Abstract
Metabolic networks are complex and highly interconnected, thus systems-level computational approaches are required to elucidate and to understand metabolic genotype-phenotype relationships. This paper has manually reconstructed the local human metabolic network based on DNA sequence data of human chromosome nine. Herein the paper describes the reconstruction process and discusses how the resulting chromosome-scale (or local) network differs from genome-scale ones. The underestimated results have revealed many gaps in the current understanding of human metabolism that require future experimental investigation. They also suggest possible problems arising from local reconstruction based on partial genome data. The study suggests further applications enabled by reconstruction of human metabolic network. The establishment of this network represents a step toward genome-scale human systems biology.
Highlights
After the Human Genome Project was finished at the beginning of 21st century, it seems that we have our own destiny under control
What is the step to deal with such a large pool of data so that they are meaningful to people? The direct approach to understanding the complex processes encoded by the human genome is studying gene products’ function, assigning these enzyme products to biochemical pathways and reconstructing biochemical networks
This paper presents the reconstruction of the local human metabolic network only with information from human chromosome 9(referred as chr9 in this paper)
Summary
After the Human Genome Project was finished at the beginning of 21st century, it seems that we have our own destiny under control. The direct approach to understanding the complex processes encoded by the human genome is studying gene products’ function, assigning these enzyme products to biochemical pathways and reconstructing biochemical networks. These biochemical pathways define regulated sequences of biochemical transformations. The procedure for integrating these diverse data types to form a network reconstruction and predictive model is well established for microorganisms [4] and has been applied to mouse hybridomas [5]. Assignment of genes to pathways permits a validation of the human genome annotation because patterns of pathway assignments spotlight likely false-positive and false-negative genome annotations
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