Analytics for Metabolic Engineering

Analytics for Metabolic Engineering

Realizing the promise of metabolic engineering has been slowed by challenges related to moving beyond proof-of-concept examples to robust and economically viable systems. Key to advancing metabolic engineering beyond trial-and-error research is access to parts with well-defined performance metrics that can be readily applied in vastly different contexts with predictable effects. As the field no...

Actionable Analytics for Software Engineering

Trill: Engineering a Library for Diverse Analytics

Trill is a streaming query processor that fulfills three requirements to serve the diverse big data analytics space: (1) Query Model: Trill is based on the tempo-relational model that enables it to handle streaming and relational queries with early results, across the latency spectrum from real-time to offline; (2) Fabric and Language Integration: Trill is architected as a high-level language l...

Genetic-Metabolic Coupling for Targeted Metabolic Engineering.

Production of chemicals in microbes often employs potent biosynthetic enzymes, which can interact with the microbial native metabolism to affect cell fitness and product yield. However, production optimization largely relies on data collected from wild-type strains in the absence of metabolic perturbations, thus limiting their relevance to specific conditions. Here, we address this issue by cou...

Analytics in Continuous Requirements Engineering

In continuous requirements engineering, timely knowledge about required and possible changes is essential. However, even one of the most important sources of knowledge – users of the systems, not always are aware of existing problems and all relevant future trends that can cause the need for changes. Therefore, to be more aware of possible changes, different analytics approaches are becoming em...