Dynamic whole-body models for infant metabolism
Zaunseder, E.; Mohammad, F. K.; Muetze, U.; Koelker, S.; Heuveline, V.; Thiele, I.
Show abstract
1Comprehensive, sex-specific whole-body models (WBMs) accounting for organ-specific metabolism have been developed to allow for the simulation of adult and infant metabolism. These WBMs are evaluated daily, giving insights into metabolic flux changes that occur in one day of an infants or adults life. However, for medical applications, such as in metabolic diseases and their treatment, an evaluation and concentration predictions on a shorter time scale would be beneficial. Therefore, we developed a dynamic infant-WBM that couples metabolite dynamics in short time frames through physiology-based pharma-cokinetic models with the existing infant whole-body models. We then tailored the dynamic infant-WBM enabling the prediction of isovalerylcarnitine (C5), a clinical biomarker used for the inherited metabolic disease isovaleric aciduria (IVA). Our results show that, as expected, the predicted C5 concentrations exceeded the newborn screening thresholds during the time (36 - 72 hours) newborn screening blood samples are taken in the IVA models but not in models simulating healthy infants. We also demonstrate how the dynamic infant-WBMs can be used to test the effect changes in dietary intake have on the biomarker. Since the dynamic infant-WBMs were parametrised with literature-derived experimental or estimated values, we show how uncertainty quantification can be applied to quantify the parameter uncertainties. We found that the fractional unbound plasma needed to be estimated correctly, as this parameter strongly impacted C5 concentration predictions of the dynamic infant-WBMs. Overall, the dynamic infant-WBMs hold promise for personalised medicine, as it enables personalised biomarker concentration predictions of healthy and diseased infant metabolism in various time intervals.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Genome scale metabolic network modelling for metabolic profile predictions 94%
- Contrasting model mechanisms of alanine aminotransferase (ALT) release from damaged and necrotic hepatocytes as an example of general biomarker mechanisms 94%
- A regularized functional regression model enabling transcriptome-wide dosage-dependent association study of cancer drug response 93%
Similar papers in this journal
- Benchmark dataset for training machine learning models to predict the pathway involvement of metabolites 92%
- MetaboListem and TABoLiSTM: Two Deep Learning Algorithms for Metabolite Named Entity Recognition 91%
- Matrix Linear Models for connecting metabolite composition to individual characteristics 91%
Similar papers in this journal
- High-resolution fecal pharmacokinetic modeling in mice with orally administered antibiotics 92%
- Bridging the gap between in silico and in vivo: modeling opioid disposition in a kidney proximal tubule microphysiological system 92%
- DeepInsight-3D for precision oncology: an improved anti-cancer drug response prediction from high-dimensional multi-omics data with convolutional neural networks 91%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.