基于Memetic多目标时变优化的全基因代谢网络重构算法研究

项目名称： 基于Memetic多目标时变优化的全基因代谢网络重构算法研究

项目编号： No.61501138

项目类型： 青年科学基金项目

立项/批准年度： 2016

项目学科： 无线电电子学、电信技术

项目作者： 周家锐

作者单位： 哈尔滨工业大学

项目金额： 19万元

中文摘要： 全基因代谢网络重构是代谢组学与系统生物学的重要研究基础。现有算法仅包含单一重构目标，无法反映生物的真实情况。为有效提升代谢网络对生理状态的预测分析能力，需设计多目标的重构方法，并兼顾时变鲁棒性。此算法属于高维、约束、多目标、时变的复杂优化问题，可使用计算智能有效求解。.本项目将研究设计基于启发式算子链的Memetic计算框架，用于多目标全基因代谢网络重构，对网络结构与代谢通量分布进行优化。一方面，使用合作协同进化与集成学习，建立约束条件下大规模优化解空间的概率分解算法。另一方面，引入基于偏好的多目标优化，使代谢网络在生理效能最佳的同时，保持通量调整稳定。通过对帕累托非支配解进行筛选，最终获得更接近真实生物的代谢网络。.本项目首次提出将计算智能用于多目标代谢网络重构，并应用此方法首次构造一种最新的模式生物-水蚤的代谢网络。预期完成后的网络可成为环境学代谢组学、功能基因组学等的有力研究平台。

中文关键词： Memetic计算；多目标优化；时变优化；代谢组学；代谢网络重构

英文摘要： Genome wide metabolic network reconstruction (GWMR) is crucial for the research of metabolomics and systems biology. Existing GWMRs consist of only one objective, which cannot faithfully reflect the reality of real-world organisms. To improve predication accuracy and analysis capabilities in physiological states, the advanced GWMR requires multiobjective modeling while taking the robustness into account. It can be formulated as a high dimensional, constraint, multiobjective, and time-varying optimization problem, which can be solved by computational intelligence satisfactorily..This research studies the usage of a novel metaheuristics chain based memetic computing to optimize metabolic network structure and flux distribution, and eventually build a multiobjective GWMR method. Particularly, the algorithm utilizes cooperative coevolution and ensemble learning to form a probabilistic decomposition method for constrained large-scale optimization solution space. Preference-based multiobjective optimization is used to trade off the biological objectives while maintaining the stability of the flux distribution adjustment. Finally, a more accurate metabolic network is selected from the optimized Pareto non-dominated solutions..For the first time, we will apply computational intelligence to multiobjective metabolic network reconstruction. We will apply our method to reconstruct the metabolic network of Daphnia, an emerging model species. We anticipate our constructed metabolic network will become a powerful platform for environmental metabolomics and functional genomics research.

英文关键词： Memetic Computing;Multiobjective Optimization;Time-Varying Optimization;Metabolomics;Metabolic Network Reconstruction