Cell Systems Informatics

Cell Systems Informatics: Fueling faster design of new gene targets or product pathways
 
Cell systems informatics permits faster design as well as efficient testing and learning about new gene targets or product pathways. This technology applies our proprietary bioinformatics software and database systems with our comprehensive knowledge of genome scale modeling, experimental data and cell production systems (including CHO, or Chinese hamster ovary, a common biomanufacturing cell platform). The result of this combination affords us a distinct advantage in the development and deployment of high value production cell lines optimized through the selection and development of gene programs and cellular systems.
 
Encompassing licensed and internally-developed technologies, this platform has been instrumental in developing comprehensive in silico models of cellular metabolism, biosynthetic pathways, and cell signaling networks responsible for controlling cell viability, productivity, and complex responses to diverse environmental stimuli.
 
Our bioinformatics platform is also central to our protein engineering expertise, which focuses on designing proteins with enhanced stability, solubility and post-translational modifications. Bioinformatics enables computer-aided drug discovery and development of novel enzyme inhibitors and fusion proteins for different markets.  Our competencies include antibody design, prediction of antibody functionality and optimization of protein pharmacokinetics. To obtain improved or novel catalytic activities, our protein engineering utilizes principles of structural biology, computational chemistry, molecular biology and bioinformatics. Our proprietary library of genetic information catalogs comprehensive information about the components based on structure-based sequence alignment, de novo and comparative protein modeling, molecular dynamics simulation and free energy analysis. Quantitative analysis with machine learning algorithms further enables the generation, optimization and prioritization of protein variant libraries for diverse applications.
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