Biology subjects

Weindl, D.

Publications and source records attributed to Weindl, D..

13 recordsLinked to original sources

bioRxiv2026

Large-scale analysis of optimisation methods for parameter estimation problems in the life sciences

Dynamic models are central to the computational life sciences but typically contain unknown parameters that must be inferred from experimental data. High-throughput measurements have made this task increasingly challenging, yielding high-dimensional search spaces and non-convex objectives with many local optima. This makes the choice of optimisation method critical. However, existing empirical studies either consider only a limited number of benchmark problems or only a narrow spectrum of local, global and hybrid optimisation methods. Here, we present a comprehensive benchmark of a broad range of optimisation methods on a curated collection of parameter estimation problems, comprising 990 method-problem-pairs executed on two independent supercomputing infrastructures. Our evaluation quantifies success rates, solution quality and computational cost, revealing characteristic strengths and limitations of each approach. We find that optimisation methods separated into clear performance tiers. Building on these results, we implemented a new hybrid strategy that combines enhanced scatter search with the best-performing local solver, which showed robust performance and improved on the other scatter-search variants we tested. Our results provide practical guidance for selecting optimisation methods and thereby support more accurate and reliable model calibration.

systems biology↗

bioRxiv2026

A mechanistic model of protein kinase A dynamics under pro- and anti-nociceptive inputs

Protein kinase A (PKA) is a central integrator of nociceptive signaling, yet a quantitative account of how pro- and anti-nociceptive inputs shape its dynamics remains incomplete. Here, we develop a mechanistic model of PKA activity in nociceptive neurons that explicitly links receptor activation to downstream kinase regulation. Using time-course and dose-response measurements, we infer unknown process parameters and quantify parameter and prediction uncertainties to ensure robust conclusions. The model captures the activation of PKA by serotonin and forskolin and its suppression by opioids. We show how the model can be used for the assessment of alternative circuit topologies, and demonstrate that receptor context and stimulation history reconfigure PKA responsiveness, providing testable predictions for opioid modulation under clinically relevant dosing. This framework offers a principled basis for integrating PKA with broader pain-signaling networks, supports rational exploration of combination therapies, and establishes a general strategy for disentangling neuromodulatory control of kinase activity. Author summaryPain perception is modulated by a complex network of signaling pathways activated by different receptors with opposing effects. A key player in this process is protein kinase A (PKA), whose regulation by both serotonin and opioid receptors is not yet fully understood. In this study, we developed a mathematical model to investigate how these opposing signals affect PKA activity in sensory neurons. After estimating the unknown model parameters from a comprehensive dataset, we were able to quantitatively analyze the dynamic behavior of the system and use it for comparison of alternative circuit topologies. Our model provides a valuable tool for integrating diverse molecular interactions involved in pain processing and could help guide future efforts to develop better treatments for chronic pain and reduce opioid tolerance.

systems biology↗

bioRxiv2025

PEtab Select: specification standard and supporting software for automated model selection

A central question in mathematical modeling of biological systems is determining which processes are most relevant and how they can be described. There are often competing hypotheses, which yield different models. Model comparison requires parameter optimization and sampling methods. Yet, standards for the specification of model selection problems and the swift evaluation of a broad spectrum of approaches are not available. PEtab Select addresses this challenge by providing a concise, standardized specification of model selection and its associated calibration problems through a new file format standard and software package. The standard facilitates the compact representation of even very large model selection problems; in one example, billions of model alternatives. PEtab Select builds on the PEtab standard for the specification of parameter estimation problems, and enables the use of state-of-the-art modelling and calibration workflows utilizing COPASI, Data2Dynamics, PEtab.jl, and pyPESTO. PEtab Select supports common model selection criteria (e.g., Akaike and Bayesian information criteria) and can be easily extended to use others. To ensure flexibility, PEtab Select implements several model space exploration approaches, including basic brute-force, forward, and backward selection, and also advanced, flexible selection methods. PEtab Select introduces the first standardization of model selection tasks, filling a critical gap in existing computational pipelines. It constitutes an essential contribution to FAIR research software in systems biology by promoting interoperability and reusability in model selection. Author summaryModel selection is a crucial step in mathematical modeling, guiding the choice of components to include in a model. PEtab Select automates this process across diverse modeling frameworks and programming languages via (1) a new interoperable, language-agnostic standard for specifying large-scale model selection problems, and (2) a comprehensive software package that implements these selection methods. Developed through a community effort, PEtab Select has been integrated into multiple modeling frameworks and is accessible to users of COPASI, Julia, MATLAB, and Python.

