Source 1primary paper2025MED
Toolkit/computational modeling
computational modeling
Taxonomy: Technique Branch / Method. Workflows sit above the mechanism and technique branches rather than replacing them.
Summary
Computational modeling was used to analyze how promoters decode light-driven transcription factor nuclear translocation dynamics. In the cited work, the modeling identified promoter kinetic regimes that enable efficient expression under short light pulses and proposed a multi-stage, thresholded activation scheme to explain opposite promoter-response phenotypes.
Usefulness & Problems
Why this is useful
This approach is useful for interpreting how promoter activation and inactivation kinetics shape transcriptional responses to optogenetically controlled nuclear localization inputs. It provides a framework for linking dynamic light stimulation to promoter-specific gene expression behavior and for guiding construct design decisions in related systems.
Source:
we present CLASP (Controllable Light Activated Shuttling and Plasma membrane sequestration), a tool that enables precise, modular, and reversible control of TF localization using a combination of two optimized LOV2 optogenetic constructs
Source:
By expressing AsLOV2 chimeras in Neuro2a cells, we achieved light-dependent modulation of REST target genes
Source:
and boosted Na(+) currents and neuronal firing
Problem solved
It addresses the problem of explaining why different promoters can respond differently to the same light-driven transcription factor translocation dynamics. Specifically, it helps identify kinetic features associated with efficient short-pulse expression and offers a mechanistic explanation for opposite response phenotypes through thresholded multi-stage activation.
Problem links
This is directly a computational modeling approach, which aligns with the gap's need for better simulation and modeling. However, the provided summary is narrowly about gene expression dynamics, so relevance to mechanical systems is weakly supported.
Taxonomy & Function
Primary hierarchy
Technique Branch
Method: A concrete computational method used to design, rank, or analyze an engineered system.
Mechanisms
multi-stage promoter activationmulti-stage promoter activationpromoter activation kineticspromoter activation kineticspromoter inactivation kineticspromoter inactivation kineticsthresholded state transitionthresholded state transitionTarget processes
No target processes tagged yet.
Input: Light
Implementation Constraints
cofactor dependency: cofactor requirement unknownencoding mode: genetically encodedimplementation constraint: context specific validationimplementation constraint: spectral hardware requirementoperating role: builder
The relevant biological context involved light-responsive control of transcription factor localization, including a CLASP system in which blue light releases cargo from plasma membrane sequestration and reveals a nuclear localization sequence for nuclear import. The evidence does not specify software, mathematical formalism, training data, or implementation requirements for the computational modeling itself.
The supplied evidence supports modeling conclusions but does not provide detailed information on model structure, parameterization, predictive accuracy, or external benchmarking. Validation appears limited to the cited studies, and no independent replication or broad cross-system generalization is documented in the provided evidence.
Validation
Cell-freeBacteriaMammalianMouseHumanTherapeuticIndep. Replication
Supporting Sources
Source 2primary paper2019
Source 3primary paper2015Proceedings of the National Academy of Sciences
Source 4primary paper2025MED
Ranked Claims
Future progress in vascular disease research should prioritize multi-center large-scale validation studies, harmonization of assay protocols, and integration with clinical datasets and human samples.
Multi-omics approaches and computational modeling hold promise for unraveling disease complexity, and digital twins may accelerate personalized medicine in vascular disease research and treatment.
In CLASP, one light-responsive construct sequesters cargo at the plasma membrane in the dark and releases it upon blue light exposure, while a second light-responsive construct reveals a nuclear localization sequence that shuttles released cargo to the nucleus.
The first sequesters the cargo in the dark at the plasma membrane and releases it upon exposure to blue light, while light exposure of the second reveals a nuclear localization sequence that shuttles the released cargo to the nucleus.
In CLASP, one light-responsive construct sequesters cargo at the plasma membrane in the dark and releases it upon blue light exposure, while a second light-responsive construct reveals a nuclear localization sequence that shuttles released cargo to the nucleus.
The first sequesters the cargo in the dark at the plasma membrane and releases it upon exposure to blue light, while light exposure of the second reveals a nuclear localization sequence that shuttles the released cargo to the nucleus.
In CLASP, one light-responsive construct sequesters cargo at the plasma membrane in the dark and releases it upon blue light exposure, while a second light-responsive construct reveals a nuclear localization sequence that shuttles released cargo to the nucleus.
The first sequesters the cargo in the dark at the plasma membrane and releases it upon exposure to blue light, while light exposure of the second reveals a nuclear localization sequence that shuttles the released cargo to the nucleus.
