Network modelling of gene regulation

Ho, Joshua W. K.; Charleston, Michael A.

doi:10.1007/s12551-010-0041-4

Cited by 5 publications

(4 citation statements)

References 103 publications

(100 reference statements)

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“…Beyond climate science, the fundamental idea of a model evaluation based on functional networks applies to numerous kinds of real-world dynamical systems. For instance, it could be used to evaluate models of social dynamics [ 9 ], financial markets [ 10 ], neural activity [ 11 , 12 ] or genetic regulatory systems [ 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

Local Difference Measures between Complex Networks for Dynamical System Model Evaluation

et al. 2015

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A faithful modeling of real-world dynamical systems necessitates model evaluation. A recent promising methodological approach to this problem has been based on complex networks, which in turn have proven useful for the characterization of dynamical systems. In this context, we introduce three local network difference measures and demonstrate their capabilities in the field of climate modeling, where these measures facilitate a spatially explicit model evaluation. Building on a recent study by Feldhoff et al. [1] we comparatively analyze statistical and dynamical regional climate simulations of the South American monsoon system. Three types of climate networks representing different aspects of rainfall dynamics are constructed from the modeled precipitation space-time series. Specifically, we define simple graphs based on positive as well as negative rank correlations between rainfall anomaly time series at different locations, and such based on spatial synchronizations of extreme rain events. An evaluation against respective networks built from daily satellite data provided by the Tropical Rainfall Measuring Mission 3B42 V7 reveals far greater differences in model performance between network types for a fixed but arbitrary climate model than between climate models for a fixed but arbitrary network type. We identify two sources of uncertainty in this respect. Firstly, climate variability limits fidelity, particularly in the case of the extreme event network; and secondly, larger geographical link lengths render link misplacements more likely, most notably in the case of the anticorrelation network; both contributions are quantified using suitable ensembles of surrogate networks. Our model evaluation approach is applicable to any multidimensional dynamical system and especially our simple graph difference measures are highly versatile as the graphs to be compared may be constructed in whatever way required. Generalizations to directed as well as edge- and node-weighted graphs are discussed.

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Section: Introductionmentioning

confidence: 99%

Local Difference Measures between Complex Networks for Dynamical System Model Evaluation

et al. 2015

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“…A gene regulatory network is a useful tool for hypothesis generation, critical evaluation of alternative hypotheses, and summarization of knowledge of a system (Ho and Charleston 2011). In the context of studying developmental gene regulation, a properly generated network of transcription factors, signaling pathways, and important downstream target genes can be very helpful in elucidating the properties of complex developmental processes, such as robustness, self-sustaining feedback, and stochastic fate changes.…”

Section: Advances In Regulatory Network Modeling and Analysismentioning

confidence: 99%

“…To translate the network structure into gene expression (or signaling pathway activity) dynamics, one can use ordinary differential equations or other network modeling techniques to simulate gene expression behavior under different parameter settings (Ho and Charleston 2011). When analyzing the structure of a network, one should pay attention to specific topological features, such as local clustering and feedback circuits.…”

Section: Systems Biology Of Developmental Gene Regulationmentioning

confidence: 99%

Application of a systems approach to study developmental gene regulation

2012

Biophys Rev

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All cells in a multicellular organism contain the same genome, yet different cell types express different sets of genes. Recent advances in high throughput genomic technologies have opened up new opportunities to understand the gene regulatory network in diverse cell types in a genome-wide manner. Here, I discuss recent advances in experimental and computational approaches for the study of gene regulation in embryonic development from a systems perspective. This review is written for computational biologists who have an interest in studying developmental gene regulation through integrative analysis of gene expression, chromatin landscape, and signaling pathways. I highlight the utility of publicly available data and tools, as well as some common analysis approaches.

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“…The static approaches assume that the genes are expressed in a steady state and thus cannot exploit and describe the dynamic process of gene regulatory process [4] because of changes in mRNA concentration over time during the cell cycle. Rather, the structure of genetic regulatory network is highly dynamic, in the sense that edges may present or absent in the response to different stress conditions over time [5]. Hence, the dynamic modelling GRN is a valuable tool to understand the complexity of gene regulation.…”

Section: Introductionmentioning

confidence: 99%

Modeling of Gene Regulatory Networks Using State Space Models

S¹

2017

CTBEB

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Modeling of gene regulatory networks is becoming popular to understand the complex molecular mechanism of gene regulation due to the availability of high throughput genomic data. Such techniques have encouraged the researchers to understand not only the structure of gene regulatory networks and proteomicnetworks but also gene-gene associations or interactions. State space models are a relatively new approach to infer gene regulatory networks. It has the unique feature of capturing the dynamicity of the gene regulation which is inherent to the biological networks as well as computationally efficiency. The nonlinear state space models also considered the non-linear associations between genes, which all linear modeling approaches fails to capture. Performance evaluation criteria for the approaches used for modeling genetic regulatory networks are also discussed.

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Network modelling of gene regulation

Cited by 5 publications

References 103 publications

Local Difference Measures between Complex Networks for Dynamical System Model Evaluation

Local Difference Measures between Complex Networks for Dynamical System Model Evaluation

Application of a systems approach to study developmental gene regulation

Modeling of Gene Regulatory Networks Using State Space Models

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