A5085002161- Gene Regulatory Network Analysis
- Advanced Breast Cancer Therapies
- Protein Structure and Dynamics
- Breast Cancer Treatment Studies
- Microbial Metabolic Engineering and Bioproduction
- Mathematical Biology Tumor Growth
- Bioinformatics and Genomic Networks
- Computational Drug Discovery Methods
- Control Systems and Identification
- HER2/EGFR in Cancer Research
- Cell Image Analysis Techniques
- Advanced Control Systems Optimization
- Numerical methods for differential equations
- Reservoir Engineering and Simulation Methods
- Energy Load and Power Forecasting
- Machine Learning and Data Classification
- Model Reduction and Neural Networks
- Multiple Myeloma Research and Treatments
- Single-cell and spatial transcriptomics
- Machine Learning in Bioinformatics
- Cancer Genomics and Diagnostics
- Estrogen and related hormone effects
- Evolution and Genetic Dynamics
- Gaussian Processes and Bayesian Inference
- SARS-CoV-2 and COVID-19 Research
University of Oslo
2023-2024
Helmholtz Zentrum München
2017-2023
Technical University of Munich
2017-2023
University of Bonn
2020-2021
Center for Environmental Health
2019
University Medical Center Hamburg-Eppendorf
2017
Universität Hamburg
2017
Mechanistic models are essential to deepen the understanding of complex diseases at molecular level. Nowadays, high-throughput and phenotypic characterizations possible, but integration such data with prior knowledge on signaling pathways is limited by availability scalable computational methods. Here, we present a framework for parameterization large-scale mechanistic its application prediction drug response cancer cell lines from exome transcriptome sequencing data. This over 104 times...
Reproducibility and reusability of the results data-based modeling studies are essential. Yet, there has been—so far—no broadly supported format for specification parameter estimation problems in systems biology. Here, we introduce PEtab, a which facilitates using Systems Biology Markup Language (SBML) models set tab-separated value files describing observation model experimental data as well parameters to be estimated. We already implemented PEtab support into eight well-established...
We need to effectively combine the knowledge from surging literature with complex datasets propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets intervention. Here, we describe a large-scale community effort build an open access, interoperable computable repository COVID-19 molecular mechanisms. The Disease Map (C19DMap) is graphical, interactive representation disease-relevant mechanisms linking many sources. Notably, it computational...
Mechanistic models are important tools to describe and understand biological processes. However, they typically rely on unknown parameters, the estimation of which can be challenging for large complex systems. pyPESTO is a modular framework systematic parameter estimation, with scalable algorithms optimization uncertainty quantification. While tailored ordinary differential equation problems, broadly applicable black-box problems. Besides own implementations, it provides unified interface...
Ordinary differential equation (ODE) models are a key tool to understand complex mechanisms in systems biology. These studied using various approaches, including stability and bifurcation analysis, but most frequently by numerical simulations. The number of required simulations is often large, e.g., when unknown parameters need be inferred. This renders efficient reliable integration methods essential. However, these depend on hyperparameters, which strongly impact the ODE solution. Despite...
Abstract Motivation Mechanistic models of biochemical reaction networks facilitate the quantitative understanding biological processes and integration heterogeneous datasets. However, some require consideration comprehensive therefore large-scale models. Parameter estimation for such poses great challenges, in particular when data are on a relative scale. Results Here, we propose novel hierarchical approach combining (i) efficient analytic evaluation optimal scaling, offset error model...
Abstract Quantitative dynamic models are widely used to study cellular signal processing. A critical step in modelling is the estimation of unknown model parameters from experimental data. As sizes and datasets steadily growing, established parameter optimization approaches for mechanistic become computationally extremely challenging. Mini-batch methods, as employed deep learning, have better scaling properties. In this work, we adapt, apply, benchmark mini-batch ordinary differential...
Image reconstruction in magnetic particle imaging (MPI) is done using an algebraic approach for Lissajous-type measurement sequences. By solving a large linear system of equations, the spatial distribution nanoparticles can be determined. Despite use iterative solvers that converge rapidly, size MPI matrix leads to times are typically much longer than actual data acquisition time. For this reason, compression techniques have been introduced transform into sparse domain and then utilize...
Tumor heterogeneity is an important driver of treatment failure in cancer since therapies often select for drug-tolerant or drug-resistant cellular subpopulations that drive tumor growth and recurrence. Profiling the drug-response samples using traditional genomic deconvolution methods has yielded limited results, due part to imperfect mapping between variation functional characteristics. Here, we leverage mechanistic population modeling develop a statistical framework profiling phenotypic...
Survival or apoptosis is a binary decision in individual cells. However, at the cell-population level, graded increase survival of colony-forming unit-erythroid (CFU-E) cells observed upon stimulation with erythropoietin (Epo). To identify components Janus kinase 2/signal transducer and activator transcription 5 (JAK2/STAT5) signal transduction that contribute to population response, we extended cell-population-level model calibrated experimental data study behavior single The single-cell...
Unknown parameters of dynamical models are commonly estimated from experimental data. However, while various efficient optimization and uncertainty analysis methods have been proposed for quantitative data, qualitative data rare suffer bad scaling convergence.Here, we propose an reliable framework estimating the ordinary differential equation In this framework, derive a semi-analytical algorithm gradient calculation optimal method developed This enables use gradient-based algorithms. We...
