1.
Balancing doses of EL222 and light improves optogenetic induction of protein production in Komagataella phaffii.
Abstract:
Komagataella phaffii, also known as Pichia pastoris, is a powerful host for recombinant protein production, in part due to its exceptionally strong and tightly controlled PAOX1 promoter. Most K. phaffii bioprocesses for recombinant protein production rely on PAOX1 to achieve dynamic control in two-phase processes. Cells are first grown under conditions that repress PAOX1 (growth phase), followed by methanol-induced recombinant protein expression (production phase). In this study, we propose a methanol-free approach for dynamic metabolic control in K. phaffii using optogenetics, which can help enhance input tunability and flexibility in process optimization and control. The light-responsive transcription factor EL222 from Erythrobacter litoralis is used to regulate protein production from the PC120 promoter in K. phaffii with blue light. We used two system designs to explore the advantages and disadvantages of coupling or decoupling EL222 integration with that of the gene of interest. We investigate the relationship between EL222 gene copy number and light dosage to improve production efficiency for intracellular and secreted proteins. Experiments in lab-scale bioreactors demonstrate the feasibility of the outlined optogenetic systems as potential alternatives to conventional methanol-inducible bioprocesses using K. phaffii.
2.
Toward a modeling, optimization, and predictive control framework for fed-batch metabolic cybergenetics.
Abstract:
Biotechnology offers many opportunities for the sustainable manufacturing of valuable products. The toolbox to optimize bioprocesses includes extracellular process elements such as the bioreactor design and mode of operation, medium formulation, culture conditions, feeding rates, and so on. However, these elements are frequently insufficient for achieving optimal process performance or precise product composition. One can use metabolic and genetic engineering methods for optimization at the intracellular level. Nevertheless, those are often of static nature, failing when applied to dynamic processes or if disturbances occur. Furthermore, many bioprocesses are optimized empirically and implemented with little-to-no feedback control to counteract disturbances. The concept of cybergenetics has opened new possibilities to optimize bioprocesses by enabling online modulation of the gene expression of metabolism-relevant proteins via external inputs (e.g., light intensity in optogenetics). Here, we fuse cybergenetics with model-based optimization and predictive control for optimizing dynamic bioprocesses. To do so, we propose to use dynamic constraint-based models that integrate the dynamics of metabolic reactions, resource allocation, and inducible gene expression. We formulate a model-based optimal control problem to find the optimal process inputs. Furthermore, we propose using model predictive control to address uncertainties via online feedback. We focus on fed-batch processes, where the substrate feeding rate is an additional optimization variable. As a simulation example, we show the optogenetic control of the ATPase enzyme complex for dynamic modulation of enforced ATP wasting to adjust product yield and productivity.
