DNA strands

Taming complexity

Our Technology

Cell factories need to be not only created, but also improved. Using a Design-Build-Test cycle allows rapid yeast cell factory development, which is fundamental for improving production titres, yields and rates. Our strong expertise in combining computational methods, advanced synthetic biology tools and high throughput screening enables targeted strain construction and testing.



Design: Creativity meets Prediction Power

Biology is complex. To be able to engineer biology, robust algorithms are needed in order to understand and predict its outcome. To achieve this, we combine large sets of data, predictive models and vast knowledge of yeast metabolism to confidently design the cell factories we need.



YEASTdesigner™ is our computational toolbox. Using genome scale metabolic models, we can predict engineering targets and optimal protein allocation under production conditions. With this approach, the biological system can be modeled in a holistic way for optimal usage of resources, channeling metabolic fluxes towards the product of interest.


Collaborations with Chalmers University of Technology gives us access to world-leading expertise in metabolic modelling and systems biology.

Relevant publications

1. Nielsen, Jens. “Systems Biology of Metabolism.” Annual Review of Biochemistry 86:245-275 (2017)
2. Österlund, Tobias, et al. “Mapping condition-dependent regulation of metabolism in yeast through genome-scale modeling.” BMC systems biology 7:36 (2013)

Build: Concept becomes reality

Building a yeast strain means bringing an idea to life. After the design process, the yeast cells need to be engineered at the DNA level. This is usually a laborious and time-consuming process. Our processes are optimized and designed for maximum efficiency to bring a design into reality.



Our YEASTbuilder™ pipeline allows rapid construction of yeast cell factories for production of target compounds. We continuously generate and maintain collections of genetic parts and strategies to precisely engineer yeast metabolism. This includes:

Library of genetic parts (plasmids, integration sites, promoters, terminators, linkers)

Pathway control modules

Validated fine-tuned gene expression

Libraries of enzymes and strains


Relevant publications

1. Teixeira, Paulo G., et al. “Dynamic regulation of fatty acid pools for improved production of fatty alcohols in Saccharomyces cerevisiae.” Microbial cell factories 16:45 (2017)
2. David, Florian and Siewers, Verena. “Advances in yeast genome engineering.” FEMS yeast research 15:1-14 (2015)
3. Fletcher, Eugene, Anastasia Krivoruchko and Jens Nielsen. “Industrial systems biology and its impact on synthetic biology of yeast cell factories.” Biotechnology and Bioengineering 113:1164-1170 (2016)


Laboratory Evolution

Adaptive laboratory evolution is used to create strains with increased robustness and product tolerance. By combining laboratory evolution with biosensors, cells with higher production capacities can be selected.

Test: Screen, Learn, Improve

Complex biological systems have a large variety of variables, some of these unknown. For that reason, millions of yeast cell variants need to be continuously tested for the best performance. Use of biosensors for target and intermediate compounds allows high-throughput analysis of all these cell varieties.



Our YeastSense™ platform allows for high-throughput screening and development of many different yeast cell variants. We currently use several biosensors for important precursors and end products. We are constantly working on developing new biosensors to further accelerate yeast cell factory design.


Biosensor use can be extended to many applications:

Screening for high producers

Screening for engineered enzymes

Metabolic pathway control

Identification of new engineering targets

Stabilization of culture for high producers


Bioreactor cultivation combined with OMICs

Strong candidate strains for production are grown and characterized in fully-controlled bioreactors (up to 3L) and data is collected on physiology and OMICs levels. Large datasets are gathered at different levels such as:




We get information on cellular response to metabolic modifications thereby uncovering potential bottlenecks, leading to new target identification. At the same time this information is fed back into the design module, including models, to allow for better target prediction.