These emerging systems biology approach requires the development of high-throughput computational environments that integrate (i) large amounts of genomic and experimental data and (ii) powerful tools and algorithms for knowledge discovery and data mining. Most of these tools and algorithms are CPU-intensive, requiring computational resources beyond those available to researchers at a single location. The aggregated and distributed computational and storage infrastructure of the Grid offers an ideal platform for mining biological information at this large scale. Based on systems biology, e.g. bioinformatics on metabolic syndrome and Grid computing platform, we build a new metabolic prediction system with nano-sensors integrated workflow computing platform. By getting genetic information that are related to metabolic disorder and by constructing various information that can be referenced upon the prediction system, we can understand the protein-protein interaction that happens among various kinds of genes and their own products, proteins. Also, we can examine if there exist any groups that reveal together from certain tissue, or whether there exists no certain genetic groups. Moreover, combining conventional heart disease measurement technology such as ECG, MCG can help predict early diagnosis of disease.
Nano Sensor System for cell metabolism measurement
By combining the information of the metabolic variation of mitochondria, which is the new technique of measuring the body signal established based upon the KNIH genomic data and KETI / Seoul National University; we are expecting the establishment of the new analysis. (KNIH is the genomic data that was made based upon combining the public genome database and the special genetic information of Koreans.) These researches, by allowing the key to the genetic mechanisms that are related to various kinds of metallic-related diseases, provide us with the assumption that are necessary to the later-on functional analysis and also provide certain information that are essential to the disease prediction system.
To identify metabolism with genome data, it is needed a process of data integration and confirmation procedure. Namely we need cell metabolism measurement to identify human cells¡¯ dynamics. KETI is developing a cell metabolism measurement system that has very high fidelity of measurement accuracy in mitochondrial metabolic process, e.g. heat and carbon dioxide by oxygen and material consumption (Refer to Fig. 2). Our system can measure infinitesimal signal difference simultaneously during cell metabolic process. It also is able to measure the various metabolic parameters simultaneously without measuring crosstalk between different parameters. There are several parameters which current system can measure and analyze.
O<= High resolution temperature difference measurement O<= Noncontact measurement module of dissolved oxygen(DO) contents O<= Noncontact measurement module of hydrogen ion concentration (pH) O<= Noncontact measurement module of dissolved carbon dioxide concentration - DO, pH, and CO2 contents in solution is able to be obtained from the change of chromaticity coordinates and optical absorption which is followed by chemical indicator in the solutions with contained quantity. The resolution of this system is less than 1% for each parameter.
Until now, Korea NIH has done Genome-Wide Association Study (GWAS) by gathering SNP genotype data of about ten thousand people that are from the regional Cohort study. From each Cohort study, various information of metabolism was also gathered, and right now, GWAS of checking the SNPs that are thought to have relations with diabetes is almost done and the results are under arrangements. The genomic SNP information that is thought to have relations to the diabetes is gathered from other diabetes association study and is going to be put into Di-SNO DB, the KNIH¡¯s own database, additionally. (Figure 3) Such results are expected to provide us with information whether there exist certain genetic factors in various kinds of ethnic groups that lead to the onset or the development of complex diseases such as diabetes.
To identify metabolism with genome data, it is needed a process of data integration and confirmation procedure. Namely we need cell metabolism measurement to identify human cells¡¯ dynamics. KETI is developing a cell metabolism measurement system that has very high fidelity of measurement accuracy in mitochondrial metabolic process, e.g. heat and carbon dioxide by oxygen and material consumption.(see Fig. Our system can measure infinitesimal signal difference simultaneously during cell metabolic process. It also is able to measure the various metabolic parameters simultaneously without measuring crosstalk between different parameters. There are several parameters which current system can measure and analyze.By gathering data, we expect that a scenario would be possible. Among the ten thousand people who are investigated under Korea NIH, about one thousand are the diabetes patients. By doing some experiments upon those people such as measuring mitochondria metabolism, we can find out the difference between normal people and those who suffer from diabetes. Therefore, this is the same as the cohort study of KNIH except that mitochondria metabolism measurement is included in addition. And in medical school of Seoul National University, the SNP genotyping was already performed upon those people whose mitochondria metabolism was measured. Therefore, among those experiment groups of KNIH, we are going to select those who are normal (who does not suffer from diabetes) and going to perform the mitochondria metabolism measurement test once again in order to find the difference between the original measurement results. This is the same as using mainly the normal groups of people among KNIH data.