In CLASP, one light-responsive construct sequesters cargo at the plasma membrane in the dark and releases it upon blue light exposure, while a second light-responsive construct reveals a nuclear localization sequence that shuttles released cargo to the nucleus.
The first sequesters the cargo in the dark at the plasma membrane and releases it upon exposure to blue light, while light exposure of the second reveals a nuclear localization sequence that shuttles the released cargo to the nucleus.
In CLASP, one light-responsive construct sequesters cargo at the plasma membrane in the dark and releases it upon blue light exposure, while a second light-responsive construct reveals a nuclear localization sequence that shuttles released cargo to the nucleus.
The first sequesters the cargo in the dark at the plasma membrane and releases it upon exposure to blue light, while light exposure of the second reveals a nuclear localization sequence that shuttles the released cargo to the nucleus.
In CLASP, one light-responsive construct sequesters cargo at the plasma membrane in the dark and releases it upon blue light exposure, while a second light-responsive construct reveals a nuclear localization sequence that shuttles released cargo to the nucleus.
The first sequesters the cargo in the dark at the plasma membrane and releases it upon exposure to blue light, while light exposure of the second reveals a nuclear localization sequence that shuttles the released cargo to the nucleus.
In CLASP, one light-responsive construct sequesters cargo at the plasma membrane in the dark and releases it upon blue light exposure, while a second light-responsive construct reveals a nuclear localization sequence that shuttles released cargo to the nucleus.
The first sequesters the cargo in the dark at the plasma membrane and releases it upon exposure to blue light, while light exposure of the second reveals a nuclear localization sequence that shuttles the released cargo to the nucleus.
In CLASP, one light-responsive construct sequesters cargo at the plasma membrane in the dark and releases it upon blue light exposure, while a second light-responsive construct reveals a nuclear localization sequence that shuttles released cargo to the nucleus.
The first sequesters the cargo in the dark at the plasma membrane and releases it upon exposure to blue light, while light exposure of the second reveals a nuclear localization sequence that shuttles the released cargo to the nucleus.
In CLASP, one light-responsive construct sequesters cargo at the plasma membrane in the dark and releases it upon blue light exposure, while a second light-responsive construct reveals a nuclear localization sequence that shuttles released cargo to the nucleus.
The first sequesters the cargo in the dark at the plasma membrane and releases it upon exposure to blue light, while light exposure of the second reveals a nuclear localization sequence that shuttles the released cargo to the nucleus.
In CLASP, one light-responsive construct sequesters cargo at the plasma membrane in the dark and releases it upon blue light exposure, while a second light-responsive construct reveals a nuclear localization sequence that shuttles released cargo to the nucleus.
The first sequesters the cargo in the dark at the plasma membrane and releases it upon exposure to blue light, while light exposure of the second reveals a nuclear localization sequence that shuttles the released cargo to the nucleus.
Computational modeling indicates that efficient gene expression in response to short pulsing requires fast promoter activation and slow inactivation.
We show using computational modeling that efficient gene expression in response to short pulsing requires fast promoter activation and slow inactivation
Computational modeling indicates that efficient gene expression in response to short pulsing requires fast promoter activation and slow inactivation.
We show using computational modeling that efficient gene expression in response to short pulsing requires fast promoter activation and slow inactivation
Computational modeling indicates that efficient gene expression in response to short pulsing requires fast promoter activation and slow inactivation.
We show using computational modeling that efficient gene expression in response to short pulsing requires fast promoter activation and slow inactivation
Computational modeling indicates that efficient gene expression in response to short pulsing requires fast promoter activation and slow inactivation.
We show using computational modeling that efficient gene expression in response to short pulsing requires fast promoter activation and slow inactivation
Computational modeling indicates that efficient gene expression in response to short pulsing requires fast promoter activation and slow inactivation.
We show using computational modeling that efficient gene expression in response to short pulsing requires fast promoter activation and slow inactivation
Computational modeling indicates that efficient gene expression in response to short pulsing requires fast promoter activation and slow inactivation.
We show using computational modeling that efficient gene expression in response to short pulsing requires fast promoter activation and slow inactivation
Computational modeling indicates that efficient gene expression in response to short pulsing requires fast promoter activation and slow inactivation.
We show using computational modeling that efficient gene expression in response to short pulsing requires fast promoter activation and slow inactivation
Computational modeling indicates that efficient gene expression in response to short pulsing requires fast promoter activation and slow inactivation.
We show using computational modeling that efficient gene expression in response to short pulsing requires fast promoter activation and slow inactivation
Computational modeling indicates that efficient gene expression in response to short pulsing requires fast promoter activation and slow inactivation.
