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See detailA dynamic multi-tissue model to study human metabolism.
Martins Conde, Patricia UL; Pfau, Thomas; Pires Pacheco, Maria Irene UL et al

in NPJ systems biology and applications (2021), 7(1), 5

Metabolic modeling enables the study of human metabolism in healthy and in diseased conditions, e.g., the prediction of new drug targets and biomarkers for metabolic diseases. To accurately describe blood ... [more ▼]

Metabolic modeling enables the study of human metabolism in healthy and in diseased conditions, e.g., the prediction of new drug targets and biomarkers for metabolic diseases. To accurately describe blood and urine metabolite dynamics, the integration of multiple metabolically active tissues is necessary. We developed a dynamic multi-tissue model, which recapitulates key properties of human metabolism at the molecular and physiological level based on the integration of transcriptomics data. It enables the simulation of the dynamics of intra-cellular and extra-cellular metabolites at the genome scale. The predictive capacity of the model is shown through the accurate simulation of different healthy conditions (i.e., during fasting, while consuming meals or during exercise), and the prediction of biomarkers for a set of Inborn Errors of Metabolism with a precision of 83%. This novel approach is useful to prioritize new biomarkers for many metabolic diseases, as well as for the integration of various types of personal omics data, towards the personalized analysis of blood and urine metabolites. [less ▲]

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See detailAn efficient machine learning-based approach for screening individuals at risk of hereditary haemochromatosis.
Martins Conde, Patricia UL; Sauter, Thomas UL; Nguyen, Thanh-Phuong UL

in Scientific reports (2020), 10(1), 20613

Hereditary haemochromatosis (HH) is an autosomal recessive disease, where HFE C282Y homozygosity accounts for 80-85% of clinical cases among the Caucasian population. HH is characterised by the ... [more ▼]

Hereditary haemochromatosis (HH) is an autosomal recessive disease, where HFE C282Y homozygosity accounts for 80-85% of clinical cases among the Caucasian population. HH is characterised by the accumulation of iron, which, if untreated, can lead to the development of liver cirrhosis and liver cancer. Since iron overload is preventable and treatable if diagnosed early, high-risk individuals can be identified through effective screening employing artificial intelligence-based approaches. However, such tools expose novel challenges associated with the handling and integration of large heterogeneous datasets. We have developed an efficient computational model to screen individuals for HH using the family study data of the Hemochromatosis and Iron Overload Screening (HEIRS) cohort. This dataset, consisting of 254 cases and 701 controls, contains variables extracted from questionnaires and laboratory blood tests. The final model was trained on an extreme gradient boosting classifier using the most relevant risk factors: HFE C282Y homozygosity, age, mean corpuscular volume, iron level, serum ferritin level, transferrin saturation, and unsaturated iron-binding capacity. Hyperparameter optimisation was carried out with multiple runs, resulting in 0.94 ± 0.02 area under the receiving operating characteristic curve (AUCROC) for tenfold stratified cross-validation, demonstrating its outperformance when compared to the iron overload screening (IRON) tool. [less ▲]

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See detailMODELING HUMAN METABOLISM: A DYNAMIC MULTI-TISSUE APPROACH
Martins Conde, Patricia UL

Doctoral thesis (2019)

Despite significant advances in constraint-based modelling, a methodology for modelling dynamic multi-tissue models of human metabolism is still missing. Additionally, prior to analysing diseased models ... [more ▼]

Despite significant advances in constraint-based modelling, a methodology for modelling dynamic multi-tissue models of human metabolism is still missing. Additionally, prior to analysing diseased models, it is important to develop a good methodology, as it would not only enable us to capture the effects of metabolism-associated diseases, but it would also allow us to recapitulate known physiological healthy properties of human metabolism. Therefore, a dynamic multi-tissue model using a new methodology was developed. The objective function comprises a set of complex functions that the multi-tissue model needs to perform. To demonstrate the capabilities of this new approach, different healthy, and unhealthy conditions were simulated. In a first step, the effect of different healthy conditions was analysed (i.e. the fasting, the ingestion of different meals, and exercising at various intensities, and conditions), demonstrating the model’s capability to correctly predict metabolic changes occurring on energy-associated pathways. In the second step, biomarkers for a range of inborn errors of metabolism were predicted, and the predictions were shown to be in good agreement with previous data. Finally, after verifying the capability of the dynamic multi-tissue model to review known physiological aspects of human metabolism, this model was further integrated with a physiologically- based pharmacokinetic model of glucose metabolism, previously developed by Schaller et al. (2013). Contrasting conditions, such as healthy and diabetic, were simulated using the multi-scale model during fasting and after an oral glucose tolerance test and candidate drugs to treat type 2 diabetes mellitus were predicted. Five out of the 80 simulated drug targets were predicted as candidate anti-diabetic targets, and the majority of drugs known to inhibit the predicted drug targets, have already been shown to have anti-diabetic effects. The developed approach can be applied to any metabolic disease and to any system where homeostasis plays an important role, or where a simple biomass optimization function is not applicable. Furthermore, the large amount of data collected for the multi-tissue model generation is of significant value for tissue constraint-based metabolic modellers who need data to constrain their models. [less ▲]

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See detailConstraint based modelling going multicellular
Martins Conde, Patricia UL; Sauter, Thomas UL; Pfau, Thomas UL

in Frontiers in Molecular Biosciences (2016), 3(3),

Constraint based modelling has seen applications in many microorganisms. For example, there are now established methods to determine potential genetic modifications and external interventions to increase ... [more ▼]

Constraint based modelling has seen applications in many microorganisms. For example, there are now established methods to determine potential genetic modifications and external interventions to increase the efficiency of microbial strains in chemical production pipelines. In addition, multiple models of multicellular organisms have been created including plants and humans. While initially the focus here was on modelling individual cell types of the multicellular organism, this focus recently started to switch. Models of microbial communities, as well as multitissue models of higher organisms have been constructed. These models thereby can include different parts of a plant, like root, stem or different tissue types in the same organ. Such models can elucidate details of the interplay between symbiotic organisms, as well as the concerted efforts of multiple tissues and can be applied to analyse the effects of drugs or mutations on a more systemic level. In this review we give an overview of the recent development of multi-tissue models using constraint based techniques and the methods employed when investigating these models. We further highlight advances in combining constraint based models with dynamic and regulatory information and give an overview of these types of hybrid or multi-level approaches. [less ▲]

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