DEMSIM: a discrete event based mechanistic simulation platform for gene expression and regulation dynamics

Dasika, Madhukar S.; Gupta, Amit Kumar; Maranas, Costas D.

doi:10.1016/j.jtbi.2004.07.020

Cited by 5 publications

(3 citation statements)

References 56 publications

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“…Recently, it has been shown possible to demonstrate stochastic transcription initiation in prokaryotes and also to gather evidence for such a phenomenon in eukaryotes 14., 15.. These observations have rapidly spurred interest in the theoretical biology community, leading to the proposition of several models 16., 17., 18., 19., 20., 21.. At least in eukaryotic organisms several levels of stochasticity should be distinct.…”

Section: Resultsmentioning

confidence: 99%

High-Sensitivity Transcriptome Data Structure and Implications for Analysis and Biologic Interpretation

Noth

Brysbaert

Pellay

et al. 2006

Genomics, Proteomics &Amp; Bioinformatics

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Novel microarray technologies such as the AB1700 platform from Applied Biosystems promise significant increases in the signal dynamic range and a higher sensitivity for weakly expressed transcripts. We have compared a representative set of AB1700 data with a similarly representative Affymetrix HG-U133A dataset. The AB1700 design extends the signal dynamic detection range at the lower bound by one order of magnitude. The lognormal signal distribution profiles of these high-sensitivity data need to be represented by two independent distributions. The additional second distribution covers those transcripts that would have gone undetected using the Affymetrix technology. The signal-dependent variance distribution in the AB1700 data is a non-trivial function of signal intensity, describable using a composite function. The drastically different structure of these high-sensitivity transcriptome profiles requires adaptation or even redevelopment of the standard microarray analysis methods. Based on the statistical properties, we have derived a signal variance distribution model for AB1700 data that is necessary for such development. Interestingly, the dual lognormal distribution observed in the AB1700 data reflects two fundamentally different biologic mechanisms of transcription initiation.

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Section: Resultsmentioning

confidence: 99%

High-Sensitivity Transcriptome Data Structure and Implications for Analysis and Biologic Interpretation

Noth

Brysbaert

Pellay

et al. 2006

Genomics, Proteomics &Amp; Bioinformatics

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“…A series of mathematical models have been established to simulate the regulation of SOS response (7)(8)(9). These works are based on experiments that investigate the dynamics of responses at population level, using Western blot (10) or mRNA microarray (5) technology.…”

Section: Introductionmentioning

confidence: 99%

“…In this article, we present a model to describe the SOS response process in E. coli. Different from previously models for this system (7)(8)(9), our model describes the dynamics at single cell level, and takes into account the stochastic and discrete characteristic of the inducing signal, RecA*, which shows up only when the replication fork encounters a DNA damage point. We show that the digital oscillator-like modulation is mainly due to the fluctuations of RecA*.…”

Section: Introductionmentioning

confidence: 99%

Simulating the Temporal Modulation of Inducible DNA Damage Response in Escherichia coli

Wang

et al. 2007

Biophysical Journal

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Living organisms make great efforts to maintain their genetic information integrity. However, DNA is vulnerable to many chemical or physical agents. To rescue the cell timely and effectively, the DNA damage response system must be well controlled. Recently, single cell experiments showing that after DNA damage, expression of the key DNA damage response regulatory protein oscillates with time. This phenomenon is observed both in eukaryotic and bacterial cells. We establish a model to simulate the DNA damage response (SOS response) in bacterial cell Escherichia coli. The simulation results are compared to the experimental data. Our simulation results suggest that the modulation observed in the experiment is due to the fluctuation of inducing signal, which is coupled with DNA replication. The inducing signal increases when replication is blocked by DNA damage and decreases when replication resumes.

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Simulating Bacterial Transcription and Translation in a Stochastic π Calculus

Kuttler

2006

Transactions on Computational Systems Biology VI

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Abstract. Stochastic simulation of genetic networks based on models in the stochastic π-calculus is a promising recent approach. This paper contributes an extensible model of the central mechanisms of gene expression i.e. transcription and translation, at the prototypical instance of bacteria. We reach extensibility through object-oriented abstractions, that are expressible in a stochastic π-calculus with pattern guarded inputs. We illustrate our generic model by simulating the effect of translational bursting in bacterial gene expression.

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DEMSIM: a discrete event based mechanistic simulation platform for gene expression and regulation dynamics

Cited by 5 publications

References 56 publications

High-Sensitivity Transcriptome Data Structure and Implications for Analysis and Biologic Interpretation

High-Sensitivity Transcriptome Data Structure and Implications for Analysis and Biologic Interpretation

Simulating the Temporal Modulation of Inducible DNA Damage Response in Escherichia coli

Simulating Bacterial Transcription and Translation in a Stochastic π Calculus

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