Source 1primary paper2021ACS Synthetic Biology
Toolkit/mathematical modeling
mathematical modeling
Also known as: quantitative model systems
Taxonomy: Technique Branch / Method. Workflows sit above the mechanism and technique branches rather than replacing them.
Summary
Mathematical modeling is a computational method used to guide the rational design of synthetic gene circuits. The cited literature also places it alongside live-cell imaging and within quantitative model systems used to study microbial drug resistance and spatial-temporal features of cancer in mammalian cells.
Usefulness & Problems
Why this is useful
This method is useful for quantitatively informing synthetic gene circuit design rather than relying solely on trial-and-error construction. The cited sources further indicate utility in model systems for analyzing drug resistance in microbes and spatial-temporal cancer biology in mammalian cells.
Source:
Our findings highlight key sources of imprecision within light-inducible dimer systems and provide tools that allow greater control of subcellular protein localization across diverse cell biological applications.
Source:
the improved Light-Inducible Dimer (iLID) system, offer the ability to selectively recruit components to subcellular locations, such as micron-scale regions of the plasma membrane
Source:
These tools, including the improved Light-Inducible Dimer (iLID) system, offer the ability to selectively recruit components to subcellular locations, such as micron-scale regions of the plasma membrane.
Source:
Mathematical models and synthetic gene circuits are powerful tools to develop novel treatments for patients with drug-resistant infections and cancers.
Problem solved
Mathematical modeling helps address the problem of rationally designing synthetic gene circuits. The available evidence also supports its use in studying complex biological behaviors, specifically antimicrobial resistance and cancer-related spatial-temporal dynamics.
Source:
Our findings highlight key sources of imprecision within light-inducible dimer systems and provide tools that allow greater control of subcellular protein localization across diverse cell biological applications.
Taxonomy & Function
Primary hierarchy
Technique Branch
Method: A concrete computational method used to design, rank, or analyze an engineered system.
Mechanisms
No mechanism tags yet.
Techniques
Computational DesignTarget processes
No target processes tagged yet.
Input: Chemical
Implementation Constraints
The evidence states that mathematical modeling was used together with live-cell imaging and for rational design of synthetic gene circuits. No further implementation details are provided regarding equations, training data, simulation platforms, or integration with specific experimental workflows.
The supplied evidence does not report specific model formalisms, predictive accuracy, software frameworks, or benchmarking results. It also does not define the chemical inputs, parameterization strategy, or whether the cited applications were experimentally validated through the modeling itself.
Validation
Cell-freeBacteriaMammalianMouseHumanTherapeuticIndep. Replication
Supporting Sources
Source 2primary paper2021IntechOpen eBooks
Ranked Claims
The findings provide tools that allow greater control of subcellular protein localization across diverse cell biological applications.
Our findings highlight key sources of imprecision within light-inducible dimer systems and provide tools that allow greater control of subcellular protein localization across diverse cell biological applications.
The findings provide tools that allow greater control of subcellular protein localization across diverse cell biological applications.
Our findings highlight key sources of imprecision within light-inducible dimer systems and provide tools that allow greater control of subcellular protein localization across diverse cell biological applications.
The findings provide tools that allow greater control of subcellular protein localization across diverse cell biological applications.
Our findings highlight key sources of imprecision within light-inducible dimer systems and provide tools that allow greater control of subcellular protein localization across diverse cell biological applications.
The findings provide tools that allow greater control of subcellular protein localization across diverse cell biological applications.
Our findings highlight key sources of imprecision within light-inducible dimer systems and provide tools that allow greater control of subcellular protein localization across diverse cell biological applications.
The findings provide tools that allow greater control of subcellular protein localization across diverse cell biological applications.
Our findings highlight key sources of imprecision within light-inducible dimer systems and provide tools that allow greater control of subcellular protein localization across diverse cell biological applications.
The findings provide tools that allow greater control of subcellular protein localization across diverse cell biological applications.
Our findings highlight key sources of imprecision within light-inducible dimer systems and provide tools that allow greater control of subcellular protein localization across diverse cell biological applications.
The findings provide tools that allow greater control of subcellular protein localization across diverse cell biological applications.
Our findings highlight key sources of imprecision within light-inducible dimer systems and provide tools that allow greater control of subcellular protein localization across diverse cell biological applications.