Abstract Quantitative dynamical models facilitate the understanding of biological processes and prediction their dynamics. These usually comprise unknown parameters, which have to be inferred from experimental data. For quantitative data, there are several methods software tools available. However, for qualitative data available approaches limited computationally demanding. Here, we consider optimal scaling method has been developed in statistics categorical applied systems. This approach...
The response of cancer cells to drugs is determined by various factors, including the cells’ mutations and gene expression levels. These factors can be assessed using next-generation sequencing. Their integration with vast prior knowledge on signaling pathways is, however, limited availability mathematical models scalable computational methods. Here, we present a framework for parameterization large-scale mechanistic its application prediction drug cell lines from exome transcriptome...
Abstract Purpose: Development of a computational biomarker to predict, prior treatment, the response CDK4/6 inhibition (CDK4/6i) in combination with endocrine therapy patients breast cancer. Experimental Design: A mechanistic mathematical model that accounts for protein signaling and drug mechanisms action was developed trained on extensive, publicly available data from cancer cell lines. The built provide patient-specific score based expression six genes (CCND1, CCNE1, ESR1, RB1, MYC,...
Abstract Motivation Mechanistic models of biochemical reaction networks facilitate the quantitative understanding biological processes and integration heterogeneous datasets. However, some require consideration comprehensive therefore large-scale models. Parameter estimation for such poses great challenges, in particular when data are on a relative scale. Results Here, we propose novel hierarchical approach combining (i) efficient analytic evaluation optimal scaling, offset, error model...
Abstract We describe a large-scale community effort to build an open-access, interoperable, and computable repository of COVID-19 molecular mechanisms - the Disease Map. discuss tools, platforms, guidelines necessary for distributed development its contents by multi-faceted biocurators, domain experts, bioinformaticians, computational biologists. highlight role relevant databases text mining approaches in enrichment validation curated mechanisms. Map their relevance pathophysiology...
In systems and computational biology, ordinary differential equations are used for the mechanistic modelling of biochemical networks. These models can easily have hundreds states parameters. Typically most parameters unknown estimated by fitting model output to observation. During parameter estimation needs be solved repeatedly, sometimes millions times. This then a bottleneck, limits employment such models. many situations experimental data provides information about steady state reaction...
Abstract Motivation Unknown parameters of dynamical models are commonly estimated from experimental data. However, while various efficient optimization and uncertainty analysis methods have been proposed for quantitative data, qualitative data rare suffer bad scaling convergence. Results Here, we propose an reliable framework estimating the ordinary differential equation In this framework, derive a semi-analytical algorithm gradient calculation optimal method developed This enables use...
Mechanistic models are important tools to describe and understand biological processes. However, they typically rely on unknown parameters, the estimation of which can be challenging for large complex systems. We present pyPESTO, a modular framework systematic parameter estimation, with scalable algorithms optimization uncertainty quantification. While tailored ordinary differential equation problems, pyPESTO is broadly applicable black-box problems. Besides own implementations, it provides...
Abstract Ordinary differential equation (ODE) models are a key tool to understand complex mechanisms in systems biology. These studied using various approaches, including stability and bifurcation analysis, but most frequently by numerical simulations. The number of required simulations is often large, e.g., when unknown parameters need be inferred. This renders efficient reliable integration methods essential. However, these depend on hyperparameters, which strongly impact the ODE solution....
Abstract BackgroundAromatase inhibitors (AIs) are commonly used in the management of estrogen receptor-positive (ER+) breast cancer as they effectively reduce levels which inhibits tumor growth and recurrence. Aromatase increasingly selected patients neoadjuvant therapy. Clinical trialThe NEOLETEXE is a neoadjuvant, randomized, open-label, intra-patient, cross-over, single-center phase II clinical trial, aimed to treat postmenopausal with locally advanced cancer. Most presented large T3/T4...
Abstract Cancer and immune cells evolutionary trajectories under treatment with the aromatase inhibitor letrozole CDK4/6 ribociclib. Marie Fongaard1, Leonard Schmiester2, Salim Ghannoum1, Pål Marius Bjørnstad1, Tatjana Bosnjak3, Signe Meltzer Kleivbo3, Knut Selsås4, Stephanie Beate Geisler5, Kamilla Fjermeros5, Sameer Bhargava5, Manouchehr Seyedzadeh6, Unn-Cathrin Buvarp5, Aino Katri Rosenskiold5, Nam Thi Nguyen5, Torben Lüders7, Diether Lambrechts8, Marianne Lyngra9, Arnoldo Frigessi2,...
Abstract Background Aromatase inhibitors (AIs) are commonly used in the management of estrogen receptor-positive (ER+) breast cancer, as they effectively reduce levels which inhibits tumor growth and recurrence. In postmenopausal women with ER+ aromatase increasingly selected patients neoadjuvant therapy to burden before surgery improve overall treatment outcomes. Clinical trial The NEOLETEXE aimed treat locally advanced cancer. It was a neoadjuvant, randomized, open-label, intra-patient,...
Abstract Background: Hormone receptor-positive/HER2-negative (HR+/HER2-) breast cancer (BC) is clinically and biologically heterogeneous. CDK4/6 inhibitors (CDK4/6i) are proven to be effective in different molecular subtypes of HR+/HER2- BC, including the PAM50 luminal B. However, not all patients benefit same extent new biomarkers for response needed. The aim this study was develop a computational workflow predict with B BC treatment CDK4/6i combination endocrine therapy (ET). Methods: main...