We show using computational modeling that efficient gene expression in response to short pulsing requires fast promoter activation and slow inactivation
Computational modeling indicates that efficient gene expression in response to short pulsing requires fast promoter activation and slow inactivation.
We show using computational modeling that efficient gene expression in response to short pulsing requires fast promoter activation and slow inactivation
Computational modeling indicates that efficient gene expression in response to short pulsing requires fast promoter activation and slow inactivation.
We show using computational modeling that efficient gene expression in response to short pulsing requires fast promoter activation and slow inactivation
Computational modeling indicates that efficient gene expression in response to short pulsing requires fast promoter activation and slow inactivation.
We show using computational modeling that efficient gene expression in response to short pulsing requires fast promoter activation and slow inactivation
Computational modeling indicates that efficient gene expression in response to short pulsing requires fast promoter activation and slow inactivation.
We show using computational modeling that efficient gene expression in response to short pulsing requires fast promoter activation and slow inactivation
Computational modeling indicates that efficient gene expression in response to short pulsing requires fast promoter activation and slow inactivation.
We show using computational modeling that efficient gene expression in response to short pulsing requires fast promoter activation and slow inactivation
Computational modeling indicates that efficient gene expression in response to short pulsing requires fast promoter activation and slow inactivation.
We show using computational modeling that efficient gene expression in response to short pulsing requires fast promoter activation and slow inactivation
Computational modeling indicates that efficient gene expression in response to short pulsing requires fast promoter activation and slow inactivation.
We show using computational modeling that efficient gene expression in response to short pulsing requires fast promoter activation and slow inactivation
Computational modeling indicates that efficient gene expression in response to short pulsing requires fast promoter activation and slow inactivation.
We show using computational modeling that efficient gene expression in response to short pulsing requires fast promoter activation and slow inactivation
Computational modeling indicates that the opposite promoter-response phenotype can arise from multi-stage promoter activation in which a transition in the first stage is thresholded.
and that the opposite phenotype can ensue from a multi-stage promoter activation, where a transition in the first stage is thresholded
Computational modeling indicates that the opposite promoter-response phenotype can arise from multi-stage promoter activation in which a transition in the first stage is thresholded.
and that the opposite phenotype can ensue from a multi-stage promoter activation, where a transition in the first stage is thresholded
Computational modeling indicates that the opposite promoter-response phenotype can arise from multi-stage promoter activation in which a transition in the first stage is thresholded.
and that the opposite phenotype can ensue from a multi-stage promoter activation, where a transition in the first stage is thresholded
Computational modeling indicates that the opposite promoter-response phenotype can arise from multi-stage promoter activation in which a transition in the first stage is thresholded.
and that the opposite phenotype can ensue from a multi-stage promoter activation, where a transition in the first stage is thresholded
Computational modeling indicates that the opposite promoter-response phenotype can arise from multi-stage promoter activation in which a transition in the first stage is thresholded.
and that the opposite phenotype can ensue from a multi-stage promoter activation, where a transition in the first stage is thresholded
Computational modeling indicates that the opposite promoter-response phenotype can arise from multi-stage promoter activation in which a transition in the first stage is thresholded.
and that the opposite phenotype can ensue from a multi-stage promoter activation, where a transition in the first stage is thresholded
Computational modeling indicates that the opposite promoter-response phenotype can arise from multi-stage promoter activation in which a transition in the first stage is thresholded.
and that the opposite phenotype can ensue from a multi-stage promoter activation, where a transition in the first stage is thresholded
Computational modeling indicates that the opposite promoter-response phenotype can arise from multi-stage promoter activation in which a transition in the first stage is thresholded.
and that the opposite phenotype can ensue from a multi-stage promoter activation, where a transition in the first stage is thresholded
Computational modeling indicates that the opposite promoter-response phenotype can arise from multi-stage promoter activation in which a transition in the first stage is thresholded.
and that the opposite phenotype can ensue from a multi-stage promoter activation, where a transition in the first stage is thresholded
Computational modeling indicates that the opposite promoter-response phenotype can arise from multi-stage promoter activation in which a transition in the first stage is thresholded.
and that the opposite phenotype can ensue from a multi-stage promoter activation, where a transition in the first stage is thresholded
Computational modeling indicates that the opposite promoter-response phenotype can arise from multi-stage promoter activation in which a transition in the first stage is thresholded.
and that the opposite phenotype can ensue from a multi-stage promoter activation, where a transition in the first stage is thresholded
Computational modeling indicates that the opposite promoter-response phenotype can arise from multi-stage promoter activation in which a transition in the first stage is thresholded.
