Bayesian inference of reaction kinetics from single-cell recordings across a heterogeneous cell population

Bronstein, Leo; Zechner, Christoph; Koeppl, Heinz

doi:10.1016/j.ymeth.2015.05.012

Cited by 14 publications

(12 citation statements)

References 73 publications

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“…In order to model the process of transcription and extract the kinetic parameters of promoter switching, we augmented classic HMMs to account for memory (details about implementation of the method are given in Appendix 3). Similar approaches were recently introduced to study transcriptional dynamics in cell culture and tissue samples (Suter et al, 2011; Molina et al, 2013; Zechner et al, 2014; Zoller et al, 2015; Hey et al, 2015; Bronstein et al, 2015; Corrigan et al, 2016; Featherstone et al, 2016). We used simulated data to establish that mHMM reliably extracts the kinetic parameters of transcriptional bursting from live-imaging data (Appendix 4), providing an ideal tool for dissecting the contributions from individual bursting parameters to observed patterns of transcriptional activity across space and time.…”

Section: Resultsmentioning

confidence: 99%

Multimodal transcriptional control of pattern formation in embryonic development

Lammers

Galstyan

Reimer

et al. 2018

Preprint

110

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Predicting developmental outcomes from regulatory DNA sequence and transcription 17 factor patterns remains an open challenge in physical biology. Using stripe 2 of the even-skipped 18 gene in Drosophila embryos as a case study, we dissect the regulatory forces underpinning a key 19 step along the developmental decision-making cascade: the generation of cytoplasmic mRNA 20 patterns via the control of transcription in individual cells. Using live imaging and computational 21 approaches, we found that the transcriptional burst frequency is modulated across the stripe to 22 control the mRNA production rate. However, we discovered that bursting alone cannot 23 quantitatively recapitulate the formation of the stripe, and that control of the window of time over 24 which each nucleus transcribes even-skipped plays a critical role in stripe formation. Theoretical 25 modeling revealed that these regulatory strategies-bursting and the time window-obey different 26 kinds of regulatory logic, suggesting that the stripe is shaped by the interplay of two distinct 27 underlying molecular processes. 28 29 35the regulatory logic of the enhancer elements that dictate the behavior of these networks, the 36 precise prediction of how gene expression patterns and developmental outcomes are driven by 37 transcription factor concentrations remains a central challenge in the field (Vincent et al., 2016). 38 Predicting developmental outcomes demands a quantitative understanding of the flow of 39 information along the central dogma: how input transcription factors dictate the output rate of 40 1 of 69 Manuscript submitted to bioRxiv mRNA production, how this rate of mRNA production dictates cytoplasmic patterns of mRNA, 41 and how these mRNA patterns lead to protein patterns that feed back into the gene regulatory 42 network. While the connection between transcription factor concentration and output mRNA 43 production rate has been the subject of active research over the last three decades (Lawrence 44 et al.connection between this output rate and 47 In order to uncover the quantitative contribution of these three regulatory strategies to pattern 62 formation, and to determine whether other regulatory strategies are at play, it is necessary to 63 measure the rate of RNA polymerase loading in individual nuclei, in real time, in a living embryo. 64 2 of 69 Manuscript submitted to bioRxiv However, to date, most studies have relied on fixed-tissue techniques such as mRNA FISH and 65 immunofluorescence in order to obtain snapshots of the cytoplasmic distributions of mRNA and 66 protein as development progresses (Jaeger et al., 2004; Fakhouri et al., 2010; Parker et al., 2011; 67 Estrada et al., 2016; Crocker et al., 2016; Verd et al., 2017; Park et al., 2018). Such techniques 68 are virtually silent regarding the regulation of single-cell gene expression over time, and are thus 69 ill-suited to the study of how spatiotemporal variations in transcriptional dynamics give rise to 70 patterns of cytoplasmic mRNA. 71...

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Section: Resultsmentioning

confidence: 99%

Multimodal transcriptional control of pattern formation in embryonic development

Lammers

Galstyan

Reimer

et al. 2018

Preprint

110

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“…More generally, mechanistic models are obtained by assuming that biological systems are built up from actual or perceived components which are governed by physical laws (Fröhlich et al, 2017;Hasenauer, 2013;Pullen and Morris, 2014;White et al, 2016). It is a different strategy to empirical models which are reverse engineered from observations (Bronstein et al, 2015;Dattner, 2015;Geffen et al, 2008). Black-box modeling can be used with some limitations when there is little knowledge about the underlying biological processes (Chou and Voit, 2009).…”

