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Hsiao YC, Dutta A. Network Modeling and Control of Dynamic Disease Pathways, Review and Perspectives. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2024; 21:1211-1230. [PMID: 38498762 DOI: 10.1109/tcbb.2024.3378155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/20/2024]
Abstract
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.
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Chernozhukov V, Karl Härdle W, Huang C, Wang W. LASSO-driven inference in time and space. Ann Stat 2021. [DOI: 10.1214/20-aos2019] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Affiliation(s)
- Victor Chernozhukov
- Department of Economics and Operations Research Center, Massachusetts Institute of Technology
| | | | - Chen Huang
- Department of Economics and Business Economics and CREATES, Aarhus University
| | - Weining Wang
- Department of Economics and Related Studies, University of York
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Dong F, He Y, Wang T, Han D, Lu H, Zhao H. Predicting viral exposure response from modeling the changes of co-expression networks using time series gene expression data. BMC Bioinformatics 2020; 21:370. [PMID: 32842958 PMCID: PMC7449007 DOI: 10.1186/s12859-020-03705-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2019] [Accepted: 07/29/2020] [Indexed: 02/02/2023] Open
Abstract
BACKGROUND Deciphering the relationship between clinical responses and gene expression profiles may shed light on the mechanisms underlying diseases. Most existing literature has focused on exploring such relationship from cross-sectional gene expression data. It is likely that the dynamic nature of time-series gene expression data is more informative in predicting clinical response and revealing the physiological process of disease development. However, it remains challenging to extract useful dynamic information from time-series gene expression data. RESULTS We propose a statistical framework built on considering co-expression network changes across time from time series gene expression data. It first detects change point for co-expression networks and then employs a Bayesian multiple kernel learning method to predict exposure response. There are two main novelties in our method: the use of change point detection to characterize the co-expression network dynamics, and the use of kernel function to measure the similarity between subjects. Our algorithm allows exposure response prediction using dynamic network information across a collection of informative gene sets. Through parameter estimations, our model has clear biological interpretations. The performance of our method on the simulated data under different scenarios demonstrates that the proposed algorithm has better explanatory power and classification accuracy than commonly used machine learning algorithms. The application of our method to time series gene expression profiles measured in peripheral blood from a group of subjects with respiratory viral exposure shows that our method can predict exposure response at early stage (within 24 h) and the informative gene sets are enriched for pathways related to respiratory and influenza virus infection. CONCLUSIONS The biological hypothesis in this paper is that the dynamic changes of the biological system are related to the clinical response. Our results suggest that when the relationship between the clinical response and a single gene or a gene set is not significant, we may benefit from studying the relationships among genes in gene sets that may lead to novel biological insights.
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Affiliation(s)
- Fangli Dong
- School of Mathematical Sciences, Shanghai Jiao Tong University, Dongchuan Road, Shanghai, 200240, China.,SJTU-Yale Joint Center of Biostatistics and Data Science, Shanghai Jiao Tong University, Dongchuan Road, Shanghai, 200240, China
| | - Yong He
- Institute for Financial Studies, Shandong University, No. 27 Shanda South Road, Jinan, 250100, China
| | - Tao Wang
- SJTU-Yale Joint Center of Biostatistics and Data Science, Shanghai Jiao Tong University, Dongchuan Road, Shanghai, 200240, China.,School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Dongchuan Road, Shanghai, 200240, China
| | - Dong Han
- School of Mathematical Sciences, Shanghai Jiao Tong University, Dongchuan Road, Shanghai, 200240, China
| | - Hui Lu
- SJTU-Yale Joint Center of Biostatistics and Data Science, Shanghai Jiao Tong University, Dongchuan Road, Shanghai, 200240, China.,School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Dongchuan Road, Shanghai, 200240, China
| | - Hongyu Zhao
- SJTU-Yale Joint Center of Biostatistics and Data Science, Shanghai Jiao Tong University, Dongchuan Road, Shanghai, 200240, China. .,Department of Biostatistics, Yale School of Public Health, 60 College Street, New Haven, CT 06520, USA.
