Inference of dynamic networks using time-course data

Kim, Yongsoo; Han, Seungmin; Choi, Seungjin; Hwang, Daehee

doi:10.1093/bib/bbt028

Cited by 51 publications

(43 citation statements)

References 82 publications

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“…151 As a consequence, an emerging component of systems proteomics is the application of spatiotemporal modeling, including recent forays into the cardiovascular proteomic field. 137,152 Ordinary differential equations, partial differential equations, and stochastic differential equations have been applied to model temporal and spatial changes of biological and physical variables in continuous format.…”

Section: Network Modelingmentioning

confidence: 99%

“…Beyond differential equation models in continuous format, there also exist Boolean network models, network ontology analysis, and switching state space models for the nonstationary nature of the network. 151,152,[154][155][156] These modeling methods generally require prior information of associated regulatory mechanisms, including protein-protein interactions or configuration of specific pathways. Available analysis methods also integrate structural properties of networks to classify proteins into functional groups.…”

Section: Network Modelingmentioning

confidence: 99%

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Transformative Impact of Proteomics on Cardiovascular Health and Disease

Lindsey¹,

Mayr²,

Gomes³

et al. 2015

Circulation

150

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Abstract-The year 2014 marked the 20th anniversary of the coining of the term proteomics. The purpose of this scientific statement is to summarize advances over this period that have catalyzed our capacity to address the experimental, translational, and clinical implications of proteomics as applied to cardiovascular health and disease and to evaluate the current status of the field. Key successes that have energized the field are delineated; opportunities for proteomics to drive basic science research, facilitate clinical translation, and establish diagnostic and therapeutic healthcare algorithms are discussed; and challenges that remain to be solved before proteomic technologies can be readily translated from scientific discoveries to meaningful advances in cardiovascular care are addressed. Proteomics is the result of disruptive technologies, namely, mass spectrometry and database searching, which drove protein analysis from 1 protein at a time to protein mixture analyses that enable large-scale analysis of proteins and facilitate paradigm shifts in biological concepts that address important clinical questions. Over the past 20 years, the field of proteomics has matured, yet it is still developing rapidly. The scope of this statement will extend beyond the reaches of a typical review article and offer guidance on the use of next-generation proteomics for future scientific discovery in the basic research laboratory and clinical settings.

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Section: Network Modelingmentioning

confidence: 99%

Section: Network Modelingmentioning

confidence: 99%

Transformative Impact of Proteomics on Cardiovascular Health and Disease

Lindsey¹,

Mayr²,

Gomes³

et al. 2015

Circulation

150

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“…I used relevance networks, as they have been shown to perform well in the high-throughput case [80]. Nodes in the reconstructed networks represented the different integrated 'omes' variables and edges the connection of 'omes' (between and within 'omes').…”

Section: Development Of Statistical Methods To Analyse Single Time Comentioning

confidence: 99%

“…I focus on methods developed for type A) and refer the reader to Yongsoo et al [80] for further information about type B) and C) networks. Assessment of network structure.…”

Section: Definition Of Networkmentioning

confidence: 99%

“…While there are DBN and RN methods [184,87] that use time delays in form of lagging the time series to identify delayed associations between molecules and to infer causality of reactions, none of these methods enable to extract the estimated lag to use the information in the network visualisation. One big advantage of RN methods compared to BN and DBN methods is the large number of nodes they can process [80], which is very relevant for high-throughput data. A drawback in forming biological inferences based on networks derived only from gene expression data is that it can lead to misinterpretation, as there are numerous regulatory mechanisms involved in turning a transcript into a fully operative protein.…”

Section: Introductionmentioning

confidence: 99%

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Development of statistical tools for integrating time course ‘omics’ data

Straube¹

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Biological systems adapt to environmental and genetic changes via dynamic molecular expression and regulation. During the past decade, the use of 'omics' technologies to take a snapshot of this molecular behaviour has become ubiquitous. Expression quantities of a single molecular level like the transcriptome, proteome or metabolome have been a focus of study, however this is not sufficient to understand the complex intermolecular level regulations. Instead, taking a series of snapshots of molecular levels over time allows the study of molecular expression dynamics. In addition, the integration of multiple time course 'omics' experiments may result in better understanding of how molecular interactions give rise to the functions and behaviours of that system. The analysis of time course 'omics' data has various objectives. One is to identify molecules that change expression over time or between groups to infer their contribution to biological processes.Another is to cluster molecules with similar expression profiles. These co-expressed molecules are believed to be co-regulated or have similar molecular functions and are hypothesised to be the result of networks of molecule interaction. Therefore, researchers use time course 'omics' data to identify co-expressed molecules and differentially expressed molecules to understand the dynamics of biological systems at the molecular level. These data can also be used to model molecular interaction networks with the objective to understand the relationships of molecules that drive a biological system.Time course 'omics' experiments quantify thousands of molecules for each biological sample, but those experiments often only include very few biological and technical replications. The data generated are difficult to analyse because of the high number of missing values, as well as potentially high within and between individual variability. Furthermore, expression changes of co-expressed molecules can occur delayed in time. Therefore, powerful statistical methods are critical for answering key questions about system response and function. They need to efficiently analyse highdimensional data that are noisy and complex. However, so far researchers have been limited in capitalising on the wealth of 'omics' data because of the lack of powerful statistical methods. This thesis outlines the development of novel and user-friendly statistical tools to analyse time course 'omics' data. It consists of two core projects: firstly, the development of a framework to analyse time course 'omics' data obtained from a single molecular level (e.g. proteome); secondly, the development of statistical methods and tools to integrate and analyse time course 'omics' data from several functional levels (e.g. transcriptome and metabolome).For my first project, I developed a framework consisting of three stages: quality assessment and filtering, profile modelling, and analysis. The quality control approach was developed to remove molecules for which expression was highly noisy. I then further deve...

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Protein‐protein interaction networks as miners of biological discovery

Wang

Lü

et al. 2022

Proteomics

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Protein-protein interactions (PPIs) form the basis of a myriad of biological pathways and mechanism, such as the formation of protein complexes or the components of signaling cascades. Here, we reviewed experimental methods for identifying PPI pairs, including yeast two-hybrid (Y2H), mass spectrometry (MS), co-localization, and coimmunoprecipitation. Furthermore, a range of computational methods leveraging biochemical properties, evolution history, protein structures and more have enabled identification of additional PPIs. Given the wealth of known PPIs, we reviewed important network methods to construct and analyze networks of PPIs. These methods aid biological discovery through identifying hub genes and dynamic changes in the network, and have been thoroughly applied in various fields of biological research. Lastly, we discussed the challenges and future direction of research utilizing the power of PPI networks.

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Inference of dynamic networks using time-course data

Cited by 51 publications

References 82 publications

Transformative Impact of Proteomics on Cardiovascular Health and Disease

Transformative Impact of Proteomics on Cardiovascular Health and Disease

Development of statistical tools for integrating time course ‘omics’ data

Protein‐protein interaction networks as miners of biological discovery

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