These quantitative model systems are used to study drug resistance in microbes and to probe the spatial-temporal dimensions of cancer in mammalian cells.
Specifically, we demonstrate how these quantitative model systems are being used to study drug resistance in microbes and to probe the spatial–temporal dimensions of cancer in mammalian cells.
These quantitative model systems are used to study drug resistance in microbes and to probe the spatial-temporal dimensions of cancer in mammalian cells.
Specifically, we demonstrate how these quantitative model systems are being used to study drug resistance in microbes and to probe the spatial–temporal dimensions of cancer in mammalian cells.
These quantitative model systems are used to study drug resistance in microbes and to probe the spatial-temporal dimensions of cancer in mammalian cells.
Specifically, we demonstrate how these quantitative model systems are being used to study drug resistance in microbes and to probe the spatial–temporal dimensions of cancer in mammalian cells.
These quantitative model systems are used to study drug resistance in microbes and to probe the spatial-temporal dimensions of cancer in mammalian cells.
Specifically, we demonstrate how these quantitative model systems are being used to study drug resistance in microbes and to probe the spatial–temporal dimensions of cancer in mammalian cells.
These quantitative model systems are used to study drug resistance in microbes and to probe the spatial-temporal dimensions of cancer in mammalian cells.
Specifically, we demonstrate how these quantitative model systems are being used to study drug resistance in microbes and to probe the spatial–temporal dimensions of cancer in mammalian cells.
These quantitative model systems are used to study drug resistance in microbes and to probe the spatial-temporal dimensions of cancer in mammalian cells.
Specifically, we demonstrate how these quantitative model systems are being used to study drug resistance in microbes and to probe the spatial–temporal dimensions of cancer in mammalian cells.
These quantitative model systems are used to study drug resistance in microbes and to probe the spatial-temporal dimensions of cancer in mammalian cells.
Specifically, we demonstrate how these quantitative model systems are being used to study drug resistance in microbes and to probe the spatial–temporal dimensions of cancer in mammalian cells.
The iLID system enables selective recruitment of components to subcellular locations, including micron-scale regions of the plasma membrane.
These tools, including the improved Light-Inducible Dimer (iLID) system, offer the ability to selectively recruit components to subcellular locations, such as micron-scale regions of the plasma membrane.
The iLID system enables selective recruitment of components to subcellular locations, including micron-scale regions of the plasma membrane.
These tools, including the improved Light-Inducible Dimer (iLID) system, offer the ability to selectively recruit components to subcellular locations, such as micron-scale regions of the plasma membrane.
The iLID system enables selective recruitment of components to subcellular locations, including micron-scale regions of the plasma membrane.
These tools, including the improved Light-Inducible Dimer (iLID) system, offer the ability to selectively recruit components to subcellular locations, such as micron-scale regions of the plasma membrane.
The iLID system enables selective recruitment of components to subcellular locations, including micron-scale regions of the plasma membrane.
These tools, including the improved Light-Inducible Dimer (iLID) system, offer the ability to selectively recruit components to subcellular locations, such as micron-scale regions of the plasma membrane.
The iLID system enables selective recruitment of components to subcellular locations, including micron-scale regions of the plasma membrane.
These tools, including the improved Light-Inducible Dimer (iLID) system, offer the ability to selectively recruit components to subcellular locations, such as micron-scale regions of the plasma membrane.
The iLID system enables selective recruitment of components to subcellular locations, including micron-scale regions of the plasma membrane.
These tools, including the improved Light-Inducible Dimer (iLID) system, offer the ability to selectively recruit components to subcellular locations, such as micron-scale regions of the plasma membrane.
The iLID system enables selective recruitment of components to subcellular locations, including micron-scale regions of the plasma membrane.
These tools, including the improved Light-Inducible Dimer (iLID) system, offer the ability to selectively recruit components to subcellular locations, such as micron-scale regions of the plasma membrane.
The iLID system enables selective recruitment of components to subcellular locations, including micron-scale regions of the plasma membrane.
These tools, including the improved Light-Inducible Dimer (iLID) system, offer the ability to selectively recruit components to subcellular locations, such as micron-scale regions of the plasma membrane.
