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reductive science > discovery science > systems biology > biological complexity Operationally defined, reductive science includes the vast majority of current biological and biomedical research in which a scientist examines a single gene, protein or pathway, reduces it to its simplest functional system, and attempts to ascertain and understand the structure and/or function of the reduced system. Discovery Science was first driven by genome sequencing, but now includes the characterization of the transcriptome, proteome and metabolome . Systems Biology entails an examination of global interactions in a system (molecular, pathway, organelle, cell, organism or ecosystem) in order to elucidate function. Complexity scientists acknowledge that life is "complex" and cannot be understood by the straightforward methods of the past. Living systems are a product of synergistic interactions that evoke emergent properties that are inexplicable by simple dissection of the underlying parts; i.e., the whole is greater than a sum of the parts. Thus, reductive methods of the past centuries will not provide the biological researcher with insights into or explanations for life processes. Many biomedical investigators have begun to appreciate the value of the approaches of Discovery Science, and some have begun to apply systems approaches in their work. However, very few invoke the principles of Complexity Science in their studies. Complexity scientists extend beyond the realm of systems biology. They assume that biological and biomedical processes cannot be understood via reductionist approaches, that systems are more than a sum of the parts, and that nonlinear interactions of components and processes result in emergent properties that can not be predicted from knowledge of the individual components and their behavioral processes. These scientists invoke mathematical and computational methods and models/simulations in order to explain system behavior. These models and simulations permit development of predictions and hypotheses that affects changes in the system. Because of the importance of the computational side of complexity theoretic approaches, the terminology “in silico” has emerged to describe the computational modeling of living systems.
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