systems biology↗

bioRxiv2025

Pseudo-time trajectory of single-cell lipidomics: Suggestion for experimental setup and computational analysis

Cellular heterogeneity is a fundamental facet of cell biology, influencing cellular signaling, metabolism, and gene regulation. Its accurate quantification requires measurements at the single-cell level. Most high-throughput single-cell technologies provide only a snapshot of cellular heterogeneity at a specific time point because the measurement is destructive. This limits our current ability to understand the dynamics of cellular behavior and quantify cell-specific parameters. We propose an experimental setup combined with a model-based analysis framework, enabling the extraction of longitudinal data from a single destructive measurement. Although broadly applicable, we focus on lipid metabolism, a domain where obtaining longitudinal single-cell data has remained elusive due to technical constraints. Our method leverages multiple labels whose measurements are linked to a shared dynamic. This allows the estimation of cell-specific parameters and the quantification of heterogeneity. This framework establishes a foundation for future investigations, providing a roadmap toward a deeper understanding of dynamic cellular processes.

systems biology↗

bioRxiv2023

A more expressive spline representation for SBML models improves code generation performance in AMICI

AbstractsSpline interpolants are commonly used for discretizing and estimating functions in mathematical models. While splines can be encoded in the Systems Biology Markup Language (SBML) using piecewise functions, the resulting formulas are very complex and difficult to derive by hand. Tools to create such formulas exist but only deal with numeric data and thus cannot be used for function estimation. Similarly, simulation tools suffer from several limitations when handling splines. For example, in the AMICI library splines with large numbers of nodes lead to long model import times. We have developed a set of SBML annotations to mark assignment rules as spline formulas. These compact representations are human-readable and easy to edit, in contrast to the piecewise representation. Different boundary conditions and extrapolation methods can also be specified. By extending AMICI to create and recognize these annotations, model import can be sped up significantly. This allows practitioners to increase the expressivity of their models. While the performance improvement is limited to AMICI, our tools for creating spline formulas can be used for other tools as well and our syntax for compact spline representation may be a starting point for an SBML-native way to represent spline interpolants.

bioinformatics↗

bioRxiv2023

Reusable rule-based cell cycle model explains compartment-resolved dynamics of 16 observables in RPE-1 cells

The mammalian cell cycle is regulated by a well-studied but complex biochemical reaction system. Computational models provide a particularly systematic and systemic description of the mechanisms governing mammalian cell cycle control. By combining both state-of-the-art multiplexed experimental methods and powerful computational tools, this work aims at improving on these models along four dimensions: model structure, validation data, validation methodology and model reusability. We developed a comprehensive model structure of the full cell cycle that qualitatively explains the behaviour of human retinal pigment epithelial-1 cells. To estimate the model parameters, time courses of eight cell cycle regulators in two compartments were reconstructed from single cell snapshot measurements. After optimisation with a parallel global optimisation metaheuristic we obtained excellent agreements between simulations and measurements. The PEtab specification of the optimisation problem facilitates reuse of model, data and/or optimisation results. Future perturbation experiments will improve parameter identifiability and allow for testing model predictive power. Such a predictive model may aid in drug discovery for cell cycle-related disorders. Author SummaryWhile there are numerous cell cycle models in the literature, mammalian cell cycle models typically suffer from four limitations. Firstly, the descriptions of biological mechanisms are often overly complicated yet insufficiently comprehensive and detailed. Secondly, there is a lack of experimental data to validate the model. Thirdly, inadequate parameter estimation procedures are used. Lastly, there is no standardized description of the model and/or optimization problem. To overcome these limitations, we combine best-in-class technology to address all four simultaneously. We use a rule-based model description to provide a concise and less error-prone representation of complex biology. By applying trajectory reconstruction algorithms to existing data from highly multiplexed immunofluorescence measurements, we obtained a rich dataset for model validation. Using a parallel global metaheuristic for parameter estimation allowed us to bring simulations and data in very good agreement. To maximize reproducibility and reusability of our work, the results are available in three popular formats: BioNetGen, SBML, and PEtab. Our model is generalizable to many healthy and transformed cell types. The PEtab specification of the optimization problem makes it straightforward to re-optimize the parameters for other cell lines. This may guide hypotheses on cell type-specific regulation of the cell cycle, potentially with clinical relevance.

