Computational Biology and Machine Learning for Metabolic Engineering and Synthetic Biology

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.