Source 1primary paper2022Sensors
Toolkit/Fernando's model
Fernando's model
Taxonomy: Technique Branch / Method. Workflows sit above the mechanism and technique branches rather than replacing them.
Summary
Fernando's model is a computational model of a synthetic molecular circuit designed to mimic Hebbian learning in a neural network architecture. It is described as one of the earliest models in this area to use Hill equation-based regulatory modeling, and computational analysis indicated that a reinforcement effect can be obtained with appropriate parameter choices.
Usefulness & Problems
Why this is useful
The model is useful as an early conceptual framework for analyzing how synthetic molecular circuits might implement associative or Hebbian-like learning behavior. It provides a parameterized computational setting in which reinforcement behavior can be examined before experimental construction.
Problem solved
Fernando's model addresses the problem of how to represent Hebbian learning-like behavior in a synthetic molecular circuit using a tractable mathematical formalism. Specifically, it supports analysis of whether reinforcement effects can emerge in such a circuit under suitable parameter regimes.
Taxonomy & Function
Primary hierarchy
Technique Branch
Method: A concrete computational method used to design, rank, or analyze an engineered system.
Mechanisms
hill equation-based regulatory modelinghill equation-based regulatory modelingreinforcement effectreinforcement effectTarget processes
No target processes tagged yet.
Implementation Constraints
cofactor dependency: cofactor requirement unknownencoding mode: genetically encodedimplementation constraint: context specific validationoperating role: builder
The available evidence supports only that this is a computational, Hill equation-based model of a synthetic molecular circuit. No specific molecular species, host organism, expression system, cofactors, or construct design details are provided in the supplied evidence.
Available evidence is limited to computational description and comparative discussion, with no experimental validation details provided. A later study states that forced dissociation was not observed in Fernando's model, indicating a behavioral capability gap relative to a newer circuit.
Validation
Cell-freeBacteriaMammalianMouseHumanTherapeuticIndep. Replication
Supporting Sources
Ranked Claims
The authors constructed a novel circuit that can demonstrate forced dissociation, a behavior not observed in Fernando's model.
We also construct a novel circuit that can demonstrate forced dissociation, which was not observed in Fernando's model.
The authors constructed a novel circuit that can demonstrate forced dissociation, a behavior not observed in Fernando's model.
We also construct a novel circuit that can demonstrate forced dissociation, which was not observed in Fernando's model.
The authors constructed a novel circuit that can demonstrate forced dissociation, a behavior not observed in Fernando's model.
We also construct a novel circuit that can demonstrate forced dissociation, which was not observed in Fernando's model.
The authors constructed a novel circuit that can demonstrate forced dissociation, a behavior not observed in Fernando's model.
We also construct a novel circuit that can demonstrate forced dissociation, which was not observed in the Fernando’s model.
Section: abstract
The authors constructed a novel circuit that can demonstrate forced dissociation, a behavior not observed in Fernando's model.
We also construct a novel circuit that can demonstrate forced dissociation, which was not observed in the Fernando’s model.
Section: abstract
The authors constructed a novel circuit that can demonstrate forced dissociation, a behavior not observed in Fernando's model.
We also construct a novel circuit that can demonstrate forced dissociation, which was not observed in the Fernando’s model.
Section: abstract
The authors constructed a novel circuit that can demonstrate forced dissociation, a behavior not observed in Fernando's model.
We also construct a novel circuit that can demonstrate forced dissociation, which was not observed in Fernando's model.
The authors constructed a novel circuit that can demonstrate forced dissociation, a behavior not observed in Fernando's model.
We also construct a novel circuit that can demonstrate forced dissociation, which was not observed in Fernando's model.
The authors constructed a novel circuit that can demonstrate forced dissociation, a behavior not observed in Fernando's model.
We also construct a novel circuit that can demonstrate forced dissociation, which was not observed in Fernando's model.
The authors constructed a novel circuit that can demonstrate forced dissociation, a behavior not observed in Fernando's model.
We also construct a novel circuit that can demonstrate forced dissociation, which was not observed in the Fernando’s model.
Section: abstract
The authors constructed a novel circuit that can demonstrate forced dissociation, a behavior not observed in Fernando's model.
