1
|
Alamoudi E, Schälte Y, Müller R, Starruß J, Bundgaard N, Graw F, Brusch L, Hasenauer J. FitMultiCell: simulating and parameterizing computational models of multi-scale and multi-cellular processes. Bioinformatics 2023; 39:btad674. [PMID: 37947308 PMCID: PMC10666203 DOI: 10.1093/bioinformatics/btad674] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 10/25/2023] [Accepted: 11/07/2023] [Indexed: 11/12/2023] Open
Abstract
MOTIVATION Biological tissues are dynamic and highly organized. Multi-scale models are helpful tools to analyse and understand the processes determining tissue dynamics. These models usually depend on parameters that need to be inferred from experimental data to achieve a quantitative understanding, to predict the response to perturbations, and to evaluate competing hypotheses. However, even advanced inference approaches such as approximate Bayesian computation (ABC) are difficult to apply due to the computational complexity of the simulation of multi-scale models. Thus, there is a need for a scalable pipeline for modeling, simulating, and parameterizing multi-scale models of multi-cellular processes. RESULTS Here, we present FitMultiCell, a computationally efficient and user-friendly open-source pipeline that can handle the full workflow of modeling, simulating, and parameterizing for multi-scale models of multi-cellular processes. The pipeline is modular and integrates the modeling and simulation tool Morpheus and the statistical inference tool pyABC. The easy integration of high-performance infrastructure allows to scale to computationally expensive problems. The introduction of a novel standard for the formulation of parameter inference problems for multi-scale models additionally ensures reproducibility and reusability. By applying the pipeline to multiple biological problems, we demonstrate its broad applicability, which will benefit in particular image-based systems biology. AVAILABILITY AND IMPLEMENTATION FitMultiCell is available open-source at https://gitlab.com/fitmulticell/fit.
Collapse
Affiliation(s)
- Emad Alamoudi
- Life and Medical Sciences Institute, University of Bonn, Bonn 53113, Germany
| | - Yannik Schälte
- Life and Medical Sciences Institute, University of Bonn, Bonn 53113, Germany
- Institute of Computational Biology, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg 85764, Germany
- Center for Mathematics, Chair of Mathematical Modeling of Biological Systems, Technische Universität München, Garching 85748, Germany
| | - Robert Müller
- Center of Information Services and High Performance Computing (ZIH), Technische Universität Dresden, Dresden 01062, Germany
| | - Jörn Starruß
- Center of Information Services and High Performance Computing (ZIH), Technische Universität Dresden, Dresden 01062, Germany
| | - Nils Bundgaard
- BioQuant—Center for Quantitative Biology, Heidelberg University, Heidelberg 69120, Germany
| | - Frederik Graw
- BioQuant—Center for Quantitative Biology, Heidelberg University, Heidelberg 69120, Germany
- Interdisciplinary Center for Scientific Computing, Heidelberg University, Heidelberg 69120, Germany
- Department of Medicine 5, Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen 91054, Germany
| | - Lutz Brusch
- Center of Information Services and High Performance Computing (ZIH), Technische Universität Dresden, Dresden 01062, Germany
| | - Jan Hasenauer
- Life and Medical Sciences Institute, University of Bonn, Bonn 53113, Germany
- Institute of Computational Biology, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg 85764, Germany
- Center for Mathematics, Chair of Mathematical Modeling of Biological Systems, Technische Universität München, Garching 85748, Germany
| |
Collapse
|
2
|
Varella V, Quintela BDM, Kasztelnik M, Viceconti M. Effect of particularisation size on the accuracy and efficiency of a multiscale tumours' growth model. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2022; 38:e3657. [PMID: 36282099 DOI: 10.1002/cnm.3657] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 10/05/2022] [Indexed: 06/16/2023]
Abstract
In silico, medicine models are frequently used to represent a phenomenon across multiples space-time scales. Most of these multiscale models require impracticable execution times to be solved, even using high performance computing systems, because typically each representative volume element in the upper scale model is coupled to an instance of the lower scale model; this causes a combinatory explosion of the computational cost, which increases exponentially as the number of scales to be modelled increases. To attenuate this problem, it is a common practice to interpose between the two models a particularisation operator, which maps the upper-scale model results into a smaller number of lower-scale models, and an operator, which maps the fewer results of the lower-scale models on the whole space-time homogenisation domain of upper-scale model. The aim of this study is to explore what is the simplest particularisation / homogenisation scheme that can couple a model aimed to predict the growth of a whole solid tumour (neuroblastoma) to a tissue-scale model of the cell-tissue biology with an acceptable approximation error and a viable computational cost. Using an idealised initial dataset with spatial gradients representative of those of real neuroblastomas, but small enough to be solved without any particularisation, we determined the approximation error and the computational cost of a very simple particularisation strategy based on binning. We found that even such simple algorithm can significantly reduce the computational cost with negligible approximation errors.
Collapse
Affiliation(s)
- Vinicius Varella
- Department of Industrial Engineering, Alma Mater Studiorum, University of Bologna, Bologna, Italy
- Medical Technology Lab, IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy
| | - Barbara de M Quintela
- Departamento de Ciencia da Computacao, Universidade Federal de Juiz de Fora, Juiz de Fora, Brazil
| | - Marek Kasztelnik
- Academic Computer Center Cyfronet AGH, University of Science and Technology, Krakow, Poland
| | - Marco Viceconti
- Department of Industrial Engineering, Alma Mater Studiorum, University of Bologna, Bologna, Italy
- Medical Technology Lab, IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy
| |
Collapse
|
3
|
Kondylakis H, Ciarrocchi E, Cerda-Alberich L, Chouvarda I, Fromont LA, Garcia-Aznar JM, Kalokyri V, Kosvyra A, Walker D, Yang G, Neri E. Position of the AI for Health Imaging (AI4HI) network on metadata models for imaging biobanks. Eur Radiol Exp 2022; 6:29. [PMID: 35773546 PMCID: PMC9247122 DOI: 10.1186/s41747-022-00281-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Accepted: 04/20/2022] [Indexed: 11/10/2022] Open
Abstract
A huge amount of imaging data is becoming available worldwide and an incredible range of possible improvements can be provided by artificial intelligence algorithms in clinical care for diagnosis and decision support. In this context, it has become essential to properly manage and handle these medical images and to define which metadata have to be considered, in order for the images to provide their full potential. Metadata are additional data associated with the images, which provide a complete description of the image acquisition, curation, analysis, and of the relevant clinical variables associated with the images. Currently, several data models are available to describe one or more subcategories of metadata, but a unique, common, and standard data model capable of fully representing the heterogeneity of medical metadata has not been yet developed. This paper reports the state of the art on metadata models for medical imaging, the current limitations and further developments, and describes the strategy adopted by the Horizon 2020 "AI for Health Imaging" projects, which are all dedicated to the creation of imaging biobanks.
Collapse
Affiliation(s)
| | - Esther Ciarrocchi
- grid.5395.a0000 0004 1757 3729Department of Translational Research, University of Pisa, Pisa, Italy
| | | | - Ioanna Chouvarda
- grid.4793.90000000109457005Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Lauren A. Fromont
- grid.11478.3b0000 0004 1766 3695Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | | | - Varvara Kalokyri
- grid.5395.a0000 0004 1757 3729Department of Translational Research, University of Pisa, Pisa, Italy
| | - Alexandra Kosvyra
- grid.4793.90000000109457005Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Dawn Walker
- grid.11835.3e0000 0004 1936 9262Department of Computer Science and Insigneo Institute of in silico Medicine, University of Sheffield, Sheffield, UK
| | - Guang Yang
- grid.7445.20000 0001 2113 8111National Heart and Lung Institute, Imperial College London, London, UK
| | - Emanuele Neri
- grid.5395.a0000 0004 1757 3729Department of Translational Research, University of Pisa, Pisa, Italy
| | | |
Collapse
|
4
|
Breitwieser L, Hesam A, de Montigny J, Vavourakis V, Iosif A, Jennings J, Kaiser M, Manca M, Di Meglio A, Al-Ars Z, Rademakers F, Mutlu O, Bauer R. BioDynaMo: a modular platform for high-performance agent-based simulation. Bioinformatics 2022; 38:453-460. [PMID: 34529036 PMCID: PMC8723141 DOI: 10.1093/bioinformatics/btab649] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Revised: 09/02/2021] [Accepted: 09/13/2021] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION Agent-based modeling is an indispensable tool for studying complex biological systems. However, existing simulation platforms do not always take full advantage of modern hardware and often have a field-specific software design. RESULTS We present a novel simulation platform called BioDynaMo that alleviates both of these problems. BioDynaMo features a modular and high-performance simulation engine. We demonstrate that BioDynaMo can be used to simulate use cases in: neuroscience, oncology and epidemiology. For each use case, we validate our findings with experimental data or an analytical solution. Our performance results show that BioDynaMo performs up to three orders of magnitude faster than the state-of-the-art baselines. This improvement makes it feasible to simulate each use case with one billion agents on a single server, showcasing the potential BioDynaMo has for computational biology research. AVAILABILITY AND IMPLEMENTATION BioDynaMo is an open-source project under the Apache 2.0 license and is available at www.biodynamo.org. Instructions to reproduce the results are available in the supplementary information. SUPPLEMENTARY INFORMATION Available at https://doi.org/10.5281/zenodo.5121618.
