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Bioeconomy International
2027
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Simon Maria Zumkeller
Germany
Forschungszentrum Juelich
Research organisation
Machine Learning, Plant science, Regulation, Design, AI, Gene Editing
Cooperation with Japan
AI-Driven Precision Design of Plant Gene Regulation (crop.IAEGER)
I am reaching out to explore potential synergies and offer the use of our in development platform, crop.IAEGER, which aligns with the goals of advancing the bioeconomy through predictive plant trait design.
As traditional gene editing faces complex regulatory frameworks and public skepticism, future market acceptance will likely be restricted to technologies making minimal, non-transgenic changes. To overcome these bottlenecks, our team at Forschungszentrum Jülich (Szymanski Group - Systems Biology and Machine Learning for Better Crops) has developed a solution that shifts from disruptive genome modifications to minimally invasive regulatory tuning.
We would be highly interested in offering our platform to support your trait optimization pipelines. Specifically, we can integrate the following capabilities:
Deep Learning Models for Automated Gene Optimization: Our explainable AI models, deepCRE and deepCIS, predict gene expression levels and transcription factor binding directly from DNA sequences. We integrate these into genRE, an AI-driven ensemble modeling tool for the multi-objective optimization of regulatory DNA. Beyond this, we are very much interested to integrate other modalities via DL modelling approaches to the genRE approach.
Targeted Mutagenesis on Trait-Related Regulators: Instead of severe gene knockouts, we focus on identifying single nucleotide polymorphisms (SNPs) and cis-regulatory variations that drive precise gain or loss of function in critical transcription factors (e.g., MYB, bZIP, ERF/DREB), that at best are relevant for growth and development.
I would welcome the opportunity to briefly present how our automated target search could specifically benefit your upcoming projects.
Best regards,
Dr. Simon Zumkeller
I am reaching out to explore potential synergies and offer the use of our in development platform, crop.IAEGER, which aligns with the goals of advancing the bioeconomy through predictive plant trait design.
As traditional gene editing faces complex regulatory frameworks and public skepticism, future market acceptance will likely be restricted to technologies making minimal, non-transgenic changes. To overcome these bottlenecks, our team at Forschungszentrum Jülich (Szymanski Group - Systems Biology and Machine Learning for Better Crops) has developed a solution that shifts from disruptive genome modifications to minimally invasive regulatory tuning.
We would be highly interested in offering our platform to support your trait optimization pipelines. Specifically, we can integrate the following capabilities:
Deep Learning Models for Automated Gene Optimization: Our explainable AI models, deepCRE and deepCIS, predict gene expression levels and transcription factor binding directly from DNA sequences. We integrate these into genRE, an AI-driven ensemble modeling tool for the multi-objective optimization of regulatory DNA. Beyond this, we are very much interested to integrate other modalities via DL modelling approaches to the genRE approach.
Targeted Mutagenesis on Trait-Related Regulators: Instead of severe gene knockouts, we focus on identifying single nucleotide polymorphisms (SNPs) and cis-regulatory variations that drive precise gain or loss of function in critical transcription factors (e.g., MYB, bZIP, ERF/DREB), that at best are relevant for growth and development.
I would welcome the opportunity to briefly present how our automated target search could specifically benefit your upcoming projects.
Best regards,
Dr. Simon Zumkeller
The Forschungszentrum Jülich operates as a limited liability company, officially registered as Forschungszentrum Jülich GmbH. It was initially established as a registered association (e.V.) but transitioned to its current GmbH status in 1967.
The center's research focuses heavily on solving grand societal challenges across three primary domains: Energy, Information, and Sustainable Bioeconomy. This includes developing new applications from biological resources and making agriculture fit for climate change.
The computational backbone of FZJ is managed by the Jülich Supercomputing Centre (JSC), which operates some of the most powerful supercomputing and data infrastructures in the world. Amongst, others leading infrastructure, FZJ is the host site for JUPITER, Europe's first exascale supercomputer. It is designed to surpass one quintillion calculations per second (1 ExaFLOP/s) and provides unmatched performance for artificial intelligence research and large-scale simulations.
The Szymanski Lab, officially recognized for its work in "Systems Biology and Machine Learning for Better Crops," is led by Dr. Jędrzej Jakub Szymański. The lab operates at the cutting edge of computational plant biology through a prominent dual affiliation at two of Germany's leading research institutions. The team utilizes deep learning models trained on extensive multi-species sequence and omics datasets to decipher the cis-regulatory code and protein-DNA interactions. Their highly accurate tools (such as deepCRE and deepCIS) reconstruct gene networks, assess how genetic variation impacts phenotypes, and ultimately guide targeted gene editing for precise expression modulation. By integrating multi-modal data—including genomic, transcriptomic, metabolomic, and phenomic information—the lab pinpoints exactly how genetic variations and metabolites interact during plant development and environmental stress responses.
The center's research focuses heavily on solving grand societal challenges across three primary domains: Energy, Information, and Sustainable Bioeconomy. This includes developing new applications from biological resources and making agriculture fit for climate change.
The computational backbone of FZJ is managed by the Jülich Supercomputing Centre (JSC), which operates some of the most powerful supercomputing and data infrastructures in the world. Amongst, others leading infrastructure, FZJ is the host site for JUPITER, Europe's first exascale supercomputer. It is designed to surpass one quintillion calculations per second (1 ExaFLOP/s) and provides unmatched performance for artificial intelligence research and large-scale simulations.
The Szymanski Lab, officially recognized for its work in "Systems Biology and Machine Learning for Better Crops," is led by Dr. Jędrzej Jakub Szymański. The lab operates at the cutting edge of computational plant biology through a prominent dual affiliation at two of Germany's leading research institutions. The team utilizes deep learning models trained on extensive multi-species sequence and omics datasets to decipher the cis-regulatory code and protein-DNA interactions. Their highly accurate tools (such as deepCRE and deepCIS) reconstruct gene networks, assess how genetic variation impacts phenotypes, and ultimately guide targeted gene editing for precise expression modulation. By integrating multi-modal data—including genomic, transcriptomic, metabolomic, and phenomic information—the lab pinpoints exactly how genetic variations and metabolites interact during plant development and environmental stress responses.