and that the opposite phenotype can ensue from a multi-stage promoter activation, where a transition in the first stage is thresholded
Computational modeling indicates that the opposite promoter-response phenotype can arise from multi-stage promoter activation in which a transition in the first stage is thresholded.
and that the opposite phenotype can ensue from a multi-stage promoter activation, where a transition in the first stage is thresholded
Computational modeling indicates that the opposite promoter-response phenotype can arise from multi-stage promoter activation in which a transition in the first stage is thresholded.
and that the opposite phenotype can ensue from a multi-stage promoter activation, where a transition in the first stage is thresholded
Computational modeling indicates that the opposite promoter-response phenotype can arise from multi-stage promoter activation in which a transition in the first stage is thresholded.
and that the opposite phenotype can ensue from a multi-stage promoter activation, where a transition in the first stage is thresholded
Computational modeling indicates that the opposite promoter-response phenotype can arise from multi-stage promoter activation in which a transition in the first stage is thresholded.
and that the opposite phenotype can ensue from a multi-stage promoter activation, where a transition in the first stage is thresholded
Computational modeling indicates that the opposite promoter-response phenotype can arise from multi-stage promoter activation in which a transition in the first stage is thresholded.
and that the opposite phenotype can ensue from a multi-stage promoter activation, where a transition in the first stage is thresholded
CLASP achieves minute-level resolution, reversible translocation of many transcription factor cargos, large dynamic range, and tunable target gene expression.
CLASP achieves minute-level resolution, reversible translocation of many TF cargos, large dynamic range, and tunable target gene expression.
temporal resolution minute-level
CLASP achieves minute-level resolution, reversible translocation of many transcription factor cargos, large dynamic range, and tunable target gene expression.
CLASP achieves minute-level resolution, reversible translocation of many TF cargos, large dynamic range, and tunable target gene expression.
temporal resolution minute-level
CLASP achieves minute-level resolution, reversible translocation of many transcription factor cargos, large dynamic range, and tunable target gene expression.
CLASP achieves minute-level resolution, reversible translocation of many TF cargos, large dynamic range, and tunable target gene expression.
temporal resolution minute-level
CLASP achieves minute-level resolution, reversible translocation of many transcription factor cargos, large dynamic range, and tunable target gene expression.
CLASP achieves minute-level resolution, reversible translocation of many TF cargos, large dynamic range, and tunable target gene expression.
temporal resolution minute-level
CLASP achieves minute-level resolution, reversible translocation of many transcription factor cargos, large dynamic range, and tunable target gene expression.
CLASP achieves minute-level resolution, reversible translocation of many TF cargos, large dynamic range, and tunable target gene expression.
temporal resolution minute-level
CLASP achieves minute-level resolution, reversible translocation of many transcription factor cargos, large dynamic range, and tunable target gene expression.
CLASP achieves minute-level resolution, reversible translocation of many TF cargos, large dynamic range, and tunable target gene expression.
temporal resolution minute-level
CLASP achieves minute-level resolution, reversible translocation of many transcription factor cargos, large dynamic range, and tunable target gene expression.
CLASP achieves minute-level resolution, reversible translocation of many TF cargos, large dynamic range, and tunable target gene expression.
temporal resolution minute-level
CLASP achieves minute-level resolution, reversible translocation of many transcription factor cargos, large dynamic range, and tunable target gene expression.
CLASP achieves minute-level resolution, reversible translocation of many TF cargos, large dynamic range, and tunable target gene expression.
temporal resolution minute-level
CLASP achieves minute-level resolution, reversible translocation of many transcription factor cargos, large dynamic range, and tunable target gene expression.
CLASP achieves minute-level resolution, reversible translocation of many TF cargos, large dynamic range, and tunable target gene expression.
temporal resolution minute-level
CLASP achieves minute-level resolution, reversible translocation of many transcription factor cargos, large dynamic range, and tunable target gene expression.
