A5019497989- Gene Regulatory Network Analysis
- Mathematical Biology Tumor Growth
- COVID-19 epidemiological studies
- Probabilistic and Robust Engineering Design
- Influenza Virus Research Studies
- Advanced Mathematical Modeling in Engineering
- Simulation Techniques and Applications
- Neural dynamics and brain function
- Advanced Fluorescence Microscopy Techniques
- Bioinformatics and Genomic Networks
- Electromagnetic Scattering and Analysis
- Electromagnetic Simulation and Numerical Methods
- Advanced X-ray Imaging Techniques
- Animal Disease Management and Epidemiology
- stochastic dynamics and bifurcation
- Soil Moisture and Remote Sensing
- Mathematical and Theoretical Epidemiology and Ecology Models
- Advanced Multi-Objective Optimization Algorithms
- Neuroscience and Neural Engineering
- Distributed and Parallel Computing Systems
- Advanced Electron Microscopy Techniques and Applications
- Fluid Dynamics and Thin Films
- Advanced Causal Inference Techniques
- Statistical Methods and Bayesian Inference
- Lattice Boltzmann Simulation Studies
Uppsala University
2015-2024
Informa (Sweden)
2012-2024
Swedish Veterinary Agency
2018
Institut national de recherche en informatique et en automatique
2018
Research Centre Inria Sophia Antipolis - Méditerranée
2018
KTH Royal Institute of Technology
2011
University of California, Santa Barbara
2009
Trent University
2003
Åbo Akademi University
1992
Stockholm County Council
1989
Abstract Background Experiments in silico using stochastic reaction-diffusion models have emerged as an important tool molecular systems biology. Designing computational software for such applications poses several challenges. Firstly, realistic lattice-based modeling biological requires a consistent way of handling complex geometries, including curved inner- and outer boundaries. Secondly, spatiotemporal simulations are computationally expensive due to the fast time scales individual...
We model stochastic chemical systems with diffusion by a reaction-diffusion master equation. On macroscopic level, the governing equation is for averages of species. mesoscopic well stirred system combined discretized Brownian motion in space to obtain The covered our method an unstructured mesh, and coefficients on mesoscale are obtained from finite element discretization Laplace operator macroscale. resulting flexible hybrid algorithm that can be handled either meso- or macroscale level....
Abstract The app-based COVID Symptom Study was launched in Sweden April 2020 to contribute real-time COVID-19 surveillance. We enrolled 143,531 study participants (≥18 years) who contributed 10.6 million daily symptom reports between 29, and February 10, 2021. Here, we include data from 19,161 self-reported PCR tests create a symptom-based model estimate the individual probability of symptomatic COVID-19, with an AUC 0.78 (95% CI 0.74–0.83) external dataset. These probabilities are employed...
Abstract An existing phase-fieldmodel of two immiscible fluids with a single soluble surfactant present is discussed in detail. We analyze the well-posedness model and provide strong evidence that it mathematically ill-posed for large set physically relevant parameters. As consequence, critical modifications to are suggested substantially increase domain validity. Carefully designed numerical simulations offer informative demonstrations as sharpness our theoretical results qualities physical...
We present the R package SimInf which provides an efficient and very flexible framework to conduct data-driven epidemiological modeling in realistic large scale disease spread simulations. The integrates infection dynamics subpopulations as continuous-time Markov chains using Gillespie stochastic simulation algorithm incorporates available data such births, deaths movements scheduled events at predefined time-points. Using C code for numerical solvers OpenMP divide work over multiple...
A version of the time-parallel algorithm parareal is analyzed and applied to stochastic models in chemical kinetics. fast predictor at macroscopic scale (evaluated serial) available form usual reaction rate equation. simulation used obtain an exact realization process mesoscopic (in parallel). The underlying description a jump driven by Poisson measure. convergence result this arguably difficult setting established, suggesting that homogenization solution advantageous. We devise simple but...
Motivated by the lack of a suitable constructive framework for analyzing popular stochastic models Systems Biology, we devise conditions existence and uniqueness solutions to certain jump differential equations (SDEs). Working from simple examples find reasonable explicit assumptions on driving coefficients SDE representation make sense. By “reasonable” mean that stronger generally do not hold systems practical interest. In particular, argue against traditional use global Lipschitz common...
