The Network Analysis and Modelling group is devoted to investigate molecular mechanisms of phenotype emergence in crops by means of large-scale data integration on the genomic, transcriptomic, metabolomic, and phenomic level. In practical terms, we develop and implement machine learning approaches and network analysis algorithms that help us (or others) to discover new gene functions, or new interactions between genes, metabolites and phenotypes.

Some of our specific activities include e.g. implementations of Deep Learning for crops phenotype prediction and breeding, motif discovery in large multi-omic genome-to-phenome networks, and “gamification” of plant development. Our favourite crops include cereals but we have special affinity to solanaceous plants too. Furthermore, we provide statistical expertise, machine learning solutions, and data visualisation tools for interpretation of high-throughput data in IPK.

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AI for Gene Regulation

We are working on a toolbox of deep learning algorithms that can elucidate predictive relationships between sequence of non-coding gene regulatory elements, protein-DNA binding events, and expression patterns of their target genes. We train our algorithms on large resources of genetic variation, DNA binding and gene expression data across multiple species, tissues and treatments. Our models achieve high accuracy and represent a novel and promising approach to reconstruct mechanism-based gene regulatory networks. We use our toolbox to estimate the regulatory impact of structural genetic variation, highlight gene candidates in GWAS studies and design gene editing strategies for gene expression modulation.


Systems Genetics

Our other focus is are methods for integration of multi-omic data and identification of causal/predictive interactions between genetic variation, gene expression, metabolite levels and emergence of crop quality traits. In GWAS experiments, we integrate multi-omic data to identify the most likely gene-mechanism-phenotype paths. In time series experiments, we characterize the molecular events determing developmental and stress responses events in time. Here, we collaborate with multiple labs interested in elucidation of molecular mechanisms of genetic associations or identification of molecular traits for targeted breeding.


Gamification of Plant Life

In our lab, we explore the life of a plant as a captivating game of survival. We construct intricate molecular networks that serve as the game engines, driving the complex interactions that govern plant life and responses to environmental challenges. These networks become dynamic, interactive models that mirror the plants’ strategies for growth, defense, and adaptation. Harnessing the power of educational computer games, we invite students and researchers to step into this vibrant world. Through engaging, game-like simulations, participants can directly interact with our scientific models, gaining an immersive and intuitive understanding of plant biology. Our goal is to bridge science and education, providing a novel way to explore, learn, and contribute to our understanding of plant systems biology.

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Knoch D, Meyer R C, Heuermann M C, Riewe D, Peleke F F, Szymański J, Abbadi A, Snowdon R J, Altmann T:

Integrated multi-omics analyses and genome-wide association studies reveal prime candidate genes of metabolic and vegetative growth variation in canola. Plant J. 117 (2024) 713-728. https://dx.doi.org/10.1111/tpj.16524


Luzarowska U, Russ A-K, Joubès J, Batsale M, Szymański J, Thirumalaikumar V P, Luzarowski M, Wu S, Zhu F, Endres N, Khedhayir S, Schumacher J, Jasinska W, Xu K, Cordoba S M C, Weil S, Skirycz A, Fernie A R, Li-Beisson Y, Fusari C M, Brotman Y:

Hello darkness, my old friend: 3-KETOACYL-COENZYME A SYNTHASE4 is a branch point in the regulation of triacylglycerol synthesis in Arabidopsis thaliana. Plant Cell 35 (2023) 1984-2005. https://dx.doi.org/10.1093/plcell/koad059

Radchuk V, Belew Z M, Gündel A, Mayer S, Hilo A, Hensel G, Sharma R, Neumann K, Ortleb S, Wagner S, Muszynska A, Crocoll C, Xu D, Hoffie I, Kumlehn J, Fuchs J, Peleke F F, Szymanski J J, Rolletschek H, Nour-Eldin H H, Borisjuk L:

SWEET11b transports both sugar and cytokinin in developing barley grains. Plant Cell 35 (2023) 2186-2207. https://dx.doi.org/10.1093/plcell/koad055


Arend D, Psaroudakis D, Memon J A, Rey-Mazón E, Schüler D, Szymanski J J, Scholz U, Junker A, Lange M:

From data to knowledge - big data needs stewardship, a plant phenomics perspective. Plant J. 111 (2022) 335-347. https://dx.doi.org/10.1111/tpj.15804

Fattel L, Psaroudakis D, Yanarella C F, Chiteri K O, Dostalik H A, Joshi P, Starr D C, Vu H, Wimalanathan K, Lawrence-Dill C J:

Standardized genome-wide function prediction enables comparative functional genomics: a new application area for Gene Ontologies in plants. GigaScience 11 (2022) giac023. https://dx.doi.org/10.1093/gigascience/giac023

Koch D:

PlantEd: A game about growing plants and learning how. (Master Thesis) Konstanz, Hochschule Konstanz, Technik, Wirtschaft und Gestaltung (2022)

Osatohanmwen B E:

Multi-omics analysis of barley HEB-25 population - Linking genotype with phenotype using genomic networking. (Master Thesis) Göttingen, Georg-August-Universität Göttingen, Fakultät für Agrarwissenschaften (2022) 59 pp.

Panda S, Jozwiak A, Sonawane P D, Szymanski J, Kazachkova Y, Vainer A, Vasuki H, Almekias-Siegl E, Dikaya V, Bocobza S, Shohat H, Meir S, Wizler G, Giri A P, Schuurink R, Weiss D, Yasuor H, Kamble A, Aharoni A:

Steroidal alkaloids defense metabolism and plant growth are modulated by the joint action of gibberellin and jasmonate signaling. New Phytol. 233 (2022) 1220-1237. https://dx.doi.org/10.1111/nph.17845

Treves H, Küken A, Arrivault S, Ishihara H, Hoppe I, Erban A, Höhne M, Moraes T A, Kopka J, Szymanski J, Nikoloski Z, Stitt M:

Carbon flux through photosynthesis and central carbon metabolism show distinct patterns between algae, C3 and C4 plants. Nat. Plants 8 (2022) 78–91. https://dx.doi.org/10.1038/s41477-021-01042-5

Zheng S, Szymański J, Shahaf N, Malitsky S, Meir S, Wang X, Aharoni A, Rogachev I:

Metabolic diversity in a collection of wild and cultivated Brassica rapa subspecies. Front. Mol. Biosci. 9 (2022) 953189. https://dx.doi.org/10.3389/fmolb.2022.953189


Blätke M-A, Beier S, Scholz U, Gladilin E, Szymanski J J (Eds.):

Front. Plant Sci., Frontiers Research Topic “Advances in Applied Bioinformatics in Crops." Lausanne: Frontiers Media SA (2021) https://dx.doi.org/10.3389/978-2-88966-620-1

Blätke M-A, Szymanski J J, Gladilin E, Scholz U, Beier S:

Editorial: Advances in applied bioinformatics in crops. Front. Plant Sci. 12 (2021) 640394. https://dx.doi.org/10.3389/fpls.2021.640394

Khayer A:

Characterization of co-evolution and co-expression patterns in tomato heat-stress response - systems approach for gene function discovery. (Master Thesis) Göttingen & Kassel, Georg-August-Universität Göttingen, Fakultät für Agrarwissenschaften & Universität Kassel, Fachbereich Ökologische Agrarwissenschaften (2021)

Machado M, Vaz M G M V, Bromke M A, Rosa R M, Covell L, de Souza L P, Rocha D I, Martins M A, Araújo W L, Szymański J, Nunes-Nesi A:

Metabolic stability of freshwater Nitzschia palea strains under silicon stress associated with triacylglycerol accumulation. Algal Res. 60 (2021) 102554. https://dx.doi.org/10.1016/j.algal.2021.102554

Municio C, Antosz W, Grasser K, Kornobis E, van Bel M, Eguinoa I, Coppens F, Bräutigam A, Lermontova I, Bruckmann A, Zelkowska K, Houben A, Schubert V:

The Arabidopsis condensin CAP-D subunits arrange interphase chromatin. New Phytol. 230 (2021) 972-987. https://dx.doi.org/10.1111/nph.17221

Sahu A, Blätke M-A, Szymański J J, Töpfer N:

Advances in flux balance analysis by integrating machine learning and mechanism-based models. Comput. Struct. Biotechnol. J. 19 (2021) 4626-4640. https://doi.org/10.1016/j.csbj.2021.08.004


Forbang Peleke F:

Impact of genetic variation on the activity of gene promoters in Arabidopsis thaliana - Deep learning meets plant genomics. (Master Thesis) Mittweida, Hochschule Mittweida, Fakultät Angewandte Computer- und Biowissenschaften (2020)

Korenblum E, Dong Y, Szymanski J, Panda S, Jozwiak A, Massalha H, Meir S, Rogachev I, Aharoni A:

