Quantitative inference of dynamic regulatory pathways via microarray data

Chang, Wenkai; Li, Chengwei; Chen, Bor‐Sen

doi:10.1186/1471-2105-6-44

Cited by 45 publications

(16 citation statements)

References 69 publications

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“…In addition, in order to differentiate mRNA expressions from protein expressions, we define two state variables X i and Y i to represent the 3-D spatio-temporal mRNA profiles of the i th target gene and its corresponding TFs, respectively. According to the transcription regulation model proposed in previous studies,6,33,36,40 the stochastic 3-DEST model for the i th target gene and their upstream regulatory TFs in the gene/protein interaction network of Drosophila development is proposed as follows:

\begin{matrix} left \\ \frac{\partial X_{i} (t, x, y)}{\partial t} = κ_{i} (x, y) - α_{i} (x, y) X_{i} (t, x, y) \\ + \sum_{j = 1}^{14} β_{i j} false(x, y false) f false(Y_{j} false(t - τ_{j}, x, y false) false) \\ + υ_{i} (t, x, y) \\ \frac{\partial Y_{j} (t, x, y)}{\partial t} = ϖ_{j} (x, y) + α_{j} (x, y) X_{i} (t, x, y) \\ - λ_{j} (x, y) Y_{j} (t, x, y) \\ + γ_{j} (x, y) \nabla^{2} \end{matrix}

…”

Section: Methodsmentioning

confidence: 99%

“…Hence, the TF in a spatial region that diffuses to (from) the neighboring spatial regions, is called a donor (acceptor). In addition, from previous studies we know that transcription regulations can be inferred by a dynamic model via microarray data 33,36,40. However, how to sieve out the insignificant transcription regulations from the whole gene/protein interaction network is still a problem.…”

Section: Introductionmentioning

confidence: 99%

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Stochastic Spatio-Temporal Dynamic Model for Gene/Protein Interaction Network in EarlyDrosophilaDevelopment

Chen

2009

Gene�Regul Syst Bio

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In order to investigate the possible mechanisms for eve stripe formation of Drosophila embryo, a spatio-temporal gene/ protein interaction network model is proposed to mimic dynamic behaviors of protein synthesis, protein decay, mRNA decay, protein diffusion, transcription regulations and autoregulation to analyze the interplay of genes and proteins at different compartments in early embryogenesis. In this study, we use the maximum likelihood (ML) method to identify the stochastic 3-D Embryo Space-Time (3-DEST) dynamic model for gene/protein interaction network via 3-D mRNA and protein expression data and then use the Akaike Information Criterion (AIC) to prune the gene/protein interaction network. The identified gene/protein interaction network allows us not only to analyze the dynamic interplay of genes and proteins on the border of eve stripes but also to infer that eve stripes are established and maintained by network motifs built by the cooperation between transcription regulations and diffusion mechanisms in early embryogenesis. Literature reference with the wet experiments of gene mutations provides a clue for validating the identified network. The proposed spatio-temporal dynamic model can be extended to gene/protein network construction of different biological phenotypes, which depend on compartments, e.g. postnatal stem/progenitor cell differentiation.

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\begin{matrix} left \\ \frac{\partial X_{i} (t, x, y)}{\partial t} = κ_{i} (x, y) - α_{i} (x, y) X_{i} (t, x, y) \\ + \sum_{j = 1}^{14} β_{i j} false(x, y false) f false(Y_{j} false(t - τ_{j}, x, y false) false) \\ + υ_{i} (t, x, y) \\ \frac{\partial Y_{j} (t, x, y)}{\partial t} = ϖ_{j} (x, y) + α_{j} (x, y) X_{i} (t, x, y) \\ - λ_{j} (x, y) Y_{j} (t, x, y) \\ + γ_{j} (x, y) \nabla^{2} \end{matrix}

…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Stochastic Spatio-Temporal Dynamic Model for Gene/Protein Interaction Network in EarlyDrosophilaDevelopment

Chen

2009

Gene�Regul Syst Bio

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“…Cubic spline interpolation is also used in the simulation to obtain sufficient time samplings in the silico simulation and parameter estimation without data overfitting or deviating data (Chang et al 2005). …”

Section: Methodsmentioning

confidence: 99%

Comparisons of Robustness and Sensitivity between Cancer and Normal Cells by Microarray Data

Chu

Chen

2008

Cancer Inform

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Robustness is defined as the ability to uphold performance in face of perturbations and uncertainties, and sensitivity is a measure of the system deviations generated by perturbations to the system. While cancer appears as a robust but fragile system, few computational and quantitative evidences demonstrate robustness tradeoffs in cancer. Microarrays have been widely applied to decipher gene expression signatures in human cancer research, and quantification of global gene expression profiles facilitates precise prediction and modeling of cancer in systems biology. We provide several efficient computational methods based on system and control theory to compare robustness and sensitivity between cancer and normal cells by microarray data. Measurement of robustness and sensitivity by linear stochastic model is introduced in this study, which shows oscillations in feedback loops of p53 and demonstrates robustness tradeoffs that cancer is a robust system with some extreme fragilities. In addition, we measure sensitivity of gene expression to perturbations in other gene expression and kinetic parameters, discuss nonlinear effects in feedback loops of p53 and extend our method to robustness-based cancer drug design.

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“…The collection of large quantities of data using high-content assays to characterize shifts in gene and metabolite expression need to be coordinated with new tools and techniques that synthesize these data and give insight not only into signalling networks but also their dose response for chemical perturbations (40-42). Therefore, further to network construction, a computational systems biology pathway (CSBP) model is needed to describe dose-and temporal response of target and “adverse’” gene expression will be performed.…”

Section: Pot Validationmentioning

confidence: 99%

Mapping the Human Toxome by Systems Toxicology

Bouhifd

Högberg

Kleensang

et al. 2014

Basic Clin Pharma Tox

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Toxicity testing typically involves studying adverse health outcomes in animals subjected to high doses of toxicants with subsequent extrapolation to expected human responses at lower doses. The low-throughput of current toxicity testing approaches (which are largely the same for industrial chemicals, pesticides and drugs) has led to a backlog of more than 80,000 chemicals to which human beings are potentially exposed whose potential toxicity remains largely unknown. Employing new testing strategies that employ the use of predictive, high-throughput cell-based assays (of human origin) to evaluate perturbations in key pathways, referred as pathways of toxicity, and to conduct targeted testing against those pathways, we can begin to greatly accelerate our ability to test the vast “storehouses” of chemical compounds using a rational, risk-based approach to chemical prioritization, and provide test results that are more predictive of human toxicity than current methods. The NIH Transformative Research Grant project Mapping the Human Toxome by Systems Toxicology aims at developing the tools for pathway mapping, annotation and validation as well as the respective knowledge base to share this information.

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Quantitative inference of dynamic regulatory pathways via microarray data

Cited by 45 publications

References 69 publications

Stochastic Spatio-Temporal Dynamic Model for Gene/Protein Interaction Network in EarlyDrosophilaDevelopment

Stochastic Spatio-Temporal Dynamic Model for Gene/Protein Interaction Network in EarlyDrosophilaDevelopment

Comparisons of Robustness and Sensitivity between Cancer and Normal Cells by Microarray Data

Mapping the Human Toxome by Systems Toxicology

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