Ribosomes are responsible for the synthesis of proteins, major component cellular biomass. Classical experiments have established a linear relationship between fraction resources invested in ribosomal proteins and rate balanced growth microbial population. Very little is known, however, about how investment ribosomes varies over individual cells We therefore extended study resource allocation from populations to single cells, using combination time-lapse fluorescence microscopy statistical...

Significant cell-to-cell heterogeneity is ubiquitously observed in isogenic cell populations. Consequently, parameters of models intracellular processes, usually fitted to population-averaged data, should rather be individual cells obtain a population similar but non-identical individuals. Here, we propose quantitative modeling framework that attributes specific parameter values single for standard model gene expression. We combine high quality single-cell measurements the response yeast...

Synthetic microbial consortia have been increasingly utilized in biotechnology and experimental evidence shows that suitably engineered can outperform individual species the synthesis of valuable products. Despite significant achievements, though, a quantitative understanding conditions make this possible, trade-offs due to concurrent growth multiple species, is still limited. In work, we contribute filling gap by investigation known prototypical synthetic consortium. A first E. coli strain,...

ABSTRACT In the bacterium Escherichia coli , posttranscriptional regulatory system Csr was postulated to influence transition from glycolysis gluconeogenesis. Here, we explored role of in glucose-acetate as a model glycolysis-to-gluconeogenesis switch. Mutations reorganization gene expression after glucose exhaustion and disturb timing acetate reconsumption exhaustion. Analysis metabolite concentrations during revealed that has major effect on energy levels cells This demonstrated result...

Abstract Motivation: Identification of regulatory networks is typically based on deterministic models gene expression. Increasing experimental evidence suggests that the regulation process intrinsically random. To ensure accurate and thorough processing data, stochasticity must be explicitly accounted for both at modelling stage in design identification algorithms. Results: We propose a model expression prokaryotes where transcription described as probabilistic event, whereas protein...

Abstract Motivation: Modern experimental techniques for time course measurement of gene expression enable the identification dynamical models genetic regulatory networks. In general, involves fitting appropriate network structures and parameters to data. For a given set genes, exploring all possible is clearly prohibitive. Modelling methods priori selection compatible with biological knowledge data are necessary make problem tractable. Results: We propose differential equation modelling...

The inference of regulatory interactions and quantitative models gene regulation from time-series transcriptomics data has been extensively studied applied to a range problems in drug discovery, cancer research, biotechnology. application existing methods is commonly based on implicit assumptions the biological processes under study. First, measurements mRNA abundance obtained experiments are taken be representative protein concentrations. Second, observed changes expression assumed solely...

Abstract Motivation: High-throughput measurement techniques for metabolism and gene expression provide a wealth of information the identification metabolic network models. Yet, missing observations scattered over dataset restrict number effectively available datapoints make classical regression inaccurate or inapplicable. Thorough exploitation data by that explicitly cope with is therefore major importance. Results: We develop maximum-likelihood approach estimation unknown parameters models...

We address online estimation of microbial growth dynamics in bioreactors from measurements a fluorescent reporter protein synthesized along with growth.We consider an extended version standard models that accounts for the synthesis.We develop state sampled, noisy cases known and unknown rate functions.Leveraging conservation laws regularized techniques, we reduce these nonlinear problems to linear timevarying ones, solve them via Kalman filtering.We establish convergence results absence...

This paper presents methods for the parameter identification of a model subtilin production by Bacillus subtilis. Based on stochastic hybrid model, is split in two subproblems: estimation genetic network regulating from gene expression data, and population dynamics based nutrient level data. Techniques switching sparse irregularly sampled observations are developed applied to simulated Numerical results provided show effectiveness our methods.

We study the problem of receding horizon control stochastic discrete-time systems with bounded inputs and incomplete state information. Given a suitable choice causal policies, we first present slight extension Kalman filter to estimate optimally in mean-square sense. then show how augment underlying optimization negative drift-like constraint, yielding second-order cone program be solved periodically online. Finally, prove that implementation resulting policies renders overall system under...

We investigate constrained optimal control problems for linear stochastic dynamical systems evolving in discrete time. consider minimization of an expected value cost over a finite horizon. Hard constraints are introduced first, and then reformulated terms probabilistic constraints. It is shown that, suitable parametrization the policy, wide class resulting optimization either convex or amenable to relaxations.

Inference of biochemical network models from experimental data is a crucial problem in systems and synthetic biology that includes parameter calibration but also identification unknown interactions. Stochastic modelling single-cell known to improve identifiability reaction parameters for specific systems. However, general results are lacking, the advantage over deterministic, population-average approaches has not been explored reconstruction. In this work, we study propose new reconstruction...

Fluorescence recovery after photobleaching (FRAP) is a functional live cell imaging technique that permits the exploration of protein dynamics in living cells. To extract kinetic parameters from FRAP data, number analytical models have been developed. Simplifications are inherent these models, which may lead to inexhaustive or inaccurate exploitation experimental data. An appealing alternative offered by simulation biological processes realistic environments at particle level. However,...

This paper concerns parameter identification for the class of jump Markov linear systems. We present recursive formulae computation second-order statistics a process and discuss their application to estimation model parameters given data from single or repeated experiments. Simulation results are provided show effectiveness our approach.

Modern experimental technologies enable monitoring of gene expression dynamics in individual cells and quantification its variability isogenic microbial populations. Among the sources this is randomness that affects inheritance factors at cell division. Known parental relationships among individually observed provide invaluable information for characterization extrinsic source noise. Despite fact, most existing methods to infer stochastic models from single-cell data dedicate little...

The advent of experimental techniques for the time-course monitoring gene expression at single-cell level has paved way to model-based study variability within- an across-cells. A number approaches inference models accounting over isogenic cell populations have been developed and applied real-world scenarios. development a systematic approach validation population is however lagging behind, accuracy obtained often assessed on semi-empirical basis. In this paper we problem validating network...