Boada2016 - Incoherent type 1 feed-forward loop (I1-FFL)

Boada2016 - Incoherent type 1 feed-forward
loop (I1-FFL)
A synthetic-biology mathematical
modelling framework that was constructed to provide guidelines for
experimental implementation and parameter optimisation resulted in
a biological device demonstrating desired behaviour.

This model is described in the article:

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 Syst Biol 2016 Mar; 10: 27
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.

Boada2016 - 不连贯型1型前馈环（Incoherent type 1 feed-forward loop, I1-FFL）
本研究构建了合成生物学（synthetic biology）数学建模框架，旨在为实验实施与参数优化提供指导，最终得到了可展现预期行为的生物学装置。

本模型的相关描述见于以下论文：
《多目标优化框架用于获取调控生物合成装置的模型化指导：以自适应网络为例》
Boada Y, Reynoso-Meza G, Picó J, Vignoni A.
发表于BMC Syst Biol 2016年3月；10: 27

论文摘要：
基于模型的设计在合成生物学领域中具有基础性地位。利用模块化特性，即通过获取生物元件并将其互连以构建新型复杂生物回路，是该领域的核心研究方向之一。在此背景下，数学模型被用于预测所设计装置的行为。设计者不仅希望在确定所有组件后能够预测回路行为，还期望该模型可辅助生物元件的设计与筛选，即提供实验实施的指导准则。这本质上等同于获取模型参数的合理取值，因为回路行为由模型结构与参数调控的共同作用决定。然而，为所涉及的生物元件确定精确的参数值并不具备现实可行性。生物学领域普遍存在不确定性，生物元件的特性表征亦无法规避该问题。此外，所设计回路的预期动态行为，通常需要在多个待优化目标之间进行权衡。
我们提出采用多目标优化调控框架，以获取基于模型的一系列指导准则，用于筛选可构建具备预期行为的生物学装置所需的动力学参数。设计准则被编码至目标函数与优化问题的构建过程之中。最终，一方面设计者可得到可产生预设回路行为的电路参数的定性取值区间；另一方面，还可获得其在实施过程中的有效指导信息。所选取的参数需确保可在湿实验（wet-lab）中有效调控，即它们属于可实际操作的生物学调控旋钮。
为展示所提方法，本研究将该框架应用于经典生物回路的设计：即展现自适应行为的遗传型不连贯前馈回路。
所提出的多目标优化设计框架，能够提供有效的生物学参数调控指导，以实现预期的回路行为。此外，该框架便于分析实验环境对待设计合成生物装置的影响，即可以分析下游负载对所设计回路性能的影响，并将该影响纳入考量范围。

本模型托管于BioModels数据库（BioModels Database），其编号为BIOMD0000000696。
若需引用BioModels数据库，请参考以下文献：Chelliah V等. BioModels：十年历程. 《核酸研究（Nucleic Acids Research）》2015, 43(Database issue):D542-8。
在适用法律允许的范围内，本编码模型的所有版权及相关或邻接权利已无偿奉献至全球公共领域。如需更多信息，请参阅CC0公共领域贡献声明（CC0 Public Domain Dedication）。