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Development in Systems Biology so Far

  • Subject: Life
  • Topic: Development
  • Page 1
  • Words: 680
  • Published: 11 February 2019
  • Downloads: 61
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Systems biology is the study of biological systems whose behaviour cannot be reduced to the linear sum of their parts’ functions. Systems biology does not necessarily involve large number of components or vast datasets, as in genomics or connectomics, but often requires quantitive modelling methods borrowed from physics.

This is an interdisciplinary Specialty that features the modelling and discovery of emergent properties in complex biological systems. One of the main objectives is to relate the structure and dynamics of biological systems with their physiology and phenotypic traits through an approach that integrates theoretical models, computational analysis and empirical data. Such analysis can be conducted at any scale of biological organization, ranging from single cells to ecological systems. Contrary to the reductionist paradigm commonly used in Molecular Biology, in Systems Biology the understanding of the behaviour and evolution of complex biological systems need not necessarily be based on a detailed molecular description of the interactions between the system’s constituent parts. Therefore, we welcome Systems Biology research with or without underlying molecular mechanisms justifying the system-level description. Translation of system-level approaches into computer-aided diagnosis and treatment of disease, health-maintenance interventions, and the clinical practice are also welcome.

SYSTEMS BIOLOGY IN DRUG DISCOVERY AND DEVELOPMENT

Systems biology omics-based efforts have led to an explosion of high-throughput data and focus is now shifting to the integration of diverse data types to connect molecular and pathway information to predict disease outcomes. Better models of human disease biology, including more integrated network-based models that can accommodate multiple omics data types, as well as more relevant experimental systems, will help predict drug effects in patients, enabling personalized medicine, improvement of the success rate of new drugs in the clinic, and the finding of new uses for existing drugs.

The ultimate goal of systems biology is an understanding of physiology and disease across the multiple hierarchical levels of organization, from chemical and molecular interactions to pathways and pathway networks, at the cell–cell and tissue level, organs and organ systems and, ultimately, to the functioning of the whole organism [1,2]. Systems biology research encompasses the generation of high-throughput datasets of system components (omics data), experimental methods of analysis and data integration, as well as the development and application of network approaches and computationally derived models.

In pharmaceutical research, systems biology efforts are directed towards the identification of drug targets, the development of novel therapeutics and new indications for existing drugs. Studies tend to be compound-centric, concerned with the identification and characterization of small molecules or biologics that selectively inhibit (or activate) specific molecular targets or pathway mechanisms. Thus, studies related to drug mechanisms of action and those that support drug development goals, such as clinical indication selection and patient stratification, are of particular interest.

Omics tools, developed over the past several decades, can provide global information on the levels and dynamic changes in cellular and tissue components at specific time points in samples from cell-based assays, preclinical animal models or human studies. Omics data sets derived from transcriptomics (mRNA transcripts), proteomics (protein levels and post-translational modifications and interactions) and metabolomics (small molecule metabolites or chemicals) are being used and integrated with each other as well as genomics information and other data types to construct models of cell signaling, pathway and disease networks to identify new targets as well as to help better understand and predict drug action in vivo. In addition to experimentally derived data sets, there is the wealth of literature information and accumulated knowledge that can be incorporated by converting to some type of formal representation. This is accomplished through the use of a defined ontology by expert curation and/or natural language processing (NLP) – based methods into a series of semantic statements.

The term ‘network medicine’ or network pharmacology has been used for systems biology studies in biomedical research, and is a particularly apt term as researchers take on the challenge of combining and integrating data sets and begin to change how they do medicine.

ADVANTAGES OF SYSTEMS BIOLOGY IN DRUG DISCOVERY:

  • Decreases drug development time by half.
  • Decreases the cost of drug development by 70% and can further reduce cost if coupled with traditional medicenes.

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DEVELOPMENT IN SYSTEMS BIOLOGY SO FAR. (2019, February 11). GradesFixer. Retrieved May 16, 2022, from https://gradesfixer.com/free-essay-examples/development-in-systems-biology-so-far/
“DEVELOPMENT IN SYSTEMS BIOLOGY SO FAR.” GradesFixer, 11 Feb. 2019, gradesfixer.com/free-essay-examples/development-in-systems-biology-so-far/
DEVELOPMENT IN SYSTEMS BIOLOGY SO FAR. [online]. Available at: <https://gradesfixer.com/free-essay-examples/development-in-systems-biology-so-far/> [Accessed 16 May 2022].
DEVELOPMENT IN SYSTEMS BIOLOGY SO FAR [Internet]. GradesFixer. 2019 Feb 11 [cited 2022 May 16]. Available from: https://gradesfixer.com/free-essay-examples/development-in-systems-biology-so-far/
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