LOICA: Integrating Models with Data for Genetic Network Design Automation

Vidal, Gonzalo; Vidal-Céspedes, Carlos; Rudge, Tim

doi:10.1021/acssynbio.1c00603

Cited by 7 publications

(5 citation statements)

References 30 publications

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“…pyFlapjack () was also improved in this work by extending the Create function to support overwrite prevention during the bulk upload of data. Simulated data for test and examples were generated using LOICA …”

Section: Methodsmentioning

confidence: 99%

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Experimental Data Connector (XDC): Integrating the Capture of Experimental Data and Metadata Using Standard Formats and Digital Repositories

et al. 2023

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Accelerating the development of synthetic biology applications requires reproducible experimental findings. Different standards and repositories exist to exchange experimental data and metadata. However, the associated software tools often do not support a uniform data capture, encoding, and exchange of information. A connection between digital repositories is required to prevent siloing and loss of information. To this end, we developed the Experimental Data Connector (XDC). It captures experimental data and related metadata by encoding it in standard formats and storing the converted data in digital repositories. Experimental data is then uploaded to Flapjack and the metadata to SynBioHub in a consistent manner linking these repositories. This produces complete connected experimental data sets that are exchangeable. The information is captured using a single template Excel Workbook, which can be integrated into existing experimental workflow automation processes and semiautomated capture of results.

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Section: Methodsmentioning

confidence: 99%

“…Simulated data for test and examples were generated using LOICA. 11 The macro enabled XDC template uses a combination of macros and cell formulas as explained in Algorithm 3.…”

Section: ■ Methodsmentioning

confidence: 99%

Experimental Data Connector (XDC): Integrating the Capture of Experimental Data and Metadata Using Standard Formats and Digital Repositories

et al. 2023

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“… \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$$ B_t = (B(t) + B^{\prime}) (1 + \epsilon_t) $$\end{document} \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$$ y_t = (y(t) + y^{\prime}) (1 + \zeta_t),$$\end{document} where ϵ t and ζ t are uncorrelated white noise with variance σ 2 , due to the measurement process. Simulated measurements were generated using LOICA ( 57 ), then uploaded to Flapjack ( 26 ) and analyzed using the API via Python (see Supporting information Figures S11 and S12 for an example of reporter and biomass raw data, respectively).…”

Section: Methodsmentioning

confidence: 99%

Accurate characterization of dynamic microbial gene expression and growth rate profiles

et al. 2022

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Genetic circuits are subject to variability due to cellular and compositional contexts. Cells face changing internal states and environments, the cellular context, to which they sense and respond by changing their gene expression and growth rates. Furthermore, each gene in a genetic circuit operates in a compositional context of genes which may interact with each other and the host cell in complex ways. The context of genetic circuits can therefore change gene expression and growth rates, and measuring their dynamics is essential to understanding natural and synthetic regulatory networks that give rise to functional phenotypes. However, reconstruction of microbial gene expression and growth rate profiles from typical noisy measurements of cell populations is difficult due to the effects of noise at low cell densities among other factors. We present here a method for estimation of dynamic microbial gene expression rates and growth rates from noisy measurement data. Compared to the current state-of-the-art, our method significantly reduced the mean squared error of reconstructions from simulated data of growth and gene expression rates, improving the estimation of timing and magnitude of relevant shapes of profiles. We applied our method to characterize a triple-reporter plasmid library combining multiple transcription units in different compositional and cellular contexts in Escherichia coli. Our analysis reveals cellular and compositional context effects on microbial growth and gene expression rate dynamics, and suggests a method for dynamic ratiometric characterization of constitutive promoters relative to an in vivo reference.

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“…All simulations were performed using the LOICA (Logical Operators for Integrated Cell Algorithms) Python package ( 26 ). Plots were generated using the Matplotlib Python package ( 27 ) and Jupyter notebooks ( 28 ).…”

Section: Methodsmentioning

confidence: 99%

Phase-based genetic logic circuits

Rudge

Vidal

2022

Preprint

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Bio-computation is the implementation of computational operations using biological substrates, such as cells engineered with synthetic genetic circuits. These genetic circuits can be composed of DNA parts with specific functions, such as promoters that initiate transcription, ribosome binding sites that initiate translation, and coding sequences that are translated into proteins. Compositions of such parts can encode genetic circuits in which the proteins produced by genes regulate each other in different ways. The expression level of each gene may be considered as a signal which may be high or low, encoding binary logic, and the combinations of genes can then encode logic circuits. This is equivalent to the level-based logic used in modern electronic computers in which the voltage forms the logical high or low states. A different approach to binary logic is to encode the high and low states in the phase of an oscillating signal. In this approach a signal in phase with a reference represents high, and a signal antiphase with the reference represents the logical low state. We present here designs and models for phase-based genetic NOT, OR, AND, MAJORITY and complementary MAJORITY gates, which together form multiple complete logical sets. We derive analytical expressions for the optimal model parameters for circuit function. Our simulation results suggest that this approach to genetic logic is feasible and could be less sensitive to gate input-output mismatch than level-based genetic logic. To demonstrate the scaleability of phase-based genetic logic, we used our complementary MAJORITY and NOT gates to design and simulate a fully functional 4-bit ripple adder circuit, and showed that it was robust to molecular noise.

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LOICA: Integrating Models with Data for Genetic Network Design Automation

Cited by 7 publications

References 30 publications

Experimental Data Connector (XDC): Integrating the Capture of Experimental Data and Metadata Using Standard Formats and Digital Repositories

Experimental Data Connector (XDC): Integrating the Capture of Experimental Data and Metadata Using Standard Formats and Digital Repositories

Accurate characterization of dynamic microbial gene expression and growth rate profiles

Phase-based genetic logic circuits

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