The iLID system enables selective recruitment of components to subcellular locations such as micron-scale regions of the plasma membrane.
the improved Light-Inducible Dimer (iLID) system, offer the ability to selectively recruit components to subcellular locations, such as micron-scale regions of the plasma membrane
The iLID system enables selective recruitment of components to subcellular locations such as micron-scale regions of the plasma membrane.
the improved Light-Inducible Dimer (iLID) system, offer the ability to selectively recruit components to subcellular locations, such as micron-scale regions of the plasma membrane
The iLID system enables selective recruitment of components to subcellular locations such as micron-scale regions of the plasma membrane.
the improved Light-Inducible Dimer (iLID) system, offer the ability to selectively recruit components to subcellular locations, such as micron-scale regions of the plasma membrane
The iLID system enables selective recruitment of components to subcellular locations such as micron-scale regions of the plasma membrane.
the improved Light-Inducible Dimer (iLID) system, offer the ability to selectively recruit components to subcellular locations, such as micron-scale regions of the plasma membrane
The iLID system enables selective recruitment of components to subcellular locations such as micron-scale regions of the plasma membrane.
the improved Light-Inducible Dimer (iLID) system, offer the ability to selectively recruit components to subcellular locations, such as micron-scale regions of the plasma membrane
The iLID system enables selective recruitment of components to subcellular locations such as micron-scale regions of the plasma membrane.
the improved Light-Inducible Dimer (iLID) system, offer the ability to selectively recruit components to subcellular locations, such as micron-scale regions of the plasma membrane
The iLID system enables selective recruitment of components to subcellular locations such as micron-scale regions of the plasma membrane.
the improved Light-Inducible Dimer (iLID) system, offer the ability to selectively recruit components to subcellular locations, such as micron-scale regions of the plasma membrane
Compared with the commonly used C-terminal iLID fusion, fusion proteins with large N-terminal anchors show stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs.
Compared to the commonly used C-terminal iLID fusion, fusion proteins with large N-terminal anchors show stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs.
Compared with the commonly used C-terminal iLID fusion, fusion proteins with large N-terminal anchors show stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs.
Compared to the commonly used C-terminal iLID fusion, fusion proteins with large N-terminal anchors show stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs.
Compared with the commonly used C-terminal iLID fusion, fusion proteins with large N-terminal anchors show stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs.
Compared to the commonly used C-terminal iLID fusion, fusion proteins with large N-terminal anchors show stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs.
Compared with the commonly used C-terminal iLID fusion, fusion proteins with large N-terminal anchors show stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs.
Compared to the commonly used C-terminal iLID fusion, fusion proteins with large N-terminal anchors show stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs.
Compared with the commonly used C-terminal iLID fusion, fusion proteins with large N-terminal anchors show stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs.
Compared to the commonly used C-terminal iLID fusion, fusion proteins with large N-terminal anchors show stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs.
Compared with the commonly used C-terminal iLID fusion, fusion proteins with large N-terminal anchors show stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs.
Compared to the commonly used C-terminal iLID fusion, fusion proteins with large N-terminal anchors show stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs.
Compared with the commonly used C-terminal iLID fusion, fusion proteins with large N-terminal anchors show stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs.
Compared to the commonly used C-terminal iLID fusion, fusion proteins with large N-terminal anchors show stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs.
Compared with the commonly used C-terminal iLID fusion, large N-terminal anchor fusions provide stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs.
Compared to the commonly used C-terminal iLID fusion, fusion proteins with large N-terminal anchors show stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs.
Compared with the commonly used C-terminal iLID fusion, large N-terminal anchor fusions provide stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs.
Compared to the commonly used C-terminal iLID fusion, fusion proteins with large N-terminal anchors show stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs.
Compared with the commonly used C-terminal iLID fusion, large N-terminal anchor fusions provide stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs.
Compared to the commonly used C-terminal iLID fusion, fusion proteins with large N-terminal anchors show stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs.
Compared with the commonly used C-terminal iLID fusion, large N-terminal anchor fusions provide stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs.
Compared to the commonly used C-terminal iLID fusion, fusion proteins with large N-terminal anchors show stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs.
Compared with the commonly used C-terminal iLID fusion, large N-terminal anchor fusions provide stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs.
