Biology subjects

Hellander, A.

Publications and source records attributed to Hellander, A..

8 recordsLinked to original sources

bioRxiv2024

Federated Learning for Predicting Compound Mechanism of Action Based on Image-data from Cell Painting

Having access to sufficient data is essential in order to train accurate machine learning models, but much data is not publicly available. In drug discovery this is particularly evident, as much data is withheld at pharmaceutical companies for various reasons. Federated Learning (FL) aims at training a joint model between multiple parties but without disclosing data between the parties. In this work, we leverage Federated Learning to predict compound Mechanism of Action (MoA) using fluorescence image data from cell painting. Our study evaluates the effectiveness and efficiency of FL, comparing to non-collaborative and data-sharing collaborative learning in diverse scenarios. Specifically, we investigate the impact of data heterogeneity across participants on MoA prediction, an essential concern in real-life applications of FL, and demonstrate the benefits for all involved parties. This work highlights the potential of federated learning in multi-institutional collaborative machine learning for drug discovery and assessment of chemicals, offering a promising avenue to overcome data-sharing constraints.

bioinformatics↗

bioRxiv2022

Contributions of cell behavior to geometric order in embryonic cartilage

During early development, cartilage provides shape and stability to the embryo while serving as a precursor for the skeleton. Correct formation of embryonic cartilage is hence essential for healthy development. In vertebrate cranial cartilage, it has been observed that a flat and laterally extended macroscopic geometry is linked to regular microscopic structure consisting of tightly packed, short, transversal clonar columns. However, it remains an ongoing challenge to identify how individual cells coordinate to successfully shape the tissue, and more precisely which mechanical interactions and cell behaviors contribute to the generation and maintenance of this columnar cartilage geometry during embryogenesis. Here, we apply a three-dimensional cell-based computational model to investigate mechanical principles contributing to column formation. The model accounts for clonal expansion, anisotropic proliferation and the geometrical arrangement of progenitor cells in space. We confirm that oriented cell divisions and repulsive mechanical interactions between cells are key drivers of column formation. In addition, the model suggests that column formation benefits from the spatial gaps created by the extracellular matrix in the initial configuration, and that column maintenance is facilitated by sequential proliferative phases. Our model thus correctly predicts the dependence of local order on division orientation and tissue thickness. The present study presents the first cell-based simulations of cell mechanics during cranial cartilage formation and we anticipate that it will be useful in future studies on the formation and growth of other cartilage geometries. Author SummaryIn embryos, the initial skeleton is made out of cartilage. As the embryo grows, this cartilage needs to increase in size while correctly maintaining shape. A recent study revealed that for cartilage found in growing skulls, a flat sheet-like geometry is reflected in a distinct arrangement of cells at the microscopic level. Cells sharing a common ancestor are arranged into short columns such that the sheet grows in thickness by lengthening columns, and expands length-wise by adding new columns from single precursor cells. In this work we investigate the mechanical principles underlying column formation and insertion using a computational model that individually represents cells and their behavior. We confirm that arrangement of clonal columns perpendicular to the main expansion direction of the sheet requires oriented cell division. Moreover, we find that column order benefits from an increased amount of extracellular matrix between cells. Similarly, our model suggests that new clonal columns are able to insert themselves into pre-existing cartilage if sufficient matrix is available. Our model constitutes an important step to study cartilage formation and growth in different geometries which will be useful for understanding skeletal developmental disorders as well as for applications in tissue engineering.

developmental biology↗

bioRxiv2021

A pipeline for systematic comparison of model levels and parameter inference settings applied to negative feedback gene regulation

