|
|
|
|
| LEADER |
03736nmm a2200289 u 4500 |
| 001 |
EB002120810 |
| 003 |
EBX01000000000000001258867 |
| 005 |
00000000000000.0 |
| 007 |
cr||||||||||||||||||||| |
| 008 |
221107 ||| eng |
| 020 |
|
|
|a 9781071626177
|
| 100 |
1 |
|
|a Selvarajoo, Kumar
|e [editor]
|
| 245 |
0 |
0 |
|a Computational Biology and Machine Learning for Metabolic Engineering and Synthetic Biology
|h Elektronische Ressource
|c edited by Kumar Selvarajoo
|
| 250 |
|
|
|a 1st ed. 2023
|
| 260 |
|
|
|a New York, NY
|b Humana
|c 2023, 2023
|
| 300 |
|
|
|a XII, 455 p. 160 illus., 133 illus. in color
|b online resource
|
| 505 |
0 |
|
|a Challenges to Ensure a Better Translation of Metabolic Engineering for Industrial Applications -- Synthetic Biology Meets Machine Learning -- Design and Analysis of Massively Parallel Reporter Assays using FORECAST -- Modelling Protein Complexes and Molecular Assemblies using Computational Method -- From Genome Mining to Protein Engineering: A Structural Bioinformatics Route -- Creating De Novo Overlapped Genes -- Design of Gene Boolean Gates and Circuits with Convergent Promoters -- Computational Methods for the Design of Recombinase Logic Circuits with Adaptable Circuit Specifications -- Designing a Model-Driven Approach Towards Rational Experimental Design in Bioprocess Optimization -- Modeling Subcellular Protein Recruitment Dynamics for Synthetic Biology -- Genome-Scale Modeling and Systems Metabolic Engineering of Vibrio Natriegens for the Production of 1,3-Propanediol -- Application of GeneCloudOmics: Transcriptomics Data Analytics for Synthetic Biology -- Overview of Bioinformatics Software and Databases for Metabolic Engineering -- Computational Simulation of Tumor-Induced Angiogenesis -- Computational Methods and Deep Learning for Elucidating Protein Interaction Networks -- Machine Learning Methods for Survival Analysis with Clinical and Transcriptomics Data of Breast Cancer -- Machine Learning Using Neural Networks for Metabolomic Pathway Analyses -- Machine Learning and Hybrid Methods for Metabolic Pathway Modeling -- A Machine Learning Based Approach Using Multi Omics Data to Predict Metabolic Pathways
|
| 653 |
|
|
|a Bioinformatics
|
| 653 |
|
|
|a Computational and Systems Biology
|
| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
|
| 989 |
|
|
|b Springer
|a Springer eBooks 2005-
|
| 490 |
0 |
|
|a Methods in Molecular Biology
|
| 028 |
5 |
0 |
|a 10.1007/978-1-0716-2617-7
|
| 856 |
4 |
0 |
|u https://doi.org/10.1007/978-1-0716-2617-7?nosfx=y
|x Verlag
|3 Volltext
|
| 082 |
0 |
|
|a 570.113
|
| 082 |
0 |
|
|a 570.285
|
| 520 |
|
|
|a This 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 SyntheticBiology is a valuable resource for any researcher or scientist who wants to learn more about the latest computational methods and how they are applied toward the understanding and prediction of complex biology.
|