arXiv - QuanBio - Molecular Networks最新文献_第4页

Designing Machine Learning Tools to Characterize Multistationarity of Fully Open Reaction Networks 设计机器学习工具表征全开放反应网络的多稳定性

arXiv - QuanBio - Molecular Networks Pub Date : 2024-07-01 DOI: arxiv-2407.01760

Shenghao Yao, AmirHosein Sadeghimanesh, Matthew England

{"title":"Designing Machine Learning Tools to Characterize Multistationarity of Fully Open Reaction Networks","authors":"Shenghao Yao, AmirHosein Sadeghimanesh, Matthew England","doi":"arxiv-2407.01760","DOIUrl":"https://doi.org/arxiv-2407.01760","url":null,"abstract":"We present the first use of machine learning tools to predict\u0000multistationarity of reaction networks. Chemical Reaction Networks (CRNs) are the mathematical formulation of how the\u0000quantities associated to a set of species (molecules, proteins, cells, or\u0000animals) vary as time passes with respect to their interactions with each\u0000other. Their mathematics does not describe just chemical reactions but many\u0000other areas of the life sciences such as ecology, epidemiology, and population\u0000dynamics. We say a CRN is at a steady state when the concentration (or number)\u0000of species do not vary anymore. Some CRNs do not attain a steady state while\u0000some others may have more than one possible steady state. The CRNs in the later\u0000group are called multistationary. Multistationarity is an important property,\u0000e.g. switch-like behaviour in cells needs multistationarity to occur. Existing\u0000algorithms to detect whether a CRN is multistationary or not are either\u0000extremely expensive or restricted in the type of CRNs they can be used on,\u0000motivating a new machine learning approach. We address the problem of representing variable-length CRN data to machine\u0000learning models by developing a new graph representation of CRNs for use with\u0000graph learning algorithms. We contribute a large dataset of labelled fully open\u0000CRNs whose production necessitated the development of new CRN theory. Then we\u0000present experimental results on the training and testing of a graph attention\u0000network model on this dataset, showing excellent levels of performance. We\u0000finish by testing the model predictions on validation data produced\u0000independently, demonstrating generalisability of the model to different types\u0000of CRN.","PeriodicalId":501325,"journal":{"name":"arXiv - QuanBio - Molecular Networks","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141518267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Immediate Neighbours of Monotone Boolean Functions 单调布尔函数的直接邻域

arXiv - QuanBio - Molecular Networks Pub Date : 2024-07-01 DOI: arxiv-2407.01337

José E. R. Cury, Patrícia Tenera Roxo, Vasco Manquinho, Claudine Chaouiya, Pedro T. Monteiro

引用次数: 0

Graphical Conditions ensuring Equality between Differential and Mean Stochastic Dynamics 确保微分和平均随机动力学相等的图形条件

arXiv - QuanBio - Molecular Networks Pub Date : 2024-06-26 DOI: arxiv-2406.18126

Hugo BuscemiENS Paris Saclay, Lifeware, François FagesLifeware

{"title":"Graphical Conditions ensuring Equality between Differential and Mean Stochastic Dynamics","authors":"Hugo BuscemiENS Paris Saclay, Lifeware, François FagesLifeware","doi":"arxiv-2406.18126","DOIUrl":"https://doi.org/arxiv-2406.18126","url":null,"abstract":"Complex systems can be advantageously modeled by formal reaction systems\u0000(RS), a.k.a. chemical reaction networks in chemistry. Reaction-based models can\u0000indeed be interpreted in a hierarchy of semantics, depending on the question at\u0000hand, most notably by Ordinary Differential Equations (ODEs), Continuous Time\u0000Markov Chains (CTMCs), discrete Petri nets and asynchronous Boolean transition\u0000systems. The last three semantics can be easily related in the framework of\u0000abstract interpretation. The first two are classically related by Kurtz's limit\u0000theorem which states that if reactions are density-dependent families, then, as\u0000the volume goes to infinity, the mean reactant concentrations of the CTMC tends\u0000towards the solution of the ODE. In the more realistic context of bounded\u0000volumes, it is easy to show, by moment closure, that the restriction to\u0000reactions with at most one reactant ensures similarly that the mean of the CTMC\u0000trajectories is equal to the solution of the ODE at all time points. In this\u0000paper, we generalize that result in presence of polyreactant reactions, by\u0000introducing the Stoichiometric Influence and Modification Graph (SIMG) of an\u0000RS, and by showing that the equality between the two interpretations holds for\u0000the variables that belong to distinct SIMG ancestors of polyreactant reactions.\u0000We illustrate this approach with several examples. Evaluation on BioModels\u0000reveals that the condition for all variables is satisfied on models with no\u0000polymolecular reaction only. However, our theorem can be applied selectively to\u0000certain variables of the model to provide insights into their behaviour within\u0000more complex systems. Interestingly, we also show that the equality holds for a\u0000basic oscillatory RS implementing the sine and cosine functions of time.","PeriodicalId":501325,"journal":{"name":"arXiv - QuanBio - Molecular Networks","volume":"49 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141503779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Graph Representation Learning Strategies for Omics Data: A Case Study on Parkinson's Disease Omics 数据的图形表示学习策略：帕金森病案例研究

arXiv - QuanBio - Molecular Networks Pub Date : 2024-06-20 DOI: arxiv-2406.14442

Elisa Gómez de Lope, Saurabh Deshpande, Ramón Viñas Torné, Pietro Liò, Enrico Glaab, Stéphane P. A. Bordas