systems biology↗

bioRxiv2022

Efficient computation of adjoint sensitivities at steady-state in ODE models of biochemical reaction networks

Dynamical models in the form of systems of ordinary differential equations have become a standard tool in systems biology. Many parameters of such models are usually unknown and have to be inferred from experimental data. Gradient-based optimization has proven to be effective for parameter estimation. However, computing gradients becomes increasingly costly for larger models, which are required for capturing the complex interactions of multiple biochemical pathways. Adjoint sensitivity analysis has been pivotal for working with such large models, but methods tailored for steady-state data are currently not available. We propose a new adjoint method for computing gradients, which is applicable if the experimental data include steady-state measurements. The method is based on a reformulation of the backward integration problem to a system of linear algebraic equations. The evaluation of the proposed method using real-world problems shows a speedup of total simulation time by a factor of up to 4.4. Our results demonstrate that the proposed approach can achieve a substantial improvement in computation time, in particular for large-scale models, where computational efficiency is critical. Author summaryLarge-scale dynamical models are nowadays widely used for the analysis of complex processes and the integration of large-scale data sets. However, computational cost is often a bottleneck. Here, we propose a new gradient computation method that facilitates the parameterization of large-scale models based on steady-state measurements. The method can be combined with existing gradient computation methods for time-course measurements. Accordingly, it is an essential contribution to the environment of computationally efficient approaches for the study of large-scale screening and omics data, but not tailored to biological applications, and, therefore, also useful beyond the field of computational biology.

systems biology↗

bioRxiv2021

Efficient gradient-based parameter estimation for dynamic models using qualitative data

MotivationUnknown 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, methods for qualitative data are rare and suffer from bad scaling and convergence. ResultsHere, we propose an efficient and reliable framework for estimating the parameters of ordinary differential equation models from qualitative data. In this framework, we derive a semi-analytical algorithm for gradient calculation of the optimal scaling method developed for qualitative data. This enables the use of efficient gradient-based optimization algorithms. We demonstrate that the use of gradient information improves performance of optimization and uncertainty quantification on several application examples. On average, we achieve a speedup of more than one order of magnitude compared to gradient-free optimization. Additionally, in some examples, the gradient-based approach yields substantially improved objective function values and quality of the fits. Accordingly, the proposed framework substantially improves the parameterization of models from qualitative data. AvailabilityThe proposed approach is implemented in the open-source Python Parameter EStimation TOolbox (pyPESTO). All application examples and code to reproduce this study are available at https://doi.org/10.5281/zenodo.4507613.

systems biology↗

bioRxiv2019

Mini-batch optimization enables training of ODE models on large-scale datasets

Quantitative dynamical models are widely used to study cellular signal processing. A critical step in modeling is the estimation of unknown model parameters from experimental data. As model sizes and datasets are steadily growing, established parameter optimization approaches for mechanistic models become computationally extremely challenging. However, mini-batch optimization methods, as employed in deep learning, have better scaling properties. In this work, we adapt, apply, and benchmark mini-batch optimization for ordinary differential equation (ODE) models thereby establishing a direct link between dynamic modeling and machine learning. On our main application example, a large-scale model of cancer signaling, we benchmark mini-batch optimization against established methods, achieving better optimization results and reducing computation by more than an order of magnitude. We expect that our work will serve as a first step towards mini-batch optimization tailored to ODE models and enable modeling of even larger and more complex systems than what is currently possible.

systems biology↗

bioRxiv2019

Statistical inference of mechanistic models from qualitative data using an efficient optimal scaling approach

Quantitative dynamical models facilitate the understanding of biological processes and the prediction of their dynamics. These models usually comprise unknown parameters, which have to be inferred from experimental data. For quantitative experimental data, there are several methods and software tools available. However, for qualitative data the available approaches are limited and computationally demanding. Here, we consider the optimal scaling method which has been developed in statistics for categorical data and has been applied to dynamical systems. This approach turns qualitative variables into quantitative ones, accounting for constraints on their relation. We derive a reduced formulation for the optimization problem defining the optimal scaling. The reduced formulation possesses the same optimal points as the established formulation but requires less degrees of freedom. Parameter estimation for dynamical models of cellular pathways revealed that the reduced formulation improves the robustness and convergence of optimizers. This resulted in substantially reduced computation times. We implemented the proposed approach in the open-source Python Parameter EStimation TOolbox (pyPESTO) to facilitate reuse and extension. The proposed approach enables efficient parameterization of quantitative dynamical models using qualitative data.

