Research Group for Nutriinformatics

The Nutriinformatics Research Group uses metabolic modelling approaches to gain a deeper understanding of how microbial metabolism contributes to the human metabolome in health and disease.

Research areas

  • Metabolic modelling
  • Constraint-based analysis of metabolic networks and cell communities
  • Reconstruction of genome-scale metabolic models from metagenomes
  • Computational metabolomics
  • Theoretical microbial ecology


Research focus

Microorganisms rarely live in isolation, but co-exist and interact in a tremendous variety of ways. The metabolic activities that are collectively performed by microbial communities through the exchange of metabolites shape the chemical composition of the environment. The human gut microbiome represents such a complex community and is densely-populated with a vast diversity of microorganisms. In the Nutriinformatics Research Group, we develop novel approaches to construct predictive models of host-microbiome metabolism by combining ecological theory with (meta-)genomic and metabolomic information.

Agent-based simulation of four human gut bacteria
Figure: Example of an in-silico individual-based metabolic simulation of a gut microbial community of four bacterial strains at µ-meter-scale. The metabolic models used in the simulation were reconstructed using gapseq based on the strains’ genome sequences. The simulation shown here depicts central metabolic interactions including the production of acetate by Bacteroides thetaiotaomicron and Bifidobacterium adolescentis; lactate production by B. adolescentis; and the production of butyrate by acetate-consuming Faecalibacterium prausnitzii and lactate-consuming Anaerobutyricum hallii.



Development of novel approaches to predict microbial metabolic interactions


Selected publications

  1. Zimmermann J, Kaleta C, Waschina S.
    gapseq: informed prediction of bacterial metabolic pathways and reconstruction of accurate metabolic models.
    Genome Biol. 2021/;22. DOI
  2. D’Souza G, Shitut S, Preussger D, Yousif G, Waschina S, Kost C.
    Ecology and evolution of metabolic cross-feeding interactions in bacteria.
    Nat Prod Rep. 2018/;35:455–88. DOI
  3. Marinos G, Kaleta C, Waschina S.
    Defining the nutritional input for genome-scale metabolic models: A roadmap.
    PLoS ONE. 2020/;15:e0236890. DOI


Systems biology of the gut microbiome in preterm infants


Selected publications

  1. Graspeuntner S, Waschina S, Künzel S, Twisselmann N, Rausch TK, Cloppenborg-Schmidt K, et al.
    Gut Dysbiosis With Bacilli Dominance and Accumulation of Fermentation Products Precedes Late-onset Sepsis in Preterm Infants.
    Clinical Infectious Diseases. 2018/;69:268–77. DOI
  2. Pagel J, Twisselmann N, Rausch TK, Waschina S, Hartz A, Steinbeis M, et al.
    Increased Regulatory T Cells Precede the Development of Bronchopulmonary Dysplasia in Preterm Infants.
    Front Immunol. 2020/;11. DOI


Interplay between gut microbial ecology and host immunological activity in chronic inflammation


Selected publications

  1. Aden K, Rehman A, Waschina S, Pan W-H, Walker A, Lucio M, et al.
    Metabolic Functions of Gut Microbes Associate With Efficacy of Tumor Necrosis Factor Antagonists in Patients With Inflammatory Bowel Diseases.
    Gastroenterology. 2019/;157:1279-1292.e11. DOI
  2. Effenberger M, Reider S, Waschina S, Bronowski C, Enrich B, Adolph TE, et al.
    Microbial Butyrate Synthesis Indicates Therapeutic Efficacy of Azathioprine in IBD Patients.
    Journal of Crohn’s and Colitis. 2020/;15:88–98. DOI
  3. Demetrowitsch TJ, Schlicht K, Knappe C, Zimmermann J, Jensen-Kroll J, Pisarevskaja A, et al.
    Precision Nutrition in Chronic Inflammation.
    Front Immunol. 2020/;11. DOI