Name: Workshop on Digital Bioeconomy: Convergence towards a bio-based society
Start: 2024-02-20T00:00:00+01:00
End: 2024-02-20T18:00:00+01:00
Location: Forschungszentrum Jülich

Workshop on Digital Bioeconomy: Convergence towards a bio-based society

Tuesday 20 February 2024 - 00:00

Monday 19 February 2024
Tuesday 20 February 2024
09:00 Introduction / Welcome address - Holger Gohlke (IBG-4, Forschungszentrum Jülich GmbH) Peter Jansens (VS, Forschungszentrum Jülich GmbH)
Introduction / Welcome address
- Holger Gohlke (IBG-4, Forschungszentrum Jülich GmbH)
- Peter Jansens (VS, Forschungszentrum Jülich GmbH)
09:00 - 09:15
Room: Lecture hall of the central library
09:15 FairAGRO in a nutshell - making agrosystem domain data fair and sustainable - Björn Usadel (IBG-4, Forschungszentrum Jülich GmbH)
FairAGRO in a nutshell - making agrosystem domain data fair and sustainable
- Björn Usadel (IBG-4, Forschungszentrum Jülich GmbH)
09:15 - 09:45
Room: Lecture hall of the central library Here, we will present the NFDI FairAGRO dedicated to agrosystem data. These data require an integrated systems perspective to develop sustainable crop production systems, consider interactions between agriculture and the environment (e.g., plants, soil, microbiota), and relationships between scales (space, time, and organisms). Special features of agrosystem research data are geospatial information which might be coupled to data collected on private land (cropland, pasture). Accordingly, data protection aspects must be taken into account here. The goal of the community-driven FairAGRO consortium is to provide researchers with a FAIR and quality-assured research data management (RDM) for generating, publishing, and accessing research data, to provide innovative and user-friendly RDM services, and to create modern data science methods for advancing agrosystems research. Using a Use case driven approach FairAGRO addresses key RDM challenges and contributes to the implementation of standards and services for selected research data infrastuctures. We will give an overview of FairAGRO the challenges in agrosystems research and FairAGRO's approach.
09:45 EnzymeML – a data exchange format for biocatalysis and enzymology - Jan Range (University Stuttgart)
EnzymeML – a data exchange format for biocatalysis and enzymology
- Jan Range (University Stuttgart)
09:45 - 10:15
Room: Lecture hall of the central library The design of biocatalytic reaction systems can be quite intricate, as numerous factors can impact the estimated kinetic parameters, including the enzyme itself, reaction conditions, and the chosen modelling method. This complexity can make reproducing enzymatic experiments and reusing enzymatic data challenging. EnzymeML 1 was created as an XML-based markup language to address this issue, enabling the storage and exchange of enzymatic data, including reaction conditions, substrate and product time course, kinetic parameters, and kinetic models. This approach makes enzymatic data accessible, findable, interoperable, and reusable (FAIR). Furthermore, the EnzymeML team and community have developed a toolbox that helps researchers report on experiments, perform modelling tasks, and upload EnzymeML documents to data repositories. The usefulness and feasibility of the EnzymeML toolbox have been demonstrated in various scenarios by collecting and analysing the data and metadata of different enzymatic reactions 2 . EnzymeML is a seamless communication channel that connects experimental platforms, electronic lab notebooks, tools for modelling enzyme kinetics, publication platforms, and enzymatic reaction databases. EnzymeML is an open standard and available via https://github.com/EnzymeML and http://enzymeml.org.
10:15 Enhancing Bioeconomy Research Through FAIR Data Management: The Development and Application of LISTER for Efficient Metadata Extraction - Fathoni Musyaffa (University Düsseldorf)
Enhancing Bioeconomy Research Through FAIR Data Management: The Development and Application of LISTER for Efficient Metadata Extraction
- Fathoni Musyaffa (University Düsseldorf)
10:15 - 10:45
Room: Lecture hall of the central library The importance of scientific methods, code, and data availability for replicating experiments is vital. This is particularly true in the context of the bioeconomy, where research data should be made available following the FAIR principle (findable, accessible, interoperable, and reusable). To facilitate this, we have developed LISTER, a solution designed to create and extract metadata from annotated, template-based experimental documentation, avoiding manual annotation effort for the experiment data generated during the research activities. LISTER is designed to integrate with existing platforms, using eLabFTW as the electronic lab notebook and the ISA (investigation, study, assay) model as the abstract data model framework. It consists of four components: an annotation language for metadata extraction, customized eLabFTW entries to structure scientific documentation, a "container" concept in eLabFTW for easy metadata extraction, and a Python-based app for semiautomated metadata extraction. The metadata produced by LISTER can be used as a base to create or extend domain-specific controlled vocabulary and ontologies, enhancing the value of published research data when applied. This is particularly relevant in the bioeconomy sector, where data-driven insights can drive sustainable solutions for global challenges such as food security and climate change. LISTER outputs metadata in machine-readable .json and human-readable .xlsx formats, and Material and Methods (MM) descriptions in .docx format that could be used in a thesis or manuscript. The extracted metadata and research data are hosted on DSpace, a data cataloging platform. We have successfully applied LISTER to computational biophysical chemistry, protein biochemistry, and molecular biology. We believe our concept can be extended to other areas of the life sciences, including those relevant to the bioeconomy, such as plant science research.
10:45 Poster session/Coffee break
Poster session/Coffee break
10:45 - 11:45
Room: Lecture hall of the central library
Contributions