systems biology↗

bioRxiv2019

Efficient parameterization of large-scale dynamic models based on relative measurements

MotivationMechanistic models of biochemical reaction networks facilitate the quantitative understanding of biological processes and the integration of heterogeneous datasets. However, some biological processes require the consideration of comprehensive reaction networks and therefore large-scale models. Parameter estimation for such models poses great challenges, in particular when the data are on a relative scale.\n\nResultsHere, we propose a novel hierarchical approach combining (i) the efficient analytic evaluation of optimal scaling, offset, and error model parameters with (ii) the scalable evaluation of objective function gradients using adjoint sensitivity analysis. We evaluate the properties of the methods by parameterizing a pan-cancer ordinary differential equation model (> 1000 state variables, > 4000 parameters) using relative protein, phospho-protein and viability measurements. The hierarchical formulation improves optimizer performance considerably. Furthermore, we show that this approach allows estimating error model parameters with negligible computational overhead when no experimental estimates are available, pro-viding an unbiased way to weight heterogeneous data. Overall, our hierarchical formulation is applicable to a wide range of models, and allows for the efficient parameterization of large-scale models based on heterogeneous relative measurements.\n\nContactjan.hasenauer@helmholtz-muenchen.de\n\nSupplementary informationSupplementary information are available at bioRxiv online. Supplementary code and data are available online at http://doi.org/10.5281/zenodo.2593839 and http://doi.org/10.5281/zenodo.2592186.

systems biology↗

bioRxiv2018

Benchmarking optimization methods for parameter estimation in large kinetic models

MotivationMechanistic kinetic models usually contain unknown parameters, which need to be estimated by optimizing the fit of the model to experimental data. This task can be computationally challenging due to the presence of local optima and ill-conditioning. While a variety of optimization methods have been suggested to surmount these issues, it is not obvious how to choose the best one for a given problem a priori, since many factors can influence their performance. A systematic comparison of methods that are suited to parameter estimation problems of sizes ranging from tens to hundreds of optimization variables is currently missing, and smaller studies indeed provided contradictory findings.\n\nResultsHere, we use a collection of benchmark problems to evaluate the performance of two families of optimization methods: (i) a multi-start of deterministic local searches; and (ii) a hybrid metaheuristic combining stochastic global search with deterministic local searches. A fair comparison is ensured through a collaborative evaluation, involving researchers applying each method on a daily basis, and a consideration of multiple performance metrics capturing the trade-off between computational efficiency and robustness. Our results show that, thanks to recent advances in the calculation of parametric sensitivities, a multi-start of gradient-based local methods is often a successful strategy, but a better performance can be obtained with a hybrid metaheuristic. The best performer is a combination of a global scatter search metaheuristic with an interior point local method, provided with gradients estimated with adjoint-based sensitivities. We provide an implementation of this novel method in an open-source software toolbox to render it available to the scientific community.\n\nAvailability and ImplementationThe code to reproduce the results is available at Zenodo https://doi.org/10.5281/zenodo.1160343\n\nContactjan.hasenauer@helmholtz-muenchen.de, julio@iim.csic.es

systems biology↗

bioRxiv2017

Efficient parameterization of large-scale mechanistic models enables drug response prediction for cancer cell lines

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 by the availability of mathematical models and scalable computational methods. Here, we present a computational framework for the parameterization of large-scale mechanistic models and its application to the prediction of drug response of cancer cell lines from exome and transcriptome sequencing data. With this framework, we parameterized a mechanistic model describing major cancer-associated signaling pathways (>1200 species and >2600 reactions) using drug response data. For the parameterized mechanistic model, we found a prediction accuracy, which exceeds that of the considered statistical approaches. Our results demonstrate for the first time the massive integration of heterogeneous datasets using large-scale mechanistic models, and how these models facilitate individualized predictions of drug response. We anticipate our parameterized model to be a starting point for the development of more comprehensive, curated models of signaling pathways, accounting for additional pathways and drugs.

systems biology↗