Alper H. Machine Learning and Big Data-enabled Biotechnology 2026

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Alper H. Machine Learning and Big Data-enabled Biotechnology 2026

Textbook in PDF format 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 (ML) 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 ML provides a powerful framework for developing computational algorithms that learn from experimental data. By leveraging a diverse range of algorithms, ML enables the automatic construction of data-driven models for descriptive, predictive and prescriptive ends. Descriptors can be used to understand bioprocess dynamics for knowledge acquisition, predictors can estimate latent features of interest or iteratively recommend the next steps in lab experiments, and prescriptors can leverage longitudinal bioreactor studies to explore the suitability of unseen conditions for ultimately enhancing various process outcomes. Numerous ML algorithms have been developed and are readily available via open-source Python packages. Generally, ML models can be categorized into two main classes: supervised and unsupervised learning methods (reinforcement learning (RL) will not be addressed due to the limited examples in the context of engineering microbial cell factories). The selection of the most suitable ML method depends on the desired outputs. 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. Preface FromGenometoActionableInsightsinBiotechnology AutomatedApproachesfortheDevelopmentofGenome-Scale MetabolicNetworkModels Machine-GuidedApproachesforSyntheticBiologyPart Design UQAqJ3KzD1l6VDGbaijIcsSeOss0FSBZggu_gmpy2kcvzuIE PredictionofEnzymeFunctionsbyArtificialIntelligence DesignofBiochemicalPathwaysviaAI/ML-Enabled Retrobiosynthesis MachineLearningtoAcceleratetheDiscoveryofTherapeutic Peptides LbGfVMx3N35jSYrAcJpywgjtCmB1tCLgga-ThroughputMicrobial Identification/Culturing GenerativeAIforKnowledgeMiningofSyntheticBiologyand BioprocessEngineeringLiterature Metabolomics:BigDataApproaches StrainEngineering,FluxDesign,andMetabolicProduction UsingBigData:OngoingAdvancesandOpportunities Next-GenerationMetabolicFluxAnalysisUsingMachine Learning StreamliningtheDesign-Build-Test-LearnProcessin AutomatedBiofoundries MachineLearning-EnhancedHybridModelingforPhenotype PredictionandBioreactorOptimization