Compared to the commonly used C-terminal iLID fusion, fusion proteins with large N-terminal anchors show stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs.
Compared with the commonly used C-terminal iLID fusion, large N-terminal anchor fusions provide stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs.
Compared to the commonly used C-terminal iLID fusion, fusion proteins with large N-terminal anchors show stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs.
Compared with the commonly used C-terminal iLID fusion, large N-terminal anchor fusions provide stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs.
Compared to the commonly used C-terminal iLID fusion, fusion proteins with large N-terminal anchors show stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs.
Compared with the commonly used C-terminal iLID fusion, large N-terminal anchor fusions provide stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs.
Compared to the commonly used C-terminal iLID fusion, fusion proteins with large N-terminal anchors show stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs.
Mathematical modeling guides the rational design of synthetic gene circuits.
Mathematical modeling guides the rational design of synthetic gene circuits.
Mathematical modeling guides the rational design of synthetic gene circuits.
Mathematical modeling guides the rational design of synthetic gene circuits.
Mathematical modeling guides the rational design of synthetic gene circuits.
Mathematical modeling guides the rational design of synthetic gene circuits.
Mathematical modeling guides the rational design of synthetic gene circuits.
Mathematical modeling guides the rational design of synthetic gene circuits.
Mathematical modeling guides the rational design of synthetic gene circuits.
Mathematical modeling guides the rational design of synthetic gene circuits.
Mathematical modeling guides the rational design of synthetic gene circuits.
Mathematical modeling guides the rational design of synthetic gene circuits.
Mathematical modeling guides the rational design of synthetic gene circuits.
Mathematical modeling guides the rational design of synthetic gene circuits.
The study defines guidelines for component expression regimes for optimal recruitment for both cell-wide and subcellular recruitment strategies.
We also define guidelines for component expression regimes for optimal recruitment for both cell-wide and subcellular recruitment strategies.
The study defines guidelines for component expression regimes for optimal recruitment for both cell-wide and subcellular recruitment strategies.
We also define guidelines for component expression regimes for optimal recruitment for both cell-wide and subcellular recruitment strategies.
The study defines guidelines for component expression regimes for optimal recruitment for both cell-wide and subcellular recruitment strategies.
We also define guidelines for component expression regimes for optimal recruitment for both cell-wide and subcellular recruitment strategies.
The study defines guidelines for component expression regimes for optimal recruitment for both cell-wide and subcellular recruitment strategies.
We also define guidelines for component expression regimes for optimal recruitment for both cell-wide and subcellular recruitment strategies.
The study defines guidelines for component expression regimes for optimal recruitment for both cell-wide and subcellular recruitment strategies.
We also define guidelines for component expression regimes for optimal recruitment for both cell-wide and subcellular recruitment strategies.
The study defines guidelines for component expression regimes for optimal recruitment for both cell-wide and subcellular recruitment strategies.
We also define guidelines for component expression regimes for optimal recruitment for both cell-wide and subcellular recruitment strategies.
The study defines guidelines for component expression regimes for optimal recruitment for both cell-wide and subcellular recruitment strategies.
We also define guidelines for component expression regimes for optimal recruitment for both cell-wide and subcellular recruitment strategies.
Consistent recruitment in optogenetic dimerization systems is limited by heterogeneous optogenetic component expression, and spatial precision is diminished by protein diffusion, especially over long time scales.
Currently, consistent recruitment is limited by heterogeneous optogenetic component expression, and spatial precision is diminished by protein diffusion, especially over long time scales.
Consistent recruitment in optogenetic dimerization systems is limited by heterogeneous optogenetic component expression, and spatial precision is diminished by protein diffusion, especially over long time scales.
Currently, consistent recruitment is limited by heterogeneous optogenetic component expression, and spatial precision is diminished by protein diffusion, especially over long time scales.
Consistent recruitment in optogenetic dimerization systems is limited by heterogeneous optogenetic component expression, and spatial precision is diminished by protein diffusion, especially over long time scales.
Currently, consistent recruitment is limited by heterogeneous optogenetic component expression, and spatial precision is diminished by protein diffusion, especially over long time scales.
Consistent recruitment in optogenetic dimerization systems is limited by heterogeneous optogenetic component expression, and spatial precision is diminished by protein diffusion, especially over long time scales.