Quantitative stochastic models of gene regulatory networks are important tools for studying cellular regulation. Such models can be formulated at many different levels of fidelity. A practical challenge is to determine what model fidelity to use in order to get accurate and representative results. The choice is important, because models of successively higher fidelity come at a rapidly increasing computational cost. In some situations, the level of detail is clearly motivated by the question under study. In many situations however, many model options could qualitatively agree with available data, depending on the amount of data and the nature of the observations. Here, an important distinction is whether we are interested in inferring the true (but unknown) physical parameters of the model or if it is sufficient to be able to capture and explain available data. The situation becomes complicated from a computational perspective because inference and model selection need to be approximate. Most often it is based on likelihood-free Approximate Bayesian Computation (ABC) and here determining which summary statistics to use, as well as how much data is needed to reach the desired level of accuracy, are difficult tasks. Ultimately, all of these aspects - the model fidelity, the available data, and the numerical choices for inference and model selection - interplay in a complex manner. In this paper we develop a computational pipeline designed to systematically evaluate inference accuracy for a wide range of true known parameters. We then use it to explore inference settings for negative feedback gene regulation. In particular, we compare a spatial stochastic model, a coarse-grained multiscale model, and a simple well-mixed model for several data-scenarios and for multiple numerical options for parameter inference. Practically speaking, this pipeline can be used as a preliminary step to guide modelers prior to gathering experimental data. By training Gaussian processes to approximate the distance metric, we are able to significantly reduce the computational cost of running the pipeline.

systems biology↗

bioRxiv2021

CBMOS: a GPU-enabled Python framework for the numerical study of center-based models

Cell-based models are becoming increasingly popular for applications in developmental biology. However, the impact of numerical choices on the accuracy and efficiency of the simulation of these models is rarely meticulously tested. We present CBMOS, a Python framework for the simulation of the center-based or cell-centered model. Contrary to other implementations, CBMOS focus is on facilitating numerical study of center-based models by providing access to multiple ODE solvers and force functions through a flexible, user-friendly API. We show-case its potential by evaluating the use of the backward Euler method for calculating the trajectories of two-dimensional cell populations. We confirm that although for moderate accuracy levels the backward Euler method allows for larger time step sizes than the commonly used forward Euler method, its additional computational cost due to being an implicit method prohibits its use for practical test cases. CBMOS is available on GitHub1 and PyPI under an MIT license. It allows for fast prototyping on a CPU for small systems through the use of NumPy. Using CuPy on a GPU, cell populations of up to 10,000 cells can be simulated within a few seconds. As such, we hope it can also be of use to modelers interested in testing preliminary hypotheses before committing to more complex center-based model frameworks. AMS subject classification65Z05, 92C15, 92-10

biophysics↗

bioRxiv2020

Rapid development of cloud-native intelligent data pipelines for scientific data streams using the HASTE Toolkit

This paper introduces the HASTE Toolkit, a cloud-native software toolkit capable of partitioning data streams in order to prioritize usage of limited resources. This in turn enables more efficient data-intensive experiments. We propose a model that introduces automated, autonomous decision making in data pipelines, such that a stream of data can be partitioned into a tiered or ordered data hierarchy. Importantly, the partitioning is online and based on data content rather than a priori metadata. At the core of the model are interestingness functions and policies. Interestingness functions assign a quantitative measure of interestingness to a single data object in the stream, an interestingness score. Based on this score, a policy guides decisions on how to prioritize computational resource usage for a given object. The HASTE Toolkit is a collection of tools to adapt data stream processing to this pipeline model. The result is smart data pipelines capable of effective or even optimal use of e.g. storage, compute and network bandwidth, to support experiments involving rapid processing of scientific data characterized by large individual data object sizes. We demonstrate the proposed model and our toolkit through two microscopy imaging case studies, each with their own interestingness functions, policies, and data hierarchies. The first deals with a high content screening experiment, where images are analyzed in an on-premise container cloud with the goal of prioritizing the images for storage and subsequent computation. The second considers edge processing of images for upload into the public cloud for a real-time control loop for a transmission electron microscope. Key PointsO_LIWe propose a pipeline model for building intelligent pipelines for streams, accounting for actual information content in data rather than a priori metadata, and present the HASTE Toolkit, a cloud-native software toolkit for supporting rapid development according to the proposed model. C_LIO_LIWe demonstrate how the HASTE Toolkit enables intelligent resource optimization in two image analysis case studies based on a) high-content imaging and b) transmission electron microscopy. C_LIO_LIWe highlight the challenges of storage, processing and transfer in streamed high volume, high velocity scientific data for both cloud and cloud-edge use cases. C_LI

