Biology subjects

Baum, K.

Publications and source records attributed to Baum, K..

13 recordsLinked to original sources

bioRxiv2026

Differential multi-omics analysis of pulmonary arterial hypertension microvascular endothelial cells for differential drug response

Pulmonary arterial hypertension (PAH) represents a heterogeneous group of disorders that involves complex molecular dysregulations, which are not fully captured by single-omics analyses. We apply our network-based multi-omics analysis framework, DrDimont, to transcriptomic, proteomic, phosphoproteomic, and kinase screening data from lung microvascular endothelial cells of PAH patients and controls. Thereby, we extend the functionality of DrDimont to incorporate kinase-kinase interactions during the construction of condition-specific multi-omics networks. Kinase interactions are inferred from phosphorylations of screened substrates that are weighted by kinase-substrate predictions. Differential interaction scores from the network-based analysis between PAH and control uncover alterations centered on kinases, in particular top hits relating to MAPK signaling, such as MAPK13, MAP2K, or upstream IRAK1, and other MAPK/MAP2K family members. Further highly differential nodes were ACADSB, GPX7, DSE (for proteins), and AIM1, LY96, CHSY3 (for mRNAs). When prioritizing drug candidates by mapping drug targets onto the differential network, we find high scores for the drug tacrolimus (FK506) and several anti-neoplastic MAPK inhibitors (e.g., selumetinib, trametinib), as well as agents acting on general proliferation via (mitochondrial) DNA transcription (e.g., epirubicin, topotecan). Integrating kinase activity screens into our explainable multi-omics network-based analyses reveals kinase-centered alterations and therapeutic hypotheses in PAH that complement single-layer classical differential expression analyses.

bioinformatics↗

bioRxiv2026

Leveraging Uncertainty Estimates for Drug Response Prediction in Cancer Cell Lines

Machine learning models for drug response prediction in cancer cell lines carry the potential to advance precision oncology by tailoring treatments to the molecular tumor profile. Their application is challenged by variability in prediction quality and distribution shifts between training and application. Uncertainty estimation provides more information on the predictive distribution than point estimates, enabling comprehensive decision support and downstream analysis of predictions. Yet, the most effective uncertainty estimator is domain-specific. In this work, we benchmark uncertainty-aware models for drug response prediction. We focus on epistemic uncertainty via ensemble agreement, and aleatoric uncertainty via distributional modeling, or both. We find that ensemble-based estimates are more sensitive to distribution shift and can flag out-of-distribution examples. In contrast, distributional models yield stronger prediction error reductions among high-confidence subsets. Despite higher computational cost, the combination can provide both advantages: an ensemble of neural networks that estimate a Gaussian predictive distribution can reduce the mean squared error by 64 percent when restricting predictions to the 10 percent most confident drug-cell line pairs, and reliably indicates distribution shifts and platform differences. Beyond benchmarking, probabilistic predictions can identify drugs whose uncertainty bounds overlap with therapeutically relevant ranges. We also show that uncertainty estimates enable a new dimension of model interpretability: by attributing predicted uncertainty to input features, we identify genes that signal unpredictability of drug response rather than sensitivity or resistance. We further demonstrate uncertainty-guided selection of measurements for active learning. In summary, including uncertainty in drug response prediction supports better-informed model application. The code is available at https://github.com/PascalIversen/LUDRP.

bioinformatics↗

bioRxiv2025

From Hype to Health Check: Critical Evaluation of Drug Response Prediction Models with DrEval

MotivationLarge-scale drug sensitivity screens have enabled training drug response prediction models based on cancer cell line omics profiles to facilitate personalized medicine. While model performances reported in the literature appear promising, no successful translation to the clinic has been reported. ResultsWe identify six primary obstacles to this: non-reproducible models, data leakage leading to poor generalization, pseudoreplication, biased evaluation metrics, missing ablation studies, and inconsistent viability data reducing comparability across models. Together, these issues lead to overly optimistic performance estimates of state-of-the-art models and make it challenging to track progress in the field. To address this, we present DrEval, a pipeline for unbiased and biologically meaningful evaluation of cancer drug response models. It includes baseline and literature models with consistent hyperparameter tuning, statistically sound evaluations, and cross-study benchmarks. DrEval enables ablation studies and publication-ready visualizations. It allows researchers to focus on model development without implementing their own evaluation protocol. We find that deep learning models barely outperform a naive model predicting the mean drug and cell line effects, while no complex model significantly outperforms properly tuned tree-based ensemble baselines in relevant settings. We advocate making our pipeline a standard benchmark for cancer drug response prediction, ensuring a clinically relevant and robust assessment. Availability and implementationDrEval consists of a Python package, available on PyPI (drevalpy) and GitHub (github.com/daisybio/drevalpy), and an accompanying nf-core pipeline (github.com/nf-core/drugresponseeval). All data is available on Zenodo (DOI: 10.5281/zenodo.12633909), preprocessing scripts on github.com/daisybio/preprocess_drp_data.

