| 摘要 |
<p>A method has been developed for using genome-wide transcription profile (i.e., gene-expression level) values to derive a gene expression index used as a kinetic value for every biological reaction and process assigned to each and every gene. This kinetic value is used in computational biology programs, i.e., mathematical models integrating genome, transcriptome, proteome, reactome, fluxome, metabolome, physiome, and phenome, in any combination, for simulations or theoretical systematic analyses of all life forms. This approach allows a model to be generated for any individual organism at any state of life, health condition, or disease/traumatic process. The model can include any or all biological reactions and processes, because an exact kinetic value becomes available; and, thereby, the outcomes represent stable or dynamic states of the individual organism at the time the biological specimen or sample was collected. Model systems without and with regulatory steps and mechanisms can be used to assess the present state of the specimen or sample and an acute response to an intervention within the system for the former and to predict some future state or status of treatment by testing single or multiple interventions within the regulated, dynamically responsive system for the latter; providing a prognostic value. Additionally, for multicellular organisms, the model can be tissue or cell type specific, depending on the source of the sample. Because of this capability, combined simulations can be generated with subsets of cells/tissues/organs/organ systems represented in a single model, in essence a reconstruction of the partial or complete organism in a single (or separate but integrated) computational model(s). Because all gene-expression values become available with genome-wide transcriptomic methods, surrogate tissue or cell samples can be used to predict other cells, tissues, or whole organism-level status; a utility essential for personalized individual medical care and history recording. This hierarchical computational approach is based upon the assumption that the transcriptome drives the reactome; and the proteome and metabolome, and other organism-level functions thereby effected, are resultant accompaniments to this basic integrative process in all organisms. If the genome and gene annotation (function) are known, or once they become known, for an organism and the transcriptome can be generated (even if from the genome of another related species, e.g., bovine genome used for buffalo), then this method can be used to generate a computational model representing that organism, inclusive of all living domains, Archaea, Bacteria, and Eukarya. The secondary data sets generated by the simulations are used for commercial and health care or promotion purposes of maximized yield or biomass production, health monitoring for improvement or sustained quality (for plants and animals, as well as smaller multicellular or unicellular organisms, such as insects and parasites, and microbes in ecological and environmental management, toxicology, agriculture, horticulture, and health management in general), bioremediation and biomining of pollutants, toxic substances, and precious metals, metabolic management for weight control, biomarker identification for commercial value (e.g., novel biofuels and sources), disease identification and management for prognosis, drug target identification, development, and testing, wound and tissue healing, overcoming drug resistances of bacteria, fungus, and cancer cells, development of novel singular or multiple therapies to individualize cancer treatments to the patient and specific molecular characteristics of the cancer cells or for treatment of metabolic disorders, and, in general, any biology-based approach to impact the improvement of humankind where study and testing of cellular based specimens is included. Additionally, the linking of the biological reactions to the life-sustaining and life-reproducing processes within the simulations generates data sets on individuals and ever increasing numbers of group samples in diverse categories in order that more global applications such as epidemiology, ecobiology, longitudinal growth and development analytics, and population dynamics studies can be implemented and performed.</p> |