bioinformatics↗

bioRxiv2020

Deep learning models for lipid-nanoparticle-based drug delivery

Large-scale time-lapse microscopy experiments are useful to understand delivery and expression in RNA-based therapeutics. The resulting data has high dimensionality and high (but sparse) information content, making it challenging and costly to store and process. Early prediction of experimental outcome enables intelligent data management and decision making. We start from time-lapse data of HepG2 cells exposed to lipid-nanoparticles loaded with mRNA for expression of green fluorescent protein (GFP). We hypothesize that it is possible to predict if a cell will express GFP or not based on cell morphology at time-points prior to GFP expression. Here we present results on per-cell classification (GFP expression/no GFP expression) and regression (level of GFP expression) using three different approaches. In the first approach we use a convolutional neural network extracting per-cell features at each time point. We then utilize the same features combined with: a long-short-term memory (LSTM) network encoding temporal dynamics (approach 2); and time-series feature extraction using the python package tsfresh followed by principal component analysis and gradient boosting machines (approach 3), to reach a final classification or regression result. Application of the three approaches to a previously unanalyzed test set of cells showed good predictive performance of all three approaches but that accounting for the temporal dynamics via LSTMs or tsfresh led to significantly improved performance. The predictions made by the LSTM and tsfresh applications were not significantly different. The results highlight the benefit of accounting for temporal dynamics when studying drug delivery using high content imaging.

cell biology↗

bioRxiv2020

Impact of force function formulations on the numerical simulation of centre-based models

Centre-based, or cell-centre models are a framework for the computational study of multicellular systems with widespread use in cancer modelling and computational developmental biology. At the core of these models are the numerical method used to update cell positions and the force functions that encode the pairwise mechanical interactions of cells. For the latter there are multiple choices that could potentially affect both the biological behaviour captured, and the robustness and efficiency of simulation. For example, available open-source software implementations of centre-based models rely on different force functions for their default behaviour and it is not straightforward for a modeler to know if these are interchangeable. Our study addresses this problem and contributes to the understanding of the potential and limitations of three popular force functions from a numerical perspective. We show empirically that choosing the force parameters such that the relaxation time for two cells after cell division is consistent between different force functions results in good agreement of the population radius of a growing monolayer. Furthermore, we report that numerical stability is not sufficient to prevent unphysical cell trajectories following cell division, and consequently, that too large time steps can cause geometrical differences at the population level.

biophysics↗

bioRxiv2018

Smart computational exploration of stochastic gene regulatory network models using human-in-the-loop semi-supervised learning

Discrete stochastic models of gene regulatory network models are indispensable tools for biological inquiry since they allow the modeler to predict how molecular interactions give rise to nonlinear system output. Model exploration with the objective of generating qualitative hypotheses about the workings of a pathway is usually the first step in the modeling process. It involves simulating the gene network model under a very large range of conditions, due to the the large uncertainty in interactions and kinetic parameters. This makes model exploration highly computational demanding. Furthermore, with no prior information about the model behavior, labour-intensive manual inspection of very large amounts of simulation results becomes necessary. This limits systematic computational exploration to simplistic models. We address this by developing an interactive, smart workflow for model exploration based on semi-supervised learning and human-in-the-loop labeling of data. The proposed methodology lets a modeler rapidly discover ranges of interesting behaviors predicted by a model. Utilizing the notion that similar simulation output is in proximity of each other in feature space, the modeler can focus on informing the system about what behaviors are more interesting than others instead of configuring and analyzing simulation results. This large reduction in time-consuming manual work by the modeler early in a modeling project can substantially reduce the time needed to go from an initial model to testable predictions and downstream analysis.

systems biology↗