{"title":"Graph Representation Learning Strategies for Omics Data: A Case Study on Parkinson's Disease","authors":"Elisa Gómez de Lope, Saurabh Deshpande, Ramón Viñas Torné, Pietro Liò, Enrico Glaab, Stéphane P. A. Bordas","doi":"arxiv-2406.14442","DOIUrl":"https://doi.org/arxiv-2406.14442","url":null,"abstract":"Omics data analysis is crucial for studying complex diseases, but its high\u0000dimensionality and heterogeneity challenge classical statistical and machine\u0000learning methods. Graph neural networks have emerged as promising alternatives,\u0000yet the optimal strategies for their design and optimization in real-world\u0000biomedical challenges remain unclear. This study evaluates various graph\u0000representation learning models for case-control classification using\u0000high-throughput biological data from Parkinson's disease and control samples.\u0000We compare topologies derived from sample similarity networks and molecular\u0000interaction networks, including protein-protein and metabolite-metabolite\u0000interactions (PPI, MMI). Graph Convolutional Network (GCNs), Chebyshev spectral\u0000graph convolution (ChebyNet), and Graph Attention Network (GAT), are evaluated\u0000alongside advanced architectures like graph transformers, the graph U-net, and\u0000simpler models like multilayer perceptron (MLP). These models are systematically applied to transcriptomics and metabolomics\u0000data independently. Our comparative analysis highlights the benefits and\u0000limitations of various architectures in extracting patterns from omics data,\u0000paving the way for more accurate and interpretable models in biomedical\u0000research.","PeriodicalId":501325,"journal":{"name":"arXiv - QuanBio - Molecular Networks","volume":"24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141503780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Gene Regulatory Network Inference with Covariance Dynamics 利用协方差动力学推断基因调控网络

arXiv - QuanBio - Molecular Networks Pub Date : 2024-06-17 DOI: arxiv-2407.00754

Yue Wang, Peng Zheng, Yu-Chen Cheng, Zikun Wang, Aleksandr Aravkin

引用次数: 0

Planar chemical reaction systems with algebraic and non-algebraic limit cycles 具有代数和非代数极限循环的平面化学反应系统

arXiv - QuanBio - Molecular Networks Pub Date : 2024-06-07 DOI: arxiv-2406.05057

Gheorghe Craciun, Radek Erban

{"title":"Planar chemical reaction systems with algebraic and non-algebraic limit cycles","authors":"Gheorghe Craciun, Radek Erban","doi":"arxiv-2406.05057","DOIUrl":"https://doi.org/arxiv-2406.05057","url":null,"abstract":"The Hilbert number $H(n)$ is defined as the maximum number of limit cycles of\u0000a planar autonomous system of ordinary differential equations (ODEs) with\u0000right-hand sides containing polynomials of degree at most $n in {mathbb N}$.\u0000The dynamics of chemical reaction systems with two chemical species can be\u0000(under mass-action kinetics) described by such planar autonomous ODEs, where\u0000$n$ is equal to the maximum order of the chemical reactions in the system.\u0000Generalizations of the Hilbert number $H(n)$ to three different classes of\u0000chemical reaction networks are investigated: (i) chemical systems with\u0000reactions up to the $n$-th order; (ii) systems with up to $n$-molecular\u0000chemical reactions; and (iii) weakly reversible chemical reaction networks. In\u0000each case (i), (ii) and (iii), the question on the number of limit cycles is\u0000considered. Lower bounds on the generalized Hilbert numbers are provided for\u0000both algebraic and non-algebraic limit cycles. Furthermore, given a general\u0000algebraic curve $h(x,y)=0$ of degree $n_h in {mathbb N}$ and containing one\u0000or more ovals in the positive quadrant, a chemical system is constructed which\u0000has the oval(s) as its stable algebraic limit cycle(s). The ODEs describing the\u0000dynamics of the constructed chemical system contain polynomials of degree at\u0000most $n=2,n_h+1.$ Considering $n_h ge 4,$ the algebraic curve $h(x,y)=0$ can\u0000contain multiple closed components with the maximum number of ovals given by\u0000Harnack's curve theorem as $1+(n_h-1)(n_h-2)/2$, which is equal to 4 for\u0000$n_h=4.$ Algebraic curve $h(x,y)=0$ with $n_h=4$ and the maximum number of four\u0000ovals is used to construct a chemical system which has four stable algebraic\u0000limit cycles.","PeriodicalId":501325,"journal":{"name":"arXiv - QuanBio - Molecular Networks","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141503781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Mapping dynamical systems into chemical reactions 将动力系统映射到化学反应中

arXiv - QuanBio - Molecular Networks Pub Date : 2024-06-05 DOI: arxiv-2406.03473

Tomislav Plesa

引用次数: 0

Recurrent neural chemical reaction networks that approximate arbitrary dynamics 近似任意动力学的递归神经化学反应网络

arXiv - QuanBio - Molecular Networks Pub Date : 2024-06-05 DOI: arxiv-2406.03456

Alexander Dack, Benjamin Qureshi, Thomas E. Ouldridge, Tomislav Plesa

引用次数: 0

Mathematical models of the Arabidopsis circadian oscillator 拟南芥昼夜节律振荡器的数学模型

arXiv - QuanBio - Molecular Networks Pub Date : 2024-05-28 DOI: arxiv-2405.18006

Lucas Henao, Saúl Ares, Pablo Catalán

引用次数: 0

BioBERT-based Deep Learning and Merged ChemProt-DrugProt for Enhanced Biomedical Relation Extraction 基于 BioBERT 的深度学习和合并 ChemProt-DrugProt 用于增强生物医学关系提取

arXiv - QuanBio - Molecular Networks Pub Date : 2024-05-28 DOI: arxiv-2405.18605

Bridget T. McInnes, Jiawei Tang, Darshini Mahendran, Mai H. Nguyen

引用次数: 0