Dear Colleagues,

The landscape of R&D in biotechnology is rapidly changing. Innovations are supported not only by advanced technologies to access versatile information of living systems but also by increasingly automated and parallelized operations in robots as well as by more efficient and self-learning computational approaches. A major pillar unifying these trends is the potential for rapidly generated large datasets with the associated challenge to store, standardize, and process such data. From gene sequencing, through drug screening, to simulation of the dynamic behavior of industrial bioreactors, new methods and tools are being developed, enabling improved understanding, confidence, and decision support. This Special Issue is devoted to the impact of computer-aided methods on the diverse stages of development and manufacture of biopharmaceutical drugs. The focus is set on computer methods that support the different stages and scales in R&D: bioinformatics (gene-level modeling), molecular dynamics simulation, cell-level modeling including MFA and FBA, reactor-scale modeling such as CFD and model-based process control, system-level modeling for high-throughput and sequential experimental design and operation, as well as modeling approaches supporting the regulatory PAT (e.g., soft sensors) and QbD approaches (e.g., process design and optimization).

Possible topics of interest of this Special Issue include, but are not limited to:

• Big Data in gene sequencing
• Genome, metabolic, and flux modeling
• Big Data in bio-analytics and soft sensors
• Big Data in high-throughput systems
• Dynamical modeling of biosystems
• Computational fluid dynamics for bioreactors
• Bioprocess design, optimization, and upscaling

Dr. Michael Sokolov
Dr. Mariano Nicolas Cruz Bournazou
Guest Editors

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• big data
• data analytics
• gene sequencing
• omics
• flux balances analysis
• bioprocess modeling
• design of experiments
• multivariate data analysis
• hybrid modeling
• computer aided bioprocess engineering
• bioprocess optimization
• bioprocess automation