Chemical Master Equation Closure for Computer-Aided Synthetic Biology

Smadbeck, Patrick; Kaznessis, Yiannis N.

doi:10.1007/978-1-4939-1878-2_9

Cited by 4 publications

(3 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For this reason, the deterministic model of our proposed biological circuit expressed in terms of RREs in (7) and (8) is only accounting for the average behavior of the biological circuit, and a proper stochastic model should be utilized to represent the chemical reaction noise. In this paper, in place of more general formulations based on the chemical master equation [20], or the τ -leaping approximate stochastic method [5], which, similarly to the diffusion noise, is based on a Poisson counting process, we make use of the Chemical Langevin Equation (CLE) [7] formulation. For this, we can rewrite the biological circuit model expressed in Section II-B through the RREs by adding the noise contribution as a Gaussian Process [7], as detailed in the following.…”

Section: Biological Circuit Noisementioning

confidence: 99%

The Gaussian approximation in soft detection for molecular communication via biological circuits

Marcone

Pierobon

Magarini

2017

2017 IEEE 18th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

View full text Add to dashboard Cite

The programming of biological cells by genetic circuit engineering is enabling the development of man-made devices and systems in the biochemical environment, with applications in the areas of biomedicine, security, and environmental sensing and control, amongst others. The exchange of information through biochemical reactions and molecule transport, i.e., Molecular Communication (MC), stands as one of the foundational paradigms for the design and characterization of these systems. In a previous work, the same authors developed an analog soft decoder design for MC based on biological circuits inspired by the analog information processing in biochemical reactions. While such a design was optimized for an MC channel affected by Gaussian noise, realistic noise models in molecule transport processes and biochemical reactions tend to deviate from this assumption. In this paper, these models are discussed together with the validity of their Gaussian approximations in light of the performance of the log-likelihood ratio calculation of the aforementioned design, numerically evaluated through biochemical simulation. These models, which are directly derived from the theory of molecular diffusion and stochastic chemical reaction analysis, are formulated with a general validity with respect to any future MC system design based on biological circuits.

show abstract

Section: Biological Circuit Noisementioning

confidence: 99%

The Gaussian approximation in soft detection for molecular communication via biological circuits

Marcone

Pierobon

Magarini

2017

2017 IEEE 18th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

View full text Add to dashboard Cite

show abstract

“…Thus, a wide range of experimental observations of biomolecular interactions might be mathematically conceptualized. The authors anticipate that models based on this closure scheme might assist in rationally designing synthetic biological systems [208].…”

Section: Novel Perspectives For the Near Futurementioning

confidence: 99%

Synthetic Biology: Computational Modeling Bridging the Gap between In Vitro and In Vivo Reactions

Duschak¹

2015

Curr Synthetic Sys Biol

View full text Add to dashboard Cite

The synthetic biology firstly refers to the design and fabrication of biological components and systems that do not already exist in the natural world and to the redesign and fabrication of existing biological systems. The link of computational tools to cell-free systems, converts to synthetic biology is an emerging field expert to build artificial biological systems through the combination of molecular biology and engineering approaches. Herein, most findings describing the differences between in vivo and in vitro reactions and systems have been extensively described. The specific applications of computational tools to the design of an in vitro gene expression platform known as the artificial cell, its components and the strategies developed to predict activities of processor modules and to control the expression of genes have been discussed in detail. Potential applications of artificial cells in drug delivery, in biosynthesis, among others, have been described. Two sources of models for the possible developing of the computational toolbox for cell-free synthetic biology include: i) Physical models of single cellular components able to be created from original principles, guiding to focus on tools to predict structure and dynamics of particular components;ii) A wide-range of mathematical models for predicting system dynamics of natural cells.Regarding modeling algorithms, there is a broad kind of models available for synthetic biologists and some areas of potential growth identified for researchers interested in developing tools for cell-free systems. Among them, deterministic, exploratory, molecular dynamic, stochastic, all atom models, among others, have been described and discussed. By using computational models to set up quantitative differences between in vitro reactions and in vivo systems, could identify specific mechanisms in living organisms to be further used in in vitro reactions in order to facilitate their processes. Thus, computational modeling would bridge the gap between in vitro and in vivo reactions. CSSB, an open access journalThe synthetic biology refers to • The design and fabrication of biological components and systems that do not already exist in the natural world and the redesign and fabrication of existing biological systems.

show abstract

“…Solving the CME yields a time evolution of mRNA and protein distributions. However, in most cases, the CME is mathematically intractable, so it is solved numerically using simulations [21][22][23]. Mathematical models and simulation algorithms can also inform experimental techniques [20].…”

Section: Introductionmentioning

confidence: 99%

Inferring distributions from observed mRNA and protein copy counts in genetic circuits

Atitey

Loskot

Rees

2018

Biomed. Phys. Eng. Express

View full text Add to dashboard Cite

Defining distributions of molecule counts produced in the cell can elucidate stochastic dynamics of the underlying biological circuits. For genetic circuits, only a few distributions of messenger RNA and protein counts were reported in literature, so the task is to decide which of these candidate distributions best fit the observed data. In this paper, we present a statistical method to infer distributions of mRNA and protein counts from observed data. The main advantage of this method is that it does not require any prior assumptions or knowledge about underlying chemical reactions. In particular, a given distribution is fitted to the observed copy counts using a histogram with optimized bin sizes in order to reduce the fitting error. The goodness of fit is evaluated by Kolmogorov-Smirnov and chi-square statistical tests to accept or reject the hypothesis that observed molecule counts were generated from given distribution. The distribution fitting also yields the values of distribution parameters, or they can be estimated using the Bayes theorem. These parameters appear to be themselves random processes. The presented statistical framework for analyzing the observed mRNA and protein copy counts is illustrated for a simulated model of lac genetic circuit in Escherichia coli. For reaction rates assumed in the model, the results in literature predict that mRNA and protein counts at steady-state are gamma distributed. Our analysis shows that both mRNA and protein in the lac circuit model can be considered gamma distributed in at least 70% of times from the initial state until steady-state. The shape and scale parameters of observed gamma distributions are also gamma distributed, giving rise to double stochastic processes. More importantly, as shown previously, the distribution parameters are functions of transcription and translation rates, so presented statistical framework can be used to estimate or optimize reaction rates in biochemical systems.

show abstract

Chemical Master Equation Closure for Computer-Aided Synthetic Biology

Cited by 4 publications

References 42 publications

The Gaussian approximation in soft detection for molecular communication via biological circuits

The Gaussian approximation in soft detection for molecular communication via biological circuits

Synthetic Biology: Computational Modeling Bridging the Gap between In Vitro and In Vivo Reactions

Inferring distributions from observed mRNA and protein copy counts in genetic circuits

Contact Info

Product

Resources

About