Biological Systems
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Biological Systems
A biological system is a complex Biological network inference, network which connects several biologically relevant entities. Biological organization spans several scales and are determined based different structures depending on what the system is. Examples of biological systems at the macro scale are populations of organisms. On the organ (anatomy), organ and Tissue (biology), tissue scale in mammals and other animals, examples include the circulatory system, the respiratory system, and the nervous system. On the Micrometre, micro to the Nanometre, nanoscopic scale, examples of biological systems are cell (biology), cells, organelles, macromolecular complexes and Regulatory T cell, regulatory pathways. A biological system is not to be confused with a Living systems, living system, such as a living organism. Organ and tissue systems These specific systems are widely studied in human anatomy and are also present in many other animals. * Respiratory system: the organs used for b ...
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Biological Network Inference
Biological network inference is the process of making inferences and predictions about biological networks. By using these networks to analyze patterns in biological systems, such as food-webs, we can visualize the nature and strength of these interactions between species, DNA, proteins, and more. The analysis of biological networks with respect to diseases has led to the development of the field of network medicine. Recent examples of application of network theory in biology include applications to understanding the cell cycle as well as a quantitative framework for developmental processes. Good network inference requires proper planning and execution of an experiment, thereby ensuring quality data acquisition. Optimal experimental design in principle refers to the use of statistical and or mathematical concepts to plan for data acquisition. This must be done in such a way that the data information content is enriched, and a sufficient amount of data is collected with enough tech ...
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