{"product_id":"computational-biology-and-machine-learning-for-metabolic-engineering-and-synthetic-biology-9781071626191","title":"Computational Biology and Machine Learning for Metabolic Engineering and Synthetic Biology","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis volume provides protocols for computational, statistical, and machine learning methods that are mainly applied to the study of metabolic engineering, synthetic biology, and disease applications. These techniques support the latest progress in cross-disciplinary research that integrates the different scales of biological complexity. The topics covered in this book are geared toward researchers with a background in engineering, computational analytical, and modeling experience and cover a broad range of topics in computational and machine learning approaches. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to 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 practical, Computational Biology and Machine Learning for Metabolic Engineering and Synthetic Biology is a valuabl\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003ePreface…\u003cbr\u003eTable of Contents…\u003cbr\u003eContributing Authors…\u003cbr\u003e\u003cbr\u003e1.\tChallenges to Ensure a Better Translation of Metabolic Engineering for Industrial Applications\u003cbr\u003eFayza Daboussi and Nic D. Lindley\u003cbr\u003e\u003cbr\u003e2.\tSynthetic Biology Meets Machine Learning\u003cbr\u003eBrendan Fu-Long Sieow, Ryan De Sotto, Zhi Ren Darren Seet, In Young Hwang, and Matthew Wook Chang\u003cbr\u003e\u003cbr\u003e3.\tDesign and Analysis of Massively Parallel Reporter Assays using FORECAST\u003cbr\u003ePierre-Aurelien Gilliot and Thomas E. Gorochowski\u003cbr\u003e\u003cbr\u003e4.\tModelling Protein Complexes and Molecular Assemblies using Computational Method\u003cbr\u003eRomain Launay, Elin Teppa, Jérémy Esque, and Isabelle André\u003cbr\u003e\u003cbr\u003e5.\tFrom Genome Mining to Protein Engineering: A Structural Bioinformatics Route\u003cbr\u003eDerek J. Smith\u003cbr\u003e\u003cbr\u003e6.\tCreating De Novo Overlapped Genes\u003cbr\u003eDominic Y. Logel and Paul R. Jaschke \u003cbr\u003e\u003cbr\u003e7.\tDesign of Gene Boolean Gates and Circuits with Convergent Promoters\u003cbr\u003eBiruck Woldai Abraha and Mario Andrea Marchisio\u003cbr\u003e\u003cbr\u003e8.\tComputational Methods for the Design of Recombinase Logic Circuits with Adaptable Circuit Specifications\u003cbr\u003eAna Zúñiga, Jérôme Bonnet, and Sarah Guiziou\u003cbr\u003e\u003cbr\u003e9.\tDesigning a Model-Driven Approach Towards Rational Experimental Design in Bioprocess Optimization\u003cbr\u003eJing Wui Yeoh and Chueh Loo Poh\u003cbr\u003e\u003cbr\u003e10.\tModeling Subcellular Protein Recruitment Dynamics for Synthetic Biology\u003cbr\u003eKwabena A. Badu-Nkansah, Diana Sernas, Dean E. Natwick, and Sean R. Collins\u003cbr\u003e\u003cbr\u003e11.\tGenome-Scale Modeling and Systems Metabolic Engineering of Vibrio Natriegens for the Production of 1,3-Propanediol\u003cbr\u003eYe Zhang, Dehua Liu, and Zhen Chen\u003cbr\u003e\u003cbr\u003e12.\tApplication of GeneCloudOmics: Transcriptomics Data Analytics for Synthetic Biology\u003cbr\u003eMohamed Helmy and Kumar Selvarajoo\u003cbr\u003e\u003cbr\u003e13.\tOverview of Bioinformatics Software and Databases for Metabolic Engineering\u003cbr\u003eDeena M.A. Gendoo\u003cbr\u003e\u003cbr\u003e14.\tComputational Simulation of Tumor-Induced Angiogenesis\u003cbr\u003eMasahiro Sugimoto\u003cbr\u003e\u003cbr\u003e15.\tComputational Methods and Deep Learning for Elucidating Protein Interaction Networks\u003cbr\u003eDhvani Sandip Vora, Yogesh Kalakoti, and Durai Sundar\u003cbr\u003e\u003cbr\u003e16.\tMachine Learning Methods for Survival Analysis with Clinical and Transcriptomics Data of Breast Cancer\u003cbr\u003eLe Minh Thao Doan, Claudio Angione, and Annalisa Occhipinti\u003cbr\u003e\u003cbr\u003e17.\tMachine Learning Using Neural Networks for Metabolomic Pathway Analyses\u003cbr\u003eRosalin Bonetta Valentino, Jean-Paul Ebejer, and Ingc Gianluca Valentino\u003cbr\u003e\u003cbr\u003e18.\tMachine Learning and Hybrid Methods for Metabolic Pathway Modeling\u003cbr\u003eMiroslava Cuperlovic-Culf, Thao Nguyen-Tran, and Steffany A.L. Bennett\u003cbr\u003e\u003cbr\u003e19.\tA Machine Learning Based Approach Using Multi Omics Data to Predict Metabolic Pathways\u003cbr\u003eVidya Niranjan, Akshay Uttarkar, Aakaanksha Kaul, and Maryanne Varghese\u003cbr\u003e\u003cbr\u003eSubject Index List…\u003c\/p\u003e\u003cbr\u003e","brand":"Springer-Verlag New York Inc.","offers":[{"title":"Default Title","offer_id":52090796245335,"sku":"9781071626191","price":89.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781071626191.jpg?v=1762273491","url":"https:\/\/bookcurl.com\/products\/computational-biology-and-machine-learning-for-metabolic-engineering-and-synthetic-biology-9781071626191","provider":"Book Curl","version":"1.0","type":"link"}