Significant cell-to-cell heterogeneity is ubiquitously observed in isogenic cell populations. Consequently, parameters of models of intracellular processes, usually fitted to population-averaged data, should rather be fitted to individual cells to obtain a population of models of similar but non-identical individuals. Here, we propose a quantitative modeling framework that attributes specific parameter values to single cells for a standard model of gene expression. We combine high quality single-cell measurements of the response of yeast cells to repeated hyperosmotic shocks and state-of-the-art statistical inference approaches for mixed-effects models to infer multidimensional parameter distributions describing the population, and then derive specific parameters for individual cells. The analysis of single-cell parameters shows that single-cell identity (e.g. gene expression dynamics, cell size, growth rate, mother-daughter relationships) is, at least partially, captured by the parameter values of gene expression models (e.g. rates of transcription, translation and degradation). Our approach shows how to use the rich information contained into longitudinal single-cell data to infer parameters that can faithfully represent single-cell identity.
With the continuous expansion of single cell biology, the observation of the behaviour of individual cells over extended durations and with high accuracy has become a problem of central importance. Surprisingly, even for yeast cells that have relatively regular shapes, no solution has been proposed that reaches the high quality required for long-term experiments for segmentation and tracking (S&T) based on brightfield images. Here, we present CellStar, a tool chain designed to achieve good performance in long-term experiments. The key features are the use of a new variant of parametrized active rays for segmentation, a neighbourhood-preserving criterion for tracking, and the use of an iterative approach that incrementally improves S&T quality. A graphical user interface enables manual corrections of S&T errors and their use for the automated correction of other, related errors and for parameter learning. We created a benchmark dataset with manually analysed images and compared CellStar with six other tools, showing its high performance, notably in long-term tracking. As a community effort, we set up a website, the Yeast Image Toolkit, with the benchmark and the Evaluation Platform to gather this and additional information provided by others.
A Bacillus subtilis mutant strain overexpressing surfactin biosynthetic genes was previously constructed. In order to further increase the production of this biosurfactant, our hypothesis is that the surfactin precursors, especially leucine, must be overproduced. We present a three step approach for leucine overproduction directed by methods from computational biology. Firstly, we develop a new algorithm for gene knockout prediction based on abstract interpretation, which applies to a recent modeling language for reaction networks with partial kinetic information. Secondly, we model the leucine metabolic pathway as a reaction network in this language, and apply the knockout prediction algorithm with the target of leucine overproduction. Out of the 21 reactions corresponding to potential gene knockouts, the prediction algorithm selects 12 reactions. Six knockouts were introduced in B. subtilis 168 derivatives strains to verify their effects on surfactin production. For all generated mutants, the specific surfactin production is increased from 1.6-to 20.9-fold during the exponential growth phase, depending on the medium composition. These results show the effectiveness of the knockout prediction approach based on formal models for metabolic reaction networks with partial kinetic information, and confirms our hypothesis that precursors supply is one of the main parameters to optimize surfactin overproduction. Keywords: Abstract interpretation · Bacillus subtilis · Knockout prediction · Modeling language · SurfactinCorresponding author: Dr. François Coutte, Research Institute for Food and Biotechnology -Charles Viollette, Polytech-Lille, Université de Lille, Sciences et Technologies, 59655 Villeneuve d'Ascq, France. E-mail: francois.coutte@polytech-lille.fr Abbreviations: Acyl-CoA, acyl coenzyme A; Akb, L-2-amino-acetoacetate; BCAA, branched chain amino acid; Glu, glutamate; Gtp, guanosine triphosphate; Ile, isoleucine; Ket a , 2-keto-3-methylvalerate;Ket b , 2-keto-isovalerate; Ket c , 2-keto-isocaproate; Leu, leucine; NRPS, nonribosomal peptide synthetase; OxoGlu, oxoglutarate; P Ilv-Leu , ilv-leu operon promoter; Pyr, pyruvate; Thr, threonine; Val, valine; XML, eXtensible Markup Language; XSLT, eXtensible Stylesheet Language Transformations Biotechnology JournalSupporting information available online * These authors contributed equally to this work. Biotechnol. J. 2015Biotechnol. J. , 10, 1216Biotechnol. J. -1234 surfactin is composed of a ring of seven amino acid residues connected to a β-hydroxylated fatty acid chain of different length and isomery [1,2]. The peptide moiety contains four leucines (Fig. 1). Genetic engineering of B. subtilis has already been made in order to increase the lipopeptide production. In previous work [3], the overproduction of surfactin was obtained by replacing the native promoter of the surfactin operon (srfA) by a constitutive one and disrupted the plipastatin operon (ppsA) to save the precursor availability. The same approach was recently developed for the mycosubtilin producti...
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.