Network Modeling and Control of Dynamic Disease Pathways, Review and Perspectives

被引:3
作者
Hsiao, Yen-Che [1 ]
Dutta, Abhishek [1 ]
机构
[1] Univ Connecticut, Dept Elect & Comp Engn, Storrs, CT 06269 USA
关键词
Biological interaction networks; biological system modeling; biological control systems; diseases; gene expression; GENETIC REGULATORY NETWORKS; PRE-MESSENGER-RNA; SYSTEMS BIOLOGY; SIGNALING NETWORKS; DIFFERENTIAL-EQUATIONS; COMPUTATIONAL METHODS; SENSITIVITY-ANALYSIS; METABOLIC NETWORKS; SYNTHETIC BIOLOGY; BAYESIAN NETWORKS;
D O I
10.1109/TCBB.2024.3378155
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Dynamic disease pathways are a combination of complex dynamical processes among bio-molecules in a cell that leads to diseases. Network modeling of disease pathways considers disease-related bio-molecules (e.g. DNA, RNA, transcription factors, enzymes, proteins, and metabolites) and their interaction (e.g. DNA methylation, histone modification, alternative splicing, and protein modification) to study disease progression and predict therapeutic responses. These bio-molecules and their interactions are the basic elements in the study of the misregulation in the disease-related gene expression that lead to abnormal cellular responses. Gene regulatory networks, cell signaling networks, and metabolic networks are the three major types of intracellular networks for the study of the cellular responses elicited from extracellular signals. The disease-related cellular responses can be prevented or regulated by designing control strategies to manipulate these extracellular or other intracellular signals. The paper reviews the regulatory mechanisms, the dynamic models, and the control strategies for each intracellular network. The applications, limitations and the prospective for modeling and control are also discussed.
引用
收藏
页码:1211 / 1230
页数:20
相关论文
共 301 条
[1]   Applications of Bayesian network models in predicting types of hematological malignancies [J].
Agrahari, Rupesh ;
Foroushani, Amir ;
Docking, T. Roderick ;
Chang, Linda ;
Duns, Gerben ;
Hudoba, Monika ;
Karsan, Aly ;
Zare, Habil .
SCIENTIFIC REPORTS, 2018, 8
[2]   In vitro implementation of robust gene regulation in a synthetic biomolecular integral controller [J].
Agrawal, Deepak K. ;
Marshall, Ryan ;
Noireaux, Vincent ;
Sontag, Eduardo D. .
NATURE COMMUNICATIONS, 2019, 10 (1)
[3]  
Aibar S, 2017, NAT METHODS, V14, P1083, DOI [10.1038/NMETH.4463, 10.1038/nmeth.4463]
[4]   Graph-based methods for analysing networks in cell biology [J].
Aittokallio, Tero ;
Schwikowski, Benno .
BRIEFINGS IN BIOINFORMATICS, 2006, 7 (03) :243-255
[5]   Inferring qualitative relations in genetic networks and metabolic pathways [J].
Akutsu, T ;
Miyano, S ;
Kuhara, S .
BIOINFORMATICS, 2000, 16 (08) :727-734
[6]   Analyzing a co-occurrence gene-interaction network to identify disease-gene association [J].
Al-Aamri, Amira ;
Taha, Kamal ;
Al-Hammadi, Yousof ;
Maalouf, Maher ;
Homouz, Dirar .
BMC BIOINFORMATICS, 2019, 20 (1)
[7]  
Albert R, 2004, LECT NOTES PHYS, V650, P459
[8]   DISCRETE DYNAMIC MODELING OF CELLULAR SIGNALING NETWORKS [J].
Albert, Reka ;
Wang, Rui-Sheng .
METHODS IN ENZYMOLOGY: COMPUTER METHODS, PART B, 2009, 467 :281-306
[9]   Mathematical modeling of the molecular switch of TNFR1-mediated signaling pathways applying Petri net formalism and in silico knockout analysis [J].
Amstein, Leonie K. ;
Ackermann, Joerg ;
Hannig, Jennifer ;
Ikic, Ivan ;
Fulda, Simone ;
Koch, Ina .
PLOS COMPUTATIONAL BIOLOGY, 2022, 18 (08)
[10]  
Andreou Artemisia M., 2009, Biotechnology Journal, V4, P1740, DOI 10.1002/biot.200900219