Research

Together with the bioprocess experts in the team, we will also investigate whether improved general forecasting models can be ‘distilled’ into improved mechanistic models.

One essential aspect of the modelling is active learning or sequential optimal experimental design. The purpose is to propose the most informative experiments to reduce the number and cost of experiments needed to achieve a certain forecasting quality.  The active learning methods will have to compare to the established methods of (sequential) optimal experimental design for classical parametrized models.

The ultimate goal of the process forecasting is to enable the optimization of culture conditions and feeding profiles with respect to maximal yield of some product. In collaboration with Task Force 2, we will use general and hybrid forecasting models for model predictive control, but also work on a reinforcement-learning based control mechanism in end-to-end learning.

Together with the University of Greifswald, Task Force 1 is working on methods for an improved enzyme screening. In a ‘directed evolution’ scenario, we want to go beyond the selection of best variants towards the recommendation of new variants by means of machine learning models, using previous screening, sequence information and possibly structural information. The open-source and free software LARA suite, developed at the University of Greifswald for automated high-throughput screening, is to be extended in order to facilitate the integration of machine learning models (as developed in the KIWI-biolab) into the screening workflow.

Original project archive

The nMPC framework, including model free methods (e.g. reinforcement learning (Kim 2020)), hybrid models (e.g. Step-Wise Gaussian Process models (Bradford 2018), symbolic regression models), and first principle dynamical models (e.g. macrokinetic growth models (Anane 2017)), will be directly coupled with the DataBase and low level automation in TF4.

Optimization techniques that shall enforce autonomous and parallel bioprocess development to search for the most sustainable production strategy will be screened, further developed, and integrated. To this aim proper relaxations of large optimization problems with sustainable cost functions (e.g. planetary boundaries (Algunaibet 2019)) in parallel structures need to be developed to cope with the computational efficiency required in the nMPC framework of the 48 minibioreactor facility (Krausch 2019).

Finally, this implementation will be highly modular (running in micro systems) and fully compatible with the tools and programs developed in Task Force 1 (ML models for forecast, active learning, and optimization) and Task Force 3 (automated signal processing tools) so as to enable a systematic computation, analysis, and evaluation of corresponding large and complex optimization problems (Martinez 2013), and seamless integration of all elements developed by the KIWI-biolab and partners in one workflow.

References:

Abt 2008 – Abt, Vinzenz, et al. “Model-based tools for optimal experiments in bioprocess engineering.” Current opinion in chemical engineering 22 (2018): 244-252.

Algunaibet 2019 – Algunaibet, Ibrahim M., et al. “Powering sustainable development within planetary boundaries.” Energy & environmental science 12.6 (2019): 1890-1900.

Anane 2017 – Anane, Emmanuel; Neubauer, Peter; and Cruz Bournazou, M. Nicolas. “Modelling overflow metabolism in Escherichia coli by acetate cycling.” Biochemical Engineering Journal 125 (2017): 23-30.

Bradford 2018 – Bradford, Eric, et al. “Dynamic modeling and optimization of sustainable algal production with uncertainty using multivariate Gaussian processes.” Computers & Chemical Engineering 118 (2018): 143-158.

Kim 2020 – Kim, Jong Woo, et al. “A model-based deep reinforcement learning method applied to finite-horizon optimal control of nonlinear control-affine system.” Journal of Process Control 87 (2020): 166-178.

Krausch 2019 – Krausch, Niels, et al. “From Screening to Production: a Holistic Approach of High-throughput Model-based Screening for Recombinant Protein Production.” Computer Aided Chemical Engineering. Vol. 48. Elsevier, 2020. 1723-1728.

Martinez 2013 – Martínez, Ernesto C.; Cristaldi, Mariano D.; and Grau, Ricardo J. “Dynamic optimization of bioreactors using probabilistic tendency models and Bayesian active learning.” Computers & Chemical Engineering 49 (2013): 37-49.

Original project archive

Bright field (left) and fluorescence microscopic (right) image with a 1:5 magnification of a single E.coli cell 135 min after induction with IPTG. Image: Irmgard Schäffl.

Bright field (left) and fluorescence microscopic (middle) image with a 1:5 magnification of a single cell 45 min after cultivation start. The panels on the right show a layered bright field and fluorescence image. Cells exhibit a “triple” structure of inclusion bodies (two at polar region, one in the middle). Image: Irmgard Schäffl.

Original project archive

The structure, connection and data architecture developed in here creates the basis for the KIWI-biolab project and its success. In order to give the algorithms maximum freedom for optimization, a constant rescheduling of the experiment is necessary, including the automated process and the inputs. At the same time, the efficiency, robustness and traceability of the experiments must be ensured. For this challenge, we will use AI algorithms to address the scheduling problem. The automated data processing, which serves as the basis for the tools in the KIWI-biolab, will be expanded. The recorded data will undergo an automated data pre-treatment step (outliers, correlated parameters, etc.). In order to be able to master the high-dimensional process with up to 500 variables to be monitored, the process data processing, visualization and storage will be developed in close coordination with the subcontractors.

The experimental work takes place in automated high-throughput laboratories at the TU Berlin and at the University of Greifswald. The test planning, test execution and test evaluation will meet the F.A.I.R data principles. The aim is to create a F.A.I.R data pipeline by generating and using open (source) data and communication standards.

To enable a standardized experimental framework SiLA standards will be implemented for controlling and coupling the laboratory instruments. Uniform data storage and processing will be reached via the AnIML standard. The standardized and automated biotechnology laboratory enables interoperative work. This ensures that the achievements of this project can be transferred directly to external laboratories and bioprocess development projects.

Original project archive