Andreas Hellander
Uppsala University
AlgorithmMachine learningDistributed computingReaction–diffusion systemStatistical physicsStochastic modellingMaster equationFinite element methodDiffusion (business)Artificial intelligenceWorkflowMesoscopic physicsStochastic simulationInferenceComputational mathematicsSoftwareMathematicsComputer scienceComputational scienceScalabilityCloud computingStochastic process
88Publications
18H-index
1,327Citations
Publications 64
Newest
#1Ola Spjuth (Science for Life Laboratory)H-Index: 23
Last. Andreas Hellander (Uppsala University)H-Index: 18
view all 3 authors...
Introduction: Artificial intelligence (AI) and machine learning (ML) are increasingly used in many aspects of drug discovery. Larger data sizes and methods such as Deep Neural Networks contribute to challenges in data management, the required software stack, and computational infrastructure. There is an increasing need in drug discovery to continuously re-train models and make them available in production environments.Areas covered: This article describes how cloud computing can aid the ML life ...
Source
#1Adrien Coulier (Uppsala University)H-Index: 2
#2Prashant Singh (Uppsala University)H-Index: 7
Last. Andreas Hellander (Uppsala University)H-Index: 18
view all 4 authors...
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 und...
Source
#1Adrien Coulier (Uppsala University)H-Index: 2
#2Stefan Hellander (Uppsala University)H-Index: 10
Last. Andreas Hellander (Uppsala University)H-Index: 18
view all 3 authors...
Spatial stochastic models of single cell kinetics are capable of capturing both fluctuations in molecular numbers and the spatial dependencies of the key steps of intracellular regulatory networks. The spatial stochastic model can be simulated both on a detailed microscopic level using particle tracking and on a mesoscopic level using the reaction–diffusion master equation. However, despite substantial progress on simulation efficiency for spatial models in the last years, the computational cost...
1 CitationsSource
#1Sonja Mathias (Uppsala University)H-Index: 1
#2Adrien Coulier (Uppsala University)H-Index: 2
Last. Andreas Hellander (Uppsala University)H-Index: 18
view all 3 authors...
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, CBMOS9 focus is on facilitating numerical study of center-based models by providing access to multiple ODE solvers and force function...
Source
#1Ben Blamey (Uppsala University)H-Index: 3
#2Salman Toor (Uppsala University)H-Index: 7
Last. Andreas Hellander (Uppsala University)H-Index: 18
view all 10 authors...
BACKGROUND Large streamed datasets, characteristic of life science applications, are often resource-intensive to process, transport and store. We propose a pipeline model, a design pattern for scientific pipelines, where an incoming stream of scientific data is organized into a tiered or ordered "data hierarchy". We introduce the HASTE Toolkit, a proof-of-concept cloud-native software toolkit based on this pipeline model, to partition and prioritize data streams to optimize use of limited comput...
Source
#1Morgan EkmefjordH-Index: 1
#2Addi Ait-Mlouk (Uppsala University)H-Index: 5
Last. Andreas Hellander (Uppsala University)H-Index: 18
view all 8 authors...
Federated machine learning has great promise to overcome the input privacy challenge in machine learning. The appearance of several projects capable of simulating federated learning has led to a corresponding rapid progress on algorithmic aspects of the problem. However, there is still a lack of federated machine learning frameworks that focus on fundamental aspects such as scalability, robustness, security, and performance in a geographically distributed setting. To bridge this gap we have desi...
#1Richard M. Jiang (UCSB: University of California, Santa Barbara)H-Index: 2
#2Fredrik Wrede (Uppsala University)H-Index: 3
Last. Linda R. Petzold (UCSB: University of California, Santa Barbara)H-Index: 74
view all 0 authors...
Approximate Bayesian Computation (ABC) has become a key tool for calibrating the parameters of discrete stochastic biochemical models. For higher dimensional models and data, its performance is strongly dependent on having a representative set of summary statistics. While regression-based methods have been demonstrated to allow for the automatic construction of effective summary statistics, their reliance on first simulating a large training set creates a significant overhead when applying these...
Source
#1Sonja Mathias (Uppsala University)H-Index: 1
#2Adrien Coulier (Uppsala University)H-Index: 2
Last. Andreas Hellander (Uppsala University)H-Index: 18
view all 4 authors...
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...
2 CitationsSource
#1Philip J. Harrison (Uppsala University)H-Index: 12
#2Håkan Wieslander (Uppsala University)H-Index: 3
Last. Ola Spjuth (Uppsala University)H-Index: 23
view all 8 authors...
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 (G...
Source
#1Stefan Hellander (Uppsala University)H-Index: 10
#2Andreas Hellander (Uppsala University)H-Index: 18
We have developed an algorithm coupling mesoscopic simulations on different levels in a hierarchy of Cartesian meshes. Based on the multiscale nature of the chemical reactions, some molecules in the system will live on a fine-grained mesh, while others live on a coarse-grained mesh. By allowing molecules to transfer from the fine levels to the coarse levels when appropriate, we show that we can save up to three orders of magnitude of computational time compared to microscopic simulations or high...
Source