bioinformatics↗

bioRxiv2025

A computational model elucidates the effect of oncogene induced expression alterations on the energy metabolism of neuroblastoma

An altered energy metabolism is recognized as a hallmark of cancer. Experimental evidence shows that oncogenes play a key role in the reprogramming of metabolism. In neuroblastoma the oncogene MYCN, a main risk factor of poor prognosis, has been demonstrated to lead to expression changes in numerous glycolytic enzymes. Since it is not clear whether all these targets are required and how the overall metabolic response is shaped, we here dissect the effect of MYCN targets on the pathway individually and in combination using a computational modeling approach. We develop a first mathematical model of the energy metabolism in neuroblastoma cells based on our published experimental data. The analysis shows that MYCN overexpression overall leads to Warburg-like flux alterations with characteristic changes for the individual MYCN targets highlighting that MYCN targets can have opposing and sometimes unexpected effects. Interestingly, not all described MYCN targets contribute to notable flux alterations, at least in glycolysis. Our model moreover predicts a potential bistability of the cellular metabolism with an occurrence of a low-flux state likely representing quiescence. Overall, our study highlights the importance of analysing perturbations such as expression changes in the context of realistic pathways capturing their specific interactions and complex regulations.

systems biology↗

bioRxiv2025

CombDNF: Disease-specific drug combination predictions with network-based features on clinically validated data

Drug combinations are increasingly applied to treat a wide range of complex diseases. Drug action and thus also drug combination effects can differ between diseases, e.g., due to molecular differences. Therefore, disease-specific predictions are required for treatments. A plethora of methods based on cell-line screening data in cancer has been proposed. However, their extendability to other diseases is limited, as is their applicability in the clinical context due to the in-vivo-in-vitro gap. In contrast, only few approaches rely on clinically validated data. Here, we propose CombDNF, a novel machine-learning based method for disease-specific drug combination prediction on clinically validated data. CombDNF is trained and predicts both on clinically approved (effective) and clinically reported adverse drug combinations from a broad collection of data sources. It can cope with the highly imbalanced label distribution in drug combination data. Further, CombDNF leverages network-derived features based on drug target and disease gene relationships. To incorporate uncertainty of the network topology it relies on edge weights in the underlying network. We systematically evaluate CombDNF against available state-of-the-art methods in four diseases with different underlying mechanisms and ground truth data characteristics. CombDNF outperforms all state-of-the-art methods in all four diseases by at least 84% in the AUPR. This translates, for example, to an enrichment of effective drug combinations in the top ten hypertension-specific predictions of four, compared to one for the best competing method. In addition, network edge weighting by interaction confidence scores indeed yields improved predictions. Further, we find evidence for biological plausibility of our top ranked drug combinations. We make the training and evaluation pipeline for CombDNF available ready-to-use at https://github.com/DILiS-lab/CombDNF.

bioinformatics↗

bioRxiv2024

Optimizing ODE-derived Synthetic Data for Transfer Learning in Dynamical Biological Systems

BackgroundAccurate prediction of the temporal dynamics of biological systems is crucial for informing timely and effective interventions, e.g., in ecological or epidemiological contexts, or for treatment adjustments in therapy. While machine learning has proven its capabilities in generalizing the underlying non-linear dynamics of such systems, unlocking its predictive power is often restrained by the limited availability of large, curated datasets. To supplement real-world data, informing machine learning by transfer learning with synthetic data derived from simulations using ordinary differential equations has emerged as a promising solution. However, the success of this approach highly depends on the designed characteristics of the synthetic data. ResultsWe suggest scrutinizing these characteristics, such as size, diversity, and noise, of ordinary differential equation-based synthetic time series datasets. Here, we demonstrate how to systematically evaluate the influence of such design choices on transfer learning performance. We conduct a proof-of-concept study on three simple, but widely used systems and four real-world datasets. We find a strong interdependency between synthetic dataset size and diversity effects. Good transfer learning settings heavily rely on real-world data characteristics as well as the datas coherence with the dynamics of the model underlying the synthetic data. We achieve a performance improvement of up to 92% in mean absolute error for simulation-based transfer learning compared to non-informed deep learning. ConclusionsOur work emphasizes the relevance of carefully selecting properties of synthetic data for leveraging the valuable domain knowledge contained in ordinary differential equation models for machine-learning based predictions. The code is available at https://github.com/DILiS-lab/opt-synthdata-4tl.

bioinformatics↗

bioRxiv2024

PEPerMINT: Peptide Abundance Imputation in Mass Spectrometry-based Proteomics using Graph Neural Networks

