Efficient parameter estimation for ODE models of cellular processes using semi-quantitative data.

MOTIVATION: Quantitative dynamical models facilitate the understanding of biological processes and the prediction of their dynamics. The parameters of these models are commonly estimated from experimental data. Yet, experimental data generated from different techniques do not provide direct information about the state of the system but a nonlinear (monotonic) transformation of it. For such semi-quantitative data, when this transformation is unknown, it is not apparent how the model simulations and the experimental data can be compared. RESULTS: We propose a versatile spline-based approach for the integration of a broad spectrum of semi-quantitative data into parameter estimation. We derive analytical formulas for the gradients of the hierarchical objective function and show that this substantially increases the estimation efficiency. Subsequently, we demonstrate that the method allows for the reliable discovery of unknown measurement transformations. Furthermore, we show that this approach can significantly improve the parameter inference based on semi-quantitative data in comparison to available methods. AVAILABILITY AND IMPLEMENTATION: Modelers can easily apply our method by using our implementation in the open-source Python Parameter EStimation TOolbox (pyPESTO) available at https://github.com/ICB-DCM/pyPESTO.

From Planning Stage Towards FAIR Data: A Practical Metadatasheet For Biomedical Scientists.

Datasets consist of measurement data and metadata. Metadata provides context, essential for understanding and (re-)using data. Various metadata standards exist for different methods, systems and contexts. However, relevant information resides at differing stages across the data-lifecycle. Often, this information is defined and standardized only at publication stage, which can lead to data loss and workload increase. In this study, we developed Metadatasheet, a metadata standard based on interviews with members of two biomedical consortia and systematic screening of data repositories. It aligns with the data-lifecycle allowing synchronous metadata recording within Microsoft Excel, a widespread data recording software. Additionally, we provide an implementation, the Metadata Workbook, that offers user-friendly features like automation, dynamic adaption, metadata integrity checks, and export options for various metadata standards. By design and due to its extensive documentation, the proposed metadata standard simplifies recording and structuring of metadata for biomedical scientists, promoting practicality and convenience in data management. This framework can accelerate scientific progress by enhancing collaboration and knowledge transfer throughout the intermediate steps of data creation.

pyPESTO: a modular and scalable tool for parameter estimation for dynamic models.

SUMMARY: 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 and complex systems. pyPESTO is a modular framework for systematic parameter estimation, with scalable algorithms for optimization and uncertainty quantification. While tailored to ordinary differential equation problems, pyPESTO is broadly applicable to black-box parameter estimation problems. Besides own implementations, it provides a unified interface to various popular simulation and inference methods. AVAILABILITY AND IMPLEMENTATION: pyPESTO is implemented in Python, open-source under a 3-Clause BSD license. Code and documentation are available on GitHub (https://github.com/icb-dcm/pypesto).

IntestLine: a shiny-based application to map the rolled intestinal tissue onto a line.

SUMMARY: To allow the comprehensive histological analysis of the whole intestine, it is often rolled to a spiral before imaging. This Swiss-rolling technique facilitates robust experimental procedures, but it limits the possibilities to comprehend changes along the intestine. Here, we present IntestLine, a Shiny-based open-source application for processing imaging data of (rolled) intestinal tissues and subsequent mapping onto a line. The visualization of the mapped data facilitates the assessment of the whole intestine in both proximal-distal and serosa-luminal axis, and enables the observation of location-specific cell types and markers. Accordingly, IntestLine can serve as a tool to characterize the intestine in multi-modal imaging studies. AVAILABILITY AND IMPLEMENTATION: Source code can be found at Zenodo (https://doi.org/10.5281/zenodo.7081864) and GitHub (https://github.com/SchlitzerLab/IntestLine).

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.

Iron-Bound Lipocalin-2 from Tumor-Associated Macrophages Drives Breast Cancer Progression Independent of Ferroportin.

Macrophages supply iron to the breast tumor microenvironment by enforced secretion of lipocalin-2 (Lcn-2)-bound iron as well as the increased expression of the iron exporter ferroportin (FPN). We aimed at identifying the contribution of each pathway in supplying iron for the growing tumor, thereby fostering tumor progression. Analyzing the expression profiles of Lcn-2 and FPN using the spontaneous polyoma-middle-T oncogene (PyMT) breast cancer model as well as mining publicly available TCGA (The Cancer Genome Atlas) and GEO Series(GSE) datasets from the Gene Expression Omnibus database (GEO), we found no association between tumor parameters and Lcn-2 or FPN. However, stromal/macrophage-expression of Lcn-2 correlated with tumor onset, lung metastases, and recurrence, whereas FPN did not. While the total iron amount in wildtype and Lcn-2-/- PyMT tumors showed no difference, we observed that tumor-associated macrophages from Lcn-2-/- compared to wildtype tumors stored more iron. In contrast, Lcn-2-/- tumor cells accumulated less iron than their wildtype counterparts, translating into a low migratory and proliferative capacity of Lcn-2-/- tumor cells in a 3D tumor spheroid model in vitro. Our data suggest a pivotal role of Lcn-2 in tumor iron-management, affecting tumor growth. This study underscores the role of iron for tumor progression and the need for a better understanding of iron-targeted therapy approaches.

