Cybernetic Modeling for Bioreaction Engineering (Cambridge Series in Chemical Engineering)

Ramkrishna, Doraiswami; Song, Hyun-Seob

ISBN 10: 1107000521 / ISBN 13: 9781107000520

Published by Cambridge University Press, 2018

About this Item

Seller Inventory # 18384194714

Bibliographic Details

• Title: Cybernetic Modeling for Bioreaction Engineering (Cambridge Series in Chemical Engineering)
• Publisher: Cambridge University Press
• Publication Date: 2018
• Language: English
• ISBN 10: 1107000521
• ISBN 13: 9781107000520
• Binding: Hardcover
• Condition: New

About this title

Synopsis

Uniquely focusing on dynamic modeling, this volume incorporates metabolic regulation as a survival mechanism for cells, by driving metabolism through optimal investment of its resources for control of enzyme synthesis and activity. Consequently, the models have a proven record of describing various uptake patterns of mixed carbon substrates that have become significant in modern applications of biomass for the production of bioenergy. The models accurately describe dynamic behavior of microbes in nutrient environments with mixtures of complementary substrates, such as carbon and nitrogen. Modeling of large metabolic networks (including prospects for extension to genome scale) is enabled by lumped hybrid cybernetic models with an unparalleled capacity to predict dynamic behavior of knockout strains. This is an invaluable, must-have reference for bio-researchers and practicing engineers.

About the Authors

Doraiswami Ramkrishna is the Harry Creighton Peffer Distinguished Professor of Chemical Engineering at Purdue University, Indiana. He pioneered the development of dynamic metabolic modeling, and has been active in the area for over thirty years. He is a member of the National Academy of Engineering, coined the term 'cybernetic modeling', and has authored several academic books.

Hyun-Seob Song is a Senior Research Scientist at Pacific Northwest National Laboratory, Washington (PNNL). His new development of metabolic pathway analysis enabled dynamic metabolic modeling for complex, large-scale networks. He is also active in the areas of network inference and microbial community modeling.