10:45 Quantum Mechanics/Molecular Mechanics (QM/MM) simulations: A modeling tool for Biomedicine and Biotechnology - Mercedes Alfonso-Prieto (Forschungszentrum Jülich)

10:45 Design and Learn: Computational tools for guiding the development of a sustainable bioprocess - Aziz Ben Ammar (institute of Applied Microbiology, RWTH Aachen University) Samira van den Bogaard (institute of Applied Microbiology, RWTH Aachen)

10:45 Open Source Tools and FAIR Data Formats for Mass Spectrometry Lipidomics - Nils Hoffmann (IBG-5, Forschungszentrum Jülich GmbH)

10:45 TopEnzyme significantly improves classification of enzyme function by incorporating computational enzyme structures. - Karel van der Weg (Forschungszentrum Juelich)

10:45 TransMEP: Transfer learning on large protein language models to predict mutation effects of proteins from a small known dataset - Birgit Strodel (Forschungszentrum Jülich)

10:45 Engineering PET-degrading enzymes – targeting the energy barrier for PET binding - Anna Jaeckering (Forschungszentrum Jülich)

10:45 Engineering of an organic solvent tolerant esterase based on computational predictions - Lara Scharbert

10:45 Exploring the Microbial Symphony - Unveiling the Dynamics of Biogas-Producing Communities - Irena Maus (IBG-5)

10:45 Seed-to-plant-tracking: Automated phenotyping and tracking of individual seeds and corresponding plants - Gregor Huber

10:45 Prediction of more thermostable ApPDC variants by Constraint Network Analysis and ProteinMPNN to enable an industrial application - Daniel Fritz Walter Becker (Heinrich-Heine-Universität)

10:45 Amine transaminase engineering based on constraint network analysis and machine learning - Steffen Docter
11:45 Learning more with less: Hybrid Models, Transfer Learning, and Calibration Designs for Efficient Knowledge Transfer in Industrial Settings - Moritz von Stosch (DataHow AG)
Learning more with less: Hybrid Models, Transfer Learning, and Calibration Designs for Efficient Knowledge Transfer in Industrial Settings
- Moritz von Stosch (DataHow AG)
11:45 - 12:15
Room: Lecture hall of the central library Industrial process development often follows a repetitive pattern, relying on the same experimental approach for each new product, despite incorporating knowledge into process templates (e.g. in form of platform processes which are widely spread in the (bio)pharmaceutical industry). While risk analysis under the Quality by Design (QbD) paradigm enables some vertical knowledge transfer, capturing the functional process behavior remains a challenge. Current efforts to develop generic mechanistic models face limitations due to their rigid structure, prompting the integration of machine-learning approaches for improved variability handling. This contribution explores the application of transfer learning in hybrid models, utilizing dummy variables, embeddings, and meta learners to transfer knowledge between molecules. Additionally, we present the concept of calibration designs, demonstrating how experiments can efficiently uncover process behavior for new molecules. Finally, we emphasize the necessity of establishing a standardized, self-learning environment for all stakeholders to effectively leverage model-derived knowledge in process development and tech transfer.
12:15 Facilitating Research Data Management for Fundamental Plant Science - Jens Krüger (Eberhard Karls Universität Tübingen)
Facilitating Research Data Management for Fundamental Plant Science
- Jens Krüger (Eberhard Karls Universität Tübingen)
12:15 - 12:45
Room: Lecture hall of the central library The evolution of digital methods and the broad availability of high throughput methods makes efficient research data management an essential necessity. This applies in particular to the field of fundamental plant science. Within the NFDI consortium DataPlant we rely on the ISA-Tab standard to annotate research data. Well annotated research data objects are called Annotated Research Context (ARC) and contain the actual research data as well as curated metadata describing its content. The specific benefit of using ARCs lies within their versioning and tracking of provenance, relying on Gitlab instances as federated repositories. DataPLANT offers various web-based services and tools to facilitate the handling of research data for scientists.
12:45 Group Photo
Group Photo
12:45 - 13:00
Room: Lecture hall of the central library

