Genome Designs leverages years of experience in metabolic engineering, including successful commercial projects both in prokaryotes and eukaryotes.
We are using a proprietary mathematical modeling technology to design bioproduction processes and improve their speed and yield. During the process, we combine portions of existing networks, evaluate possible metabolic designs, and search for new paths through the ‘substructure search’. Then we suggest targets for genome modifications, dramatically lowering wet lab development costs and increasing production limits far beyond normal forecasts.
- First, we analyze and select possible natural designs
- Then we build realistic mathematical models which allow numeric optimization for the highest yield and speed
- Then we translate the optimized model's requirements towards genome engineering
Analysis of Existing Natural Designs
Using existing, naturally optimized metabolic designs as potential building blocks can greatly increase the speed and efficiency of bioproduction processes. Here, Genome Designs holds a unique advantage: its MPW database of metabolic pathways includes all known variations of pathways throughout the organisms, as well as kinetic data on hundreds of enzymes.
Optimization for Maximum Productivity
Our optimization method goes way beyond traditional flux-balance analysis. In our metabolic engineering, we are using much more realistic kinetic and dynamic models for numeric optimization of the processes. Asymptotic methods of mathematical modeling allow us to dramatically simplify models by reducing the number of essential variables. Therefore, numeric optimization gets easier, and the number of required experimentally measured parameters drastically decreases.
Our dynamic models include descriptions of temporal organization of cellular metabolism and allow us to predict necessary regulatory mechanisms and numeric values of the system's parameters.
Metabolic Engineering: Cooperation with Wet Lab Development
The best results in metabolic engineering are achieved when in-silico modeling is supported by experimental work in the client's lab. The iteration between the mathematical simulation and experimental efforts allows us to quickly cut off numerous unrealistic or underperforming designs.
Please contact us for more information.