CLASP achieves minute-level resolution, reversible translocation of many TF cargos, large dynamic range, and tunable target gene expression.
temporal resolution minute-level
CLASP enables precise, modular, and reversible control of transcription factor localization using two optimized LOV2 optogenetic constructs.
we present CLASP (Controllable Light Activated Shuttling and Plasma membrane sequestration), a tool that enables precise, modular, and reversible control of TF localization using a combination of two optimized LOV2 optogenetic constructs
CLASP enables precise, modular, and reversible control of transcription factor localization using two optimized LOV2 optogenetic constructs.
we present CLASP (Controllable Light Activated Shuttling and Plasma membrane sequestration), a tool that enables precise, modular, and reversible control of TF localization using a combination of two optimized LOV2 optogenetic constructs
CLASP enables precise, modular, and reversible control of transcription factor localization using two optimized LOV2 optogenetic constructs.
we present CLASP (Controllable Light Activated Shuttling and Plasma membrane sequestration), a tool that enables precise, modular, and reversible control of TF localization using a combination of two optimized LOV2 optogenetic constructs
CLASP enables precise, modular, and reversible control of transcription factor localization using two optimized LOV2 optogenetic constructs.
we present CLASP (Controllable Light Activated Shuttling and Plasma membrane sequestration), a tool that enables precise, modular, and reversible control of TF localization using a combination of two optimized LOV2 optogenetic constructs
CLASP enables precise, modular, and reversible control of transcription factor localization using two optimized LOV2 optogenetic constructs.
we present CLASP (Controllable Light Activated Shuttling and Plasma membrane sequestration), a tool that enables precise, modular, and reversible control of TF localization using a combination of two optimized LOV2 optogenetic constructs
CLASP enables precise, modular, and reversible control of transcription factor localization using two optimized LOV2 optogenetic constructs.
we present CLASP (Controllable Light Activated Shuttling and Plasma membrane sequestration), a tool that enables precise, modular, and reversible control of TF localization using a combination of two optimized LOV2 optogenetic constructs
CLASP enables precise, modular, and reversible control of transcription factor localization using two optimized LOV2 optogenetic constructs.
we present CLASP (Controllable Light Activated Shuttling and Plasma membrane sequestration), a tool that enables precise, modular, and reversible control of TF localization using a combination of two optimized LOV2 optogenetic constructs
CLASP enables precise, modular, and reversible control of transcription factor localization using two optimized LOV2 optogenetic constructs.
we present CLASP (Controllable Light Activated Shuttling and Plasma membrane sequestration), a tool that enables precise, modular, and reversible control of TF localization using a combination of two optimized LOV2 optogenetic constructs
CLASP enables precise, modular, and reversible control of transcription factor localization using two optimized LOV2 optogenetic constructs.
we present CLASP (Controllable Light Activated Shuttling and Plasma membrane sequestration), a tool that enables precise, modular, and reversible control of TF localization using a combination of two optimized LOV2 optogenetic constructs
CLASP enables precise, modular, and reversible control of transcription factor localization using two optimized LOV2 optogenetic constructs.
we present CLASP (Controllable Light Activated Shuttling and Plasma membrane sequestration), a tool that enables precise, modular, and reversible control of TF localization using a combination of two optimized LOV2 optogenetic constructs
AsLOV2 REST-inhibitory chimeras enabled light-dependent modulation of REST target genes in Neuro2a cells.
By expressing AsLOV2 chimeras in Neuro2a cells, we achieved light-dependent modulation of REST target genes
AsLOV2 REST-inhibitory chimeras enabled light-dependent modulation of REST target genes in Neuro2a cells.
By expressing AsLOV2 chimeras in Neuro2a cells, we achieved light-dependent modulation of REST target genes
AsLOV2 REST-inhibitory chimeras enabled light-dependent modulation of REST target genes in Neuro2a cells.
By expressing AsLOV2 chimeras in Neuro2a cells, we achieved light-dependent modulation of REST target genes
AsLOV2 REST-inhibitory chimeras enabled light-dependent modulation of REST target genes in Neuro2a cells.
By expressing AsLOV2 chimeras in Neuro2a cells, we achieved light-dependent modulation of REST target genes
AsLOV2 REST-inhibitory chimeras enabled light-dependent modulation of REST target genes in Neuro2a cells.
By expressing AsLOV2 chimeras in Neuro2a cells, we achieved light-dependent modulation of REST target genes
AsLOV2 REST-inhibitory chimeras enabled light-dependent modulation of REST target genes in Neuro2a cells.
By expressing AsLOV2 chimeras in Neuro2a cells, we achieved light-dependent modulation of REST target genes
AsLOV2 REST-inhibitory chimeras enabled light-dependent modulation of REST target genes in Neuro2a cells.
By expressing AsLOV2 chimeras in Neuro2a cells, we achieved light-dependent modulation of REST target genes
AsLOV2 REST-inhibitory chimeras enabled light-dependent modulation of REST target genes in Neuro2a cells.
By expressing AsLOV2 chimeras in Neuro2a cells, we achieved light-dependent modulation of REST target genes
AsLOV2 REST-inhibitory chimeras enabled light-dependent modulation of REST target genes in Neuro2a cells.
