Metabolic network modularity arising from simple growth processes

Metabolic networks consist of linked functional components, or modules. The mechanism underlying metabolic network modularity is of great interest not only to researchers of basic science but also to those in fields of engineering. Previous studies have suggested a theoretical model, which proposes that a change in the evolutionary goal (system-specific purpose) increases network modularity, and this hypothesis was supported by statistical data analysis. Nevertheless, further investigation has uncovered additional possibilities that might explain the origin of network modularity. In this work, we propose an evolving network model without tuning parameters to describe metabolic networks. We demonstrate, quantitatively, that metabolic network modularity can arise from simple growth processes, independent of the change in the evolutionary goal. Our model is applicable to a wide range of organisms, and appears to suggest that metabolic network modularity can be more simply determined than previously thought. Nonetheless, our proposition does not serve to contradict the previous model; it strives to provide an insight from a different angle in the ongoing efforts to understand metabolic evolution, with the hope of eventually achieving the synthetic engineering of metabolic networks.Comment: 11 pages, 7 figure

Reconstruction of metabolic networks from high-throughput metabolite profiling data: in silico analysis of red blood cell metabolism

We investigate the ability of algorithms developed for reverse engineering of transcriptional regulatory networks to reconstruct metabolic networks from high-throughput metabolite profiling data. For this, we generate synthetic metabolic profiles for benchmarking purposes based on a well-established model for red blood cell metabolism. A variety of data sets is generated, accounting for different properties of real metabolic networks, such as experimental noise, metabolite correlations, and temporal dynamics. These data sets are made available online. We apply ARACNE, a mainstream transcriptional networks reverse engineering algorithm, to these data sets and observe performance comparable to that obtained in the transcriptional domain, for which the algorithm was originally designed.Comment: 14 pages, 3 figures. Presented at the DIMACS Workshop on Dialogue on Reverse Engineering Assessment and Methods (DREAM), Sep 200

Division of Metabolic Engineering

この論文は国立情報学研究所の学術雑誌公開支援事業により電子化されまし

Department of Metabolic Engineering

薬物代謝工学部門は和漢薬の薬効，毒性発現に関与する代謝系の分子生物学的研究を発展させることを設置目的とし，①和漢薬の薬効発現に関与する腸内細菌遺伝子の解析，②薬物代謝機能調節遺伝子の解明とその応用，③腎毒性物質産生機構の分子生物学的解明とその制御に関する研究を課題として取りあげ，和漢薬の薬効発現機構，生体へのレスポンスなどの基礎的研究を通じて，和漢薬の科学的評価や臨床応用をはかることを目指している。主な研究題目を以下に示す。1. 天然物のバイオトランスフォーメイション2. 和漢薬の薬効発現に関与する腸内細菌遺伝子の解明3. AIDSの予防および治療薬の開発4. 腎疾患における病態の解明と腎臓病治療薬の開発この論文は国立情報学研究所の学術雑誌公開支援事業により電子化されまし

Metabolic engineering of functional phytochemicals

Phytochemicals belonging to the group’s phenols, terpenes, betalains, organosulfides, indoles and protein inhibitors are important components in fruits, vegetables, legumes, whole grains and nuts that have health promoting benefits and a variety of applications in food and pharmaceutical industries. Initially only a few of these important phytochemicals are produced commercially by chemical synthesis. However, recent developments in the field of biotechnology have provided metabolic engineering strategies that use microorganisms as cell factories for high production of these products. This review will discuss the general biosynthetic pathways, metabolic engineering and optimization strategies of functional phytochemicals that have received a lot of attention from investigators