MotivationAccurate quantitative information about the protein abundance is crucial for understanding a biological system and its dynamics. Protein abundance is commonly estimated using label-free, bottom-up mass spectrometry protocols. Here, proteins are digested into peptides before quantification via mass spectrometry. However, missing peptide abundance values, which can make up more than 50% of all abundance values, are a common issue. They result in missing protein abundance values, which then hinder accurate and reliable downstream analyses. ResultsTo impute missing abundance values, we propose PEPerMINT, a graph neural network model working directly on the peptide level that flexibly takes both peptide-to-protein relationships in a graph format as well as amino acid sequence information into account. We benchmark our method against eleven common imputation methods on six diverse datasets, including cell lines, tissue, and plasma samples. We observe that PEPerMINT consistently outperforms other imputation methods. Its prediction performance remains high for varying degrees of missingness, different evaluation approaches and differential expression prediction. As an additional novel feature, PEPerMINT provides meaningful uncertainty estimates and allows for tailoring imputation to the users needs based on the reliability of imputed values. Availability and implementationThe code is available at https://github.com/DILiS-lab/pepermint.

bioinformatics↗

bioRxiv2023

Live imaging of excitable axonal microdomains in ankyrin-G-GFP mice

The axon initial segment (AIS) constitutes not only the site of action potential initiation, but also a hub for activity-dependent modulation of output generation. Recent studies shedding light on AIS function used predominantly post-hoc approaches since no robust murine in vivo live reporters exist. Here, we introduce a reporter line in which the AIS is intrinsically labeled by an ankyrin-G-GFP fusion protein activated by Cre recombinase, tagging the native Ank3 gene. Using confocal, superresolution, and two-photon microscopy as well as whole-cell patch-clamp recordings in vitro, ex vivo, and in vivo, we confirm that the subcellular scaffold of the AIS and electrophysiological parameters of labeled cells remain unchanged. We further uncover rapid AIS remodeling following increased network activity in this model system, as well as highly reproducible in vivo labeling of AIS over weeks. This novel reporter line allows longitudinal studies of AIS modulation and plasticity in vivo in real-time and thus provides a unique approach to study subcellular plasticity in a broad range of applications.

neuroscience↗

bioRxiv2022

DrDimont: Explainable drug response predictionfrom differential analysis of multi-omics networks

MotivationWhile it has been well established that drugs affect and help patients differently, personalized drug response predictions remain challenging. Solutions based on single omics measurements have been proposed, and networks provide means to incorporate molecular interactions into reasoning. However, how to integrate the wealth of information contained in multiple omics layers still poses a complex problem. ResultsWe present DrDimont, Drug response prediction from Differential analysis of multi-omics networks. It allows for comparative conclusions between two conditions and translates them into differential drug response predictions. DrDimont focuses on molecular interactions. It establishes condition-specific networks from correlation within an omics layer that are then reduced and combined into heterogeneous, multi-omics molecular networks. A novel semi-local, path-based integration step ensures integrative conclusions. Differential predictions are derived from comparing the condition-specific integrated networks. DrDimonts predictions are explainable, i.e., molecular differences that are the source of high differential drug scores can be retrieved. We predict differential drug response in breast cancer using transcriptomics, proteomics, phosphosite, and metabolomics measurements and contrast estrogen receptor positive and receptor negative patients. DrDimont performs better than drug prediction based on differential protein expression or PageRank when evaluating it on ground truth data from cancer cell lines. We find proteomic and phosphosite layers to carry most information for distinguishing drug response. AvailabilityDrDimont is available on CRAN: https://cran.r-project.org/package=DrDimont. Contactkatharina.baum@hpi.de

bioinformatics↗

bioRxiv2020

LoopDetect: Comprehensive feedback loop detection in ordinary differential equation models

SummaryThe dynamics of ordinary differential equation (ODE) models strongly depend on the model structure, in particular the existence of positive and negative feedback loops. LoopDetect offers user-friendly detection of all feedback loops in ODE models in three programming languages frequently used to solve and analyze them: MATLAB, Python, and R. The developed toolset accounts for user-defined model parametrizations and states of the modelled variables and supports feedback loop detection over ranges of values. It generates output in an easily adaptable format for further investigation. Availability and ImplementationLoopDetect is implemented in R, Python 3 and MATLAB. It is freely available at https://cran.r-project.org/web/packages/LoopDetectR/, https://pypi.org/project/loopdetect/, https://de.mathworks.com/matlabcentral/fileexchange/81928-loopdetect/ (GPLv3 or BSD license). Contactkatharina.baum@hpi.de

systems biology↗

bioRxiv2018

MYCN-induced metabolic rewiring creates novel therapeutic vulnerabilities in neuroblastoma