By expressing AsLOV2 chimeras in Neuro2a cells, we achieved light-dependent modulation of REST target genes
AsLOV2 REST-inhibitory chimeras enabled light-dependent modulation of REST target genes in Neuro2a cells.
By expressing AsLOV2 chimeras in Neuro2a cells, we achieved light-dependent modulation of REST target genes
Computational modeling guided fusion of REST-inhibitory domains to AsLOV2.
Computational modeling guided the fusion of the inhibitory domains to the light-sensitive Avena sativa light-oxygen-voltage-sensing (LOV) 2-phototrophin 1 (AsLOV2).
Computational modeling guided fusion of REST-inhibitory domains to AsLOV2.
Computational modeling guided the fusion of the inhibitory domains to the light-sensitive Avena sativa light-oxygen-voltage-sensing (LOV) 2-phototrophin 1 (AsLOV2).
Computational modeling guided fusion of REST-inhibitory domains to AsLOV2.
Computational modeling guided the fusion of the inhibitory domains to the light-sensitive Avena sativa light-oxygen-voltage-sensing (LOV) 2-phototrophin 1 (AsLOV2).
Computational modeling guided fusion of REST-inhibitory domains to AsLOV2.
Computational modeling guided the fusion of the inhibitory domains to the light-sensitive Avena sativa light-oxygen-voltage-sensing (LOV) 2-phototrophin 1 (AsLOV2).
Computational modeling guided fusion of REST-inhibitory domains to AsLOV2.
Computational modeling guided the fusion of the inhibitory domains to the light-sensitive Avena sativa light-oxygen-voltage-sensing (LOV) 2-phototrophin 1 (AsLOV2).
Computational modeling guided fusion of REST-inhibitory domains to AsLOV2.
Computational modeling guided the fusion of the inhibitory domains to the light-sensitive Avena sativa light-oxygen-voltage-sensing (LOV) 2-phototrophin 1 (AsLOV2).
Computational modeling guided fusion of REST-inhibitory domains to AsLOV2.
Computational modeling guided the fusion of the inhibitory domains to the light-sensitive Avena sativa light-oxygen-voltage-sensing (LOV) 2-phototrophin 1 (AsLOV2).
Computational modeling guided fusion of REST-inhibitory domains to AsLOV2.
Computational modeling guided the fusion of the inhibitory domains to the light-sensitive Avena sativa light-oxygen-voltage-sensing (LOV) 2-phototrophin 1 (AsLOV2).
Computational modeling guided fusion of REST-inhibitory domains to AsLOV2.
Computational modeling guided the fusion of the inhibitory domains to the light-sensitive Avena sativa light-oxygen-voltage-sensing (LOV) 2-phototrophin 1 (AsLOV2).
Computational modeling guided fusion of REST-inhibitory domains to AsLOV2.
Computational modeling guided the fusion of the inhibitory domains to the light-sensitive Avena sativa light-oxygen-voltage-sensing (LOV) 2-phototrophin 1 (AsLOV2).
Computational modeling guided fusion of REST-inhibitory domains to AsLOV2.
Computational modeling guided the fusion of the inhibitory domains to the light-sensitive Avena sativa light-oxygen-voltage-sensing (LOV) 2-phototrophin 1 (AsLOV2).
Computational modeling guided fusion of REST-inhibitory domains to AsLOV2.
Computational modeling guided the fusion of the inhibitory domains to the light-sensitive Avena sativa light-oxygen-voltage-sensing (LOV) 2-phototrophin 1 (AsLOV2).
Computational modeling guided fusion of REST-inhibitory domains to AsLOV2.
Computational modeling guided the fusion of the inhibitory domains to the light-sensitive Avena sativa light-oxygen-voltage-sensing (LOV) 2-phototrophin 1 (AsLOV2).
Computational modeling guided fusion of REST-inhibitory domains to AsLOV2.
Computational modeling guided the fusion of the inhibitory domains to the light-sensitive Avena sativa light-oxygen-voltage-sensing (LOV) 2-phototrophin 1 (AsLOV2).
Computational modeling guided fusion of REST-inhibitory domains to AsLOV2.
Computational modeling guided the fusion of the inhibitory domains to the light-sensitive Avena sativa light-oxygen-voltage-sensing (LOV) 2-phototrophin 1 (AsLOV2).
Computational modeling guided fusion of REST-inhibitory domains to AsLOV2.
Computational modeling guided the fusion of the inhibitory domains to the light-sensitive Avena sativa light-oxygen-voltage-sensing (LOV) 2-phototrophin 1 (AsLOV2).