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Dimitrakopoulou K, Dimitrakopoulos GN, Wilk E, Tsimpouris C, Sgarbas KN, Schughart K, Bezerianos A. Influenza A immunomics and public health omics: the dynamic pathway interplay in host response to H1N1 infection. OMICS-A JOURNAL OF INTEGRATIVE BIOLOGY 2014; 18:167-83. [PMID: 24512282 DOI: 10.1089/omi.2013.0062] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Towards unraveling the influenza A (H1N1) immunome, this work aims at constructing the murine host response pathway interactome. To accomplish that, an ensemble of dynamic and time-varying Gene Regulatory Network Inference methodologies was recruited to set a confident interactome based on mouse time series transcriptome data (day 1-day 60). The proposed H1N1 interactome demonstrated significant transformations among activated and suppressed pathways in time. Enhanced interplay was observed at day 1, while the maximal network complexity was reached at day 8 (correlated with viral clearance and iBALT tissue formation) and one interaction was present at day 40. Next, we searched for common interactivity features between the murine-adapted PR8 strain and other influenza A subtypes/strains. For this, two other interactomes, describing the murine host response against H5N1 and H1N1pdm, were constructed, which in turn validated many of the observed interactions (in the period day 1-day 7). The H1N1 interactome revealed the role of cell cycle both in innate and adaptive immunity (day 1-day 14). Also, pathogen sensory pathways (e.g., RIG-I) displayed long-lasting association with cytokine/chemokine signaling (until day 8). Interestingly, the above observations were also supported by the H5N1 and H1N1pdm models. It also elucidated the enhanced coupling of the activated innate pathways with the suppressed PPAR signaling to keep low inflammation until viral clearance (until day 14). Further, it showed that interactions reflecting phagocytosis processes continued long after the viral clearance and the establishment of adaptive immunity (day 8-day 40). Additionally, interactions involving B cell receptor pathway were evident since day 1. These results collectively inform the emerging field of public health omics and future clinical studies aimed at deciphering dynamic host responses to infectious agents.
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Abstract
Retinoids and rexinoids, as all other ligands of the nuclear receptor (NR) family, act as ligand-regulated trans-acting transcription factors that bind to cis-acting DNA regulatory elements in the promoter regions of target genes (for reviews see [12, 22, 23, 26, 36]). Ligand binding modulates the communication functions of the receptor with the intracellular environment, which essentially entails receptor-protein and receptor-DNA or receptor-chromatin interactions. In this communication network, the receptor simultaneously serves as both intracellular sensor and regulator of cell/organ functions. Receptors are "intelligent" mediators of the information encoded in the chemical structure of a nuclear receptor ligand, as they interpret this information in the context of cellular identity and cell-physiological status and convert it into a dynamic chain of receptor-protein and receptor-DNA interactions. To process input and output information, they are composed of a modular structure with several domains that have evolved to exert particular molecular recognition functions. As detailed in other chapters in this volume, the main functional domains are the DNA-binding (DBD) and ligand-binding (LBD) [5-7, 38, 56, 71]. The LBD serves as a dual input-output information processor. Inputs, such as ligand binding or receptor phosphorylations, induce allosteric changes in receptor surfaces that serve as docking sites for outputs, such as subunits of transcription and epigenetic machineries or enzyme complexes. The complexity of input and output signals and their interdependencies is far from being understood.