Section: Review Of Modeling Strategies For Brnsmentioning

confidence: 99%

“…Cited by Abdullah et al (2013a) Cited by Abdullah et al (2013c) Cited by Alberton et al (2013) Cited by Ale et al (2013) Cited by Ali et al (2015) Cited by Amrein and Künsch (2012) Cited by Anai et al (2006) Cited by Andreychenko et al (2011) Cited by Andreychenko et al (2012) Cited by Andrieu et al (2010) Cited by Angius and Horváth (2011) Cited by Arnold et al (2014) Cited by Ashyraliyev et al (2009) Cited by Babtie and Stumpf (2017) Cited by Backenköhler et al (2016) Cited by Baker et al (133, 2010) Cited by Baker et al (2011) Cited by Baker et al (2013) Cited by Baker et al (2015) Cited by Banga and Canto (2008) Cited by Barnes et al (2011) Cited by Bayer et al (2015) Cited by Berrones et al (2016) Cited by Besozzi et al (2009) Cited by Bhaskar et al (2010) Cited Bogomolov et al (2015) Cited by Bouraoui et al (2015) Cited by Farza et al (2016) Cited by Boys et al (2008) Cited by Bronstein et al (2015) Cited by Brunel et al (2014) Cited by Busetto and Buhmann (2009) Cited by Camacho et al (2018) Cited by Balsa-Canto et al (2008) Cited by Carmi et al (2013) Cited by Cazzaniga et al (2015) Cited by Cedersund et al (2016) Cited by Ceska et al (2014) Cited by Ceška et al (2017) Cited by…”

Section: Abdullah Et Al (2013b)mentioning

confidence: 99%

Comprehensive Review of Models and Methods for Inferences in Bio-Chemical Reaction Networks

2019

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The key processes in biological and chemical systems are described by networks of chemical reactions. From molecular biology to biotechnology applications, computational models of reaction networks are used extensively to elucidate their non-linear dynamics. The model dynamics are crucially dependent on the parameter values which are often estimated from observations. Over the past decade, the interest in parameter and state estimation in models of (bio-) chemical reaction networks (BRNs) grew considerably. The related inference problems are also encountered in many other tasks including model calibration, discrimination, identifiability, and checking, and optimum experiment design, sensitivity analysis, and bifurcation analysis. The aim of this review paper is to examine the developments in literature to understand what BRN models are commonly used, and for what inference tasks and inference methods. The initial collection of about 700 documents concerning estimation problems in BRNs excluding books and textbooks in computational biology and chemistry were screened to select over 270 research papers and 20 graduate research theses. The paper selection was facilitated by text mining scripts to automate the search for relevant keywords and terms. The outcomes are presented in tables revealing the levels of interest in different inference tasks and methods for given models in the literature as well as the research trends are uncovered. Our findings indicate that many combinations of models, tasks and methods are still relatively unexplored, and there are many new research opportunities to explore combinations that have not been considered—perhaps for good reasons. The most common models of BRNs in literature involve differential equations, Markov processes, mass action kinetics, and state space representations whereas the most common tasks are the parameter inference and model identification. The most common methods in literature are Bayesian analysis, Monte Carlo sampling strategies, and model fitting to data using evolutionary algorithms. The new research problems which cannot be directly deduced from the text mining data are also discussed.

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“…Model-based analysis of cell-to-cell variability often uses stochastic dynamics to capture variability within and between cells. However, most stochastic analysis ignores ''pre-existing'' (parameter) variability (Bronstein et al, 2015), albeit this omission compromises the analysis of stochastic noise (Hilfinger and Paulsson, 2011). Noise propagation through networks therefore remains a grand challenge for stochastic modeling (Tsimring, 2014).…”

Section: Introductionmentioning

confidence: 99%

A Simple and Flexible Computational Framework for Inferring Sources of Heterogeneity from Single-Cell Dynamics

et al. 2019

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Graphical AbstractHighlights d Dynamic single-cell data pose opportunities and challenges for mechanistic models d Non-linear mixed-effects models disentangle sources of cellular heterogeneity d Methods for flexible, scalable parameter inference and for sub-process identification d Several sub-processes contribute dynamically to heterogeneity in yeast endocytosis SUMMARY Single-cell time-lapse data provide the means for disentangling sources of cell-to-cell and intracellular variability, a key step for understanding heterogeneity in cell populations. However, single-cell analysis with dynamic models is a challenging open problem: current inference methods address only single-gene expression or neglect parameter correlations. We report on a simple, flexible, and scalable method for estimating cell-specific and populationaverage parameters of non-linear mixed-effects models of cellular networks, demonstrating its accuracy with a published model and dataset. We also propose sensitivity analysis for identifying which biological sub-processes quantitatively and dynamically contribute to cell-to-cell variability. Our application to endocytosis in yeast demonstrates that dynamic models of realistic size can be developed for the analysis of single-cell data and that shifting the focus from single reactions or parameters to nuanced and time-dependent contributions of subprocesses helps biological interpretation. Generality and simplicity of the approach will facilitate customized extensions for analyzing single-cell dynamics.

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Bayesian inference of reaction kinetics from single-cell recordings across a heterogeneous cell population

Cited by 14 publications

References 73 publications

Multimodal transcriptional control of pattern formation in embryonic development

Multimodal transcriptional control of pattern formation in embryonic development

Comprehensive Review of Models and Methods for Inferences in Bio-Chemical Reaction Networks

A Simple and Flexible Computational Framework for Inferring Sources of Heterogeneity from Single-Cell Dynamics

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