Balazs Erdos

• Kaljusmaa, Liisa-Maria; Dziubinska-Kuehn, Katarzyna Maria; Erdös, Balázs; Tubli, Diandra; Lillepea, Sander & Reile, Indrek [Show all 7 contributors for this article] (2026). Protic Ionic Liquid–H2O MixturesStructure, Interactions, and Structure–Property Relationships. Journal of Physical Chemistry B. ISSN 1520-6106. 130(5), p. 1674–1674. doi: 10.1021/acs.jpcb.5c07863. Full text in Research Archive
• Erdős, Balázs; Chatzis, Christos; Thorsen, Jonathan; Stokholm, Jakob; Smilde, Age K. & Rasmussen, Morten A. [Show all 7 contributors for this article] (2026). Extracting host-specific developmental signatures from longitudinal microbiome data. PLoS Computational Biology. ISSN 1553-734X. 22(7). doi: 10.1371/journal.pcbi.1014486. Full text in Research Archive Show summary

  Longitudinal microbiome studies provide critical insights into microbial community dynamics and their relation to host health. Tensor decompositions offer a powerful framework for the unsupervised analysis of such data, yielding interpretable low-dimensional temporal patterns. However, existing approaches based on the CANDECOMP/PARAFAC (CP) model assume common temporal dynamics for all subjects and therefore cannot capture subject-specific trajectories. To address this limitation, we introduce a novel analytical framework based on PARAFAC2 to explicitly model subject-specific variations, such as shifts and delays in temporal patterns. Through systematic comparisons on simulated and real-world datasets—including studies of infant gut maturation and dietary interventions—we demonstrate that PARAFAC2 outperforms CP in capturing subject-specific temporal trajectories, and enables the discovery of biologically relevant patterns that are overlooked by CP. Furthermore, we introduce replicability as a robust criterion for selecting the number of model components, ensuring that the extracted patterns are replicable.

• Rooij, Max de; Erdős, Balázs; Riel, Natal A. W. van & O’Donovan, Shauna D. (2025). Physiology-informed regularisation enables training of universal differential equation systems for biological applications. PLoS Computational Biology. ISSN 1553-734X. 21(1), p. 1012198–1012198. doi: 10.1371/journal.pcbi.1012198. Full text in Research Archive Show summary

  Systems biology tackles the challenge of understanding the high complexity in the internal regulation of homeostasis in the human body through mathematical modelling. These models can aid in the discovery of disease mechanisms and potential drug targets. However, on one hand the development and validation of knowledge-based mechanistic models is time-consuming and does not scale well with increasing features in medical data. On the other hand, data-driven approaches such as machine learning models require large volumes of data to produce generalisable models. The integration of neural networks and mechanistic models, forming universal differential equation (UDE) models, enables the automated learning of unknown model terms with less data than neural networks alone. Nevertheless, estimating parameters for these hybrid models remains difficult with sparse data and limited sampling durations that are common in biological applications. In this work, we propose the use of physiology-informed regularisation, penalising biologically implausible model behavior to guide the UDE towards more physiologically plausible regions of the solution space. In a simulation study we show that physiology-informed regularisation not only results in a more accurate forecasting of model behaviour, but also supports training with less data. We also applied this technique to learn a representation of the rate of glucose appearance in the glucose minimal model using meal response data measured in healthy people. In that case, the inclusion of regularisation reduces variability between UDE-embedded neural networks that were trained from different initial parameter guesses.

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