About this Research Topic
With advances in quality, reliability, and cost of high-throughput experimental assays over the last several decades, rich, quantitative snapshots of biological networks are now routinely collected in biology labs, and are being used to elucidate the gamut of normal and pathological processes in cells. As a result, there have been huge increases in the scope and resolution of molecular-level biological knowledge, and it has become evident that human disease is highly multi-factorial, in that the most effective approaches to understanding and battling disease often lie on the level of systems rather than of individual components.
Concomitant with the increased availability of data, many decades of carefully collected biological insights and the development of cheaper, faster genome-sequencing technologies have been bootstrapped into the construction of large-scale models of biological systems. Such models increasingly span the entire genomes of cells and enable quantitative, comprehensive analyses. They are ideal in scope, resolution, and design for integration with high-throughput data to address the most difficult topics in disease, and to unravel the complexities that remain unyielding to current approaches. With today’s model quality and computational capability, there is both a great need and an ideal opportunity for these models to play a major role in 21st-century medicine.
An example of genome-scale modeling is the metabolic network reconstruction (GENRE), a framework that links genotype to metabolic phenotype over the entire expanse of a cell. GENREs enable genome-wide metabolic fluxes to be predicted and related to phenotypes such as cell survival or growth. While much work with GENREs has focused on single-celled organisms, including a wide variety of pathogens, a recent GENRE of human metabolism has expanded the scope of these models to include practically the full range of human diseases, thereby enabling exciting new possibilities for large-scale mechanistic analysis.
Indeed, GENREs are now being used to build tissue-specific models of human cell types (including models of cancer cells), predict drug targets or ideal combinations of drugs against pathogens, infer fundamental insights into the life-cycles of harmful organisms, determine metabolic aberrations linked to chronic diseases, and link phenotypes at multiple temporal and spatial scales, in an effort to focus and optimize the lens of high-throughput data through which we can view with unprecedented detail the workings of cells.
Other non-metabolic genome-scale modeling frameworks, many inspired by GENREs, have also been developed in recent years. Notably, these frameworks have enabled reconstruction and analysis of large-scale intracellular signaling and transcriptional regulatory networks, and are bringing us closer to mechanistically describing structural and dynamical properties of entire cells. Such large-scale systematic descriptions will be a tremendous boon to developing cures for the complex, intransigent diseases that humanity faces today.
Systems-level analysis of disease requires a tight integration of modeling, high-throughput data generation, and genomics-level analysis, as well as vigilant experimental validation of the models and hypothesis-driven testing through traditional wet-lab approaches. This integration has the potential to broadly influence our approaches and to uncover previously inaccessible relationships even in well-studied systems. We are now at a point where the scope of the data, the capabilities of the models, and the nature of the integrative challenges increasingly overlap, and where s
Concomitant with the increased availability of data, many decades of carefully collected biological insights and the development of cheaper, faster genome-sequencing technologies have been bootstrapped into the construction of large-scale models of biological systems. Such models increasingly span the entire genomes of cells and enable quantitative, comprehensive analyses. They are ideal in scope, resolution, and design for integration with high-throughput data to address the most difficult topics in disease, and to unravel the complexities that remain unyielding to current approaches. With today’s model quality and computational capability, there is both a great need and an ideal opportunity for these models to play a major role in 21st-century medicine.
An example of genome-scale modeling is the metabolic network reconstruction (GENRE), a framework that links genotype to metabolic phenotype over the entire expanse of a cell. GENREs enable genome-wide metabolic fluxes to be predicted and related to phenotypes such as cell survival or growth. While much work with GENREs has focused on single-celled organisms, including a wide variety of pathogens, a recent GENRE of human metabolism has expanded the scope of these models to include practically the full range of human diseases, thereby enabling exciting new possibilities for large-scale mechanistic analysis.
Indeed, GENREs are now being used to build tissue-specific models of human cell types (including models of cancer cells), predict drug targets or ideal combinations of drugs against pathogens, infer fundamental insights into the life-cycles of harmful organisms, determine metabolic aberrations linked to chronic diseases, and link phenotypes at multiple temporal and spatial scales, in an effort to focus and optimize the lens of high-throughput data through which we can view with unprecedented detail the workings of cells.
Other non-metabolic genome-scale modeling frameworks, many inspired by GENREs, have also been developed in recent years. Notably, these frameworks have enabled reconstruction and analysis of large-scale intracellular signaling and transcriptional regulatory networks, and are bringing us closer to mechanistically describing structural and dynamical properties of entire cells. Such large-scale systematic descriptions will be a tremendous boon to developing cures for the complex, intransigent diseases that humanity faces today.
Systems-level analysis of disease requires a tight integration of modeling, high-throughput data generation, and genomics-level analysis, as well as vigilant experimental validation of the models and hypothesis-driven testing through traditional wet-lab approaches. This integration has the potential to broadly influence our approaches and to uncover previously inaccessible relationships even in well-studied systems. We are now at a point where the scope of the data, the capabilities of the models, and the nature of the integrative challenges increasingly overlap, and where s
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