datasets [17.61868583360252]
  We introduce ODBAE (Outlier Detection using Balanced Autoencoders), a machine learning method designed to uncover both subtle and extreme outliers. Our results highlight the utility of ODBAE in detecting joint abnormalities and advancing our understanding of homeostatic perturbations in biological systems.
  arXiv  Detail & Related papers  (2022-11-06T07:56:33Z)
• Improved Drug-target Interaction Prediction with Intermolecular Graph Transformer [98.8319016075089]
  We propose a novel approach to model intermolecular information with a three-way Transformer-based architecture. Intermolecular Graph Transformer (IGT) outperforms state-of-the-art approaches by 9.1% and 20.5% over the second best for binding activity and binding pose prediction respectively. IGT exhibits promising drug screening ability against SARS-CoV-2 by identifying 83.1% active drugs that have been validated by wet-lab experiments with near-native predicted binding poses.
  arXiv  Detail & Related papers  (2021-10-14T13:28:02Z)
• Integrating Expert ODEs into Neural ODEs: Pharmacology and Disease Progression [71.7560927415706]
  latent hybridisation model (LHM) integrates a system of expert-designed ODEs with machine-learned Neural ODEs to fully describe the dynamics of the system. We evaluate LHM on synthetic data as well as real-world intensive care data of COVID-19 patients.
  arXiv  Detail & Related papers  (2021-06-05T11:42:45Z)
• A Cross-Level Information Transmission Network for Predicting Phenotype from New Genotype: Application to Cancer Precision Medicine [37.442717660492384]
  We propose a novel Cross-LEvel Information Transmission network (CLEIT) framework. Inspired by domain adaptation, CLEIT first learns the latent representation of high-level domain then uses it as ground-truth embedding. We demonstrate the effectiveness and performance boost of CLEIT in predicting anti-cancer drug sensitivity from somatic mutations.
  arXiv  Detail & Related papers  (2020-10-09T22:01:00Z)
• SUOD: Accelerating Large-Scale Unsupervised Heterogeneous Outlier Detection [63.253850875265115]
  Outlier detection (OD) is a key machine learning (ML) task for identifying abnormal objects from general samples. We propose a modular acceleration system, called SUOD, to address it.
  arXiv  Detail & Related papers  (2020-03-11T00:22:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.