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Affiliation(s)
- Marco-Antonio Mendoza-Parra
- Department of Functional Genomics and Cancer, Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), CNRS, INSERM, Université de Strasbourg, BP 10142, 67404, Illkirch Cedex, France
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Genome-wide studies of nuclear receptors in cell fate decisions. Semin Cell Dev Biol 2013; 24:706-15. [DOI: 10.1016/j.semcdb.2013.07.001] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2013] [Revised: 07/18/2013] [Accepted: 07/22/2013] [Indexed: 12/13/2022]
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Dimitrakopoulou K, Vrahatis AG, Wilk E, Tsakalidis AK, Bezerianos A. OLYMPUS: an automated hybrid clustering method in time series gene expression. Case study: host response after Influenza A (H1N1) infection. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2013; 111:650-661. [PMID: 23796450 DOI: 10.1016/j.cmpb.2013.05.025] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2013] [Revised: 05/07/2013] [Accepted: 05/30/2013] [Indexed: 06/02/2023]
Abstract
The increasing flow of short time series microarray experiments for the study of dynamic cellular processes poses the need for efficient clustering tools. These tools must deal with three primary issues: first, to consider the multi-functionality of genes; second, to evaluate the similarity of the relative change of amplitude in the time domain rather than the absolute values; third, to cope with the constraints of conventional clustering algorithms such as the assignment of the appropriate cluster number. To address these, we propose OLYMPUS, a novel unsupervised clustering algorithm that integrates Differential Evolution (DE) method into Fuzzy Short Time Series (FSTS) algorithm with the scope to utilize efficiently the information of population of the first and enhance the performance of the latter. Our hybrid approach provides sets of genes that enable the deciphering of distinct phases in dynamic cellular processes. We proved the efficiency of OLYMPUS on synthetic as well as on experimental data. The discriminative power of OLYMPUS provided clusters, which refined the so far perspective of the dynamics of host response mechanisms to Influenza A (H1N1). Our kinetic model sets a timeline for several pathways and cell populations, implicated to participate in host response; yet no timeline was assigned to them (e.g. cell cycle, homeostasis). Regarding the activity of B cells, our approach revealed that some antibody-related mechanisms remain activated until day 60 post infection. The Matlab codes for implementing OLYMPUS, as well as example datasets, are freely accessible via the Web (http://biosignal.med.upatras.gr/wordpress/biosignal/).
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Kim Y, Han S, Choi S, Hwang D. Inference of dynamic networks using time-course data. Brief Bioinform 2013; 15:212-28. [PMID: 23698724 DOI: 10.1093/bib/bbt028] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Cells execute their functions through dynamic operations of biological networks. Dynamic networks delineate the operation of biological networks in terms of temporal changes of abundances or activities of nodes (proteins and RNAs), as well as formation of new edges and disappearance of existing edges over time. Global genomic and proteomic technologies can be used to decode dynamic networks. However, using these experimental methods, it is still challenging to identify temporal transition of nodes and edges. Thus, several computational methods for estimating dynamic topological and functional characteristics of networks have been introduced. In this review, we summarize concepts and applications of these computational methods for inferring dynamic networks and further summarize methods for estimating spatial transition of biological networks.
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Affiliation(s)
- Yongsoo Kim
- POSTECH, Pohang, 790-784, Republic of Korea. Tel.: 82-54-279-2393; Fax: 82-54-279-8409;
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Meeting report of the European mouse complex genetics network SYSGENET. Mamm Genome 2013; 24:190-7. [PMID: 23673683 DOI: 10.1007/s00335-013-9458-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2013] [Accepted: 04/26/2013] [Indexed: 10/26/2022]
Abstract
The second scientific meeting of the European systems genetics network for the study of complex genetic human disease using genetic reference populations (SYSGENET) took place at the Center for Cooperative Research in Biosciences in Bilbao, Spain, December 10-12, 2012. SYSGENET is funded by the European Cooperation in the Field of Scientific and Technological Research (COST) and represents a network of scientists in Europe that use mouse genetic reference populations (GRPs) to identify complex genetic factors influencing disease phenotypes (Schughart, Mamm Genome 21:331-336, 2010). About 50 researchers working in the field of systems genetics attended the meeting, which consisted of 27 oral presentations, a poster session, and a management committee meeting. Participants exchanged results, set up future collaborations, and shared phenotyping and data analysis methodologies. This meeting was particularly instrumental for conveying the current status of the US, Israeli, and Australian Collaborative Cross (CC) mouse GRP. The CC is an open source project initiated nearly a decade ago by members of the Complex Trait Consortium to aid the mapping of multigenetic traits (Threadgill, Mamm Genome 13:175-178, 2002). In addition, representatives of the International Mouse Phenotyping Consortium were invited to exchange ongoing activities between the knockout and complex genetics communities and to discuss and explore potential fields for future interactions.