Collapse
Affiliation(s)
- Lukas Breitwieser
- CERN openlab, IT Department, CERN, Geneva 1211, Switzerland.,Department of Computer Science, ETH Zurich, Zurich 8092, Switzerland
| | - Ahmad Hesam
- CERN openlab, IT Department, CERN, Geneva 1211, Switzerland.,Department of Quantum & Computer Engineering, Delft University of Technology, Delft 2628CD, The Netherlands
| | | | - Vasileios Vavourakis
- Department of Mechanical & Manufacturing Engineering, University of Cyprus, Nicosia 2109, Cyprus.,Department of Medical Physics & Biomedical Engineering, University College London, London WC1E 6BT, UK
| | - Alexandros Iosif
- Department of Mechanical & Manufacturing Engineering, University of Cyprus, Nicosia 2109, Cyprus
| | - Jack Jennings
- School of Computing, Newcastle University, Newcastle upon Tyne NE4 5TG, UK
| | - Marcus Kaiser
- School of Computing, Newcastle University, Newcastle upon Tyne NE4 5TG, UK.,Department of Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.,Precision Imaging Beacon, School of Medicine, University of Nottingham, Nottingham NG7 2UH, UK
| | - Marco Manca
- SCimPulse Foundation, Geleen 6162 BC, The Netherlands
| | | | - Zaid Al-Ars
- Department of Quantum & Computer Engineering, Delft University of Technology, Delft 2628CD, The Netherlands
| | | | - Onur Mutlu
- Department of Computer Science, ETH Zurich, Zurich 8092, Switzerland.,Department of Information Technology and Electrical Engineering, ETH Zurich, Zurich 8092, Switzerland
| | - Roman Bauer
- Department of Computer Science, University of Surrey, Guildford GU2 7XH, UK
| |
Collapse
|
5
|
Mousavi R, Konuru SH, Lobo D. Inference of dynamic spatial GRN models with multi-GPU evolutionary computation. Brief Bioinform 2021; 22:6217729. [PMID: 33834216 DOI: 10.1093/bib/bbab104] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Revised: 02/15/2021] [Accepted: 03/09/2021] [Indexed: 02/06/2023] Open
Abstract
Reverse engineering mechanistic gene regulatory network (GRN) models with a specific dynamic spatial behavior is an inverse problem without analytical solutions in general. Instead, heuristic machine learning algorithms have been proposed to infer the structure and parameters of a system of equations able to recapitulate a given gene expression pattern. However, these algorithms are computationally intensive as they need to simulate millions of candidate models, which limits their applicability and requires high computational resources. Graphics processing unit (GPU) computing is an affordable alternative for accelerating large-scale scientific computation, yet no method is currently available to exploit GPU technology for the reverse engineering of mechanistic GRNs from spatial phenotypes. Here we present an efficient methodology to parallelize evolutionary algorithms using GPU computing for the inference of mechanistic GRNs that can develop a given gene expression pattern in a multicellular tissue area or cell culture. The proposed approach is based on multi-CPU threads running the lightweight crossover, mutation and selection operators and launching GPU kernels asynchronously. Kernels can run in parallel in a single or multiple GPUs and each kernel simulates and scores the error of a model using the thread parallelism of the GPU. We tested this methodology for the inference of spatiotemporal mechanistic gene regulatory networks (GRNs)-including topology and parameters-that can develop a given 2D gene expression pattern. The results show a 700-fold speedup with respect to a single CPU implementation. This approach can streamline the extraction of knowledge from biological and medical datasets and accelerate the automatic design of GRNs for synthetic biology applications.
Collapse
Affiliation(s)
- Reza Mousavi
- Department of Biological Sciences at the University of Maryland, Baltimore, MD 21250, USA
| | - Sri Harsha Konuru
- Department of Biological Sciences at the University of Maryland, Baltimore, MD 21250, USA
| | - Daniel Lobo
- Department of Biological Sciences at the University of Maryland, Baltimore, MD 21250, USA
| |
Collapse
|
6
|
Ascolani G, Skerry TM, Lacroix D, Dall'Ara E, Shuaib A. Analysis of mechanotransduction dynamics during combined mechanical stimulation and modulation of the extracellular-regulated kinase cascade uncovers hidden information within the signalling noise. Interface Focus 2021; 11:20190136. [PMID: 33343875 PMCID: PMC7739911 DOI: 10.1098/rsfs.2019.0136] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/23/2020] [Indexed: 12/27/2022] Open
Abstract
Osteoporosis is a bone disease characterized by brittle bone and increased fracture incidence. With ageing societies worldwide, the disease presents a high burden on health systems. Furthermore, there are limited treatments for osteoporosis with just two anabolic pharmacological agents approved by the US Food and Drug Administration. Healthy bones are believed to be maintained via an intricate relationship between dual biochemical and mechanical (bio-mechanical) stimulations. It is widely considered that osteoporosis emerges as a result of disturbances to said relationship. The mechanotransduction process is key to this balance, and disruption of its dynamics in bone cells plays a role in osteoporosis development. Nonetheless, the exact details and mechanisms that drive and secure the health of bones are still elusive at the cellular and molecular scales. This study examined the dual modulation of mechanical stimulation and mechanotransduction activation dynamics in an osteoblast (OB). The aim was to find patterns of mechanotransduction dynamics demonstrating a significant change that can be mapped to alterations in the OB responses, specifically at the level of gene expression and osteogenic markers such as alkaline phosphatase. This was achieved using a three-dimensional hybrid multiscale computational model simulating mechanotransduction in the OB and its interaction with the extracellular matrix, combined with a numerical analytical technique. The model and the analysis method predict that within the noise of mechanotransduction, owing to modulation of the bio-mechanical stimulus and consequent gene expression, there are unique events that provide signatures for a shift in the system's dynamics. Furthermore, the study uncovered molecular interactions that can be potential drug targets.