13:00 Lunch
Lunch
13:00 - 14:15
Room: Lecture hall of the central library
14:15 Modeling soil functions for a sustainable bioeconomy - Ute Wollschläger (Helmholtz Centre for Environmental Research)
Modeling soil functions for a sustainable bioeconomy
- Ute Wollschläger (Helmholtz Centre for Environmental Research)
14:15 - 14:45
Room: Lecture hall of the central library Soils occupy a central position in the ecosystem and provide essential functions for both, humans and the environment. On the one hand, they are used to produce biomass for human nutrition and animal feed, but also for bioenergy, and the production of various industrial materials. In addition, they are the largest terrestrial carbon reservoir, filter and store water, recycle nutrients and provide a habitat for an immense diversity of organisms. For many years, agriculture was almost exclusively production-oriented. Today, climate change, biodiversity loss and a number of other environmental problems are calling for a change to more sustainable production taking into account the entire bundle of soil functions. The BODIUM model (König et al., 2023) is a systemic soil model which aims to simulate the effect of changing agricultural management practices on soil functions such as yield, water storage and filtration, nutrient recycling, carbon storage and habitat for biodiversity. For this purpose, the influence of crop rotation, soil cultivation, fertilization as well as the effect of a changing climate is taken into account site-specifically. A version of this model, the BODIUM4Farmers, is intended to serve farmers as an on-site decision support tool for long-term planning of soil management measures in response to actual economic and ecological requirements. In this presentation, we introduce soil as a central resource for a sustainable bioeconomy and provide an overview about the BODIUM and BODIUM4Farmers models.
14:45 Getting value out of digitalization in biotech R&D - Jens Erik Nielsen (Novozymes A/S)
Getting value out of digitalization in biotech R&D
- Jens Erik Nielsen (Novozymes A/S)
14:45 - 15:15
Room: Lecture hall of the central library The ability to use data and predictive computational technologies holds a large promise for developing the next generation of solutions in industrial and pharma-based biotech. Large language model technology applied to protein sequences combined with application-directed AI oracles have made spectacular progress in some cases, and there is a growing awareness that data-based design could indeed revolutionize the discovery process for many protein-based solutions. In the present talk I will outline the view of these technologies from an industrial biotech view, and discuss the dilemmas that we have encountered over the years in Novozymes and now Novonesis in harnessing the power of computational biology and machine learning. I will discuss technical, practical and organizational aspects of how one can formulate the setup of a data-driven R&D organization and discuss successes and failures of our endeavors. Finally, I will speculate on the future, whether it will resemble the past, and muse on how the computational biology community could chose to invest its resources for maximum impact in the 2020s and beyond.
15:15 AI-powered protein engineering: the next frontier in biocatalyst development - Jelena Ivanovska (Exazyme GmbH)
AI-powered protein engineering: the next frontier in biocatalyst development
- Jelena Ivanovska (Exazyme GmbH)
15:15 - 15:45
Room: Lecture hall of the central library Enzymes are a transformative tool for the path towards a digital bioeconomy due to their ability to catalyze diverse biochemical processes in sustainable and adaptable ways. Protein engineering plays a pivotal role in advancing the development of such biocatalysts with applications spanning biotechnology, biomedicine, and life sciences. The advent of protein-directed evolution, recognized with the 2018 Nobel Prize, has enabled the customization of enzyme functions for a broad range of new-to-nature applications. Despite significant progress, challenges persist due to a limited understanding of protein function and the complex multi-factorial optimization problem inherent in enzymes and proteins. Recent advancements, however, integrate machine learning (ML) techniques to address these challenges. Exazyme focuses on leveraging ML algorithms to enhance protein functionalities, including enzyme properties. Our methodology employs a two-step ML model application. Initially, our models proficiently