Biophysically Motivated Regulatory Network Inference: Progress and Prospects

Äijö, Tarmo; Bonneau, Richard

doi:10.1159/000446614

Cited by 26 publications

(13 citation statements)

References 177 publications

(142 reference statements)

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“…However, agreement between the inferred parameters and ground truth (or, for experimental data, FSP estimates) was not guaranteed, especially for k ini and k off . As in Fig 2, these gaps in performance appear to correspond to non-uniqueness in mapping from the parameter domain to the observable domain [29], and inability of the genetic algorithm to report degenerate results. We suggest that this degeneracy is best identified by running the search algorithm multiple times and examining the resulting distribution of point estimates from the centers of the search populations.…”

Section: Parameter Estimationmentioning

confidence: 86%

Stochastic simulation and statistical inference platform for visualization and estimation of transcriptional kinetics

et al. 2020

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Recent advances in single-molecule fluorescent imaging have enabled quantitative measurements of transcription at a single gene copy, yet an accurate understanding of transcriptional kinetics is still lacking due to the difficulty of solving detailed biophysical models. Here we introduce a stochastic simulation and statistical inference platform for modeling detailed transcriptional kinetics in prokaryotic systems, which has not been solved analytically. The model includes stochastic two-state gene activation, mRNA synthesis initiation and stepwise elongation, release to the cytoplasm, and stepwise co-transcriptional degradation. Using the Gillespie algorithm, the platform simulates nascent and mature mRNA kinetics of a single gene copy and predicts fluorescent signals measurable by time-lapse single-cell mRNA imaging, for different experimental conditions. To approach the inverse problem of estimating the kinetic parameters of the model from experimental data, we develop a heuristic optimization method based on the genetic algorithm and the empirical distribution of mRNA generated by simulation. As a demonstration, we show that the optimization algorithm can successfully recover the transcriptional kinetics of simulated and experimental gene expression data. The platform is available as a MATLAB software package at https:// data.caltech.

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Section: Parameter Estimationmentioning

confidence: 86%

Stochastic simulation and statistical inference platform for visualization and estimation of transcriptional kinetics

et al. 2020

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“…Single‐cell transcriptomics data represent a rich source of information to infer interactions which occur between genes and transcription factors. However, new studies are highlighting the need to not only focus on a single‐cell's transcripts, but also the methylation state of the DNA, the chromatin state and other epigenomic data that might enrich our knowledge of the gene regulation dynamics .…”

Section: Advanced Computational Approachesmentioning

confidence: 99%

Computational approaches for high‐throughput single‐cell data analysis

Todorov

Saeys

2018

The FEBS Journal

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During the past decade, the number of novel technologies to interrogate biological systems at the single-cell level has skyrocketed. Numerous approaches for measuring the proteome, genome, transcriptome and epigenome at the single-cell level have been pioneered, using a variety of technologies. All these methods have one thing in common: they generate large and high-dimensional datasets that require advanced computational modelling tools to highlight and interpret interesting patterns in these data, potentially leading to novel biological insights and hypotheses. In this work, we provide an overview of the computational approaches used to interpret various types of single-cell data in an automated and unbiased way.

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“…Gene network inference is a deeply studied problem in computational biology (Friedman, 2004;Albert, 2007;Bansal et al, 2007;Penfold and Wild, 2011;Emmert-Streib et al, 2012;Marbach et al, 2012;Äijö and Bonneau, 2016;Kiani et al, 2016). Among the many successful methods that have been devised, Bayesian networks are a powerful approach for modelling causal relationships and incorporating prior knowledge (Friedman et al, 2000;Friedman, 2004;Werhli and Husmeier, 2007;Mukherjee and Speed, 2008;Koller and Friedman, 2009;Pearl, 2009).…”

Section: Introductionmentioning

confidence: 99%