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Pommerenke C, Wilk E, Srivastava B, Schulze A, Novoselova N, Geffers R, Schughart K. Global transcriptome analysis in influenza-infected mouse lungs reveals the kinetics of innate and adaptive host immune responses. PLoS One 2012; 7:e41169. [PMID: 22815957 PMCID: PMC3398930 DOI: 10.1371/journal.pone.0041169] [Citation(s) in RCA: 87] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2012] [Accepted: 06/18/2012] [Indexed: 12/15/2022] Open
Abstract
An infection represents a highly dynamic process involving complex biological responses of the host at many levels. To describe such processes at a global level, we recorded gene expression changes in mouse lungs after a non-lethal infection with influenza A virus over a period of 60 days. Global analysis of the large data set identified distinct phases of the host response. The increase in interferon genes and up-regulation of a defined NK-specific gene set revealed the initiation of the early innate immune response phase. Subsequently, infiltration and activation of T and B cells could be observed by an augmentation of T and B cell specific signature gene expression. The changes in B cell gene expression and preceding chemokine subsets were associated with the formation of bronchus-associated lymphoid tissue. In addition, we compared the gene expression profiles from wild type mice with Rag2 mutant mice. This analysis readily demonstrated that the deficiency in the T and B cell responses in Rag2 mutants could be detected by changes in the global gene expression patterns of the whole lung. In conclusion, our comprehensive gene expression study describes for the first time the entire host response and its kinetics to an acute influenza A infection at the transcriptome level.
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Affiliation(s)
- Claudia Pommerenke
- Department of Infection Genetics, Helmholtz Centre for Infection Research and University of Veterinary Medicine Hannover, Braunschweig, Germany
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Friedel S, Usadel B, von Wirén N, Sreenivasulu N. Reverse engineering: a key component of systems biology to unravel global abiotic stress cross-talk. FRONTIERS IN PLANT SCIENCE 2012; 3:294. [PMID: 23293646 PMCID: PMC3533172 DOI: 10.3389/fpls.2012.00294] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2012] [Accepted: 12/10/2012] [Indexed: 05/18/2023]
Abstract
Understanding the global abiotic stress response is an important stepping stone for the development of universal stress tolerance in plants in the era of climate change. Although co-occurrence of several stress factors (abiotic and biotic) in nature is found to be frequent, current attempts are poor to understand the complex physiological processes impacting plant growth under combinatory factors. In this review article, we discuss the recent advances of reverse engineering approaches that led to seminal discoveries of key candidate regulatory genes involved in cross-talk of abiotic stress responses and summarized the available tools of reverse engineering and its relevant application. Among the universally induced regulators involved in various abiotic stress responses, we highlight the importance of (i) abscisic acid (ABA) and jasmonic acid (JA) hormonal cross-talks and (ii) the central role of WRKY transcription factors (TF), potentially mediating both abiotic and biotic stress responses. Such interactome networks help not only to derive hypotheses but also play a vital role in identifying key regulatory targets and interconnected hormonal responses. To explore the full potential of gene network inference in the area of abiotic stress tolerance, we need to validate hypotheses by implementing time-dependent gene expression data from genetically engineered plants with modulated expression of target genes. We further propose to combine information on gene-by-gene interactions with data from physical interaction platforms such as protein-protein or TF-gene networks.
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Affiliation(s)
- Swetlana Friedel
- Leibniz Institute of Plant Genetics and Crop Plant ResearchGatersleben, Germany
| | - Björn Usadel
- RWTH Aachen UniversityAachen, Germany
- IBG-2: Plant Sciences, Institute of Bio- and Geosciences, Forschungszentrum JülichJülich, Germany
| | - Nicolaus von Wirén
- Leibniz Institute of Plant Genetics and Crop Plant ResearchGatersleben, Germany
| | - Nese Sreenivasulu
- Leibniz Institute of Plant Genetics and Crop Plant ResearchGatersleben, Germany
- *Correspondence: Nese Sreenivasulu, Leibniz Institute of Plant Genetics and Crop Plant Research, D-06466 Gatersleben, Germany. e-mail:
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