Computational Design and Analytics

The Computational Design and Analytics (CDA) team is a key component of the Biofoundry, a state-of-the-art facility for engineering biological systems and processes. The CDA team’s main mission is to guide and accelerate the development of new cell factories, which are microorganisms that can produce valuable chemicals, fuels, materials and proteins from renewable resources. 

To achieve this mission, the CDA team leverages data-driven, model-based strain design approaches, which combine experimental data, mathematical models, and computational algorithms to optimize the genetic and metabolic features of cell factories. These approaches enable the CDA team to explore a large and complex design space to identify promising candidates. 

The CDA team consists of specialists in computational biology, bioinformatics, and machine learning, who have expertise in various aspects of data analysis, modelling, simulation, optimization, and machine learning. The CDA team mainly operates in the “D” and “L” sections of the Design – Build – Test – Learn pipeline. 

For the Design part, the CDA team provides solutions for designing cell factories based on the desired product, host organism, and production pathway. The CDA team uses tools such as genome-scale models, metabolic flux analysis and various public databases for interventions that are required to establish a cell factory or enhance the productivity, yield, robustness of a lead strain. 

For the Learn part, the CDA team provides solutions for analysing the data generated from the Test section, which involves measuring the performance of cell factories in small scale screening setups to larger scale bioreactors. The CDA team utilizes tools fitting available data - such as iModulon analytics, model fitting, or machine learning - to extract useful insights to provide feedback for the next cycle of design. 

The CDA team works closely with other Biofoundry teams to design and execute efficient and informative experiments, and to integrate data from various sources, such as genomics, transcriptomics, proteomics, metabolomics, and fluxomics. 

By applying computational design and analytics methods, the CDA team aims to enhance the capabilities of the Biofoundry, and contribute to the advancement of biomanufacturing for a sustainable future. 

Technologies

• Transcriptomics analysis (through ICA/iModulons or other methods)
• Proteomics data analysis
• Resequencing and variant analysis
• Genome scale modelling and design
• OMICS data integration
• Data mining
• Design/implement/execute data analysis workflows

Example Work

Diagnosis and troubleshooting of arrest in melatonin production strain

Selected Publications

1. Advanced Transcriptome Analysis Reveals Regulatory Basis for a Trade-Off Between Resistance and Persistence in High Cell-Density Escherichia Coli Fermentations. F. Beulig, P.E. Jensen, S.H. Kim, E. Özdemir, J.B. Rührer, L. Yang, D. Zielinski, B.O. Palsson, S. Sudarsan.(To be submitted in Jan 2024)
2. Understanding and exploitation of host stress responses to protein production using novel transcriptomic analytics. Christoffer R., Beulig F., Jönsson M., Jahn L.J., Nørholm N., Palsson B.O., Özdemir E, Yang L.
3. Machine Learning Uncovers the Transcriptional Regulatory Network for the Production Host Streptomyces albidoflavus J1074. Mathias Jönsson, Renata Sigrist, Nils Marcussen, Mykhaylo Petrov, Tetiana Gren, Bernhard O. Palsson, Lei Yang, Emre Özdemir.