We also construct a novel circuit that can demonstrate forced dissociation, which was not observed in Fernando's model.
The authors constructed a novel circuit that can demonstrate forced dissociation, a behavior not observed in Fernando's model.
We also construct a novel circuit that can demonstrate forced dissociation, which was not observed in Fernando's model.
The authors constructed a novel circuit that can demonstrate forced dissociation, a behavior not observed in Fernando's model.
We also construct a novel circuit that can demonstrate forced dissociation, which was not observed in Fernando's model.
The authors constructed a novel circuit that can demonstrate forced dissociation, a behavior not observed in Fernando's model.
We also construct a novel circuit that can demonstrate forced dissociation, which was not observed in the Fernando’s model.
Section: abstract
The authors constructed a novel circuit that can demonstrate forced dissociation, a behavior not observed in Fernando's model.
We also construct a novel circuit that can demonstrate forced dissociation, which was not observed in the Fernando’s model.
Section: abstract
The authors constructed a novel circuit that can demonstrate forced dissociation, a behavior not observed in Fernando's model.
We also construct a novel circuit that can demonstrate forced dissociation, which was not observed in Fernando's model.
The authors constructed a novel circuit that can demonstrate forced dissociation, a behavior not observed in Fernando's model.
We also construct a novel circuit that can demonstrate forced dissociation, which was not observed in the Fernando’s model.
Section: abstract
The authors constructed a novel circuit that can demonstrate forced dissociation, a behavior not observed in Fernando's model.
We also construct a novel circuit that can demonstrate forced dissociation, which was not observed in Fernando's model.
The authors constructed a novel circuit that can demonstrate forced dissociation, a behavior not observed in Fernando's model.
We also construct a novel circuit that can demonstrate forced dissociation, which was not observed in the Fernando’s model.
Section: abstract
The authors constructed a novel circuit that can demonstrate forced dissociation, a behavior not observed in Fernando's model.
We also construct a novel circuit that can demonstrate forced dissociation, which was not observed in the Fernando’s model.
Section: abstract
The authors constructed a novel circuit that can demonstrate forced dissociation, a behavior not observed in Fernando's model.
We also construct a novel circuit that can demonstrate forced dissociation, which was not observed in Fernando's model.
The authors constructed a novel circuit that can demonstrate forced dissociation, a behavior not observed in Fernando's model.
We also construct a novel circuit that can demonstrate forced dissociation, which was not observed in Fernando's model.
The authors constructed a novel circuit that can demonstrate forced dissociation, a behavior not observed in Fernando's model.
We also construct a novel circuit that can demonstrate forced dissociation, which was not observed in Fernando's model.
The authors constructed a novel circuit that can demonstrate forced dissociation, a behavior not observed in Fernando's model.
We also construct a novel circuit that can demonstrate forced dissociation, which was not observed in the Fernando’s model.
Section: abstract
The authors constructed a novel circuit that can demonstrate forced dissociation, a behavior not observed in Fernando's model.
We also construct a novel circuit that can demonstrate forced dissociation, which was not observed in the Fernando’s model.
Section: abstract
The authors constructed a novel circuit that can demonstrate forced dissociation, a behavior not observed in Fernando's model.
We also construct a novel circuit that can demonstrate forced dissociation, which was not observed in the Fernando’s model.
Section: abstract
The authors constructed a novel circuit that can demonstrate forced dissociation, a behavior not observed in Fernando's model.
We also construct a novel circuit that can demonstrate forced dissociation, which was not observed in Fernando's model.
The authors constructed a novel circuit that can demonstrate forced dissociation, a behavior not observed in Fernando's model.
We also construct a novel circuit that can demonstrate forced dissociation, which was not observed in the Fernando’s model.
Section: abstract
The authors constructed a novel circuit that can demonstrate forced dissociation, a behavior not observed in Fernando's model.
We also construct a novel circuit that can demonstrate forced dissociation, which was not observed in the Fernando’s model.
Section: abstract
The authors constructed a novel circuit that can demonstrate forced dissociation, a behavior not observed in Fernando's model.
We also construct a novel circuit that can demonstrate forced dissociation, which was not observed in Fernando's model.