Currently, consistent recruitment is limited by heterogeneous optogenetic component expression, and spatial precision is diminished by protein diffusion, especially over long time scales.
Consistent recruitment in optogenetic dimerization systems is limited by heterogeneous optogenetic component expression, and spatial precision is diminished by protein diffusion, especially over long time scales.
Currently, consistent recruitment is limited by heterogeneous optogenetic component expression, and spatial precision is diminished by protein diffusion, especially over long time scales.
Consistent recruitment in optogenetic dimerization systems is limited by heterogeneous optogenetic component expression, and spatial precision is diminished by protein diffusion, especially over long time scales.
Currently, consistent recruitment is limited by heterogeneous optogenetic component expression, and spatial precision is diminished by protein diffusion, especially over long time scales.
Consistent recruitment in optogenetic dimerization systems is limited by heterogeneous optogenetic component expression, and spatial precision is diminished by protein diffusion, especially over long time scales.
Currently, consistent recruitment is limited by heterogeneous optogenetic component expression, and spatial precision is diminished by protein diffusion, especially over long time scales.
Within iLID-based recruitment, consistent recruitment is limited by heterogeneous optogenetic component expression and spatial precision is reduced by protein diffusion, especially over long time scales.
Currently, consistent recruitment is limited by heterogeneous optogenetic component expression, and spatial precision is diminished by protein diffusion, especially over long time scales.
Within iLID-based recruitment, consistent recruitment is limited by heterogeneous optogenetic component expression and spatial precision is reduced by protein diffusion, especially over long time scales.
Currently, consistent recruitment is limited by heterogeneous optogenetic component expression, and spatial precision is diminished by protein diffusion, especially over long time scales.
Within iLID-based recruitment, consistent recruitment is limited by heterogeneous optogenetic component expression and spatial precision is reduced by protein diffusion, especially over long time scales.
Currently, consistent recruitment is limited by heterogeneous optogenetic component expression, and spatial precision is diminished by protein diffusion, especially over long time scales.
Within iLID-based recruitment, consistent recruitment is limited by heterogeneous optogenetic component expression and spatial precision is reduced by protein diffusion, especially over long time scales.
Currently, consistent recruitment is limited by heterogeneous optogenetic component expression, and spatial precision is diminished by protein diffusion, especially over long time scales.
Within iLID-based recruitment, consistent recruitment is limited by heterogeneous optogenetic component expression and spatial precision is reduced by protein diffusion, especially over long time scales.
Currently, consistent recruitment is limited by heterogeneous optogenetic component expression, and spatial precision is diminished by protein diffusion, especially over long time scales.
Within iLID-based recruitment, consistent recruitment is limited by heterogeneous optogenetic component expression and spatial precision is reduced by protein diffusion, especially over long time scales.
Currently, consistent recruitment is limited by heterogeneous optogenetic component expression, and spatial precision is diminished by protein diffusion, especially over long time scales.
Within iLID-based recruitment, consistent recruitment is limited by heterogeneous optogenetic component expression and spatial precision is reduced by protein diffusion, especially over long time scales.
Currently, consistent recruitment is limited by heterogeneous optogenetic component expression, and spatial precision is diminished by protein diffusion, especially over long time scales.
Within iLID-based recruitment, consistent recruitment is limited by heterogeneous optogenetic component expression and spatial precision is reduced by protein diffusion, especially over long time scales.
Currently, consistent recruitment is limited by heterogeneous optogenetic component expression, and spatial precision is diminished by protein diffusion, especially over long time scales.
Anchoring strategy affects component expression and diffusion, which in turn impact recruitment strength, kinetics, and spatial dynamics in the iLID system.
we demonstrate that the anchoring strategy affects both component expression and diffusion, which in turn impact recruitment strength, kinetics, and spatial dynamics
Anchoring strategy affects component expression and diffusion, which in turn impact recruitment strength, kinetics, and spatial dynamics in the iLID system.
we demonstrate that the anchoring strategy affects both component expression and diffusion, which in turn impact recruitment strength, kinetics, and spatial dynamics
Anchoring strategy affects component expression and diffusion, which in turn impact recruitment strength, kinetics, and spatial dynamics in the iLID system.
