SynTReN: a generator of synthetic gene expression data for design and analysis of structure learning algorithms

Bulcke, Tim Van den; Leemput, Koen Van; Naudts, Bart; Remortel, Piet van; Ma, Hongwu; Verschoren, A.; Moor, Bart De; Marchal, Kathleen

doi:10.1186/1471-2105-7-43

Cited by 248 publications

(175 citation statements)

References 26 publications

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“…For example, Smith et al [22] studied small networks (100 genes or fewer), and Yu et al [23] studied small dynamic networks. More recently, Van den Bulcke et al [24] proposed the SynTren scheme to combine different constraints to simulate small networks based on dynamic models. Our work is different from this previous work in many respects, but most importantly we (1) applied a unique constraint (genetics) on the data simulation, and (2) studied larger networks, including one with structure and parameters derived from biological data, allowing for more biologically realistic networks.…”

Section: Discussionmentioning

confidence: 99%

“…Our work is different from this previous work in many respects, but most importantly we (1) applied a unique constraint (genetics) on the data simulation, and (2) studied larger networks, including one with structure and parameters derived from biological data, allowing for more biologically realistic networks. In future work, it might be interesting to combine our data simulation scheme with SynTren [24] to generate more realistic data.…”

Section: Discussionmentioning

confidence: 99%

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Increasing the Power to Detect Causal Associations by Combining Genotypic and Expression Data in Segregating Populations

Zhu

Wiener²,

Zhang³

et al. 2007

PLoS Comput Biol

197

200

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To dissect common human diseases such as obesity and diabetes, a systematic approach is needed to study how genes interact with one another, and with genetic and environmental factors, to determine clinical end points or disease phenotypes. Bayesian networks provide a convenient framework for extracting relationships from noisy data and are frequently applied to large-scale data to derive causal relationships among variables of interest. Given the complexity of molecular networks underlying common human disease traits, and the fact that biological networks can change depending on environmental conditions and genetic factors, large datasets, generally involving multiple perturbations (experiments), are required to reconstruct and reliably extract information from these networks. With limited resources, the balance of coverage of multiple perturbations and multiple subjects in a single perturbation needs to be considered in the experimental design. Increasing the number of experiments, or the number of subjects in an experiment, is an expensive and time-consuming way to improve network reconstruction. Integrating multiple types of data from existing subjects might be more efficient. For example, it has recently been demonstrated that combining genotypic and gene expression data in a segregating population leads to improved network reconstruction, which in turn may lead to better predictions of the effects of experimental perturbations on any given gene. Here we simulate data based on networks reconstructed from biological data collected in a segregating mouse population and quantify the improvement in network reconstruction achieved using genotypic and gene expression data, compared with reconstruction using gene expression data alone. We demonstrate that networks reconstructed using the combined genotypic and gene expression data achieve a level of reconstruction accuracy that exceeds networks reconstructed from expression data alone, and that fewer subjects may be required to achieve this superior reconstruction accuracy. We conclude that this integrative genomics approach to reconstructing networks not only leads to more predictive network models, but also may save time and money by decreasing the amount of data that must be generated under any given condition of interest to construct predictive network models.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Increasing the Power to Detect Causal Associations by Combining Genotypic and Expression Data in Segregating Populations

Zhu

Wiener²,

Zhang³

et al. 2007

PLoS Comput Biol

197

200

View full text Add to dashboard Cite

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“…The lognormal distribution is widely used to describe noise in biological processes and, in particular, it well describes the fluorescence distribution of reporter proteins in cell populations bearing synthetic gene networks, as it is often shown by experimental measurements performed via flow cytometry [45-48]. …”

Section: Methodsmentioning

confidence: 99%

Modelling the effects of cell-to-cell variability on the output of interconnected gene networks in bacterial populations

et al. 2015

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BackgroundThe interconnection of quantitatively characterized biological devices may lead to composite systems with apparently unpredictable behaviour. Context-dependent variability of biological parts has been investigated in several studies, measuring its entity and identifying the factors contributing to variability. Such studies rely on the experimental analysis of model systems, by quantifying reporter genes via population or single-cell approaches. However, cell-to-cell variability is not commonly included in predictability analyses, thus relying on predictive models trained and tested on central tendency values. This work aims to study in silico the effects of cell-to-cell variability on the population-averaged output of interconnected biological circuits.MethodsThe steady-state deterministic transfer function of individual devices was described by Hill equations and lognormal synthetic noise was applied to their output. Two- and three-module networks were studied, where individual devices implemented inducible/repressible functions. The single-cell output of such networks was simulated as a function of noise entity; their population-averaged output was computed and used to investigate the expected variability in transfer function identification. The study was extended by testing different noise models, module logic, intrinsic/extrinsic noise proportions and network configurations.ResultsFirst, the transfer function of an individual module was identified from simulated data of a two-module network. The estimated parameter variability among different noise entities was limited (14%), while a larger difference was observed (up to 62%) when estimated and true parameters were compared. Thus, low-variability parameter estimates can be obtained for different noise entities, although deviating from the true parameters, whose measurement requires noise knowledge. Second, the black-box input-output function of a two/three-module network was predicted from the knowledge of the transfer function of individual modules, identified in the presence of noise. Estimates variability was low (16%); however, differences up to 68% were observed by simulating a typical experimental study where the predictions obtained above were compared to network outputs generated in the presence of noise. Network predictions can, thus, deviate from real outputs when modules are characterized and re-used in different noise contexts.ConclusionsThe adopted approach can support predictability studies in synthetic biology by distinguishing between actual unpredictability and contribution of noise and by guiding researchers in the design of suitable experimental measurement for gene networks.

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“…SynT ReN is a software package for synthetic gene expression data generation, which allows us to control the number of genes (n) as well as observations (m) in the generated datasets [Van den Bulcke et al, 2006]. We used SynT ReN to generate 5 gene networks TINGe [Zola et al, 2010].…”

Section: Synthetic Validation Using Syntrenmentioning

confidence: 99%

Parallel Algorithms for Bayesian Networks Structure Learning with Applications to Systems Biology

Nikolova

2011

2011 IEEE International Symposium on Parallel and Distributed Processing Workshops and PHD Forum

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SynTReN: a generator of synthetic gene expression data for design and analysis of structure learning algorithms

Cited by 248 publications

References 26 publications

Increasing the Power to Detect Causal Associations by Combining Genotypic and Expression Data in Segregating Populations

Increasing the Power to Detect Causal Associations by Combining Genotypic and Expression Data in Segregating Populations

Modelling the effects of cell-to-cell variability on the output of interconnected gene networks in bacterial populations

Parallel Algorithms for Bayesian Networks Structure Learning with Applications to Systems Biology

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