Rhizosphere microbiome mediates systemic root metabolite exudation by root-to-root signaling. Proc. Natl. Acad. Sci. U.S.A. 117 (2020) 3874-3883. https://dx.doi.org/10.1073/pnas.1912130117

Psaroudakis D, Liu F, König P, Scholz U, Junker A, Lange M, Arend D:

isa4j: a scalable Java library for creating ISA-Tab metadata [version 1; peer review: 2 approved]. F1000Research 9(ELIXIR) (2020) 1388. https://doi.org/10.12688/f1000research.27188.1

Stock J, Bräutigam A, Melzer M, Bienert G P, Bunk B, Nagel M, Overmann J, Keller E R J, Mock H P:

The transcription factor WRKY22 is required during cryo-stress acclimation in Arabidopsis shoot tips. J. Exp. Bot. 71 (2020) 4993-5009. https://dx.doi.org/10.1093/jxb/eraa224

Szymański J, Bocobza S, Panda S, Sonawane P, Cárdenas P D, Lashbrooke J, Kamble A, Shahaf N, Meir S, Bovy A, Beekwilder J, Tikunov Y, Romero de la Fuente I, Zamir D, Rogachev I, Aharoni A:

Analysis of wild tomato introgression lines elucidates the genetic basis of transcriptome and metabolome variation underlying fruit traits and pathogen response. Nat. Genet. 52 (2020) 1111-1121. https://dx.doi.org/10.1038/s41588-020-0690-6

Treves H, Siemiatkowska B, Luzarowska U, Murik O, Fernandez-Pozo N, Moraes T A, Erban A, Armbruster U, Brotman Y, Kopka J, Rensing S A, Szymanski J, Stitt M:

Multi-omics reveals mechanisms of total resistance to extreme illumination of a desert alga. Nat. Plants 6 (2020) 1031-1043. https://dx.doi.org/10.1038/s41477-020-0729-9


Blätke M A, Bräutigam A:

Evolution of C4 photosynthesis predicted by constraint-based modelling. eLife 8 (2019) e49305. https://dx.doi.org/10.7554/eLife.49305

Cárdenas P D, Sonawane P D, Heinig U, Jozwiak A, Panda S, Abebie B, Kazachkova Y, Pliner M, Unger T, Wolf D, Ofner I, Vilaprinyo E, Meir S, Davydov O, Gal-On A, Burdman S, Giri A, Zamir D, Scherf T, Szymanski J, Rogachev I, Aharoni A:

Pathways to defense metabolites and evading fruit bitterness in genus Solanum evolved through 2-oxoglutarate-dependent dioxygenases. Nat. Commun. 10 (2019) 5169. https://dx.doi.org/10.1038/s41467-019-13211-4

Cohen H, Dong Y, Szymanski J, Lashbrooke J, Meir S, Almekias-Siegl E, Zeisler-Diehl V V, Schreiber L, Aharoni A:

A multilevel study of melon fruit reticulation provides insight into skin ligno-suberization Hallmarks. Plant Physiol. 179 (2019) 1486-1501. https://dx.doi.org/10.1104/pp.18.01158

Meyer R C, Gryczka C, Neitsch C, Müller M, Bräutigam A, Schlereth A, Schön H, Weigelt-Fischer K, Altmann T:

Genetic diversity for nitrogen use efficiency in Arabidopsis thaliana. Planta 250 (2019) 41–57. https://dx.doi.org/10.1007/s00425-019-03140-3


Blätke M-A, Rohr C:

BioModelKit: spatial modelling of complex multiscale molecular biosystems based on modular models. Fundamenta Informaticae 160 (2018) 221-254. https://dx.doi.org/10.3233/FI-2018-1682

Blätke M A:

BioModelKit - an integrative framework for multi-scale biomodel-engineering. J. Integr. Bioinform. 15 (2018) 20180021. https://dx.doi.org/10.1515/jib-2018-0021

Ramírez-González R H, Borrill P, Lang D, Harrington S A, Brinton J, Venturini L, Davey M, Jacobs J, van Ex F, Pasha A, Khedikar Y, Robinson S J, Cory A T, Florio T, Concia L, Juery C, Schoonbeek H, Steuernagel B, Xiang D, Ridout C J, Chalhoub B, Mayer K F X, Benhamed M, Latrasse D, Bendahmane A, Wulff B B H, Appels R, Tiwari V, Datla R, Choulet F, Pozniak C J, Provart N J, Sharpe A G, Paux E, Spannagl M, Bräutigam A, Uauy C:

The transcriptional landscape of polyploid wheat. Science 361 (2018) eaar6089. https://dx.doi.org/10.1126/science.aar6089

Saper G, Kallmann D, Conzuelo F, Zhao F, Toth T N, Liveanu V, Meir S, Szymanski J, Aharoni A, Schuhmann W, Rothschild A, Schuster G, Adir N:

Live cyanobacteria produce photocurrent and hydrogen using both the respiratory and photosynthetic systems. Nat. Commun. 9 (2018) 2168. https://dx.doi.org/10.1038/s41467-018-04613-x


Bräutigam A, Eisenhut M, Schlüter U, Gowik U:

On the evolutionary origin of CAM photosynthesis. Plant Physiol. 174 (2017) 473-477. https://dx.doi.org/10.1104/pp.17.00195

Brouwer P, Bräutigam A, Buijs V A, Tazelaar A O E, van der Werf A, Schlüter U, Reichart G J, Bolger A, Usadel B, Weber A P M, Schluepmann H:

Metabolic adaptation, a specialized leaf organ structure and vascular responses to diurnal N2 fixation by Nostoc azollae sustain the astonishing productivity of Azolla ferns without nitrogen fertilizer. Front. Plant Sci. 8 (2017) 442. https://dx.doi.org/10.3389/Fpls.2017.00442

Denton A K, Mass J, Kulahoglu C, Lercher M J, Bräutigam A, Weber A P:

Freeze-quenched maize mesophyll and bundle sheath separation uncovers bias in previous tissue-specific RNA-Seq data. J. Exp. Bot. 68 (2017) 147-160. https://dx.doi.org/10.1093/jxb/erw463

Eisenhut M, Bräutigam A, Timm S, Florian A, Tohge T, Fernie A R, Bauwe H, Weber A P M:

Photorespiration is crucial for dynamic response of photosynthetic metabolism and stomatal movement to altered CO2 availability. Mol. Plant 10 (2017) 47-61. https://dx.doi.org/10.1016/j.molp.2016.09.011

König S, Eisenhut M, Bräutigam A, Kurz S, Weber A P M, Büchel C:

The influence of a cryptochrome on the gene expression profile in the diatom Phaeodactylum tricornutum under blue light and in darkness. Plant Cell Physiol. 58 (2017) 1914-1923. https://dx.doi.org/10.1093/pcp/pcx127

Rademacher N, Wrobel T J, Rossoni A W, Kurz S, Bräutigam A, Weber A P M, Eisenhut M:

Transcriptional response of the extremophile red alga Cyanidioschyzon merolae to changes in CO2 concentrations. J. Plant Physiol. 217 (2017) 49-56. https://dx.doi.org/10.1016/j.jplph.2017.06.014

Schlüter U, Bräutigam A, Gowik U, Melzer M, Christin P-A, Kurz S, Mettler-Altmann T, Weber A P:

Photosynthesis in C3–C4 intermediate Moricandia species. J. Exp. Bot. 68 (2017) 191-206. https://dx.doi.org/10.1093/jxb/erw391

Thirulogachandar V, Alqudah A M, Koppolu R, Rutten T, Graner A, Hensel G, Kumlehn J, Bräutigam A, Sreenivasulu N, Schnurbusch T, Kuhlmann M:

Leaf primordium size specifies leaf width and vein number among row-type classes in barley. Plant J. 91 (2017) 601-612. https://dx.doi.org/10.1111/tpj.13590


Bräutigam A, Gowik U:

Photorespiration connects C3 and C4 photosynthesis. J. Exp. Bot. 67 (2016) 2953-2962. https://dx.doi.org/10.1093/jxb/erw056

Döring F, Streubel M, Bräutigam A, Gowik U:

Most photorespiratory genes are preferentially expressed in the bundle sheath cells of the C4 grass Sorghum bicolor. J. Exp. Bot. 67 (2016) 3053-3064. https://dx.doi.org/10.1093/jxb/erw041

Schlüter U, Denton A K, Bräutigam A:

Understanding metabolite transport and metabolism in C4 plants through RNA-seq. Curr. Opin. Plant Biol. 31 (2016) 83-90. https://dx.doi.org/10.1016/j.pbi.2016.03.007

Xu J, Bräutigam A, Li Y, Weber A P M, Zhu X-G:

Systems analysis of cis-regulatory motifs in C4 photosynthesis genes using maize and rice leaf transcriptomic data during a process of de-etiolation. J. Exp. Bot. 67 (2016) 5105-5117. https://dx.doi.org/10.1093/jxb/erw275

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