we demonstrate that the anchoring strategy affects both component expression and diffusion, which in turn impact recruitment strength, kinetics, and spatial dynamics
Anchoring strategy affects component expression and diffusion, which in turn impact recruitment strength, kinetics, and spatial dynamics in the iLID system.
we demonstrate that the anchoring strategy affects both component expression and diffusion, which in turn impact recruitment strength, kinetics, and spatial dynamics
Anchoring strategy affects component expression and diffusion, which in turn impact recruitment strength, kinetics, and spatial dynamics in the iLID system.
we demonstrate that the anchoring strategy affects both component expression and diffusion, which in turn impact recruitment strength, kinetics, and spatial dynamics
Anchoring strategy affects component expression and diffusion, which in turn impact recruitment strength, kinetics, and spatial dynamics in the iLID system.
we demonstrate that the anchoring strategy affects both component expression and diffusion, which in turn impact recruitment strength, kinetics, and spatial dynamics
Anchoring strategy affects component expression and diffusion, which in turn impact recruitment strength, kinetics, and spatial dynamics in the iLID system.
we demonstrate that the anchoring strategy affects both component expression and diffusion, which in turn impact recruitment strength, kinetics, and spatial dynamics
In the iLID system, anchoring strategy affects component expression and diffusion, which in turn impact recruitment strength, kinetics, and spatial dynamics.
Using live cell imaging and mathematical modeling, we demonstrate that the anchoring strategy affects both component expression and diffusion, which in turn impact recruitment strength, kinetics, and spatial dynamics.
In the iLID system, anchoring strategy affects component expression and diffusion, which in turn impact recruitment strength, kinetics, and spatial dynamics.
Using live cell imaging and mathematical modeling, we demonstrate that the anchoring strategy affects both component expression and diffusion, which in turn impact recruitment strength, kinetics, and spatial dynamics.
In the iLID system, anchoring strategy affects component expression and diffusion, which in turn impact recruitment strength, kinetics, and spatial dynamics.
Using live cell imaging and mathematical modeling, we demonstrate that the anchoring strategy affects both component expression and diffusion, which in turn impact recruitment strength, kinetics, and spatial dynamics.
In the iLID system, anchoring strategy affects component expression and diffusion, which in turn impact recruitment strength, kinetics, and spatial dynamics.
Using live cell imaging and mathematical modeling, we demonstrate that the anchoring strategy affects both component expression and diffusion, which in turn impact recruitment strength, kinetics, and spatial dynamics.
In the iLID system, anchoring strategy affects component expression and diffusion, which in turn impact recruitment strength, kinetics, and spatial dynamics.
Using live cell imaging and mathematical modeling, we demonstrate that the anchoring strategy affects both component expression and diffusion, which in turn impact recruitment strength, kinetics, and spatial dynamics.
In the iLID system, anchoring strategy affects component expression and diffusion, which in turn impact recruitment strength, kinetics, and spatial dynamics.
Using live cell imaging and mathematical modeling, we demonstrate that the anchoring strategy affects both component expression and diffusion, which in turn impact recruitment strength, kinetics, and spatial dynamics.
In the iLID system, anchoring strategy affects component expression and diffusion, which in turn impact recruitment strength, kinetics, and spatial dynamics.
Using live cell imaging and mathematical modeling, we demonstrate that the anchoring strategy affects both component expression and diffusion, which in turn impact recruitment strength, kinetics, and spatial dynamics.
In the iLID system, anchoring strategy affects component expression and diffusion, which in turn impact recruitment strength, kinetics, and spatial dynamics.
Using live cell imaging and mathematical modeling, we demonstrate that the anchoring strategy affects both component expression and diffusion, which in turn impact recruitment strength, kinetics, and spatial dynamics.
The study defines guidelines for component expression regimes that optimize recruitment for both cell-wide and subcellular recruitment strategies.
We also define guidelines for component expression regimes for optimal recruitment for both cell-wide and subcellular recruitment strategies.
The study defines guidelines for component expression regimes that optimize recruitment for both cell-wide and subcellular recruitment strategies.
We also define guidelines for component expression regimes for optimal recruitment for both cell-wide and subcellular recruitment strategies.