Collapse
Affiliation(s)
- Gianluca Ascolani
- Department of Oncology and Metabolism, University of Sheffield, Sheffield, UK
- Insigneo Institute of In Silico Medicine, University of Sheffield, Sheffield, UK
| | - Timothy M. Skerry
- Department of Oncology and Metabolism, University of Sheffield, Sheffield, UK
| | - Damien Lacroix
- Insigneo Institute of In Silico Medicine, University of Sheffield, Sheffield, UK
- Department of Mechanical Engineering, University of Sheffield, Sheffield, UK
| | - Enrico Dall'Ara
- Department of Oncology and Metabolism, University of Sheffield, Sheffield, UK
- Insigneo Institute of In Silico Medicine, University of Sheffield, Sheffield, UK
| | - Aban Shuaib
- Department of Oncology and Metabolism, University of Sheffield, Sheffield, UK
- Insigneo Institute of In Silico Medicine, University of Sheffield, Sheffield, UK
| |
Collapse
|
7
|
Amirmahani F, Ebrahimi N, Molaei F, Faghihkhorasani F, Jamshidi Goharrizi K, Mirtaghi SM, Borjian‐Boroujeni M, Hamblin MR. Approaches for the integration of big data in translational medicine: single‐cell and computational methods. Ann N Y Acad Sci 2021; 1493:3-28. [DOI: 10.1111/nyas.14544] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Revised: 10/31/2020] [Accepted: 11/12/2020] [Indexed: 12/11/2022]
Affiliation(s)
- Farzane Amirmahani
- Genetics Division, Department of Cell and Molecular Biology and Microbiology, Faculty of Science and Technology University of Isfahan Isfahan Iran
| | - Nasim Ebrahimi
- Genetics Division, Department of Cell and Molecular Biology and Microbiology, Faculty of Science and Technology University of Isfahan Isfahan Iran
| | - Fatemeh Molaei
- Department of Anesthesiology, Faculty of Paramedical Jahrom University of Medical Sciences Jahrom Iran
| | | | | | | | | | - Michael R. Hamblin
- Laser Research Centre, Faculty of Health Science University of Johannesburg South Africa
| |
Collapse
|
8
|
Fullstone G, Guttà C, Beyer A, Rehm M. The FLAME-accelerated signalling tool (FaST) for facile parallelisation of flexible agent-based models of cell signalling. NPJ Syst Biol Appl 2020; 6:10. [PMID: 32313030 PMCID: PMC7170865 DOI: 10.1038/s41540-020-0128-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Accepted: 03/17/2020] [Indexed: 11/18/2022] Open
Abstract
Agent-based modelling is particularly adept at modelling complex features of cell signalling pathways, where heterogeneity, stochastic and spatial effects are important, thus increasing our understanding of decision processes in biology in such scenarios. However, agent-based modelling often is computationally prohibitive to implement. Parallel computing, either on central processing units (CPUs) or graphical processing units (GPUs), can provide a means to improve computational feasibility of agent-based applications but generally requires specialist coding knowledge and extensive optimisation. In this paper, we address these challenges through the development and implementation of the FLAME-accelerated signalling tool (FaST), a software that permits easy creation and parallelisation of agent-based models of cell signalling, on CPUs or GPUs. FaST incorporates validated new agent-based methods, for accurate modelling of reaction kinetics and, as proof of concept, successfully converted an ordinary differential equation (ODE) model of apoptosis execution into an agent-based model. We finally parallelised this model through FaST on CPUs and GPUs resulting in an increase in performance of 5.8× (16 CPUs) and 53.9×, respectively. The FaST takes advantage of the communicating X-machine approach used by FLAME and FLAME GPU to allow easy alteration or addition of functionality to parallel applications, but still includes inherent parallelisation optimisation. The FaST, therefore, represents a new and innovative tool to easily create and parallelise bespoke, robust, agent-based models of cell signalling.
Collapse
Affiliation(s)
- Gavin Fullstone
- Institute for Cell Biology and Immunology, University of Stuttgart, Allmandring 31, 70569, Stuttgart, Germany. .,Stuttgart Research Center Systems Biology (SRCSB), University of Stuttgart, Nobelstrasse 15, 70569, Stuttgart, Germany.
| | - Cristiano Guttà
- Institute for Cell Biology and Immunology, University of Stuttgart, Allmandring 31, 70569, Stuttgart, Germany
| | - Amatus Beyer
- Institute for Cell Biology and Immunology, University of Stuttgart, Allmandring 31, 70569, Stuttgart, Germany
| | - Markus Rehm
- Institute for Cell Biology and Immunology, University of Stuttgart, Allmandring 31, 70569, Stuttgart, Germany. .,Stuttgart Research Center Systems Biology (SRCSB), University of Stuttgart, Nobelstrasse 15, 70569, Stuttgart, Germany.
| |
Collapse
|
9
|
Martí-Bonmatí L, Alberich-Bayarri Á, Ladenstein R, Blanquer I, Segrelles JD, Cerdá-Alberich L, Gkontra P, Hero B, García-Aznar JM, Keim D, Jentner W, Seymour K, Jiménez-Pastor A, González-Valverde I, Martínez de Las Heras B, Essiaf S, Walker D, Rochette M, Bubak M, Mestres J, Viceconti M, Martí-Besa G, Cañete A, Richmond P, Wertheim KY, Gubala T, Kasztelnik M, Meizner J, Nowakowski P, Gilpérez S, Suárez A, Aznar M, Restante G, Neri E. PRIMAGE project: predictive in silico multiscale analytics to support childhood cancer personalised evaluation empowered by imaging biomarkers. Eur Radiol Exp 2020; 4:22. [PMID: 32246291 PMCID: PMC7125275 DOI: 10.1186/s41747-020-00150-9] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2019] [Accepted: 02/24/2020] [Indexed: 03/12/2023] Open
Abstract
PRIMAGE is one of the largest and more ambitious research projects dealing with medical imaging, artificial intelligence and cancer treatment in children. It is a 4-year European Commission-financed project that has 16 European partners in the consortium, including the European Society for Paediatric Oncology, two imaging biobanks, and three prominent European paediatric oncology units. The project is constructed as an observational in silico study involving high-quality anonymised datasets (imaging, clinical, molecular, and genetics) for the training and validation of machine learning and multiscale algorithms. The open cloud-based platform will offer precise clinical assistance for phenotyping (diagnosis), treatment allocation (prediction), and patient endpoints (prognosis), based on the use of imaging biomarkers, tumour growth simulation, advanced visualisation of confidence scores, and machine-learning approaches. The decision support prototype will be constructed and validated on two paediatric cancers: neuroblastoma and diffuse intrinsic pontine glioma. External validation will be performed on data recruited from independent collaborative centres. Final results will be available for the scientific community at the end of the project, and ready for translation to other malignant solid tumours.
Collapse
Affiliation(s)
- Luis Martí-Bonmatí
- Medical Imaging Department, La Fe University and Polytechnic Hospital & Biomedical Imaging Research Group (GIBI230) at La Fe University and Polytechnic Hospital and Health Research Institute, Av. Fernando Abril Martorell 106, 46026, Valencia, Spain.
| | - Ángel Alberich-Bayarri
- Quantitative Imaging Biomarkers in Medicine, QUIBIM SL, Edificio Europa, Av. de Aragón, 30, Planta 12, 46021, Valencia, Spain
| | | | - Ignacio Blanquer
- Instituto de Instrumentación para Imagen Molecular (I3M), Universitat Politècnica de València (UPV), c\ Camino de Vera s/n, 46022, Valencia, Spain
| | - J Damian Segrelles
- Instituto de Instrumentación para Imagen Molecular (I3M), Universitat Politècnica de València (UPV), c\ Camino de Vera s/n, 46022, Valencia, Spain
| | - Leonor Cerdá-Alberich
- Biomedical Imaging Research Group (GIBI230), La Fe Health Research Institute, Av. Fernando Abril Martorell 106, Torre E, 46026, Valencia, Spain
| | - Polyxeni Gkontra
- Biomedical Imaging Research Group (GIBI230), La Fe Health Research Institute, Av. Fernando Abril Martorell 106, Torre E, 46026, Valencia, Spain
| | - Barbara Hero
- Department of Pediatrics, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - J M García-Aznar
- Multiscale in Mechanical and Biological Engineering, Department of Mechanical Engineering, Universidad de Zaragoza, Zaragoza, Spain.,Aragón Institute of Engineering Research, Zaragoza, Spain
| | - Daniel Keim
- Department of Computer Science, University of Konstanz, Konstanz, Germany
| | - Wolfgang Jentner
- Department of Computer Science, University of Konstanz, Konstanz, Germany
| | | | - Ana Jiménez-Pastor
- Quantitative Imaging Biomarkers in Medicine, QUIBIM SL, Edificio Europa, Av. de Aragón, 30, Planta 12, 46021, Valencia, Spain
| | - Ismael González-Valverde
- Quantitative Imaging Biomarkers in Medicine, QUIBIM SL, Edificio Europa, Av. de Aragón, 30, Planta 12, 46021, Valencia, Spain
| | - Blanca Martínez de Las Heras
- Paediatric Oncology Unit, La Fe University and Polytechnic Hospital, Av. Fernando Abril Martorell 106, Torre G, 2 Floor, 46026, Valencia, Spain
