On Observability and Reconstruction of Promoter Activity Statistics from Reporter Protein Mean and Variance Profiles

Reporter protein systems are widely used in biology for the indirect quantitative monitoring of gene expression activity over time. At the level of population averages, the relationship between the observed reporter concentration profile and gene promoter activity is established, and effective methods have been introduced to reconstruct this information from the data. At single-cell level, the relationship between population distribution time profiles and the statistics of promoter activation is still not fully investigated, and adequate reconstruction methods are lacking.

[1]  Eugenio Cinquemani,et al.  Reconstruction of promoter activity statistics from reporter protein population snapshot data , 2015, 2015 54th IEEE Conference on Decision and Control (CDC).

[2]  Fabian J Theis,et al.  Method of conditional moments (MCM) for the Chemical Master Equation , 2013, Journal of Mathematical Biology.

[3]  M. Khammash,et al.  Systematic Identification of Signal-Activated Stochastic Gene Regulation , 2013, Science.

[4]  Eugenio Cinquemani,et al.  Reconstructing Statistics of Promoter Switching from Reporter Protein Population Snapshot Data , 2015, HSB.

[5]  M. Peter,et al.  Scalable inference of heterogeneous reaction kinetics from pooled single-cell recordings , 2013, Nature Methods.

[6]  Eugenio Cinquemani,et al.  Inference of Quantitative Models of Bacterial Promoters from Time-Series Reporter Gene Data , 2015, PLoS Comput. Biol..

[7]  T. Elston,et al.  Stochasticity in gene expression: from theories to phenotypes , 2005, Nature Reviews Genetics.

[8]  J. Hespanha.  Modelling and analysis of stochastic hybrid systems , 2006 .

[9]  Johan Paulsson,et al.  Models of stochastic gene expression , 2005 .

[10]  G. Picci,et al.  Linear Stochastic Systems: A Geometric Approach to Modeling, Estimation and Identification , 2016 .

[11]  J. Lygeros,et al.  Moment-based inference predicts bimodality in transient gene expression , 2012, Proceedings of the National Academy of Sciences.

[12]  Giuseppe De Nicolao,et al.  Nonparametric input estimation in physiological systems: Problems, methods, and case studies , 1997, Autom..

[13]  Hidde de Jong,et al.  Experimental and computational validation of models of fluorescent and luminescent reporter genes in bacteria , 2010, BMC Systems Biology.

[14]  Nir Friedman,et al.  Linking stochastic dynamics to population distribution: an analytical framework of gene expression. , 2006, Physical review letters.

[15]  Valentin Zulkower,et al.  Robust reconstruction of gene expression profiles from reporter gene data using linear inversion , 2015, Bioinform..

[16]  Peter S. Swain,et al.  The Fidelity of Dynamic Signaling by Noisy Biomolecular Networks , 2013, PLoS Comput. Biol..

[17]  Sheng Wu,et al.  StochKit2: software for discrete stochastic simulation of biochemical systems with events , 2011, Bioinform..

[18]  Frank Allgöwer,et al.  Identification of models of heterogeneous cell populations from population snapshot data , 2011, BMC Bioinformatics.

[19]  Brian Munsky,et al.  Listening to the noise: random fluctuations reveal gene network parameters , 2009, Molecular systems biology.