Computational modeling guided fusion of REST-inhibitory domains to AsLOV2.
Computational modeling guided the fusion of the inhibitory domains to the light-sensitive Avena sativa light-oxygen-voltage-sensing (LOV) 2-phototrophin 1 (AsLOV2).
In primary neurons, light-mediated REST inhibition boosted Na+ currents and neuronal firing.
and boosted Na(+) currents and neuronal firing
In primary neurons, light-mediated REST inhibition boosted Na+ currents and neuronal firing.
and boosted Na(+) currents and neuronal firing
In primary neurons, light-mediated REST inhibition boosted Na+ currents and neuronal firing.
and boosted Na(+) currents and neuronal firing
In primary neurons, light-mediated REST inhibition boosted Na+ currents and neuronal firing.
and boosted Na(+) currents and neuronal firing
In primary neurons, light-mediated REST inhibition boosted Na+ currents and neuronal firing.
and boosted Na(+) currents and neuronal firing
In primary neurons, light-mediated REST inhibition boosted Na+ currents and neuronal firing.
and boosted Na(+) currents and neuronal firing
In primary neurons, light-mediated REST inhibition boosted Na+ currents and neuronal firing.
and boosted Na(+) currents and neuronal firing
In primary neurons, light-mediated REST inhibition boosted Na+ currents and neuronal firing.
and boosted Na(+) currents and neuronal firing
In primary neurons, light-mediated REST inhibition boosted Na+ currents and neuronal firing.
and boosted Na(+) currents and neuronal firing
In primary neurons, light-mediated REST inhibition boosted Na+ currents and neuronal firing.
and boosted Na(+) currents and neuronal firing
Light-dependent modulation of REST target genes by AsLOV2 chimeras in Neuro2a cells was associated with improved neural differentiation.
we achieved light-dependent modulation of REST target genes that was associated with an improved neural differentiation
Light-dependent modulation of REST target genes by AsLOV2 chimeras in Neuro2a cells was associated with improved neural differentiation.
we achieved light-dependent modulation of REST target genes that was associated with an improved neural differentiation
Light-dependent modulation of REST target genes by AsLOV2 chimeras in Neuro2a cells was associated with improved neural differentiation.
we achieved light-dependent modulation of REST target genes that was associated with an improved neural differentiation
Light-dependent modulation of REST target genes by AsLOV2 chimeras in Neuro2a cells was associated with improved neural differentiation.
we achieved light-dependent modulation of REST target genes that was associated with an improved neural differentiation
Light-dependent modulation of REST target genes by AsLOV2 chimeras in Neuro2a cells was associated with improved neural differentiation.
we achieved light-dependent modulation of REST target genes that was associated with an improved neural differentiation
Light-dependent modulation of REST target genes by AsLOV2 chimeras in Neuro2a cells was associated with improved neural differentiation.
we achieved light-dependent modulation of REST target genes that was associated with an improved neural differentiation
Light-dependent modulation of REST target genes by AsLOV2 chimeras in Neuro2a cells was associated with improved neural differentiation.
we achieved light-dependent modulation of REST target genes that was associated with an improved neural differentiation
Light-dependent modulation of REST target genes by AsLOV2 chimeras in Neuro2a cells was associated with improved neural differentiation.
we achieved light-dependent modulation of REST target genes that was associated with an improved neural differentiation
Light-dependent modulation of REST target genes by AsLOV2 chimeras in Neuro2a cells was associated with improved neural differentiation.
we achieved light-dependent modulation of REST target genes that was associated with an improved neural differentiation
Light-dependent modulation of REST target genes by AsLOV2 chimeras in Neuro2a cells was associated with improved neural differentiation.
we achieved light-dependent modulation of REST target genes that was associated with an improved neural differentiation
In primary neurons, light-mediated REST inhibition increased Na+-channel 1.2 and brain-derived neurotrophic factor transcription.
In primary neurons, light-mediated REST inhibition increased Na(+)-channel 1.2 and brain-derived neurotrophic factor transcription
In primary neurons, light-mediated REST inhibition increased Na+-channel 1.2 and brain-derived neurotrophic factor transcription.
In primary neurons, light-mediated REST inhibition increased Na(+)-channel 1.2 and brain-derived neurotrophic factor transcription
In primary neurons, light-mediated REST inhibition increased Na+-channel 1.2 and brain-derived neurotrophic factor transcription.
In primary neurons, light-mediated REST inhibition increased Na(+)-channel 1.2 and brain-derived neurotrophic factor transcription
In primary neurons, light-mediated REST inhibition increased Na+-channel 1.2 and brain-derived neurotrophic factor transcription.