MYCN is a transcription factor that is aberrantly expressed in many tumor types and is often correlated with poor patient prognosis. Recently, several lines of evidence pointed to the fact that oncogenic activation of MYC family proteins is concomitant with reprogramming of tumor cells to cope with an enhanced need for metabolites during cell growth. These adaptions are driven by the ability of MYC proteins to act as transcriptional amplifiers in a tissue-of-origin specific manner. Here, we describe the effects of MYCN overexpression on metabolic reprogramming in neuroblastoma cells. Ectopic expression of MYCN induced a glycolytic switch that was concomitant with enhanced sensitivity towards 2-deoxyglucose, an inhibitor of glycolysis. Moreover, global metabolic profiling revealed extensive alterations in the cellular metabolome resulting from overexpression of MYCN. Limited supply with either of the two main carbon sources, glucose or glutamine, resulted in distinct shifts in steady-state metabolite levels and significant changes in glutathione metabolism. Interestingly, interference with glutamine-glutamate conversion preferentially blocked proliferation of MYCN overexpressing cells, when glutamine levels were reduced. Thus, our study uncovered MYCN induction and nutrient levels as important metabolic master switches in neuroblastoma cells and identified critical nodes that restrict tumor cell proliferation.

cancer biology↗

bioRxiv2017

Protein Interaction Screen on Peptide Matrix (PRISMA) reveals interaction footprints and the PTM-dependent interactome of intrinsically disordered C/EBPβ

CCAAT enhancer binding protein beta (C/EBP{beta}) is a pioneer transcription factor that specifies cell differentiation. C/EBP{beta} is intrinsically unstructured, a molecular feature common to many proteins involved in signal processing and epigenetics. The structure of C/EBP{beta} differs depending on alternative translation start site usage and multiple post-translational modifications (PTM). Mutation of distinct PTM sites in C/EBP{beta} alters designated protein interactions and cell differentiation, suggesting a C/EBP{beta} PTM indexing code determines epigenetic outcomes. Herein, we systematically explored the interactome of C/EBP{beta} using an array of spot-synthesised C/EBP{beta}-derived linear tiling peptides with and without PTM, combined with mass spectrometric proteomic analysis of protein interactions. We identified interaction footprints of ~1300 proteins in nuclear cell extracts, many with chromatin modifying, remodelling and RNA processing functions. The results suggest C/EBP{beta} acts as a multi-tasking molecular switchboard, integrating signal-dependent modifications and structural plasticity to orchestrate interactions with numerous protein complexes directing cell fate and function.\n\nHighlightsO_LIPeptide array based interaction proteomics map SLiM and PTM dependent C/EBP{beta} interactome\nC_LIO_LINovel links between C/EBP{beta}, RNA processing, transcription elongation, MLL, NuRD were revealed\nC_LIO_LIC/EBP{beta} structure organizes modular hub function for gene regulatory machinery\nC_LIO_LIPRISMA is suitable to resolve protein interactions and networks based on intrinsically disordered proteins\nC_LI

biochemistry↗

bioRxiv2017

Fibril branching dominates self-assembly of mutant huntingtin exon-1 aggregates in vitro

Huntingtin (HTT) fragments with extended polyglutamine (polyQ) tracts self-assemble into amyloid-like fibrillar aggregates. Elucidating the fibril formation mechanism is critical for understanding Huntingtons disease pathology and for developing novel therapeutic strategies. Here, we performed systematic experimental and theoretical studies to examine the self-assembly of an aggregation-prone N-terminal HTT exon-1 fragment with 49 glutamines (Ex1Q49). Using high resolution imaging techniques such as electron microscopy and atomic force microscopy, we show that Ex1Q49 fragments in cell-free assays spontaneously convert into large, highly complex bundles of amyloid fibrils with multiple ends and fibril branching points. Furthermore, we present experimental evidence that two nucleation mechanisms control spontaneous Ex1Q49 fibrillogenesis: (1) a relatively slow primary fibril-independent nucleation process, which involves the spontaneous formation of aggregation-competent fibrillary structures, and (2) a fast secondary fibril-dependent nucleation process, which involves nucleated branching and promotes the rapid assembly of highly complex fibril bundles with multiple ends. The proposed aggregation mechanism is supported by studies with the small molecule O4, which perturbs early events in the aggregation cascade and delays Ex1Q49 fibril assembly, comprehensive mathematical and computational modelling studies, and seeding experiments with small, preformed fibrillar Ex1Q49 aggregates that promote the assembly of amyloid fibrils. Together, our results suggest that nucleated branching in vitro plays a critical role in the formation of complex fibrillar HTT exon-1 aggregates with multiple ends.

biochemistry↗