Research Projects

The world of biomedical knowledge at your fingertip...

The Virtual Metabolic Human

Metabolism plays a pivotal role in many human diseases and is modulated by intrinsic and extrinsic factors.
The Virtual Metabolic Human database (VMH) explicitly connects human metabolism with genetics, human-associated microbial metabolism, nutrition, and diseases. At its core, there are genome-scale reconstructions of human and gut microbial metabolism, which have been assembled based on manually curated genomic, biochemical, and phenotypic information. These metabolic reconstructions are amenable for computational modeling and can be downloaded from the VMH. All VMH entities are i) accompanied with comprehensive data on their biochemical properties, ii) interconnected through a common nomenclature, allowing for complex search queries across its diverse information, and iii) connected to over 50 external databases. Importantly, the VMH also host human metabolic maps, allowing for query and overlay of experimental and computational data, as well as a genotype-to-phenotype map for Leigh disease.
We are continuously expanding the database content and its links to biomedical and clinical information. The VMH targets researchers from all life science domains, including metabolomics, microbiome, and systems biomedicine.

 

Predicting the effects of gut microbiota and diet on an individual’s drug response and safety

Precision medicine is an emerging paradigm that aims at maximizing the benefits and minimizing the harm of drugs. Realistic mechanistic models are needed to understand and limit heterogeneity in drug responses. Consequently, novel approaches are required that explicitly account for individual variations in response to environmental influences, in addition to genetic variation. The human gut microbiota metabolizes drugs and is modulated by diet, and it exhibits significant variation among individuals. However, the influence of the gut microbiota on drug failure or drug side effects is under-researched. In this project, we will combine whole-body, genome-scale molecular resolution modeling of human metabolism and human gut microbial metabolism, which represents a network of genes, proteins, and biochemical reactions, with physiological, clinically relevant modeling of drug responses. We will perform two pilot studies on human subjects to illustrate that this innovative, versatile computational modeling framework can be used to stratify patients prior to drug prescription and to optimize drug bioavailability through personalized dietary intervention. With these studies, BugTheDrug will advance mechanistic understanding of drug-microbiota-diet interactions and their contribution to individual drug responses. We will perform the first integration of cutting-edge approaches and novel insights from four distinct research areas: systems biology, quantitative systems pharmacology, microbiology, and nutrition. BugTheDrug conceptually and technologically addresses the demand for novel approaches to the study of individual variability, thereby providing breakthrough support for progress in precision medicine.

 

For large- and multiscale modelling of biochemical reaction networks.

As the complexity of the metabolic models continuously increases, more efficient modeling approaches are required. Traditionally, the COBRA field solved many important problems, including generation of tissue-specific metabolic models and in silico formulation of minimal growth medium, using mixed-integer linear programming, which scales poorly with increasing model size and complexity. We, and others, have already developed linear approximation algorithms, which have better performance on a large scale. We will continue to develop those algorithms and make them freely available to the research community.

 

We contribute all our code to the open-source effort, the COBRA Toolbox:

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Computational Tools & Methods

 

Parkinson's Disease

Identification of biomarker signatures from metagenomic and metabolomic data

My group is collaborating with numerous PD experts in Europe to analyse plasma metabolomic data for well-defined Parkinson’s disease patients and healthy controls. We are using advanced statistics together with our computational, metabolic models of human gut microbes and the human host to identify candidate biomarker signature for disease diagnosis and patient stratification. Furthermore, using the host-microbiome models we aim at identifying personalised nutritional therapeutic approaches.

 

Gut-Brain-Axis in Alzheimer's Disease

For large- and multiscale modelling of biochemical reaction networks.

My group closely collaborates with the Alzheimer's Disease Neuroimaging Initiative to analyse plasma metabolomic and metagenomic data from well-defined Alzheimer’s disease patients and healthy controls.

Our models are ideally suited to investigate the diet-gut-brain-axis and also the gut-liver-brain axis, both of which are considered crucial for brain health. Importantly, it is rather challenging to study the gut-brain axis in humans so that many investigations will be limited to mouse models. Our computational modeling approach complements these research efforts and will be very important for generating mechanistic hypotheses that could be tested in humans in a targeted manner, e.g., using well defined (i.e., based on the predictions) C13-labeling experiments. In such an experiment, an individual would drink, e.g., C13-labeled glucose and the labeling patterns could then be measured, e.g., using brain imaging (e.g., fMRI).

 

Host-Microbiome Co-Metabolism

What are the functional consequences of changes in the human gut microbiota in health and disease?

Using the COBRA approach, we have generated the first physiologically resolved whole-body, gender-specific metabolic models (WBMs) based on extensive organ-specific proteomic and metabolomic data, as well as through literature curation. The WBMs capture the metabolism of 28 anatomically interconnected organs, the gastrointestinal lumen, the systemic blood circulation, and the blood-brain barrier, represented by >80,000 reactions, >50,000 metabolites, and >1,700 gene products. We have demonstrated that the WBMs could accurately predict known blood biomarker metabolites for 57 inherited metabolic diseases. We have also generated >800 genome-scale gut microbial metabolic models containing 205 genera and 605 species based on literature-derived experimental data. The microbial models have been combined into a generic microbial community model30 and integrated with the WBMs. These generic microWBMs can be personalised based on, e.g., metagenomic, genetic, physiological, and dietary data. In average, 90% of the reads can be mapped onto the captured microbial genomes.

We use personalised microWBMs to systematically investigate host-microbiome co-metabolism, with particular emphasis along the diet-gut-brain axis.

 

© 2019 by Ines Thiele.