“Omics” research and systems medicine
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Omics is an emerging area that has many aspects in the field of science and medicine.Several exiting developments have been achieved with omics including genomics, epigenomics, transcriptomics, proteomics, metabolomics, and bioinformatics.Systems biology is another emerging scientific area to develop new approaches for investigating complex interactions within biological systems.Keywords:
Omics
Epigenomics
Systems medicine
The primary aim of “omic” technologies is the nontargeted identification of all gene products (transcripts, proteins, and metabolites) present in a specific biological sample. By their nature, these technologies reveal unexpected properties of biological systems. A second and more challenging aspect of omic technologies is the refined analysis of quantitative dynamics in biological systems. For metabolomics, gas and liquid chromatography coupled to mass spectrometry are well suited for coping with high sample numbers in reliable measurement times with respect to both technical accuracy and the identification and quantitation of small-molecular-weight metabolites. This potential is a prerequisite for the analysis of dynamic systems. Thus, metabolomics is a key technology for systems biology. The aim of this review is to (a) provide an in-depth overview about metabolomic technology, (b) explore how metabolomic networks can be connected to the underlying reaction pathway structure, and (c) discuss the need to investigate integrative biochemical networks.
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Systems biology has emerged during the past 20 years with the goal of studying organisms holistically and comprehensively. It is characterized by modeling and large datasets. The introduction of high-throughput technology in the 1990s led to a wealth of biology knowledge. On the other hand, the data at the time required computational simulations and mathematical models in order to be understood. In contrast to more conventional branches of biology such as evolutionary, molecular, and developmental biology, systems biology has had a long history of computer and mathematical research since the early 1990s. Initial systems biologists devised various methods for handling large datasets and formalizations that simulate certain channels, such as signal transduction systems, gene monitoring, and metabolic systems, to improve the technique. These developments led to the emergence of other systems biology sub-disciplines, including systems pharmacology, which also uses systems biology techniques to study the mechanisms underlying medications, and cancer systems biology, which employs computational modeling to identify cancer-causing pathways. Here, the approaches based on systems biology have enormous advantages for biologists, especially for those in life science research. First, complex biological networks, rather than just one or a few genes, play a role in many complex diseases such as diabetes, lung disease, and cardiovascular disease. Furthermore, systems biology methods permit the modeling, manipulation, and predictions of multifaceted systems, which are essential for the diagnosis and treatment of complex disorders. The systems biology concept is proactive instead of reactive for the reasons mentioned above.
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Systems biology is a novel subject in the field of life science that aims at a systems' level understanding of biological systems. Because of the significant progress in high-throughput technologies and molecular biology, systems biology occupies an important place in research during the post-genome era.The characteristics of systems biology and its applicability to traditional medicine research have been discussed from three points of view: data and databases, network analysis and inference, and modeling and systems prediction.The existing databases are mostly associated with medicinal herbs and their activities, but new databases reflecting clinical situations and platforms to extract, visualize and analyze data easily need to be constructed. Network pharmacology is a key element of systems biology, so addressing the multi-component, multi-target aspect of pharmacology is important. Studies of network pharmacology highlight the drug target network and network target. Mathematical modeling and simulation are just in their infancy, but mathematical modeling of dynamic biological processes is a central aspect of systems biology. Computational simulations allow structured systems and their functional properties to be understood and the effects of herbal medicines in clinical situations to be predicted.Systems biology based on a holistic approach is a pivotal research methodology for understanding the mechanisms of traditional medicine. If systems biology is to be incorporated into traditional medicine, computational technologies and holistic insights need to be integrated.