predict protein sequence-to-function mappings based on functionally assayed sequence variants, requiring minimal reliance on detailed mechanistic or structural data. This approach has proven particularly useful in low data regimes, where extensive screening is not possible. Subsequently, we utilize these predictions in a Bayesian optimization framework to guide the selection of candidates for experimental validation, enabling simultaneous optimization of multiple parameters, such as stability, catalytic speed, and substrate specificity. A noteworthy accomplishment of our research lies in the superior performance of our prediction algorithms, consistently outperforming current state-of-the-art methods, across various datasets and benchmarks. Practical validation of our algorithms was demonstrated through successful protein engineering campaigns, enhancing the functionality of complex enzymes from diverse families including carboxylases, hydrogenases, and phosphohydrolases. Our results underscore the capacity of ML methods to expedite the processes of directed evolution and rational design of proteins. By efficiently predicting and selectively identifying sequences with improved properties, these methods leverage existing sequence variant data to advance the field of protein engineering, and thereby a digital bioeconomy.
15:45 Coffee break
Coffee break
15:45 - 16:15
Room: Lecture hall of the central library
16:15 Computer Aided Enzyme Discovery and Engineering for Industrial Biocatalysis: From physics based MD to Machine Learning and AI - Marco Bocola (Enzymaster Deutschland GmbH)
Computer Aided Enzyme Discovery and Engineering for Industrial Biocatalysis: From physics based MD to Machine Learning and AI
- Marco Bocola (Enzymaster Deutschland GmbH)
16:15 - 16:45
Room: Lecture hall of the central library Enzyme engineering by directed evolution allows the optimisation of virtually any enzyme into a powerful industrial catalyst, which can be conveniently applied under process conditions. However, due to time and cost limitations, enzyme discovery from (meta)-genomic diversity often fails to deliver high performance catalysts ready for industrial applications and enzyme variant screening under process conditions can only cover a very limited protein sequence space. In order to overcome this limitation in Enzyme Discovery & Engineering, Enzymaster has developed the BioEngine®, a proprietary directed evolution platform with integrated computational enzyme identification/enzyme engineering toolbox BioNavigator®. This enables us to cover much larger sequence space using machine learning/AI for combining activity, 3D-structure and sequence information in our EM 2 L-platform for predictive in-sillico screening. In this presentation I will show, how Enzymaster is utilizing it`s digital toolbox in order to speed up our smart enzyme evolution platform BioEngine® by computer aided enzyme engineering. We combine high throughput screening and next generation sequencing for fast and reliable data generation with AI and machine learning approaches for virtual enzyme identification and screening. Enzymasters aim is to deliver practical solutions enabled by a powerful combination of experimental and virtual screening to overcome the hurdle from idea to product in a future data driven bioeconomy.
16:45 Soil sensing for soil health assessment – a contribution to digital bio-economy. - Robin Gebbers (University Halle-Wittenberg)
Soil sensing for soil health assessment – a contribution to digital bio-economy.
- Robin Gebbers (University Halle-Wittenberg)
16:45 - 17:15
Room: Lecture hall of the central library Soil is essential for crop production and has other important functions with regard to ecosystems and human health. Proper soil management requires detailed knowledge of soil state variables and processes. However, soil investigation is challenging because soil is a very complex and heterogeneous medium with high spatio-temporal variability. Traditional laboratory based soil analysis is expensive and time-consuming. Soil sensing, in particular proximal soil sensing, promises to provide soil data with high resolution at minimum costs. The talk will present the state-of-the art in proximal soil sensing and discusses its prospects and limitations.
17:15 Closing remarks - Holger Gohlke (IBG-4, Forschungszentrum Jülich GmbH)
Closing remarks
- Holger Gohlke (IBG-4, Forschungszentrum Jülich GmbH)
17:15 - 17:30
Room: Lecture hall of the central library

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