Hal S. Alper – författare
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3 produkter
3 produkter
1 278 kr
Skickas inom 10-15 vardagar
With the ultimate goal of systematically and robustly defining the specific perturbations necessary to alter a cellular phenotype, systems metabolic engineering has the potential to lead to a complete cell model capable of simulating cell and metabolic function as well as predicting phenotypic response to changes in media, gene knockouts/overexpressions, or the incorporation of heterologous pathways. In Systems Metabolic Engineering: Methods and Protocols, experts in the field describe the methodologies and approaches in the area of systems metabolic engineering and provide a step-by-step guide for their implementation. Four major tenants of this approach are addressed, including modeling and simulation, multiplexed genome engineering, ‘omics technologies, and large data-set incorporation and synthesis, all elucidated through the use of model host organisms. Written in the highly successful Methods in Molecular Biology™ series format, chapters include introductions on their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Comprehensive and cutting-edge, Systems Metabolic Engineering: Methods and Protocols serves as an ideal guide for metabolic engineers, molecular biologists, and microbiologists aiming to implement the most recent approaches available in the field.
1 068 kr
Skickas inom 10-15 vardagar
This book describes the methodologies and approaches in systems metabolic engineering and provides a step-by-step guide for their implementation. Includes readily reproducible laboratory protocols and tips on troubleshooting.
1 856 kr
Skickas inom 3-6 vardagar
Enables researchers and engineers to gain insights into the capabilities of machine learning approaches to power applications in their fields Machine Learning and Big Data-enabled Biotechnology discusses how machine learning and big data can be used in biotechnology for a wide breadth of topics, providing tools essential to support efforts in process control, reactor performance evaluation, and research target identification. Topics explored in Machine Learning and Big Data-enabled Biotechnology include: Deep learning approaches for synthetic biology part design and automated approaches for GSM development from DNA sequencesDe novo protein structure and design tools, pathway discovery and retrobiosynthesis, enzyme functional classifications, and proteomics machine learning approachesMetabolomics big data approaches, metabolic production, strain engineering, flux design, and use of generative AI and natural language processing for cell modelsAutomated function and learning in biofoundries and strain designsMachine learning predictions of phenotype and bioreactor performanceMachine Learning and Big Data-enabled Biotechnology earns a well-deserved spot on the bookshelves of reaction, process, catalytic, and environmental engineers seeking to explore the vast opportunities presented by rapidly developing technologies.