Machine Learning and Big Data-enabled Biotechnology -

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Résumé :

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 sequences
• De novo protein structure and design tools, pathway discovery and retrobiosynthesis, enzyme functional classifications, and proteomics machine learning approaches
• Metabolomics big data approaches, metabolic production, strain engineering, flux design, and use of generative AI and natural language processing for cell models
• Automated function and learning in biofoundries and strain designs
• Machine learning predictions of phenotype and bioreactor performance

Machine 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....

Biographie:

Dr. Hal S. Alper is the Cockrell Family Regents Chair in Engineering #1 at The University of Texas at Austin in the McKetta Department of Chemical Engineering. His research focuses on applying and extending the approaches of metabolic engineering, synthetic biology, systems biology, and protein engineering....

Sommaire:

Preface xv

1 From Genome to Actionable Insights in Biotechnology 1
James Morrissey, Benjamin Strain, and Cleo Kontoravdi

1.1 Introduction 1

1.2 From Genome to Network 2

1.2.1 Metabolic Networks 3

1.2.1.1 Bottom-Up Approaches for Network Reconstruction 3

1.2.1.2 Top-Down Approaches for Network Reconstruction 4

1.2.2 Networks Beyond Metabolism 5

1.3 From Draft to Functional Network 6

1.3.1 Additional Reactions 6

1.3.1.1 Exchange Reactions 6

1.3.1.2 Demand Reactions 6

1.3.1.3 Transport Reactions 6

1.3.1.4 Spontaneous Reactions 7

1.3.1.5 Nongrowth Associated ATP Maintenance 7

1.3.1.6 Biomass Reaction 7

1.3.2 Network Validation 7

1.3.2.1 Manual Screening 8

1.3.2.2 Screening for Dead-End Reactions and Blocked Metabolites 8

1.3.2.3 Infinite Loops 9

1.3.2.4 Leaks and Siphons 10

1.4 From Functional Network to Model 10

1.4.1 Flux Balance Analysis 11

1.4.2 Flux Variability Analysis 12

1.4.3 Flux Sampling 13

1.5 From Model to In Silico Predictions 15

1.5.1 Constraints 15

1.5.2 Objective Function 16

1.5.3 Validating In Silico Predictions 16

1.5.3.1 Growth Rate Predictions 22

1.5.3.2 Amino Acid Auxotrophies 22

1.5.3.3 Gene Essentialities 22

1.5.3.4 Known Host Traits 23

1.5.3.5 Intracellular Predictive Accuracy 23

1.5.4 Toward Multilayer, Multiscale Metabolic Networks 23

1.5.4.1 Integrating Gene Regulatory Networks 24

1.5.4.2 Integrating Transcription and Translation 25

1.5.4.3 Integrating Signaling Networks 25

1.5.4.4 Multicellular and Multitissue Models 25

1.5.4.5 Multiscale Bioreactor Models 26

1.6 From Predictions to Actionable Insights in Biotechnology 26

1.6.1 Metabolic Engineering 26

1.6.2 Cell Line Development and Metabolic Profiling 27

1.6.3 Media and Feed Design 29

1.6.4 Gene Essentiality 30

1.6.5 Kinetic Parameter Estimation 30

1.6.6 Process Monitoring and Forecasting 30

References 31

2 Automated Approaches for the Development of Genome-Scale Metabolic Network Models 43
Emma M. Glass, Deborah A. Powers, and Jason A. Papin

2.1 Introduction 43

2.2 Manual GSM Creation 44

2.3 Automated GSM Development 45

2.3.1 General Approach for Automated GSM Methods 45

2.3.2 GSM Construction Tools 46

2.3.2.1 From Raw Sequences 46

2.3.2.2 From Pre-annotated Sequences 52

2.3.2.3 From Reaction Database Information 53

2.3.2.4 Based on Existing GSMs 56

2.3.2.5 GSM Modification and Visualization Tools 57

2.4 Applications of Automatically-Generated GSM Collections 59

2.4.1 AGORA1 - 773 GSMs 59

2.4.2 EMBL GEMs - 5,587 GSMs 60

2.4.3 MetaGEM - 447 GSMs 61

2.4.4 AGORA2-7,302 GSMs 61

2.4.5 PATHGENN - 914 GSMs 62

2.5 Future Directions for the Field of Automated GSM Development 62

2.6 Conclusion 63

References 63

3 Machine-Guided Approaches for Synthetic Biology Part Design 67
Marc Amil, Leandro N. Ventimiglia, and Aleksej Zelezniak

3.1 Introduction 67

3.2 Model-Guided Sequence Design Using Deep Learning 70

3.2.1 Predictive Models for DNA Function: CNNs in Regulatory Sequence Analysis 70

3.2.1.1 Data Considerations for Supervised Learning on Genomic Sequences 72

3.2.1.2 Primer on Convolutional Neural Networks for Supervised Genomic Sequence Modeling 73

3.2.2 Generative Sequence Modeling 75

3.2.2.1 Data Preparation for Unsupervised Learning 76

3.2.2.2 GANs for the Design of Biological Se...