Quantitative modelling of physiological processes enables us to connect molecules and phenotypes. Sufficient data is critical for the development of physiological models, as models without sufficient data may fail to approximate the real-world scenario. While high-throughput technologies have significantly increased the amount of data available for biological research, the integration of divergent types of data remains a barrier to big data in biology. Transcriptomics, proteomics, metabolomics, and genomics high-throughput data must be integrated into a meaningful large dataset. It remains a challenge that, despite the massive amounts of data generated in the biological domain, the overall mechanism of a system such as phenotypes remains difficult to comprehend due to a dearth of data integration techniques. Models are quantifiable hypotheses based on available data and theory. Developing models allows us to gain insight into the function and mechanism of a physiological process. Given that biology is well-known as the science of exceptions, it is safe to assume that every quantitative model will fail at some point. However, the failure of these models motivates us to investigate what was missing and, in the process, a few new elements, pathways, molecules, and so on are integrated to improve its accuracy. As this iterative process continues, new discoveries, crosstalk between components, and a deeper understanding of an organism's, organ's, or system's normal functional processes are made. Thus, integrating multiple omics datasets and developing computational physiological models can help us gain a better knowledge of system biology and its homeostasis.
The purpose of this Research Topic is to develop techniques for integrating omics data in order to better understand physiological functions through computational modelling. Communicating the development of new models is as important as communicating the failure of existing models, as both contribute to the advancement of current understanding of physiological functions. Original Research Articles, Reviews, Mini Reviews and Perspective Articles on omics data integration and physiological model development are welcome under this Research Topic. Specific sub-topics include, but are not limited, to the following areas:
1. Omics data integration or data set generation
2. Development of computational models of physiological processes
3. Multiscale modelling using various omics and pathway data
4. Informatics in Computational Physiology and Medicine
5. Analysis of existing models based on recent biological data
Quantitative modelling of physiological processes enables us to connect molecules and phenotypes. Sufficient data is critical for the development of physiological models, as models without sufficient data may fail to approximate the real-world scenario. While high-throughput technologies have significantly increased the amount of data available for biological research, the integration of divergent types of data remains a barrier to big data in biology. Transcriptomics, proteomics, metabolomics, and genomics high-throughput data must be integrated into a meaningful large dataset. It remains a challenge that, despite the massive amounts of data generated in the biological domain, the overall mechanism of a system such as phenotypes remains difficult to comprehend due to a dearth of data integration techniques. Models are quantifiable hypotheses based on available data and theory. Developing models allows us to gain insight into the function and mechanism of a physiological process. Given that biology is well-known as the science of exceptions, it is safe to assume that every quantitative model will fail at some point. However, the failure of these models motivates us to investigate what was missing and, in the process, a few new elements, pathways, molecules, and so on are integrated to improve its accuracy. As this iterative process continues, new discoveries, crosstalk between components, and a deeper understanding of an organism's, organ's, or system's normal functional processes are made. Thus, integrating multiple omics datasets and developing computational physiological models can help us gain a better knowledge of system biology and its homeostasis.
The purpose of this Research Topic is to develop techniques for integrating omics data in order to better understand physiological functions through computational modelling. Communicating the development of new models is as important as communicating the failure of existing models, as both contribute to the advancement of current understanding of physiological functions. Original Research Articles, Reviews, Mini Reviews and Perspective Articles on omics data integration and physiological model development are welcome under this Research Topic. Specific sub-topics include, but are not limited, to the following areas:
1. Omics data integration or data set generation
2. Development of computational models of physiological processes
3. Multiscale modelling using various omics and pathway data
4. Informatics in Computational Physiology and Medicine
5. Analysis of existing models based on recent biological data
