We are a research group at the KTH Royal Institute of Technology in Stockholm, Sweden. Our laboratory is located at the Science for Life Laboratory. We work to create bacteria strains that can efficiently convert carbon dioxide into biochemicals. Along the way, we explore fundamental metabolic processes around carbon fixation.
Two CO₂-fixing bacteria with industrial potential
We work primarily with cyanobacteria. Cyanobacteria derive energy from light and fix CO₂ via the Calvin cycle, the same series of reactions present in the chloroplasts of algae and most crop plants. For biochemists, cyanobacteria are model systems for photosynthesis. In biotechnology, they are potential converters of CO₂ to chemicals or proteins. However, to realize cyanobacteria in industry, their metabolism must be extensively engineered. Futher, mechanical and materials engineering are needed to produce low-cost photobioreactors and to maximize light delivery.
We also develop CO₂-fixing lithoautotrophic bacteria. These do not derive energy from light, but instead can utilize hydrogen or waste gases such as carbon monoxide to power CO₂ fixation. Both gases are plentiful in Swedish industry as byproducts of the pulp and paper industry. Increasingly, hydrogen is prouced by electrolysis of water. For us, lithoautotrophic bacteria also provide an evolutionarily distant example of the Calvin cycle to compare to cyanobacteria.
Metabolic engineering is a cycle of design, build, and test. Our capabilities in each of these evolve with advances in technology.
Most bacteria have not evolved to synthesize molecules for mankind. We endow them with new capabilities through the metabolic engineering cycle. We use algorithms such as POPPY to design synthetic metabolic pathways to target compounds. To implement these pathways, as well as to supress the cell's native metabolism, we have adopted and refined CRISPR/Cas for gene editing and CRISPR interference to repress genes. Targeted repression of a key enzyme can force alternative metabolic pathways into use, or arrest cell growth so that cultures can be maintained at a suitable density. We also create libraries of mutant strains that can be analyzed using high-throughput screening platforms at SciLifeLab.
As we pursue metabolic engineering, we must consider which parts of metabolism are fruitful to modify. Metabolism and its regulation are complex, even in bacteria, and intuition alone cannot navigate it. Systems biology collates and interprets large datasets from different parts of metabolism. For example, our software RedMagPie examines the genome sequences of bacteria that do or don’t have the Calvin cycle, to learn how CO₂ fixation impacts the metabolic network. RNA sequencing can reveal the signalling pathways that are active when the cell environment is altered. We use quantitative proteomics allows to estimate which enzymes are most abundant, and learn how enzyme catalysis may be regulated. For example, if the quantity of an enzyme stays constant while metabolic rates change, then it may be regulated by a post-translational mechanism. To interpret and layer these large datasets, we use computational metabolic modeling. Our software K1 identifies which parts of the Calvin cycle, if perturbed, are most likely to have an impact on CO₂ fixation.
The timeframes to create the first generation of cell factories were long, but new technologies are accelerating this.
- Identifying flux control in CO₂ fixation using genetic libraries and high-throughput screening
- Exploring regulation of the Calvin cycle with proteomics
- Developing growth-arrest in CO2-fixing bacteria to enhance product synthesis
- Machine-learning based enzyme engineering
- Gene expression analysis with RNA-Seq and Ribosome profiling, using in-house Illumina machines
- Quantitative and speciality proteomics by mass spectroscopy (collaboration with Fredrik Edfors at SciLifeLab)
- Genetic libraries for gene knockdown, knockout, and overexpression
- Cell screening and sorting using droplet microfluidics (collaboration with Håkan Jönsson)
- Metabolic modeling at small- and genome-scale
- Machine learning of gene and genome sequences