PubMed · 6542887
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1984. What is biomedical engineering?. https://pubmed.ncbi.nlm.nih.gov/6542887/
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Education in biomedical engineering offers a number of challenges to all constituents of the educational process-faculty, students, and employers of graduates. Although biomedical engineering educational systems have been under development for 40 years, interest in and the pace of development of these programs has accelerated in recent years. New advances in the learning sciences have provided a framework for the reexamination of instructional paradigms in biomedical engineering. This work shows that learning environments should be learner centered, knowledge centered, assessment centered, and community centered. In addition, learning technologies offer the potential to achieve this environment with efficiency. Biomedical engineering educators are in a position to design and implement new learning systems that can take advantage of advances in learning science, learning technology, and reform in engineering education.
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Mathematical modeling is one of the key methodologies of metabolic engineering. Based on a given metabolic model different computational tools for the simulation, data evaluation, systems analysis, prediction, design and optimization of metabolic systems have been developed. The currently used metabolic modeling approaches can be subdivided into structural models, stoichiometric models, carbon flux models, stationary and nonstationary mechanistic models and models with gene regulation. However, the power of a model strongly depends on its basic modeling assumptions, the simplifications made and the data sources used. Model validation turns out to be particularly difficult for metabolic systems. The different modeling approaches are critically reviewed with respect to their potential and benefits for the metabolic engineering cycle. Several tools that have emerged from the different modeling approaches including structural pathway synthesis, stoichiometric pathway analysis, metabolic flux analysis, metabolic control analysis, optimization of regulatory architectures and the evaluation of rapid sampling experiments are discussed.