The authors constructed a novel circuit that can demonstrate forced dissociation, a behavior not observed in Fernando's model.
We also construct a novel circuit that can demonstrate forced dissociation, which was not observed in the Fernando’s model.
Section: abstract
The authors constructed a novel circuit that can demonstrate forced dissociation, a behavior not observed in Fernando's model.
We also construct a novel circuit that can demonstrate forced dissociation, which was not observed in Fernando's model.
The authors constructed a novel circuit that can demonstrate forced dissociation, a behavior not observed in Fernando's model.
We also construct a novel circuit that can demonstrate forced dissociation, which was not observed in the Fernando’s model.
Section: abstract
The authors constructed a novel circuit that can demonstrate forced dissociation, a behavior not observed in Fernando's model.
We also construct a novel circuit that can demonstrate forced dissociation, which was not observed in the Fernando’s model.
Section: abstract
Computational analysis showed that the reinforcement effect can be achieved by choosing proper parameter values in Fernando's model.
In this article, we carry out indepth computational analysis of the model and demonstrate that the reinforcement effect can be achieved by choosing the proper parameter values.
Computational analysis showed that the reinforcement effect can be achieved by choosing proper parameter values in Fernando's model.
In this article, we carry out indepth computational analysis of the model and demonstrate that the reinforcement effect can be achieved by choosing the proper parameter values.
Computational analysis showed that the reinforcement effect can be achieved by choosing proper parameter values in Fernando's model.
In this article, we carry out indepth computational analysis of the model and demonstrate that the reinforcement effect can be achieved by choosing the proper parameter values.
Computational analysis showed that the reinforcement effect can be achieved by choosing proper parameter values in Fernando's model.
In this article, we carry out indepth computational analysis of the model and demonstrate that the reinforcement effect can be achieved by choosing the proper parameter values.
Computational analysis showed that the reinforcement effect can be achieved by choosing proper parameter values in Fernando's model.
In this article, we carry out indepth computational analysis of the model and demonstrate that the reinforcement effect can be achieved by choosing the proper parameter values.
Computational analysis showed that the reinforcement effect can be achieved by choosing proper parameter values in Fernando's model.
In this article, we carry out indepth computational analysis of the model and demonstrate that the reinforcement effect can be achieved by choosing the proper parameter values.
Computational analysis showed that the reinforcement effect can be achieved by choosing proper parameter values in Fernando's model.
In this article, we carry out indepth computational analysis of the model and demonstrate that the reinforcement effect can be achieved by choosing the proper parameter values.
Computational analysis showed that the reinforcement effect can be achieved by choosing proper parameter values in Fernando's model.
In this article, we carry out indepth computational analysis of the model and demonstrate that the reinforcement effect can be achieved by choosing the proper parameter values.
Computational analysis showed that the reinforcement effect can be achieved by choosing proper parameter values in Fernando's model.
In this article, we carry out indepth computational analysis of the model and demonstrate that the reinforcement effect can be achieved by choosing the proper parameter values.
Computational analysis showed that the reinforcement effect can be achieved by choosing proper parameter values in Fernando's model.
In this article, we carry out indepth computational analysis of the model and demonstrate that the reinforcement effect can be achieved by choosing the proper parameter values.
Computational analysis showed that the reinforcement effect can be achieved by choosing proper parameter values in Fernando's model.
In this article, we carry out indepth computational analysis of the model and demonstrate that the reinforcement effect can be achieved by choosing the proper parameter values.
Computational analysis showed that the reinforcement effect can be achieved by choosing proper parameter values in Fernando's model.
In this article, we carry out indepth computational analysis of the model and demonstrate that the reinforcement effect can be achieved by choosing the proper parameter values.
Computational analysis showed that the reinforcement effect can be achieved by choosing proper parameter values in Fernando's model.
In this article, we carry out indepth computational analysis of the model and demonstrate that the reinforcement effect can be achieved by choosing the proper parameter values.
Computational analysis showed that the reinforcement effect can be achieved by choosing proper parameter values in Fernando's model.
In this article, we carry out indepth computational analysis of the model and demonstrate that the reinforcement effect can be achieved by choosing the proper parameter values.
