Multidimensional Characterization of Parts Enhances Modeling Accuracy in Genetic Circuits

Gómez-Schiavon, Mariana; Dods, Galen; El-Samad, Hana; Ng, Andrew H.

doi:10.1021/acssynbio.0c00288

Cited by 17 publications

(20 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While there exists a deep body of knowledge that can guide the model development process, from model formulation 18,19 and parameter estimation [19][20][21][22][23][24][25] , to model selection 23,26,27 , parameter identifiability analysis 25,[28][29][30][31][32][33][34][35][36][37] , and experimental design 7,29,31,38,39 , navigating these tasks can be slow and cumbersome, posing a barrier to entry. This challenge is heightened by the fact that published studies often focus on a final optimized model rather than the process by which the model was generated, which is often through laborious iteration that is understandably difficult to fully capture in a report.…”

Section: Resultsmentioning

confidence: 99%

“…Efforts to date have often used ordinary differential equations (ODEs), which describe the time evolution of the concentrations of system components and provide a framework for representing continuous dynamic systems. These models have been used to explain and predict behaviors of genetic programs that perform a growing array of functions including regulating gene expression [3][4][5][6][7] , implementing logic gates 5,6 , and implementing feedback control [8][9][10][11][12][13] . Models can generally be employed for explanation, in which the objective is to help the user understand a set of experimental observations, or for prediction, in which the objective is to simulate the response of a genetic program to a previously untested experimental condition or design choice (terms in italics are summarized in a glossary in Supplementary Information).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

GAMES: A dynamic model development workflow for rigorous characterization of synthetic genetic systems

Dray

Muldoon

Mangan

et al. 2021

Preprint

View full text Add to dashboard Cite

Mathematical modeling is invaluable for advancing understanding and design of synthetic biological systems. However, the model development process is complicated and often unintuitive, requiring iteration on various computational tasks and comparisons with experimental data. Ad hoc model development can pose a barrier to reproduction and critical analysis of the development process itself, reducing potential impact and inhibiting further model development and collaboration. To help practitioners manage these challenges, we introduce GAMES: a workflow for Generation and Analysis of Models for Exploring Synthetic systems that includes both automated and human-in-the-loop processes. We systematically consider the process of developing dynamic models, including model formulation, parameter estimation, parameter identifiability, experimental design, model reduction, model refinement, and model selection. We demonstrate the workflow with a case study on a chemically responsive transcription factor. The generalizable workflow presented in this tutorial can enable biologists to more readily build and analyze models for various applications.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

GAMES: A dynamic model development workflow for rigorous characterization of synthetic genetic systems

Dray

Muldoon

Mangan

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…The protein production rate was used as the output of each circuit module, since it is independent of growth rate, see SI Section S1.2 for theoretical justication or (24 ). The protein production rate of a protein X per cell is given as 1 OD dX dt (8) where OD is the optical density of the cells. Fluorescence measurements were taken every 5 minutes using the microplate reader.…”

Section: Discussionmentioning

confidence: 99%

“…It has previously been shown that resource competition between genes may result in more than a 60% decrease in expression levels (6 ), and that resource competition may cause signicant changes in the qualitative behavior of genetic circuits (7 ). It has also been shown in (8 ) that using the gene expression levels of a module's output helps to improve the accuracy of mathematical models in two-node transcriptional cascades, which indicates that resource competition may play a signicant role in these circuits. Resource competition has also been observed in cell-free systems (9 ), mammalian cells (10 ), and in computational models (11 13 ).…”

Section: Introductionmentioning

confidence: 99%

Predicting Composition of Genetic Circuits with Resource Competition: Demand and Sensitivity

McBride

Vecchio

2021

Preprint

View full text Add to dashboard Cite

The design of genetic circuits typically relies on characterization of constituent modules in isolation to predict the behavior of modules' composition. However, it has been shown that the behavior of a genetic module changes when other modules are in the cell due to competition for shared resources. In order to engineer multi-module circuits that behave as intended, it is thus necessary to predict changes in the behavior of a genetic module when other modules load cellular resources. Here, we introduce two characteristics of circuit modules: the demand for cellular resources and the sensitivity to resource loading. When both are known for every genetic module in a circuit, they can be used to predict any module's behavior upon addition of any other module to the cell. We develop an experimental approach to measure both characteristics for any circuit module using a resource sensor module. Using the measured resource demand and sensitivity for each module in a library, the outputs of the modules can be accurately predicted when they are inserted in the cell in arbitrary combinations. These resource competition characteristics may be used to inform the design of genetic circuits that perform as predicted despite resource competition.

show abstract

“…Finally, it is important to note that promoter leakiness can be taken into account by considering a different formulation [63], which leads to the following changes in (1):…”

Section: Mathematical Model To Account For the Scarcity Of Resourcesmentioning

confidence: 99%

Context-Dependent Stability and Robustness of Genetic Toggle Switches with Leaky Promoters

György

2021

Life

View full text Add to dashboard Cite

Multistable switches are ubiquitous building blocks in both systems and synthetic biology. Given their central role, it is thus imperative to understand how their fundamental properties depend not only on the tunable biophysical properties of the switches themselves, but also on their genetic context. To this end, we reveal in this article how these factors shape the essential characteristics of toggle switches implemented using leaky promoters such as their stability and robustness to noise, both at single-cell and population levels. In particular, our results expose the roles that competition for scarce transcriptional and translational resources, promoter leakiness, and cell-to-cell heterogeneity collectively play. For instance, the interplay between protein expression from leaky promoters and the associated cost of relying on shared cellular resources can give rise to tristable dynamics even in the absence of positive feedback. Similarly, we demonstrate that while promoter leakiness always acts against multistability, resource competition can be leveraged to counteract this undesirable phenomenon. Underpinned by a mechanistic model, our results thus enable the context-aware rational design of multistable genetic switches that are directly translatable to experimental considerations, and can be further leveraged during the synthesis of large-scale genetic systems using computer-aided biodesign automation platforms.

show abstract

Multidimensional Characterization of Parts Enhances Modeling Accuracy in Genetic Circuits

Cited by 17 publications

References 43 publications

GAMES: A dynamic model development workflow for rigorous characterization of synthetic genetic systems

GAMES: A dynamic model development workflow for rigorous characterization of synthetic genetic systems

Predicting Composition of Genetic Circuits with Resource Competition: Demand and Sensitivity

Context-Dependent Stability and Robustness of Genetic Toggle Switches with Leaky Promoters

Contact Info

Product

Resources

About