| | - Samira Essiaf
- European Society for Paediatric Oncology, Brussels, Belgium
| | - Dawn Walker
- Department of Computer Science and Insigneo Institute of In Silico Medicine, University of Sheffield, Regent Court, 211 Portobello, Sheffield, UK
| | - Michel Rochette
- Simulation, Modelling and Engineering Software, Ansys Group, Montigny-le-Bretonneux, France
| | - Marian Bubak
- ACC Cyfronet, AGH University of Science and Technology, Sano Centre for Computational Medicine, Nawojki 11, 30-950, Kraków, Poland
| | - Jordi Mestres
- Chemotargets S.L., Carrer de Baldiri Reixac, 4-8 TI05A7 Torre I, planta 5, A7, 08028, Barcelona, Spain
| | - Marco Viceconti
- Department of Industrial Engineering, Alma Mater Studiorum, University of Bologna, Bologna, Italy
| | - Gracia Martí-Besa
- Biomedical Imaging Research Group (GIBI230), La Fe Health Research Institute, Av. Fernando Abril Martorell 106, Torre E, 46026, Valencia, Spain
| | - Adela Cañete
- Paediatric Oncology Unit, La Fe University and Polytechnic Hospital, Av. Fernando Abril Martorell 106, Torre G, 2 Floor, 46026, Valencia, Spain
| | - Paul Richmond
- Department of Computer Science and Insigneo Institute of In Silico Medicine, University of Sheffield, Regent Court, 211 Portobello, Sheffield, UK
| | - Kenneth Y Wertheim
- Department of Computer Science and Insigneo Institute of In Silico Medicine, University of Sheffield, Regent Court, 211 Portobello, Sheffield, UK
| | - Tomasz Gubala
- ACC Cyfronet, AGH University of Science and Technology, Sano Centre for Computational Medicine, Nawojki 11, 30-950, Kraków, Poland
| | - Marek Kasztelnik
- ACC Cyfronet, AGH University of Science and Technology, Sano Centre for Computational Medicine, Nawojki 11, 30-950, Kraków, Poland
| | - Jan Meizner
- ACC Cyfronet, AGH University of Science and Technology, Sano Centre for Computational Medicine, Nawojki 11, 30-950, Kraków, Poland
| | - Piotr Nowakowski
- ACC Cyfronet, AGH University of Science and Technology, Sano Centre for Computational Medicine, Nawojki 11, 30-950, Kraków, Poland
| | | | - Amelia Suárez
- Matical Innovation, Calle de Torija, 5, 28013, Madrid, Spain
| | - Mario Aznar
- Matical Innovation, Calle de Torija, 5, 28013, Madrid, Spain
| | - Giuliana Restante
- Department of Translational Research, University of Pisa, Chair Radiodiagnostica 3, Pisa University Hospital, Via Roma 67, 56126, Pisa, Italy
| | - Emanuele Neri
- Department of Translational Research, University of Pisa, Chair Radiodiagnostica 3, Pisa University Hospital, Via Roma 67, 56126, Pisa, Italy
| |
Collapse
|
10
|
Ascolani G, Skerry TM, Lacroix D, Dall'Ara E, Shuaib A. Revealing hidden information in osteoblast's mechanotransduction through analysis of time patterns of critical events. BMC Bioinformatics 2020; 21:114. [PMID: 32183690 PMCID: PMC7079370 DOI: 10.1186/s12859-020-3394-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2019] [Accepted: 02/04/2020] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Mechanotransduction in bone cells plays a pivotal role in osteoblast differentiation and bone remodelling. Mechanotransduction provides the link between modulation of the extracellular matrix by mechanical load and intracellular activity. By controlling the balance between the intracellular and extracellular domains, mechanotransduction determines the optimum functionality of skeletal dynamics. Failure of this relationship was suggested to contribute to bone-related diseases such as osteoporosis. RESULTS A hybrid mechanical and agent-based model (Mech-ABM), simulating mechanotransduction in a single osteoblast under external mechanical perturbations, was utilised to simulate and examine modulation of the activation dynamics of molecules within mechanotransduction on the cellular response to mechanical stimulation. The number of molecules and their fluctuations have been analysed in terms of recurrences of critical events. A numerical approach has been developed to invert subordination processes and to extract the direction processes from the molecular signals in order to derive the distribution of recurring events. These predict that there are large fluctuations enclosing information hidden in the noise which is beyond the dynamic variations of molecular baselines. Moreover, studying the system under different mechanical load regimes and altered dynamics of feedback loops, illustrate that the waiting time distributions of each molecule are a signature of the system's state. CONCLUSIONS The behaviours of the molecular waiting times change with the changing of mechanical load regimes and altered dynamics of feedback loops, presenting the same variation of patterns for similar interacting molecules and identifying specific alterations for key molecules in mechanotransduction. This methodology could be used to provide a new tool to identify potent molecular candidates to modulate mechanotransduction, hence accelerate drug discovery towards therapeutic targets for bone mass upregulation.
Collapse
Affiliation(s)
- Gianluca Ascolani
- Department of Oncology and Metabolism, University of Sheffield, Sheffield, UK
- Insigneo Institute of In Silico Medicine, University of Sheffield, Sheffield, UK
| | - Timothy M Skerry
- Department of Oncology and Metabolism, University of Sheffield, Sheffield, UK
| | - Damien Lacroix
- Insigneo Institute of In Silico Medicine, University of Sheffield, Sheffield, UK
- Department of Mechanical Engineering, University of Sheffield, Sheffield, UK
| | - Enrico Dall'Ara
- Department of Oncology and Metabolism, University of Sheffield, Sheffield, UK
- Insigneo Institute of In Silico Medicine, University of Sheffield, Sheffield, UK
| | - Aban Shuaib
- Department of Oncology and Metabolism, University of Sheffield, Sheffield, UK.
- Insigneo Institute of In Silico Medicine, University of Sheffield, Sheffield, UK.
| |
Collapse
|
11
|
Douillet A, Ballet P. A GPU Algorithm for Agent-Based Models to Simulate the Integration of Cell Membrane Signals. Acta Biotheor 2020; 68:61-71. [PMID: 31468242 DOI: 10.1007/s10441-019-09360-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2019] [Accepted: 08/23/2019] [Indexed: 11/29/2022]
Abstract
Simulation of complex biological systems with agent-based models is becoming more relevant with the increase in Graphics Processing Unit (GPU) power. In those simulations, up to millions of virtual cells are individually computed, involving daunting processing times. An important part of computational models is the algorithm that manages how agents perceive their surroundings. This can be particularly problematic in three-dimensional environments where agents have deformable virtual membranes. This article presents a GPU algorithm that gives the possibility for agents to integrate the signals scattered on their virtual membrane. It is detailed to be coded in languages like OpenCL or Cuda. Its performances are tested to show its speed with current GPU devices. Finally, it was implemented inside an existing software to test and illustrate the possibilities it offers.
Collapse
Affiliation(s)
- Arthur Douillet
- Laboratory of Medical Data Processing, Inserm UMR 1101, University of Brest, 20 avenue Le Gorgeu, 29238, Brest, France.
| | - Pascal Ballet
- Laboratory of Medical Data Processing, Inserm UMR 1101, University of Brest, 20 avenue Le Gorgeu, 29238, Brest, France
| |
Collapse
|
12
|
Parallelisation strategies for agent based simulation of immune systems. BMC Bioinformatics 2019; 20:579. [PMID: 31823716 PMCID: PMC6905091 DOI: 10.1186/s12859-019-3181-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2019] [Accepted: 10/29/2019] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND In recent years, the study of immune response behaviour using bottom up approach, Agent Based Modeling (ABM), has attracted considerable efforts. The ABM approach is a very common technique in the biological domain due to high demand for a large scale analysis tools for the collection and interpretation of information to solve biological problems. Simulating massive multi-agent systems (i.e. simulations containing a large number of agents/entities) requires major computational effort which is only achievable through the use of parallel computing approaches. RESULTS This paper explores different approaches to parallelising the key component of biological and immune system models within an ABM model: pairwise interactions. The focus of this paper is on the performance and algorithmic design choices of cell interactions in continuous and discrete space where agents/entities are competing to interact with one another within a parallel environment. CONCLUSIONS Our performance results demonstrate the applicability of these methods to a broader class of biological systems exhibiting typical cell to cell interactions. The advantage and disadvantage of each implementation is discussed showing each can be used as the basis for developing complete immune system models on parallel hardware.
