Global parameter estimation for thermodynamic models of transcriptional regulation

Suleimenov, Yerzhan; Ay, Ahmet; Samee, Md. Abul Hassan; Dresch, Jacqueline M.; Sinha, Saurabh; Arnosti, David N.

doi:10.1016/j.ymeth.2013.05.012

Cited by 8 publications

(6 citation statements)

References 32 publications

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“…We fit quenching parameters (Q) for each of the bins. We also used the non-monotonic 'quenching' function (Q1) derived from our analysis of short-range repression by the Giant protein in synthetic enhancer constructs ( Hansen et al, 2003 ; Fakhouri et al, 2010 ; Suleimenov et al, 2013 ).…”

Section: Methodsmentioning

confidence: 99%

“…A global parameter estimation strategy, CMA-ES (Covariance Matrix Adaptation - Evolutionary Strategy) was applied to estimate the parameters ( Hansen et al, 2003 ; Segal et al, 2008 ; Fakhouri et al, 2010 ; Suleimenov et al, 2013 ). Root mean square error (RMSE) was used as a measure of performance of different cooperativity and quenching schemes, as described previously ( Matsumoto and Nishimura, 1998 ; Segal et al, 2008 ; Fakhouri et al, 2010 ).…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Quantitative perturbation-based analysis of gene expression predicts enhancer activity in early Drosophila embryo

Sayal

Dresch

Pushel

et al. 2016

eLife

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Enhancers constitute one of the major components of regulatory machinery of metazoans. Although several genome-wide studies have focused on finding and locating enhancers in the genomes, the fundamental principles governing their internal architecture and cis-regulatory grammar remain elusive. Here, we describe an extensive, quantitative perturbation analysis targeting the dorsal-ventral patterning gene regulatory network (GRN) controlled by Drosophila NF-κB homolog Dorsal. To understand transcription factor interactions on enhancers, we employed an ensemble of mathematical models, testing effects of cooperativity, repression, and factor potency. Models trained on the dataset correctly predict activity of evolutionarily divergent regulatory regions, providing insights into spatial relationships between repressor and activator binding sites. Importantly, the collective predictions of sets of models were effective at novel enhancer identification and characterization. Our study demonstrates how experimental dataset and modeling can be effectively combined to provide quantitative insights into cis-regulatory information on a genome-wide scale.DOI: http://dx.doi.org/10.7554/eLife.08445.001

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Quantitative perturbation-based analysis of gene expression predicts enhancer activity in early Drosophila embryo

Sayal

Dresch

Pushel

et al. 2016

eLife

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“…Assigning values to these free parameters specifies a model completely, allowing it to predict gene expression in any cellular context where TF concentrations are known. Typically, optimization strategies are used to identify the parameter setting(s) that accurately predict gene expression driven by a CRM in multiple cellular contexts [ 30 ].…”

Section: Resultsmentioning

confidence: 99%

An information theoretic treatment of sequence-to-expression modeling

Khajouei

Sinha

2018

PLoS Comput Biol

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Studying a gene’s regulatory mechanisms is a tedious process that involves identification of candidate regulators by transcription factor (TF) knockout or over-expression experiments, delineation of enhancers by reporter assays, and demonstration of direct TF influence by site mutagenesis, among other approaches. Such experiments are often chosen based on the biologist’s intuition, from several testable hypotheses. We pursue the goal of making this process systematic by using ideas from information theory to reason about experiments in gene regulation, in the hope of ultimately enabling rigorous experiment design strategies. For this, we make use of a state-of-the-art mathematical model of gene expression, which provides a way to formalize our current knowledge of cis- as well as trans- regulatory mechanisms of a gene. Ambiguities in such knowledge can be expressed as uncertainties in the model, which we capture formally by building an ensemble of plausible models that fit the existing data and defining a probability distribution over the ensemble. We then characterize the impact of a new experiment on our understanding of the gene’s regulation based on how the ensemble of plausible models and its probability distribution changes when challenged with results from that experiment. This allows us to assess the ‘value’ of the experiment retroactively as the reduction in entropy of the distribution (information gain) resulting from the experiment’s results. We fully formalize this novel approach to reasoning about gene regulation experiments and use it to evaluate a variety of perturbation experiments on two developmental genes of D. melanogaster. We also provide objective and ‘biologist-friendly’ descriptions of the information gained from each such experiment. The rigorously defined information theoretic approaches presented here can be used in the future to formulate systematic strategies for experiment design pertaining to studies of gene regulatory mechanisms.

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“…We chose to work with one such model, called GEMSTAT (22), which was previously reported by us and successfully used to model several developmental enhancers of Drosophila (27,40).…”

Section: Expression Data Support Diverse Mechanistic Models Of Ind Enmentioning

confidence: 99%

Model-based analysis of polymorphisms in an enhancer reveals cis-regulatory mechanisms

Khajouei

Samper

Djabrayan

et al. 2020

Preprint

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It is challenging to predict the impact of small genetic changes such as single nucleotide polymorphisms on gene expression, since mechanisms involved in gene regulation and their cis-regulatory encoding are not well-understood. Recent studies have attempted to predict the functional impact of non-coding variants based on available knowledge of cis-regulatory encoding, e.g., transcription factor (TF) motifs. In this work, we explore the relationship between regulatory variants and cis-regulatory encoding from the opposite angle, using the former to inform the latter. We employ sequence-to-expression modeling to resolve ambiguities regarding gene regulatory mechanisms using information about effects of single nucleotide variations in an enhancer. We demonstrate our methodology using a well-studied enhancer of the developmental gene intermediate neuroblasts defective (ind) in D. melanogaster. We first trained the thermodynamics-based model GEMSTAT to relate the neuroectodermal expression pattern of ind to its enhancer's sequence, and constructed an ensemble of models that represent different parameter settings consistent with available data for this gene. We then predicted the effects of every possible single nucleotide variation within this enhancer, and compared these to SNP data recorded in the Drosophila Genome Reference Panel. We chose specific SNPs for which different models in the ensemble made conflicting predictions, and tested their effect in vivo. These experiments narrowed in on one mechanistic model as capable of explaining the observed effects. We further confirmed the generalizability of this model to orthologous enhancers and other related developmental enhancers. In conclusion, mechanistic models of cis-regulatory function not only help make specific predictions of variant impact, they may also be learned more accurately using data on variants.A central issue in analyzing variations in the non-coding genome is to interpret their functional impact, and their connections to phenotype differences and disease etiology. Machine learning methods based on statistical modeling have been developed to associate genetic variants to expression changes. However, associations predicted by these models may not be functionally relevant, despite being statisticaly significant. We describe how mathematical modeling of gene expression can be employed to systematically study the non-coding sequence and its relationship to gene expression. We demonstrate our method in a well studied developmental enhancer of the fruitfly. We establish the efficacy of mathematical models in combination with the polymorphism data to reveal new mechanistic insights.

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Global parameter estimation for thermodynamic models of transcriptional regulation

Cited by 8 publications

References 32 publications

Quantitative perturbation-based analysis of gene expression predicts enhancer activity in early Drosophila embryo

Quantitative perturbation-based analysis of gene expression predicts enhancer activity in early Drosophila embryo

An information theoretic treatment of sequence-to-expression modeling

Model-based analysis of polymorphisms in an enhancer reveals cis-regulatory mechanisms

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