Hybrid Modeling of Metabolic-Regulatory Networks (Extended Abstract)

被引:0
作者
Liu, Lin [1 ]
Bockmayr, Alexander [1 ]
机构
[1] Free Univ Berlin, Dept Math & Comp Sci, Arnimallee 6, D-14195 Berlin, Germany
来源
HYBRID SYSTEMS BIOLOGY (HSB 2019) | 2019年 / 11705卷
关键词
Computational modeling; Metabolism; Resource allocation; Gene regulation; Hybrid system; OPTIMIZATION;
D O I
10.1007/978-3-030-28042-0_12
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
Computational approaches in systems biology have become a powerful tool for understanding the fundamental mechanisms of cellular metabolism and regulation. However, the interplay between the regulatory and the metabolic system is still poorly understood. In particular, there is a need for formal mathematical frameworks that allow analyzing metabolism together with dynamic enzyme resources and regulatory events. Here, we introduce a metabolic-regulatory network model (MRN) that allows integrating metabolism with transcriptional regulation, macromolecule production and enzyme resources. Using this model, we show that the dynamic interplay between these different cellular processes can be formalized by a hybrid system, combining continuous dynamics and discrete control.
引用
收藏
页码:177 / 180
页数:4
相关论文
共 50 条
  • [41] Advanced Modeling of Food Convective Drying: A Comparison Between Artificial Neural Networks and Hybrid Approaches
    Saraceno, Alessandra
    Aversa, Maria
    Curcio, Stefano
    FOOD AND BIOPROCESS TECHNOLOGY, 2012, 5 (05) : 1694 - 1705
  • [42] Dynamic Modeling and Optimal Control of Batch Reactors, Based on Structure Approaching Hybrid Neural Networks
    Wang, J.
    Cao, L. Lin
    Wu, H. Yan
    Li, X. Guang
    Jin, Q. Bing
    INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, 2011, 50 (10) : 6174 - 6186
  • [43] Behavioral Modeling of a C-Band Ring Hybrid Coupler Using Artificial Neural Networks
    Demircioglu, Erdem
    Sazli, Murat H.
    RADIOENGINEERING, 2010, 19 (04) : 645 - 652
  • [44] Online Sequential Learning of Neural Networks in Solar Radiation Modeling Using Hybrid Bayesian Hierarchical Approach
    Hussain, Sajid
    AlAlili, Ali
    JOURNAL OF SOLAR ENERGY ENGINEERING-TRANSACTIONS OF THE ASME, 2016, 138 (06):
  • [45] Compound-choking theory and artificial neural networks-based hybrid modeling for supersonic ejectors
    Zhu, Hanzeng
    Liu, Jiapeng
    Yu, Jinpeng
    Yang, Peng
    INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER, 2024, 228
  • [46] Hybrid dynamic modeling of Escherichia coli central metabolic network combining Michaelis-Menten and approximate kinetic equations
    Costa, Rafael S.
    Machado, Daniel
    Rocha, Isabel
    Ferreira, Eugenio C.
    BIOSYSTEMS, 2010, 100 (02) : 150 - 157
  • [47] Modeling and Prediction of Surface Roughness in Hybrid Manufacturing-Milling after FDM Using Artificial Neural Networks
    Djurovic, Strahinja
    Lazarevic, Dragan
    Cirkovic, Bogdan
    Misic, Milan
    Ivkovic, Milan
    Stojcetovic, Bojan
    Petkovic, Martina
    Asonja, Aleksandar
    APPLIED SCIENCES-BASEL, 2024, 14 (14):
  • [48] Dynamic modeling of exergy efficiency of turboprop engine components using hybrid genetic algorithm-artificial neural networks
    Baklacioglu, Tolga
    Turan, Onder
    Aydin, Hakan
    ENERGY, 2015, 86 : 709 - 721
  • [49] Hybrid modeling techniques for predicting chemical oxygen demand in wastewater treatment: a stacking ensemble learning approach with neural networks
    Ramya, S.
    Srinath, S.
    Tuppad, Pushpa
    ENVIRONMENTAL MONITORING AND ASSESSMENT, 2024, 196 (12)
  • [50] Parametric Modeling of EM Behavior of Microwave Components Using Combined Neural Networks and Hybrid-Based Transfer Functions
    Zhao, Zhihao
    Feng, Feng
    Zhang, Wei
    Zhang, Jianan
    Jin, Jing
    Zhang, Qi-Jun
    IEEE ACCESS, 2020, 8 : 93922 - 93938