CAREER: Integrative Synthetic Biology: A Scalable Framework for Modular Multilevel Design

职业：综合合成生物学：模块化多级设计的可扩展框架

• 批准号: 1254205
• 负责人: Ilias Tagkopoulos
• 金额: $ 60万
• 依托单位: University of California-Davis
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-03-01 至 2019-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1254205&HistoricalAwards=false
• 关键词: CAREER; Integrative; Synthetic; Biology; Scalable

There have been recent and notable advances in the field of computer-aided tools for synthetic circuit design at the system level. While most approaches focus on providing visualization and simulation capabilities for the users to explore, there is a lack of a much needed end-to-end integrated framework capable of automated optimization of synthetic circuits based on user-defined constraints. The goal of the proposed project is to develop an integrated synthetic biology framework and tools for the automated systems-level design of mixed-signal synthetic circuits. This framework will serve as a computational tool for synthetic biologists and will lay the theoretical foundations for system level synthetic circuit design. The project will explore a global optimization platform with both approximate and exact techniques for finding the optimal set of parts for any given configuration, which will provide guarantees on the optimality, reproducibility or bounds of the solution. This work will explore matching past validated designs to the circuit at hand, by employing graph-querying and graph partitioning algorithms that will transform the original design to equivalent graphs which can be solved efficiently. This will promote modular and reusable designs, and result in an increase in the level of achievable complexity and a decrease in the cost of construction.

在系统级合成电路设计的计算机辅助工具领域，最近取得了显著的进展。 虽然大多数方法专注于为用户提供可视化和仿真能力以供探索，但缺乏能够基于用户定义的约束自动优化合成电路的急需的端到端集成框架。该项目的目标是为混合信号合成电路的自动化系统级设计开发一个集成的合成生物学框架和工具。 该框架将作为合成生物学家的计算工具，并将为系统级合成电路设计奠定理论基础。该项目将探索一个全局优化平台，采用近似和精确技术，为任何给定的配置找到最佳的部件组，这将为解决方案的最优性，可重复性或边界提供保证。这项工作将探讨匹配过去验证的设计，手头的电路，采用图形查询和图形分区算法，将原来的设计，可以有效地解决等效图。这将促进模块化和可重复使用的设计，并导致可实现的复杂程度的增加和建造成本的降低。

项目成果

Genetic Neural Networks: an artificial neural network architecture for capturing gene expression relationships

• DOI: 10.1093/bioinformatics/bty945
• 发表时间: 2019-07-01
• 期刊: BIOINFORMATICS
• 影响因子: 5.8
• 作者: Eetemadi, Ameen;Tagkopoulos, Ilias
• 通讯作者: Tagkopoulos, Ilias