Computational analysis showed that the reinforcement effect can be achieved by choosing proper parameter values in Fernando's model.
In this article, we carry out indepth computational analysis of the model and demonstrate that the reinforcement effect can be achieved by choosing the proper parameter values.
Computational analysis showed that the reinforcement effect can be achieved by choosing proper parameter values in Fernando's model.
In this article, we carry out indepth computational analysis of the model and demonstrate that the reinforcement effect can be achieved by choosing the proper parameter values.
Computational analysis showed that the reinforcement effect can be achieved by choosing proper parameter values in Fernando's model.
In this article, we carry out indepth computational analysis of the model and demonstrate that the reinforcement effect can be achieved by choosing the proper parameter values.
In-depth computational analysis indicates that the reinforcement effect can be achieved by choosing proper parameter values in the modeled molecular circuit framework.
In this article, we carry out in-depth computational analysis of the model and demonstrate that the reinforcement effect can be achieved by choosing the proper parameter values.
Section: abstract
In-depth computational analysis indicates that the reinforcement effect can be achieved by choosing proper parameter values in the modeled molecular circuit framework.
In this article, we carry out in-depth computational analysis of the model and demonstrate that the reinforcement effect can be achieved by choosing the proper parameter values.
Section: abstract
In-depth computational analysis indicates that the reinforcement effect can be achieved by choosing proper parameter values in the modeled molecular circuit framework.
In this article, we carry out in-depth computational analysis of the model and demonstrate that the reinforcement effect can be achieved by choosing the proper parameter values.
Section: abstract
In-depth computational analysis indicates that the reinforcement effect can be achieved by choosing proper parameter values in the modeled molecular circuit framework.
In this article, we carry out in-depth computational analysis of the model and demonstrate that the reinforcement effect can be achieved by choosing the proper parameter values.
Section: abstract
In-depth computational analysis indicates that the reinforcement effect can be achieved by choosing proper parameter values in the modeled molecular circuit framework.
In this article, we carry out in-depth computational analysis of the model and demonstrate that the reinforcement effect can be achieved by choosing the proper parameter values.
Section: abstract
In-depth computational analysis indicates that the reinforcement effect can be achieved by choosing proper parameter values in the modeled molecular circuit framework.
In this article, we carry out in-depth computational analysis of the model and demonstrate that the reinforcement effect can be achieved by choosing the proper parameter values.
Section: abstract
In-depth computational analysis indicates that the reinforcement effect can be achieved by choosing proper parameter values in the modeled molecular circuit framework.
In this article, we carry out in-depth computational analysis of the model and demonstrate that the reinforcement effect can be achieved by choosing the proper parameter values.
Section: abstract
In-depth computational analysis indicates that the reinforcement effect can be achieved by choosing proper parameter values in the modeled molecular circuit framework.
In this article, we carry out in-depth computational analysis of the model and demonstrate that the reinforcement effect can be achieved by choosing the proper parameter values.
Section: abstract
In-depth computational analysis indicates that the reinforcement effect can be achieved by choosing proper parameter values in the modeled molecular circuit framework.
In this article, we carry out in-depth computational analysis of the model and demonstrate that the reinforcement effect can be achieved by choosing the proper parameter values.
Section: abstract
In-depth computational analysis indicates that the reinforcement effect can be achieved by choosing proper parameter values in the modeled molecular circuit framework.
In this article, we carry out in-depth computational analysis of the model and demonstrate that the reinforcement effect can be achieved by choosing the proper parameter values.
Section: abstract
In-depth computational analysis indicates that the reinforcement effect can be achieved by choosing proper parameter values in the modeled molecular circuit framework.
In this article, we carry out in-depth computational analysis of the model and demonstrate that the reinforcement effect can be achieved by choosing the proper parameter values.
Section: abstract
In-depth computational analysis indicates that the reinforcement effect can be achieved by choosing proper parameter values in the modeled molecular circuit framework.
In this article, we carry out in-depth computational analysis of the model and demonstrate that the reinforcement effect can be achieved by choosing the proper parameter values.
Section: abstract
In-depth computational analysis indicates that the reinforcement effect can be achieved by choosing proper parameter values in the modeled molecular circuit framework.