In primary neurons, light-mediated REST inhibition increased Na(+)-channel 1.2 and brain-derived neurotrophic factor transcription
In primary neurons, light-mediated REST inhibition increased Na+-channel 1.2 and brain-derived neurotrophic factor transcription.
In primary neurons, light-mediated REST inhibition increased Na(+)-channel 1.2 and brain-derived neurotrophic factor transcription
In primary neurons, light-mediated REST inhibition increased Na+-channel 1.2 and brain-derived neurotrophic factor transcription.
In primary neurons, light-mediated REST inhibition increased Na(+)-channel 1.2 and brain-derived neurotrophic factor transcription
In primary neurons, light-mediated REST inhibition increased Na+-channel 1.2 and brain-derived neurotrophic factor transcription.
In primary neurons, light-mediated REST inhibition increased Na(+)-channel 1.2 and brain-derived neurotrophic factor transcription
In primary neurons, light-mediated REST inhibition increased Na+-channel 1.2 and brain-derived neurotrophic factor transcription.
In primary neurons, light-mediated REST inhibition increased Na(+)-channel 1.2 and brain-derived neurotrophic factor transcription
In primary neurons, light-mediated REST inhibition increased Na+-channel 1.2 and brain-derived neurotrophic factor transcription.
In primary neurons, light-mediated REST inhibition increased Na(+)-channel 1.2 and brain-derived neurotrophic factor transcription
In primary neurons, light-mediated REST inhibition increased Na+-channel 1.2 and brain-derived neurotrophic factor transcription.
In primary neurons, light-mediated REST inhibition increased Na(+)-channel 1.2 and brain-derived neurotrophic factor transcription
Approval Evidence
4 sources5 linked approval claimsfirst-pass slug computational-modeling
Multi-omics approaches and computational modeling hold promise for unraveling disease complexity, while advances in regulatory science and digital simulation (such as digital twins) may further accelerate personalized medicine in vascular disease research and treatment.
Source:
Combining computational modeling and in vivo mutagenesis experiments in Escherichia coli
Source:
We show using computational modeling that efficient gene expression in response to short pulsing requires fast promoter activation and slow inactivation
Source:
Computational modeling guided the fusion of the inhibitory domains
Source:
future directionsupports
Future progress in vascular disease research should prioritize multi-center large-scale validation studies, harmonization of assay protocols, and integration with clinical datasets and human samples.
Source:
future directionsupports
Multi-omics approaches and computational modeling hold promise for unraveling disease complexity, and digital twins may accelerate personalized medicine in vascular disease research and treatment.
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modeling resultsupports
Computational modeling indicates that efficient gene expression in response to short pulsing requires fast promoter activation and slow inactivation.
We show using computational modeling that efficient gene expression in response to short pulsing requires fast promoter activation and slow inactivation
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modeling resultsupports
Computational modeling indicates that the opposite promoter-response phenotype can arise from multi-stage promoter activation in which a transition in the first stage is thresholded.
and that the opposite phenotype can ensue from a multi-stage promoter activation, where a transition in the first stage is thresholded
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design strategysupports
Computational modeling guided fusion of REST-inhibitory domains to AsLOV2.
Computational modeling guided the fusion of the inhibitory domains to the light-sensitive Avena sativa light-oxygen-voltage-sensing (LOV) 2-phototrophin 1 (AsLOV2).
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Comparisons
Source-backed strengths
The modeling generated specific mechanistic hypotheses rather than only descriptive fits, including the requirement for fast promoter activation and slow inactivation for efficient short-pulse responses. It also proposed a concrete multi-stage activation architecture with a thresholded first transition to account for opposite promoter behaviors, and one source states that computational modeling guided fusion of inhibitory domains.
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CLASP achieves minute-level resolution, reversible translocation of many TF cargos, large dynamic range, and tunable target gene expression.
Compared with mathematical model of light-induced expression kinetics
computational modeling and mathematical model of light-induced expression kinetics address a similar problem space.
Shared frame: same top-level item type; same primary input modality: light
Strengths here: appears more independently replicated; looks easier to implement in practice.
Compared with model bioinformatics analysis
computational modeling and model bioinformatics analysis address a similar problem space.
Shared frame: same top-level item type; same primary input modality: light
Strengths here: appears more independently replicated; looks easier to implement in practice.
Compared with molecular dynamics simulations
computational modeling and molecular dynamics simulations address a similar problem space.
Shared frame: same top-level item type; same primary input modality: light
Ranked Citations
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