European Union legislation requires member states to keep national databases of all bovine animals. This allows for disease spread models that includes the time-varying contact network and population demographic. However, performing data-driven simulations with a high degree detail are computationally challenging. We have developed an efficient flexible discrete-event simulator SimInf stochastic modelling divides work among multiple processors accelerate computations. The model integrates...
A spatial data-driven stochastic model was developed to explore the spread of verotoxigenic Escherichia coli O157 (VTEC O157) by livestock movements and local transmission among neighbouring holdings in complete Swedish cattle population. Livestock data were incorporated time-varying contact network between population demographics. Furthermore, meteorological with average temperature at geographical location each holding used incorporate season. The fitted against observed extensive...
We present a computational modeling framework for data-driven simulations and analysis of infectious disease spread in large populations. For the purpose efficient simulations, we devise parallel solution algorithm targeting multi-socket shared-memory architectures. The model integrates dynamics as continuous-time Markov chains available data such animal movements or aging are incorporated externally defined events. To bring out parallelism accelerate computations, decompose spatial domain...
The processes taking place inside the living cell are now understood to point where predictive computational models can be used gain detailed understanding of important biological phenomena. A key challenge is extrapolate this knowledge individual able explain at population level how cells interact and respond with each other their environment. In particular, goal understand organisms develop, maintain repair functional tissues organs. paper we propose a novel framework for modeling...
We present a highly general implementation of fast multipole methods on graphics processing units (GPUs). Our two-dimensional double precision code features an asymmetric type adaptive space discretization leading to particularly elegant and flexible implementation. All steps the algorithm are efficiently performed GPU, including initial phase which assembles topological information input data. Through careful timing experiments we investigate effects various peculiarities GPU architecture.
In this paper we propose a new method for approximating the nonstationary moment dynamics of one dimensional Markovian birth-death processes. By expanding transition probabilities Markov process in terms Poisson-Charlier polynomials, are able to estimate any even though system equations may not be closed. Using weighted discrete Sobolev spaces, derive explicit error bounds and weak priori estimates moments processs using truncated form expansion. our estimates, show that approximations...
In computational systems biology, the mesoscopic model of reaction-diffusion kinetics is described by a continuous time, discrete space Markov process. To simulate diffusion stochastically, jump coefficients are obtained discretization equation. Using unstructured meshes to represent complicated geometries may lead negative when using piecewise linear finite elements. Several methods have been proposed modify enforce nonnegativity needed in stochastic setting. this paper, we present method...
Abstract Angiogenesis, the growth of new blood vessels, is a complex process requiring orchestration numerous different cell types, factors, and chemokines. Some recently acknowledged actors in this are immune cells. They accumulate at hypoxic sites, but kinetics, dynamics, regulation that trafficking unknown. In study, we used intravital live imaging to understand how neutrophils macrophages migrate behave angiogenic sites. We developed two reproducible models angiogenesis: one by...
Mathematical epidemiological models have a broad use, including both qualitative and quantitative applications. With the increasing availability of data, large-scale disease spread can nowadays be formulated. Such great potential, e.g., in risk assessments public health. Their main challenge is model parameterization given surveillance problem which often limits their practical usage. We offer solution to this by developing Bayesian methodology suitable driven network data. The greatest...
Nature presents multiple intriguing examples of processes that proceed with high precision and regularity. This remarkable stability is frequently counter to modellers' experience the inherent stochasticity chemical reactions in regime low-copy numbers. Moreover, effects noise nonlinearities can lead ‘counterintuitive’ behaviour, as demonstrated for a basic enzymatic reaction scheme display stochastic focusing (SF). Under assumption rapid signal fluctuations, SF has been shown convert graded...
We present a new technique for controlling optimism in Parallel Discrete Event Simulation on multicores. It is designed to be suitable simulating models, which the time intervals between successive events different processes are highly variable, and have no lower bounds. In our technique, called Dynamic Local Time Window Estimates (DLTWE), each processor communicates estimates of its next inter-processor event (some of) neighbors, use as bounds advancement their local simulation time....