Collapse
|
13
|
Abstract
The complexity of morphogenesis poses a fundamental challenge to understanding the mechanisms governing the formation of biological patterns and structures. Over the past century, numerous processes have been identified as critically contributing to morphogenetic events, but the interplay between the various components and aspects of pattern formation have been much harder to grasp. The combination of traditional biology with mathematical and computational methods has had a profound effect on our current understanding of morphogenesis and led to significant insights and advancements in the field. In particular, the theoretical concepts of reaction–diffusion systems and positional information, proposed by Alan Turing and Lewis Wolpert, respectively, dramatically influenced our general view of morphogenesis, although typically in isolation from one another. In recent years, agent-based modeling has been emerging as a consolidation and implementation of the two theories within a single framework. Agent-based models (ABMs) are unique in their ability to integrate combinations of heterogeneous processes and investigate their respective dynamics, especially in the context of spatial phenomena. In this review, we highlight the benefits and technical challenges associated with ABMs as tools for examining morphogenetic events. These models display unparalleled flexibility for studying various morphogenetic phenomena at multiple levels and have the important advantage of informing future experimental work, including the targeted engineering of tissues and organs.
Collapse
|
14
|
ya||a: GPU-Powered Spheroid Models for Mesenchyme and Epithelium. Cell Syst 2019; 8:261-266.e3. [DOI: 10.1016/j.cels.2019.02.007] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2018] [Revised: 08/03/2018] [Accepted: 02/19/2019] [Indexed: 11/20/2022]
|
15
|
Salguero AG, Tomeu-Hardasmal AJ, Capel MI. Dynamic Load Balancing Strategy for Parallel Tumor Growth Simulations. J Integr Bioinform 2019; 16:/j/jib.ahead-of-print/jib-2018-0066/jib-2018-0066.xml. [PMID: 30763265 PMCID: PMC6798858 DOI: 10.1515/jib-2018-0066] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2018] [Accepted: 01/14/2019] [Indexed: 12/23/2022] Open
Abstract
In this paper, we propose a parallel cellular automaton tumor growth model that includes load balancing of cells distribution among computational threads with the introduction of adjusting parameters. The obtained results show a fair reduction in execution time and improved speedup compared with the sequential tumor growth simulation program currently referenced in tumoral biology. The dynamic data structures of the model can be extended to address additional tumor growth characteristics such as angiogenesis and nutrient intake dependencies.
Collapse
|
16
|
Vincenot CE. How new concepts become universal scientific approaches: insights from citation network analysis of agent-based complex systems science. Proc Biol Sci 2019. [PMID: 29514968 DOI: 10.1098/rspb.2017.2360] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Progress in understanding and managing complex systems comprised of decision-making agents, such as cells, organisms, ecosystems or societies, is-like many scientific endeavours-limited by disciplinary boundaries. These boundaries, however, are moving and can actively be made porous or even disappear. To study this process, I advanced an original bibliometric approach based on network analysis to track and understand the development of the model-based science of agent-based complex systems (ACS). I analysed research citations between the two communities devoted to ACS research, namely agent-based (ABM) and individual-based modelling (IBM). Both terms refer to the same approach, yet the former is preferred in engineering and social sciences, while the latter prevails in natural sciences. This situation provided a unique case study for grasping how a new concept evolves distinctly across scientific domains and how to foster convergence into a universal scientific approach. The present analysis based on novel hetero-citation metrics revealed the historical development of ABM and IBM, confirmed their past disjointedness, and detected their progressive merger. The separation between these synonymous disciplines had silently opposed the free flow of knowledge among ACS practitioners and thereby hindered the transfer of methodological advances and the emergence of general systems theories. A surprisingly small number of key publications sparked the ongoing fusion between ABM and IBM research. Beside reviews raising awareness of broad-spectrum issues, generic protocols for model formulation and boundary-transcending inference strategies were critical means of science integration. Accessible broad-spectrum software similarly contributed to this change. From the modelling viewpoint, the discovery of the unification of ABM and IBM demonstrates that a wide variety of systems substantiate the premise of ACS research that microscale behaviours of agents and system-level dynamics are inseparably bound.
Collapse
|
17
|
Reynolds ER, Himmelwright R, Sanginiti C, Pfaffmann JO. An agent-based model of the Notch signaling pathway elucidates three levels of complexity in the determination of developmental patterning. BMC SYSTEMS BIOLOGY 2019; 13:7. [PMID: 30642357 PMCID: PMC6332573 DOI: 10.1186/s12918-018-0672-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/05/2018] [Accepted: 12/17/2018] [Indexed: 01/15/2023]
Abstract
BACKGROUND The Notch signaling pathway is involved in cell fate decision and developmental patterning in diverse organisms. A receptor molecule, Notch (N), and a ligand molecule (in this case Delta or Dl) are the central molecules in this pathway. In early Drosophila embryos, these molecules determine neural vs. skin fates in a reproducible rosette pattern. RESULTS We have created an agent-based model (ABM) that simulates the molecular components for this signaling pathway as agents acting within a spatial representation of a cell. The model captures the changing levels of these components, their transition from one state to another, and their movement from the nucleus to the cell membrane and back to the nucleus again. The model introduces stochastic variation into the system using a random generator within the Netlogo programming environment. The model uses these representations to understand the biological systems at three levels: individual cell fate, the interactions between cells, and the formation of pattern across the system. Using a set of assessment tools, we show that the current model accurately reproduces the rosette pattern of neurons and skin cells in the system over a wide set of parameters. Oscillations in the level of the N agent eventually stabilize cell fate into this pattern. We found that the dynamic timing and the availability of the N and Dl agents in neighboring cells are central to the formation of a correct and stable pattern. A feedback loop to the production of both components is necessary for a correct and stable pattern. CONCLUSIONS The signaling pathways within and between cells in our model interact in real time to create a spatially correct field of neurons and skin cells. This model predicts that cells with high N and low Dl drive the formation of the pattern. This model also be used to elucidate general rules of biological self-patterning and decision-making.
Collapse
Affiliation(s)
- Elaine R. Reynolds
- Biology Department, Lafayette College, Easton, PA 18042 USA
- Neuroscience Program, Lafayette College, Easton, PA 18042 USA
| | - Ryan Himmelwright
- Neuroscience Program, Lafayette College, Easton, PA 18042 USA
- Present address: 530 Foster Street, Apt #512, Durham, NC 27701 USA
| | - Christopher Sanginiti
- Neuroscience Program, Lafayette College, Easton, PA 18042 USA
- Present address: 780 Richie Hwy, Suite S-30, Severna Park, MD 21146 USA
| | | |
Collapse
|
18
|
Soheilypour M, Mofrad MRK. Agent-Based Modeling in Molecular Systems Biology. Bioessays 2018; 40:e1800020. [PMID: 29882969 DOI: 10.1002/bies.201800020] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2018] [Revised: 04/11/2018] [Indexed: 12/13/2022]
Abstract
Molecular systems orchestrating the biology of the cell typically involve a complex web of interactions among various components and span a vast range of spatial and temporal scales. Computational methods have advanced our understanding of the behavior of molecular systems by enabling us to test assumptions and hypotheses, explore the effect of different parameters on the outcome, and eventually guide experiments. While several different mathematical and computational methods are developed to study molecular systems at different spatiotemporal scales, there is still a need for methods that bridge the gap between spatially-detailed and computationally-efficient approaches. In this review, we summarize the capabilities of agent-based modeling (ABM) as an emerging molecular systems biology technique that provides researchers with a new tool in exploring the dynamics of molecular systems/pathways in health and disease.