Modeling Drug Absorption from the Dermis after an Injection.

A dermal absorption model for small and macromolecules was previously proposed by Ibrahim et al. This model estimated absorption of therapeutics from the dermal tissue based on their molecular size and protein binding through blood and lymphatics. Blood absorption followed a two-pore theory and the lymphatic absorption was limited by the constant lymphatic flow rate. Current work builds on this steady-state concept by modeling the absorption from the dermis immediately after an injection is given (unsteady state). An injection in the dermis creates a localized pressure gradient which resolves itself over time. This phenomenon is captured in the model to estimate the impact of injection volume on the absorption rate constant. Blood absorption follows the two-pore theory but is time-dependent and the lymphatic absorption is determined based on valve opening and pressure driven convective flow, returning to steady-state as the molecule is absorbed. A direct comparison of the steady-state analysis, experimental data and the current model is made. The results indicate that accounting for the localized time-varying pressure can better predict the experimental absorption rate constants. This work significantly improves the existing understanding of macromolecule uptake from the interstitial fluid following intradermal injection.

Macrophage-Derived Iron-Bound Lipocalin-2 Correlates with Renal Recovery Markers Following Sepsis-Induced Kidney Damage.

During the course of sepsis in critically ill patients, kidney dysfunction and damage are among the first events of a complex scenario toward multi-organ failure and patient death. Acute kidney injury triggers the release of lipocalin-2 (Lcn-2), which is involved in both renal injury and recovery. Taking into account that Lcn-2 binds and transports iron with high affinity, we aimed at clarifying if Lcn-2 fulfills different biological functions according to its iron-loading status and its cellular source during sepsis-induced kidney failure. We assessed Lcn-2 levels both in serum and in the supernatant of short-term cultured renal macrophages (MΦ) as well as renal tubular epithelial cells (TEC) isolated from either Sham-operated or cecal ligation and puncture (CLP)-treated septic mice. Total kidney iron content was analyzed by Perls' staining, while Lcn-2-bound iron in the supernatants of short-term cultured cells was determined by atomic absorption spectroscopy. Lcn-2 protein in serum was rapidly up-regulated at 6 h after sepsis induction and subsequently increased up to 48 h. Lcn-2-levels in the supernatant of TEC peaked at 24 h and were low at 48 h with no change in its iron-loading. In contrast, in renal MΦ Lcn-2 was low at 24 h, but increased at 48 h, where it mainly appeared in its iron-bound form. Whereas TEC-secreted, iron-free Lcn-2 was associated with renal injury, increased MΦ-released iron-bound Lcn-2 was linked to renal recovery. Therefore, we hypothesized that both the cellular source of Lcn-2 as well as its iron-load crucially adds to its biological function during sepsis-induced renal injury.

Density Visualization Pipeline: A Tool for Cellular and Network Density Visualization and Analysis.

Neuron classification is an important component in analyzing network structure and quantifying the effect of neuron topology on signal processing. Current quantification and classification approaches rely on morphology projection onto lower-dimensional spaces. In this paper a 3D visualization and quantification tool is presented. The Density Visualization Pipeline (DVP) computes, visualizes and quantifies the density distribution, i.e., the "mass" of interneurons. We use the DVP to characterize and classify a set of GABAergic interneurons. Classification of GABAergic interneurons is of crucial importance to understand on the one hand their various functions and on the other hand their ubiquitous appearance in the neocortex. 3D density map visualization and projection to the one-dimensional x, y, z subspaces show a clear distinction between the studied cells, based on these metrics. The DVP can be coupled to computational studies of the behavior of neurons and networks, in which network topology information is derived from DVP information. The DVP reads common neuromorphological file formats, e.g., Neurolucida XML files, NeuroMorpho.org SWC files and plain ASCII files. Full 3D visualization and projections of the density to 1D and 2D manifolds are supported by the DVP. All routines are embedded within the visual programming IDE VRL-Studio for Java which allows the definition and rapid modification of analysis workflows.

Macrophage-derived lipocalin-2 transports iron in the tumor microenvironment.

While the importance of iron for tumor development is widely appreciated, the exact sources of tumor-supporting iron largely remain elusive. The possibility that iron might be provided by stromal cells in the tumor microenvironment was not taken into account so far. In the present study, we show that tumor-associated macrophages (TAM) acquire an iron-release phenotype upon their interaction with tumor cells, thereby increasing the availability of iron in the tumor microenvironment. Mechanistically, TAM expressed elevated levels of the high-affinity iron-binding protein lipocalin-2 (LCN-2), which appeared to be critical for the export of iron from TAM, and in turn enhanced tumor cell proliferation. Moreover, in PyMT-mouse tumors as well as in primary human breast tumors LCN-2 was predominantly expressed in the tumor stroma as compared to tumor cells. LCN-2 expression in the stroma further correlated with enhanced tumor proliferation in vivo. Our data suggest a dominant role of TAM in the tumor iron-management and identify LCN-2 as a critical iron transporter in this context. Targeting the LCN-2 iron export mechanism selectively in stromal cells might open for future iron-targeted tumor therapeutic approaches.