The study defines guidelines for component expression regimes that optimize recruitment for both cell-wide and subcellular recruitment strategies.
We also define guidelines for component expression regimes for optimal recruitment for both cell-wide and subcellular recruitment strategies.
The study defines guidelines for component expression regimes that optimize recruitment for both cell-wide and subcellular recruitment strategies.
We also define guidelines for component expression regimes for optimal recruitment for both cell-wide and subcellular recruitment strategies.
The study defines guidelines for component expression regimes that optimize recruitment for both cell-wide and subcellular recruitment strategies.
We also define guidelines for component expression regimes for optimal recruitment for both cell-wide and subcellular recruitment strategies.
The study defines guidelines for component expression regimes that optimize recruitment for both cell-wide and subcellular recruitment strategies.
We also define guidelines for component expression regimes for optimal recruitment for both cell-wide and subcellular recruitment strategies.
The study defines guidelines for component expression regimes that optimize recruitment for both cell-wide and subcellular recruitment strategies.
We also define guidelines for component expression regimes for optimal recruitment for both cell-wide and subcellular recruitment strategies.
The study defines guidelines for component expression regimes that optimize recruitment for both cell-wide and subcellular recruitment strategies.
We also define guidelines for component expression regimes for optimal recruitment for both cell-wide and subcellular recruitment strategies.
Mathematical models and synthetic gene circuits are powerful tools for developing novel treatments for drug-resistant infections and cancers.
Mathematical models and synthetic gene circuits are powerful tools to develop novel treatments for patients with drug-resistant infections and cancers.
Mathematical models and synthetic gene circuits are powerful tools for developing novel treatments for drug-resistant infections and cancers.
Mathematical models and synthetic gene circuits are powerful tools to develop novel treatments for patients with drug-resistant infections and cancers.
Mathematical models and synthetic gene circuits are powerful tools for developing novel treatments for drug-resistant infections and cancers.
Mathematical models and synthetic gene circuits are powerful tools to develop novel treatments for patients with drug-resistant infections and cancers.
Mathematical models and synthetic gene circuits are powerful tools for developing novel treatments for drug-resistant infections and cancers.
Mathematical models and synthetic gene circuits are powerful tools to develop novel treatments for patients with drug-resistant infections and cancers.
Mathematical models and synthetic gene circuits are powerful tools for developing novel treatments for drug-resistant infections and cancers.
Mathematical models and synthetic gene circuits are powerful tools to develop novel treatments for patients with drug-resistant infections and cancers.
Mathematical models and synthetic gene circuits are powerful tools for developing novel treatments for drug-resistant infections and cancers.
Mathematical models and synthetic gene circuits are powerful tools to develop novel treatments for patients with drug-resistant infections and cancers.
Mathematical models and synthetic gene circuits are powerful tools for developing novel treatments for drug-resistant infections and cancers.
Mathematical models and synthetic gene circuits are powerful tools to develop novel treatments for patients with drug-resistant infections and cancers.
Approval Evidence
2 sources3 linked approval claimsfirst-pass slug mathematical-modeling
Using live cell imaging and mathematical modeling
Source:
Mathematical modeling guides the rational design of synthetic gene circuits.
Source:
biomedical applicationsupports
These quantitative model systems are used to study drug resistance in microbes and to probe the spatial-temporal dimensions of cancer in mammalian cells.
Specifically, we demonstrate how these quantitative model systems are being used to study drug resistance in microbes and to probe the spatial–temporal dimensions of cancer in mammalian cells.
Source:
design guidancesupports
Mathematical modeling guides the rational design of synthetic gene circuits.
Mathematical modeling guides the rational design of synthetic gene circuits.
Source:
utilitysupports
Mathematical models and synthetic gene circuits are powerful tools for developing novel treatments for drug-resistant infections and cancers.
Mathematical models and synthetic gene circuits are powerful tools to develop novel treatments for patients with drug-resistant infections and cancers.
Source:
Comparisons
Source-backed strengths
A key strength supported by the evidence is its role in rational design of synthetic gene circuits. The literature also indicates that it can be combined with live-cell imaging and applied across both microbial and mammalian research contexts.
Source:
Compared to the commonly used C-terminal iLID fusion, fusion proteins with large N-terminal anchors show stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs.
Ranked Citations
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