Collapse
Affiliation(s)
- Mohammad Soheilypour
- Molecular Cell Biomechanics Laboratory, Departments of Bioengineering and Mechanical Engineering, University of California Berkeley, Berkeley, CA 94720, USA
| | - Mohammad R K Mofrad
- Molecular Cell Biomechanics Laboratory, Departments of Bioengineering and Mechanical Engineering, University of California Berkeley, Berkeley, CA 94720, USA
| |
Collapse
|
19
|
Liberman A, Kario D, Mussel M, Brill J, Buetow K, Efroni S, Nevo U. Cell studio: A platform for interactive, 3D graphical simulation of immunological processes. APL Bioeng 2018; 2:026107. [PMID: 31069304 PMCID: PMC6481718 DOI: 10.1063/1.5039473] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2017] [Accepted: 05/04/2018] [Indexed: 12/27/2022] Open
Abstract
The field of computer modeling and simulation of biological systems is rapidly advancing, backed by significant progress in the fields of experimentation techniques, computer hardware, and programming software. The result of a simulation may be delivered in several ways, from numerical results, through graphs of the simulated run, to a visualization of the simulation. The vision of an in-silico experiment mimicking an in-vitro or in-vivo experiment as it is viewed under a microscope is appealing but technically demanding and computationally intensive. Here, we report “Cell Studio,” a generic, hybrid platform to simulate an immune microenvironment with biological and biophysical rules. We use game engines—generic programs for game creation which offer ready-made assets and tools—to create a visualized, interactive 3D simulation. We also utilize a scalable architecture that delegates the computational load to a server. The user may view the simulation, move the “camera” around, stop, fast-forward, and rewind it and inject soluble molecules into the extracellular medium at any point in time. During simulation, graphs are created in real time for a broad view of system-wide processes. The model is parametrized using a user-friendly Graphical User Interface (GUI). We show a simple validation simulation and compare its results with those from a “classical” simulation, validated against a “wet” experiment. We believe that interactive, real-time 3D visualization may aid in generating insights from the model and encourage intuition about the immunological scenario.
Collapse
Affiliation(s)
- Asaf Liberman
- The Iby and Aladar Fleischman Faculty of Engineering, Tel Aviv University, Tel Aviv 6997801, Israel
| | | | - Matan Mussel
- Physics Department, TU Dortmund University, Dortmund 44227, Germany
| | - Jacob Brill
- Arizona State University, Tempe, Arizona 85281, USA
| | | | - Sol Efroni
- The Mina and Everard Goodman Faculty of Life Sciences, Bar Ilan University, Ramat Gan 52900, Israel
| | | |
Collapse
|
20
|
Seekhao N, Shung C, JaJa J, Mongeau L, Li-Jessen NYK. High-Performance Agent-Based Modeling Applied to Vocal Fold Inflammation and Repair. Front Physiol 2018; 9:304. [PMID: 29706894 PMCID: PMC5906585 DOI: 10.3389/fphys.2018.00304] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2018] [Accepted: 03/13/2018] [Indexed: 01/13/2023] Open
Abstract
Fast and accurate computational biology models offer the prospect of accelerating the development of personalized medicine. A tool capable of estimating treatment success can help prevent unnecessary and costly treatments and potential harmful side effects. A novel high-performance Agent-Based Model (ABM) was adopted to simulate and visualize multi-scale complex biological processes arising in vocal fold inflammation and repair. The computational scheme was designed to organize the 3D ABM sub-tasks to fully utilize the resources available on current heterogeneous platforms consisting of multi-core CPUs and many-core GPUs. Subtasks are further parallelized and convolution-based diffusion is used to enhance the performance of the ABM simulation. The scheme was implemented using a client-server protocol allowing the results of each iteration to be analyzed and visualized on the server (i.e., in-situ) while the simulation is running on the same server. The resulting simulation and visualization software enables users to interact with and steer the course of the simulation in real-time as needed. This high-resolution 3D ABM framework was used for a case study of surgical vocal fold injury and repair. The new framework is capable of completing the simulation, visualization and remote result delivery in under 7 s per iteration, where each iteration of the simulation represents 30 min in the real world. The case study model was simulated at the physiological scale of a human vocal fold. This simulation tracks 17 million biological cells as well as a total of 1.7 billion signaling chemical and structural protein data points. The visualization component processes and renders all simulated biological cells and 154 million signaling chemical data points. The proposed high-performance 3D ABM was verified through comparisons with empirical vocal fold data. Representative trends of biomarker predictions in surgically injured vocal folds were observed.
Collapse
Affiliation(s)
- Nuttiiya Seekhao
- Department of Electrical and Computer Engineering, University of Maryland, College Park, MD, United States
| | - Caroline Shung
- Department of Mechanical Engineering, McGill University, Montreal, QC, Canada
| | - Joseph JaJa
- Department of Electrical and Computer Engineering, University of Maryland, College Park, MD, United States
| | - Luc Mongeau
- Department of Mechanical Engineering, McGill University, Montreal, QC, Canada
| | - Nicole Y K Li-Jessen
- School of Communication Sciences and Disorders, McGill University, Montreal, QC, Canada
| |
Collapse
|
21
|
Song Y, Yang S, Lei J. ParaCells: A GPU Architecture for Cell-Centered Models in Computational Biology. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2018; 16:994-1006. [PMID: 29994073 DOI: 10.1109/tcbb.2018.2814570] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
In computational biology, the hierarchy of biological systems requires the development of flexible and powerful computational tools. Graphics processing unit (GPU) architecture has been a suitable device for parallel computing in simulating multi-cellular systems. However, in modeling complex biological systems, scientists often face two tasks, mathematical formulation and skillful programming. In particular, specific programming skills are needed for GPU programming. Therefore, the development of an easy-to-use computational architecture, which utilizes GPU for parallel computing and provides intuitive interfaces for simple implementation, is needed so that general scientists can perform GPU simulations without knowing much about the GPU architecture. Here, we introduce ParaCells, a cell-centered GPU simulation architecture for NVIDIA compute unified device architecture (CUDA). ParaCells was designed as a versatile architecture that connects the user logic (in C++) with NVIDIA CUDA runtime and is specific to the modeling of multi-cellular systems. An advantage of ParaCells is its object-oriented model declaration, which allows it to be widely applied to many biological systems through the combination of basic biological concepts. We test ParaCells with two applications. Both applications are significantly faster when compared with sequential as well as parallel OpenMP and OpenACC implementations. Moreover, the simulation programs based on ParaCells are cleaner and more readable than other versions.
Collapse
|
22
|
Nobile MS, Cazzaniga P, Tangherloni A, Besozzi D. Graphics processing units in bioinformatics, computational biology and systems biology. Brief Bioinform 2017; 18:870-885. [PMID: 27402792 PMCID: PMC5862309 DOI: 10.1093/bib/bbw058] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2016] [Indexed: 01/18/2023] Open
Abstract
Several studies in Bioinformatics, Computational Biology and Systems Biology rely on the definition of physico-chemical or mathematical models of biological systems at different scales and levels of complexity, ranging from the interaction of atoms in single molecules up to genome-wide interaction networks. Traditional computational methods and software tools developed in these research fields share a common trait: they can be computationally demanding on Central Processing Units (CPUs), therefore limiting their applicability in many circumstances. To overcome this issue, general-purpose Graphics Processing Units (GPUs) are gaining an increasing attention by the scientific community, as they can considerably reduce the running time required by standard CPU-based software, and allow more intensive investigations of biological systems. In this review, we present a collection of GPU tools recently developed to perform computational analyses in life science disciplines, emphasizing the advantages and the drawbacks in the use of these parallel architectures. The complete list of GPU-powered tools here reviewed is available at http://bit.ly/gputools.
Collapse
Affiliation(s)
- Marco S Nobile
- Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milano, Italy
- SYSBIO.IT Centre of Systems Biology, Milano, Italy
| | - Paolo Cazzaniga
- Department of Human and Social Sciences, University of Bergamo, Bergamo, Italy
- SYSBIO.IT Centre of Systems Biology, Milano, Italy
| | - Andrea Tangherloni
- Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milano, Italy
| | - Daniela Besozzi
- Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milano, Italy
- SYSBIO.IT Centre of Systems Biology, Milano, Italy
- Corresponding author. Daniela Besozzi, Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milano, Italy and SYSBIO.IT Centre of Systems Biology, Milano, Italy. Tel.: +39 02 6448 7874. E-mail:
| |
Collapse
|
23
|
Seekhao N, JaJa J, Mongeau L, Li-Jessen NYK. In Situ Visualization for 3D Agent-Based Vocal Fold Inflammation and Repair Simulation. SUPERCOMPUTING FRONTIERS AND INNOVATIONS 2017; 4:68-79. [PMID: 29177201 PMCID: PMC5701753 DOI: 10.14529/jsfi170304] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
A fast and insightful visualization is essential in modeling biological system behaviors and understanding underlying inter-cellular mechanisms. High fidelity models produce billions of data points per time step, making in situ visualization techniques extremely desirable as they mitigate I/O bottlenecks and provide computational steering capability. In this work, we present a novel high-performance scheme to couple in situ visualization with the simulation of the vocal fold inflammation and repair using little to no extra cost in execution time or computing resources. The visualization component is first optimized with an adaptive sampling scheme to accelerate the rendering process while maintaining the precision of the displayed visual results. Our software employs VirtualGL to perform visualization in situ. The scheme overlaps visualization and simulation, resulting in the optimal utilization of computing resources. This results in an in situ system biology simulation suite capable of remote simulation of 17 million biological cells and 1.2 billion chemical data points, remote visualization of the results, and delivery of visualized frames with aggregated statistics to remote clients in real-time.