In this article, we carry out in-depth computational analysis of the model and demonstrate that the reinforcement effect can be achieved by choosing the proper parameter values.
Section: abstract
In-depth computational analysis indicates that the reinforcement effect can be achieved by choosing proper parameter values in the modeled molecular circuit framework.
In this article, we carry out in-depth computational analysis of the model and demonstrate that the reinforcement effect can be achieved by choosing the proper parameter values.
Section: abstract
In-depth computational analysis indicates that the reinforcement effect can be achieved by choosing proper parameter values in the modeled molecular circuit framework.
In this article, we carry out in-depth computational analysis of the model and demonstrate that the reinforcement effect can be achieved by choosing the proper parameter values.
Section: abstract
In-depth computational analysis indicates that the reinforcement effect can be achieved by choosing proper parameter values in the modeled molecular circuit framework.
In this article, we carry out in-depth computational analysis of the model and demonstrate that the reinforcement effect can be achieved by choosing the proper parameter values.
Section: abstract
In-depth computational analysis indicates that the reinforcement effect can be achieved by choosing proper parameter values in the modeled molecular circuit framework.
In this article, we carry out in-depth computational analysis of the model and demonstrate that the reinforcement effect can be achieved by choosing the proper parameter values.
Section: abstract
Fernando's model attempted to design a synthetic circuit that mimics Hebbian learning in a neural network architecture.
Fernando's model, which is one of the first works in this line of research using the Hill equation, attempted to design a synthetic circuit that mimics Hebbian learning in a neural network architecture.
Fernando's model attempted to design a synthetic circuit that mimics Hebbian learning in a neural network architecture.
Fernando's model, which is one of the first works in this line of research using the Hill equation, attempted to design a synthetic circuit that mimics Hebbian learning in a neural network architecture.
Fernando's model attempted to design a synthetic circuit that mimics Hebbian learning in a neural network architecture.
Fernando's model, which is one of the first works in this line of research using the Hill equation, attempted to design a synthetic circuit that mimics Hebbian learning in a neural network architecture.
Fernando's model attempted to design a synthetic circuit that mimics Hebbian learning in a neural network architecture.
Fernando's model, which is one of the first works in this line of research using the Hill equation, attempted to design a synthetic circuit that mimics Hebbian learning in a neural network architecture.
Fernando's model attempted to design a synthetic circuit that mimics Hebbian learning in a neural network architecture.
Fernando's model, which is one of the first works in this line of research using the Hill equation, attempted to design a synthetic circuit that mimics Hebbian learning in a neural network architecture.
Fernando's model attempted to design a synthetic circuit that mimics Hebbian learning in a neural network architecture.
Fernando's model, which is one of the first works in this line of research using the Hill equation, attempted to design a synthetic circuit that mimics Hebbian learning in a neural network architecture.
Fernando's model attempted to design a synthetic circuit that mimics Hebbian learning in a neural network architecture.
Fernando's model, which is one of the first works in this line of research using the Hill equation, attempted to design a synthetic circuit that mimics Hebbian learning in a neural network architecture.
Fernando's model attempted to design a synthetic circuit that mimics Hebbian learning in a neural network architecture.
Fernando's model, which is one of the first works in this line of research using the Hill equation, attempted to design a synthetic circuit that mimics Hebbian learning in a neural network architecture.
Fernando's model attempted to design a synthetic circuit that mimics Hebbian learning in a neural network architecture.
Fernando's model, which is one of the first works in this line of research using the Hill equation, attempted to design a synthetic circuit that mimics Hebbian learning in a neural network architecture.
Fernando's model attempted to design a synthetic circuit that mimics Hebbian learning in a neural network architecture.
Fernando's model, which is one of the first works in this line of research using the Hill equation, attempted to design a synthetic circuit that mimics Hebbian learning in a neural network architecture.
Fernando's model attempted to design a synthetic circuit that mimics Hebbian learning in a neural network architecture.
Fernando's model, which is one of the first works in this line of research using the Hill equation, attempted to design a synthetic circuit that mimics Hebbian learning in a neural network architecture.