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High‐throughput experimental techniques for generating genomes, transcriptomes, proteomes, metabolomes, and interactomes have provided unprecedented opportunities to interrogate biological systems and human diseases on a global level. Systems biology integrates the mass of heterogeneous high‐throughput data and predictive computational modeling to understand biological functions as system‐level properties. Most human diseases are biological states caused by multiple components of perturbed pathways and regulatory networks rather than individual failing components. Systems biology not only facilitates basic biological research but also provides new avenues through which to understand human diseases, identify diagnostic biomarkers, and develop disease treatments. At the same time, systems biology seeks to assist in drug discovery, drug optimization, drug combinations, and drug repositioning by investigating the molecular mechanisms of action of drugs at a system's level. Indeed, systems biology is evolving to systems medicine as a new discipline that aims to offer new approaches for addressing the diagnosis and treatment of major human diseases uniquely, effectively, and with personalized precision. WIREs Syst Biol Med 2015, 7:141–161. doi: 10.1002/wsbm.1297 This article is categorized under: Analytical and Computational Methods > Computational Methods Laboratory Methods and Technologies > Macromolecular Interactions, Methods Translational, Genomic, and Systems Medicine > Translational Medicine
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Systems biology is the study of systems of biological components,which may be molecules,cells,organisms or entire species.Living systems are dynamic and complex,and their behavior may be hard to predict from the properties of individual parts.In recent years,systems biology as a new frontier developed recently in life science,has become one of the landmarks for the post genome era and widely spreadly implanted into every field of life science and medicine.Classical Chinese traditional medicine drugs considered as very old age by comparison with systems biology,although we found that classical Chinese traditional medicine drugs and current systems biology are research toward the same goal by different means.In the light of this,this review deals with origin and relationship of systems biology and traditional Chinese medicine.The systems biology-based's from aspects of its development including thought and methods.The research methods are also undergoing a change from detailed breakdown analysis to systematic integrated analysis from analysis theory to system theory.The developments of systems biology in the future are appraised in the end.Systems Biology stresses the research in the level of system and organic whole and promotes to innovate the researching method of traditional Chinese medicine.
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Systems biology has matured considerably as a discipline over the last decade, yet some of the key challenges separating current research efforts in systems biology and clinically useful results are only now becoming apparent. As these gaps are better defined, the new discipline of systems medicine is emerging as a translational extension of systems biology. How is systems medicine defined? What are relevant ontologies for systems medicine? What are the key theoretic and methodologic challenges facing computational disease modeling? How are inaccurate and incomplete data, and uncertain biologic knowledge best synthesized in useful computational models? Does network analysis provide clinically useful insight? We discuss the outstanding difficulties in translating a rapidly growing body of data into knowledge usable at the bedside. Although core-specific challenges are best met by specialized groups, it appears fundamental that such efforts should be guided by a roadmap for systems medicine drafted by a coalition of scientists from the clinical, experimental, computational, and theoretic domains.
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Metabolomics has been applied to measure the dynamic metabolic responses, to understand the systematic biological networks, to reveal the potential genetic architecture, etc., for human diseases and livestock traits. For example, the current published results include the detected relevant candidate metabolites, identified metabolic pathways, potential systematic networks, etc., for different cattle traits that can be applied for further metabolomic and integrated omics studies. Therefore, summarizing the applications of metabolomics for economic traits is required in cattle. We here provide a comprehensive review about metabolomic analysis and its integration with other omics in five aspects: (1) characterization of the metabolomic profile of cattle; (2) metabolomic applications in cattle; (3) integrated metabolomic analysis with other omics; (4) methods and tools in metabolomic analysis; and (5) further potentialities. The review aims to investigate the existing metabolomic studies by highlighting the results in cattle, integrated with other omics studies, to understand the metabolic mechanisms underlying the economic traits and to provide useful information for further research and practical breeding programs in cattle.
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The challenge of biology and medicine in the 21st century is complexity. A paradigm change that is currently emerging is the idea that biology is an informational science and that biological information is mediated by dynamic biological networks. The systems approach to biology and medicine is a general category of approaches that appear to be very effective in dealing with biological complexity. It requires a cross-disciplinary environment and the effective integration of biology, technology and computation/mathematics. I will discuss my view of systems biology. Then I will discuss a systems approach to disease and demonstrate how it profoundly alters our view of medicine. This approach is centered on the simple ideal that disease arises from disease-perturbed biological networks and that the dynamically altered patterns of information expressed explain the pathophysiology of disease. Moreover, they open new systems approaches to diagnostics and therapy. I will discuss prion infection in mice as an example of a systems approach to disease and I will discuss the implications these observations have for diagnosis and therapy. My prediction is that our current largely reactive medicine will be largely replaced with predictive, preventive, personalized and participatory (P4) medicine over the next 10 to 20 years.
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