Collapse
|
24
|
Parallelization and High-Performance Computing Enables Automated Statistical Inference of Multi-scale Models. Cell Syst 2017; 4:194-206.e9. [PMID: 28089542 DOI: 10.1016/j.cels.2016.12.002] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2016] [Revised: 09/14/2016] [Accepted: 11/30/2016] [Indexed: 01/18/2023]
Abstract
Mechanistic understanding of multi-scale biological processes, such as cell proliferation in a changing biological tissue, is readily facilitated by computational models. While tools exist to construct and simulate multi-scale models, the statistical inference of the unknown model parameters remains an open problem. Here, we present and benchmark a parallel approximate Bayesian computation sequential Monte Carlo (pABC SMC) algorithm, tailored for high-performance computing clusters. pABC SMC is fully automated and returns reliable parameter estimates and confidence intervals. By running the pABC SMC algorithm for ∼106 hr, we parameterize multi-scale models that accurately describe quantitative growth curves and histological data obtained in vivo from individual tumor spheroid growth in media droplets. The models capture the hybrid deterministic-stochastic behaviors of 105-106 of cells growing in a 3D dynamically changing nutrient environment. The pABC SMC algorithm reliably converges to a consistent set of parameters. Our study demonstrates a proof of principle for robust, data-driven modeling of multi-scale biological systems and the feasibility of multi-scale model parameterization through statistical inference.
Collapse
|
25
|
Seekhao N, Shung C, JaJa J, Mongeau L, Li-Jessen NYK. Real-Time Agent-Based Modeling Simulation with in-situ Visualization of Complex Biological Systems: A Case Study on Vocal Fold Inflammation and Healing. IEEE INTERNATIONAL SYMPOSIUM ON PARALLEL & DISTRIBUTED PROCESSING, WORKSHOPS AND PHD FORUM : [PROCEEDINGS]. IEEE INTERNATIONAL SYMPOSIUM ON PARALLEL & DISTRIBUTED PROCESSING, WORKSHOPS AND PHD FORUM 2016; 2016:463-472. [PMID: 27547508 DOI: 10.1109/ipdpsw.2016.20] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
We present an efficient and scalable scheme for implementing agent-based modeling (ABM) simulation with In Situ visualization of large complex systems on heterogeneous computing platforms. The scheme is designed to make optimal use of the resources available on a heterogeneous platform consisting of a multicore CPU and a GPU, resulting in minimal to no resource idle time. Furthermore, the scheme was implemented under a client-server paradigm that enables remote users to visualize and analyze simulation data as it is being generated at each time step of the model. Performance of a simulation case study of vocal fold inflammation and wound healing with 3.8 million agents shows 35× and 7× speedup in execution time over single-core and multi-core CPU respectively. Each iteration of the model took less than 200 ms to simulate, visualize and send the results to the client. This enables users to monitor the simulation in real-time and modify its course as needed.
Collapse
Affiliation(s)
- Nuttiiya Seekhao
- Department of Electrical and Computer Engineering - University of Maryland-College Park, Maryland, USA
| | - Caroline Shung
- Department of Mechanical Engineering - McGill University, Montreal, Canada
| | - Joseph JaJa
- Department of Electrical and Computer Engineering - University of Maryland-College Park, Maryland, USA
| | - Luc Mongeau
- Department of Mechanical Engineering - McGill University, Montreal, Canada
| | - Nicole Y K Li-Jessen
- School of Communication Sciences and Disorders - McGill University, Montreal, Canada
| |
Collapse
|
26
|
Pérez-Rodríguez G, Pérez-Pérez M, Fdez-Riverola F, Lourenço A. High performance computing for three-dimensional agent-based molecular models. J Mol Graph Model 2016; 68:68-77. [DOI: 10.1016/j.jmgm.2016.06.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2015] [Revised: 05/26/2016] [Accepted: 06/07/2016] [Indexed: 12/28/2022]
|
27
|
Spiesser T, Kühn C, Krantz M, Klipp E. The MYpop toolbox: Putting yeast stress responses in cellular context on single cell and population scales. Biotechnol J 2016; 11:1158-68. [PMID: 26952199 DOI: 10.1002/biot.201500344] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2015] [Revised: 12/15/2015] [Accepted: 03/07/2016] [Indexed: 11/05/2022]
Abstract
Systems biology holds the promise to integrate multiple sources of information in order to build ever more complete models of cellular function. To do this, the field must overcome two significant challenges. First, the current strategy to model average cells must be replaced with population based models accounting for cell-to-cell variability. Second, models must be integrated with each other and with basic cellular function. This requires a core model of cellular physiology as well as a multiscale simulation platform to support large-scale simulation of culture or tissues from single cells. Here, we present such a simulation platform with a core model of yeast physiology as scaffold to integrate and simulate SBML models. The software automates this integration helping users simulate their model of choice in context of the cell division cycle. We benchmark model merging, simulation and analysis by integrating a minimal model of osmotic stress into the core model and analyzing it. We characterize the effect of single cell differences on the dynamics of osmoadaptation, estimating when normal cell growth is resumed and obtaining an explanation for experimentally observed glycerol dynamics based on population dynamics. Hence, the platform can be used to reconcile single cell and population level data.
Collapse
Affiliation(s)
- Thomas Spiesser
- Theoretical Biophysics, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Clemens Kühn
- Theoretical Biophysics, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Marcus Krantz
- Theoretical Biophysics, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Edda Klipp
- Theoretical Biophysics, Humboldt-Universität zu Berlin, Berlin, Germany.
| |
Collapse
|
28
|
Ganesan N, Li J, Sharma V, Jiang H, Compagnoni A. Process Simulation of Complex Biological Pathways in Physical Reactive Space and Reformulated for Massively Parallel Computing Platforms. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2016; 13:365-379. [PMID: 27045833 DOI: 10.1109/tcbb.2015.2443784] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Biological systems encompass complexity that far surpasses many artificial systems. Modeling and simulation of large and complex biochemical pathways is a computationally intensive challenge. Traditional tools, such as ordinary differential equations, partial differential equations, stochastic master equations, and Gillespie type methods, are all limited either by their modeling fidelity or computational efficiency or both. In this work, we present a scalable computational framework based on modeling biochemical reactions in explicit 3D space, that is suitable for studying the behavior of large and complex biological pathways. The framework is designed to exploit parallelism and scalability offered by commodity massively parallel processors such as the graphics processing units (GPUs) and other parallel computing platforms. The reaction modeling in 3D space is aimed at enhancing the realism of the model compared to traditional modeling tools and framework. We introduce the Parallel Select algorithm that is key to breaking the sequential bottleneck limiting the performance of most other tools designed to study biochemical interactions. The algorithm is designed to be computationally tractable, handle hundreds of interacting chemical species and millions of independent agents by considering all-particle interactions within the system. We also present an implementation of the framework on the popular graphics processing units and apply it to the simulation study of JAK-STAT Signal Transduction Pathway. The computational framework will offer a deeper insight into various biological processes within the cell and help us observe key events as they unfold in space and time. This will advance the current state-of-the-art in simulation study of large scale biological systems and also enable the realistic simulation study of macro-biological cultures, where inter-cellular interactions are prevalent.
Collapse
|
29
|
Tartarini D, Mele E. Adult Stem Cell Therapies for Wound Healing: Biomaterials and Computational Models. Front Bioeng Biotechnol 2016; 3:206. [PMID: 26793702 PMCID: PMC4707872 DOI: 10.3389/fbioe.2015.00206] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2015] [Accepted: 12/17/2015] [Indexed: 12/29/2022] Open
Abstract
The increased incidence of diabetes and tumors, associated with global demographic issues (aging and life styles), has pointed out the importance to develop new strategies for the effective management of skin wounds. Individuals affected by these diseases are in fact highly exposed to the risk of delayed healing of the injured tissue that typically leads to a pathological inflammatory state and consequently to chronic wounds. Therapies based on stem cells (SCs) have been proposed for the treatment of these wounds, thanks to the ability of SCs to self-renew and specifically differentiate in response to the target bimolecular environment. Here, we discuss how advanced biomedical devices can be developed by combining SCs with properly engineered biomaterials and computational models. Examples include composite skin substitutes and bioactive dressings with controlled porosity and surface topography for controlling the infiltration and differentiation of the cells. In this scenario, mathematical frameworks for the simulation of cell population growth can provide support for the design of bioconstructs, reducing the need of expensive, time-consuming, and ethically controversial animal experimentation.