Fernando's model attempted to design a synthetic circuit that mimics Hebbian learning in a neural network architecture.
Fernando's model, which is one of the first works in this line of research using the Hill equation, attempted to design a synthetic circuit that mimics Hebbian learning in a neural network architecture.
Fernando's model attempted to design a synthetic circuit that mimics Hebbian learning in a neural network architecture.
Fernando's model, which is one of the first works in this line of research using the Hill equation, attempted to design a synthetic circuit that mimics Hebbian learning in a neural network architecture.
Fernando's model attempted to design a synthetic circuit that mimics Hebbian learning in a neural network architecture.
Fernando's model, which is one of the first works in this line of research using the Hill equation, attempted to design a synthetic circuit that mimics Hebbian learning in a neural network architecture.
Fernando's model attempted to design a synthetic circuit that mimics Hebbian learning in a neural network architecture.
Fernando's model, which is one of the first works in this line of research using the Hill equation, attempted to design a synthetic circuit that mimics Hebbian learning in a neural network architecture.
Fernando's model attempted to design a synthetic circuit that mimics Hebbian learning in a neural network architecture.
Fernando's model, which is one of the first works in this line of research using the Hill equation, attempted to design a synthetic circuit that mimics Hebbian learning in a neural network architecture.
Fernando's model attempted to design a synthetic circuit that mimics Hebbian learning in a neural network architecture.
Fernando's model, which is one of the first works in this line of research using the Hill equation, attempted to design a synthetic circuit that mimics Hebbian learning in a neural network architecture.
Approval Evidence
1 source5 linked approval claimsfirst-pass slug fernando-s-model
Fernando's model, which is one of the first works in this line of research using the Hill equation, attempted to design a synthetic circuit that mimics Hebbian learning in a neural network architecture.
Source:
The Fernando’s model, which is thought to be one of the first works in this line of research using the Hill equation, attempted to design a synthetic circuit that mimics Hebbian learning in the neural network architecture.
Source:
comparative capabilitysupports
The authors constructed a novel circuit that can demonstrate forced dissociation, a behavior not observed in Fernando's model.
We also construct a novel circuit that can demonstrate forced dissociation, which was not observed in the Fernando’s model.
Source:
comparative capabilitysupports
The authors constructed a novel circuit that can demonstrate forced dissociation, a behavior not observed in Fernando's model.
We also construct a novel circuit that can demonstrate forced dissociation, which was not observed in Fernando's model.
Source:
computational resultsupports
Computational analysis showed that the reinforcement effect can be achieved by choosing proper parameter values in Fernando's model.
In this article, we carry out indepth computational analysis of the model and demonstrate that the reinforcement effect can be achieved by choosing the proper parameter values.
Source:
computational resultsupports
In-depth computational analysis indicates that the reinforcement effect can be achieved by choosing proper parameter values in the modeled molecular circuit framework.
In this article, we carry out in-depth computational analysis of the model and demonstrate that the reinforcement effect can be achieved by choosing the proper parameter values.
Source:
design goalsupports
Fernando's model attempted to design a synthetic circuit that mimics Hebbian learning in a neural network architecture.
Fernando's model, which is one of the first works in this line of research using the Hill equation, attempted to design a synthetic circuit that mimics Hebbian learning in a neural network architecture.
Source:
Comparisons
Source-backed strengths
The model was identified as one of the first efforts in this research line to use the Hill equation for synthetic molecular learning circuits. In-depth computational analysis indicated that the modeled framework can achieve a reinforcement effect when proper parameter values are selected.
Source:
We also construct a novel circuit that can demonstrate forced dissociation, which was not observed in the Fernando’s model.
Source:
We also construct a novel circuit that can demonstrate forced dissociation, which was not observed in Fernando's model.
Compared with free-energy calculations
Fernando's model and free-energy calculations address a similar problem space.
Shared frame: same top-level item type
Compared with mathematical model
Fernando's model and mathematical model address a similar problem space.
Shared frame: same top-level item type
Strengths here: looks easier to implement in practice.
Compared with SwiftLib
Fernando's model and SwiftLib address a similar problem space.
Shared frame: same top-level item type
Ranked Citations
- 1.