Boada2016 - Incoherent type 1 feed-forward loop (I1-FFL)
This model is described in the article:
Abstract:
Model based design plays a fundamental role in synthetic biology. Exploiting modularity, i.e. using biological parts and interconnecting them to build new and more complex biological circuits is one of the key issues. In this context, mathematical models have been used to generate predictions of the behavior of the designed device. Designers not only want the ability to predict the circuit behavior once all its components have been determined, but also to help on the design and selection of its biological parts, i.e. to provide guidelines for the experimental implementation. This is tantamount to obtaining proper values of the model parameters, for the circuit behavior results from the interplay between model structure and parameters tuning. However, determining crisp values for parameters of the involved parts is not a realistic approach. Uncertainty is ubiquitous to biology, and the characterization of biological parts is not exempt from it. Moreover, the desired dynamical behavior for the designed circuit usually results from a trade-off among several goals to be optimized.We propose the use of a multi-objective optimization tuning framework to get a model-based set of guidelines for the selection of the kinetic parameters required to build a biological device with desired behavior. The design criteria are encoded in the formulation of the objectives and optimization problem itself. As a result, on the one hand the designer obtains qualitative regions/intervals of values of the circuit parameters giving rise to the predefined circuit behavior; on the other hand, he obtains useful information for its guidance in the implementation process. These parameters are chosen so that they can effectively be tuned at the wet-lab, i.e. they are effective biological tuning knobs. To show the proposed approach, the methodology is applied to the design of a well known biological circuit: a genetic incoherent feed-forward circuit showing adaptive behavior.The proposed multi-objective optimization design framework is able to provide effective guidelines to tune biological parameters so as to achieve a desired circuit behavior. Moreover, it is easy to analyze the impact of the context on the synthetic device to be designed. That is, one can analyze how the presence of a downstream load influences the performance of the designed circuit, and take it into account.
This model is hosted on BioModels Database and identified by: BIOMD0000000696.
To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.
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Multi-objective optimization framework to obtain model-based guidelines for tuning biological synthetic devices: an adaptive network case.
- Boada Y, Reynoso-Meza G, Picó J, Vignoni A
- BMC systems biology , 3/ 2016 , Volume 10 , pages: 27 , PubMed ID: 26968941
- Institut d'Automàtica i Informàtica Industrial, Universitat Politècnica de València, Valencia, Spain.
- Model based design plays a fundamental role in synthetic biology. Exploiting modularity, i.e. using biological parts and interconnecting them to build new and more complex biological circuits is one of the key issues. In this context, mathematical models have been used to generate predictions of the behavior of the designed device. Designers not only want the ability to predict the circuit behavior once all its components have been determined, but also to help on the design and selection of its biological parts, i.e. to provide guidelines for the experimental implementation. This is tantamount to obtaining proper values of the model parameters, for the circuit behavior results from the interplay between model structure and parameters tuning. However, determining crisp values for parameters of the involved parts is not a realistic approach. Uncertainty is ubiquitous to biology, and the characterization of biological parts is not exempt from it. Moreover, the desired dynamical behavior for the designed circuit usually results from a trade-off among several goals to be optimized.We propose the use of a multi-objective optimization tuning framework to get a model-based set of guidelines for the selection of the kinetic parameters required to build a biological device with desired behavior. The design criteria are encoded in the formulation of the objectives and optimization problem itself. As a result, on the one hand the designer obtains qualitative regions/intervals of values of the circuit parameters giving rise to the predefined circuit behavior; on the other hand, he obtains useful information for its guidance in the implementation process. These parameters are chosen so that they can effectively be tuned at the wet-lab, i.e. they are effective biological tuning knobs. To show the proposed approach, the methodology is applied to the design of a well known biological circuit: a genetic incoherent feed-forward circuit showing adaptive behavior.The proposed multi-objective optimization design framework is able to provide effective guidelines to tune biological parameters so as to achieve a desired circuit behavior. Moreover, it is easy to analyze the impact of the context on the synthetic device to be designed. That is, one can analyze how the presence of a downstream load influences the performance of the designed circuit, and take it into account.