Tumour stroma-derived lipocalin-2 promotes breast cancer metastasis.

Tumour cell-secreted factors skew infiltrating immune cells towards a tumour-supporting phenotype, expressing pro-tumourigenic mediators. However, the influence of lipocalin-2 (Lcn2) on the metastatic cascade in the tumour micro-environment is still not clearly defined. Here, we explored the role of stroma-derived, especially macrophage-released, Lcn2 in breast cancer progression. Knockdown studies and neutralizing antibody approaches showed that Lcn2 contributes to the early events of metastasis in vitro. The release of Lcn2 from macrophages induced an epithelial-mesenchymal transition programme in MCF-7 breast cancer cells and enhanced local migration as well as invasion into the extracellular matrix, using a three-dimensioanl (3D) spheroid model. Moreover, a global Lcn2 deficiency attenuated breast cancer metastasis in both the MMTV-PyMT breast cancer model and a xenograft model inoculating MCF-7 cells pretreated with supernatants from wild-type and Lcn2-knockdown macrophages. To dissect the role of stroma-derived Lcn2, we employed an orthotopic mammary tumour mouse model. Implantation of wild-type PyMT tumour cells into Lcn2-deficient mice left primary mammary tumour formation unaltered, but specifically reduced tumour cell dissemination into the lung. We conclude that stroma-secreted Lcn2 promotes metastasis in vitro and in vivo, thereby contributing to tumour progression. Our study highlights the tumourigenic potential of stroma-released Lcn2 and suggests Lcn2 as a putative therapeutic target. Copyright © 2016 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Anatomically Detailed and Large-Scale Simulations Studying Synapse Loss and Synchrony Using NeuroBox.

The morphology of neurons and networks plays an important role in processing electrical and biochemical signals. Based on neuronal reconstructions, which are becoming abundantly available through databases such as NeuroMorpho.org, numerical simulations of Hodgkin-Huxley-type equations, coupled to biochemical models, can be performed in order to systematically investigate the influence of cellular morphology and the connectivity pattern in networks on the underlying function. Development in the area of synthetic neural network generation and morphology reconstruction from microscopy data has brought forth the software tool NeuGen. Coupling this morphology data (either from databases, synthetic, or reconstruction) to the simulation platform UG 4 (which harbors a neuroscientific portfolio) and VRL-Studio, has brought forth the extendible toolbox NeuroBox. NeuroBox allows users to perform numerical simulations on hybrid-dimensional morphology representations. The code basis is designed in a modular way, such that e.g., new channel or synapse types can be added to the library. Workflows can be specified through scripts or through the VRL-Studio graphical workflow representation. Third-party tools, such as ImageJ, can be added to NeuroBox workflows. In this paper, NeuroBox is used to study the electrical and biochemical effects of synapse loss vs. synchrony in neurons, to investigate large morphology data sets within detailed biophysical simulations, and used to demonstrate the capability of utilizing high-performance computing infrastructure for large scale network simulations. Using new synapse distribution methods and Finite Volume based numerical solvers for compartment-type models, our results demonstrate how an increase in synaptic synchronization can compensate synapse loss at the electrical and calcium level, and how detailed neuronal morphology can be integrated in large-scale network simulations.

1D-3D hybrid modeling-from multi-compartment models to full resolution models in space and time.

Investigation of cellular and network dynamics in the brain by means of modeling and simulation has evolved into a highly interdisciplinary field, that uses sophisticated modeling and simulation approaches to understand distinct areas of brain function. Depending on the underlying complexity, these models vary in their level of detail, in order to cope with the attached computational cost. Hence for large network simulations, single neurons are typically reduced to time-dependent signal processors, dismissing the spatial aspect of each cell. For single cell or networks with relatively small numbers of neurons, general purpose simulators allow for space and time-dependent simulations of electrical signal processing, based on the cable equation theory. An emerging field in Computational Neuroscience encompasses a new level of detail by incorporating the full three-dimensional morphology of cells and organelles into three-dimensional, space and time-dependent, simulations. While every approach has its advantages and limitations, such as computational cost, integrated and methods-spanning simulation approaches, depending on the network size could establish new ways to investigate the brain. In this paper we present a hybrid simulation approach, that makes use of reduced 1D-models using e.g., the NEURON simulator-which couples to fully resolved models for simulating cellular and sub-cellular dynamics, including the detailed three-dimensional morphology of neurons and organelles. In order to couple 1D- and 3D-simulations, we present a geometry-, membrane potential- and intracellular concentration mapping framework, with which graph- based morphologies, e.g., in the swc- or hoc-format, are mapped to full surface and volume representations of the neuron and computational data from 1D-simulations can be used as boundary conditions for full 3D simulations and vice versa. Thus, established models and data, based on general purpose 1D-simulators, can be directly coupled to the emerging field of fully resolved, highly detailed 3D-modeling approaches. We present the developed general framework for 1D/3D hybrid modeling and apply it to investigate electrically active neurons and their intracellular spatio-temporal calcium dynamics.