Submitter of this revision: administrator
Modellers: administrator, Alejandro Vignoni
Metadata information
isDescribedBy (2 statements)
isVersionOf (3 statements)
Connected external resources
SBGN view in Newt Editor
| Name | Description | Size | Actions |
|---|---|---|---|
Model files |
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| BIOMD0000000696_url.xml | SBML L2V4 representation of Boada2016 - Incoherent type 1 feed-forward loop (I1-FFL) | 131.42 KB | Preview | Download |
Additional files |
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| BIOMD0000000696-biopax2.owl | Auto-generated BioPAX (Level 2) | 24.03 KB | Preview | Download |
| BIOMD0000000696-biopax3.owl | Auto-generated BioPAX (Level 3) | 39.13 KB | Preview | Download |
| BIOMD0000000696.m | Auto-generated Octave file | 11.97 KB | Preview | Download |
| BIOMD0000000696.pdf | Auto-generated PDF file | 202.79 KB | Preview | Download |
| BIOMD0000000696.png | Auto-generated Reaction graph (PNG) | 81.59 KB | Preview | Download |
| BIOMD0000000696.sci | Auto-generated Scilab file | 154.00 Bytes | Preview | Download |
| BIOMD0000000696.svg | Auto-generated Reaction graph (SVG) | 43.06 KB | Preview | Download |
| BIOMD0000000696.vcml | Auto-generated VCML file | 893.00 Bytes | Preview | Download |
| BIOMD0000000696.xpp | Auto-generated XPP file | 8.97 KB | Preview | Download |
| BIOMD0000000696_urn.xml | Auto-generated SBML file with URNs | 131.16 KB | Preview | Download |
| MODEL1511290000.cps | Curated and annotated model COPASI file with plots to generate figures 5A and 5B. | 135.20 KB | Preview | Download |
| MODEL1511290000.sedml | SED-ML file for figures 5A and 5B. A parameter scan was used to vary gamma_1 from 79 to 200 and k_mC*C_gC from 1 to 171. | 4.74 KB | Preview | Download |
- Model originally submitted by : Alejandro Vignoni
- Submitted: Nov 29, 2015 11:01:46 PM
- Last Modified: Apr 25, 2018 12:10:35 PM
Revisions
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Version: 3
- Submitted on: Apr 25, 2018 12:10:35 PM
- Submitted by: administrator
- With comment: Notes updated using online editor.
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Version: 2
- Submitted on: May 3, 2017 5:01:18 PM
- Submitted by: Alejandro Vignoni
- With comment: Current version of Boada2016 - Incoherent type 1 feed-forward loop (I1-FFL)
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Version: 1
- Submitted on: Nov 29, 2015 11:01:46 PM
- Submitted by: Alejandro Vignoni
- With comment: Original import of Incoherent type 1 feed-forward loop (I1-FFL)
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: Variable used inside SBML models
| Species | Initial Concentration/Amount |
|---|---|
| x2 protein |
28336.8022705771 nmol |
| x3 Inducer |
0.0 nmol |
| x4 urn:miriam:sbo:SBO%3A0000607 |
0.0 nmol |
| x6 protein |
0.0 nmol |
| x7 mRNA |
0.0 nmol |
| x1 mRNA |
12.3973509933775 nmol |
| Reactions | Rate | Parameters |
|---|---|---|
| x1 => x1 + x2 | Cell*k_pA*x1 | k_pA = 80.0 |
| x9 => x9 + x3 | k_d*x9 | k_d = 0.06 |
| x4 => | Cell*k_3r*x4 | k_3r = 1.0 |
| x2 + x3 => | Cell*k_2f*x2*x3 | k_2f = 0.1 |
| x6 => | Cell*d_B*x6 | d_B = 0.016 |
| => x7; x4, x6 | Cell*k_mC_C_gC*(x4+Beta_1*gamma_4*x6+Beta_2*gamma_5*x4*x6)/(gamma_2+gamma_3*x4+gamma_4*x6+gamma_5*x4*x6) | gamma_5 = 8.56; gamma_3 = 0.01; Beta_2 = 0.05; k_mC_C_gC = 1.0; gamma_4 = 1.15; gamma_2 = 0.2; Beta_1 = 0.05 |
| x7 => x7 + x8 | Cell*k_pC*x7 | k_pC = 11.42 |
(added: 25 Apr 2018, 11:26:40, updated: 25 Apr 2018, 11:26:40)