Collapse
Affiliation(s)
- Daniele Tartarini
- Department of Mechanical Engineering, Insigneo Institute for in silico Medicine, University of Sheffield , Sheffield , UK
| | - Elisa Mele
- Department of Materials, Loughborough University , Loughborough , UK
| |
Collapse
|
30
|
Abstract
As the amount of biological data in the public domain grows, so does the range of modeling and analysis techniques employed in systems biology. In recent years, a number of theoretical computer science developments have enabled modeling methodology to keep pace. The growing interest in systems biology in executable models and their analysis has necessitated the borrowing of terms and methods from computer science, such as formal analysis, model checking, static analysis, and runtime verification. Here, we discuss the most important and exciting computational methods and tools currently available to systems biologists. We believe that a deeper understanding of the concepts and theory highlighted in this review will produce better software practice, improved investigation of complex biological processes, and even new ideas and better feedback into computer science.
Collapse
Affiliation(s)
- Ezio Bartocci
- Faculty of Informatics, Technische Universität Wien, Vienna, Austria
| | - Pietro Lió
- Computer Laboratory, University of Cambridge, Cambridge, United Kingdom
| |
Collapse
|
31
|
Jeannin-Girardon A, Ballet P, Rodin V. Large Scale Tissue Morphogenesis Simulation on Heterogenous Systems Based on a Flexible Biomechanical Cell Model. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2015; 12:1021-1033. [PMID: 26451816 DOI: 10.1109/tcbb.2015.2418994] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
The complexity of biological tissue morphogenesis makes in silico simulations of such system very interesting in order to gain a better understanding of the underlying mechanisms ruling the development of multicellular tissues. This complexity is mainly due to two elements: firstly, biological tissues comprise a large amount of cells; secondly, these cells exhibit complex interactions and behaviors. To address these two issues, we propose two tools: the first one is a virtual cell model that comprise two main elements: firstly, a mechanical structure (membrane, cytoskeleton, and cortex) and secondly, the main behaviors exhibited by biological cells, i.e., mitosis, growth, differentiation, molecule consumption, and production as well as the consideration of the physical constraints issued from the environment. An artificial chemistry is also included in the model. This virtual cell model is coupled to an agent-based formalism. The second tool is a simulator that relies on the OpenCL framework. It allows efficient parallel simulations on heterogenous devices such as micro-processors or graphics processors. We present two case studies validating the implementation of our model in our simulator: cellular proliferation controlled by cell signalling and limb growth in a virtual organism.
Collapse
|
32
|
Rhodes DM, Smith SA, Holcombe M, Qwarnstrom EE. Computational Modelling of NF-κB Activation by IL-1RI and Its Co-Receptor TILRR, Predicts a Role for Cytoskeletal Sequestration of IκBα in Inflammatory Signalling. PLoS One 2015; 10:e0129888. [PMID: 26110282 PMCID: PMC4482363 DOI: 10.1371/journal.pone.0129888] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2014] [Accepted: 05/14/2015] [Indexed: 11/19/2022] Open
Abstract
The transcription factor NF-κB (nuclear factor kappa B) is activated by Toll-like receptors and controlled by mechanotransduction and changes in the cytoskeleton. In this study we combine 3-D predictive protein modelling and in vitro experiments with in silico simulations to determine the role of the cytoskeleton in regulation of NF-κB. Simulations used a comprehensive agent-based model of the NF-κB pathway, which includes the type 1 IL-1 receptor (IL-1R1) complex and signalling intermediates, as well as cytoskeletal components. Agent based modelling relies on in silico reproductions of systems through the interactions of its components, and provides a reliable tool in investigations of biological processes, which require spatial considerations and involve complex formation and translocation of regulatory components. We show that our model faithfully reproduces the multiple steps comprising the NF-κB pathway, and provides a framework from which we can explore novel aspects of the system. The analysis, using 3-D predictive protein modelling and in vitro assays, demonstrated that the NF-κB inhibitor, IκBα is sequestered to the actin/spectrin complex within the cytoskeleton of the resting cell, and released during IL-1 stimulation, through a process controlled by the IL-1RI co-receptor TILRR (Toll-like and IL-1 receptor regulator). In silico simulations using the agent-based model predict that the cytoskeletal pool of IκBα is released to adjust signal amplification in relation to input levels. The results suggest that the process provides a mechanism for signal calibration and enables efficient, activation-sensitive regulation of NF-κB and inflammatory responses.
Collapse
Affiliation(s)
- David M. Rhodes
- Department of Cardiovascular Science, Medical School, University of Sheffield, United Kingdom
- Department of Computer Science, University of Sheffield, Sheffield, United Kingdom
| | - Sarah A. Smith
- Department of Cardiovascular Science, Medical School, University of Sheffield, United Kingdom
| | - Mike Holcombe
- Department of Computer Science, University of Sheffield, Sheffield, United Kingdom
| | - Eva E. Qwarnstrom
- Department of Cardiovascular Science, Medical School, University of Sheffield, United Kingdom
- * E-mail:
| |
Collapse
|
33
|
|
34
|
Bai H, Rolfe MD, Jia W, Coakley S, Poole RK, Green J, Holcombe M. Agent-based modeling of oxygen-responsive transcription factors in Escherichia coli. PLoS Comput Biol 2014; 10:e1003595. [PMID: 24763195 PMCID: PMC3998891 DOI: 10.1371/journal.pcbi.1003595] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2013] [Accepted: 03/14/2014] [Indexed: 11/23/2022] Open
Abstract
In the presence of oxygen (O2) the model bacterium Escherichia coli is able to conserve energy by aerobic respiration. Two major terminal oxidases are involved in this process - Cyo has a relatively low affinity for O2 but is able to pump protons and hence is energetically efficient; Cyd has a high affinity for O2 but does not pump protons. When E. coli encounters environments with different O2 availabilities, the expression of the genes encoding the alternative terminal oxidases, the cydAB and cyoABCDE operons, are regulated by two O2-responsive transcription factors, ArcA (an indirect O2 sensor) and FNR (a direct O2 sensor). It has been suggested that O2-consumption by the terminal oxidases located at the cytoplasmic membrane significantly affects the activities of ArcA and FNR in the bacterial nucleoid. In this study, an agent-based modeling approach has been taken to spatially simulate the uptake and consumption of O2 by E. coli and the consequent modulation of ArcA and FNR activities based on experimental data obtained from highly controlled chemostat cultures. The molecules of O2, transcription factors and terminal oxidases are treated as individual agents and their behaviors and interactions are imitated in a simulated 3-D E. coli cell. The model implies that there are two barriers that dampen the response of FNR to O2, i.e. consumption of O2 at the membrane by the terminal oxidases and reaction of O2 with cytoplasmic FNR. Analysis of FNR variants suggested that the monomer-dimer transition is the key step in FNR-mediated repression of gene expression. The model bacterium Escherichia coli has a modular electron transport chain that allows it to successfully compete in environments with differing oxygen (O2) availabilities. It has two well-characterized terminal oxidases, Cyd and Cyo. Cyd has a very high affinity for O2, whereas Cyo has a lower affinity, but is energetically more efficient. Expression of the genes encoding Cyd and Cyo is controlled by two O2-responsive regulators, ArcBA and FNR. However, it is not clear how O2 molecules enter the E. coli cell and how the locations of the terminal oxidases and the regulators influence the system. An agent-based model is presented that simulates the interactions of O2 with the regulators and the oxidases in an E. coli cell. The model suggests that O2 consumption by the oxidases at the cytoplasmic membrane and by FNR in the cytoplasm protects FNR bound to DNA in the nucleoid from inactivation and that dimerization of FNR in response to O2 depletion is the key step in FNR-mediated repression. Thus, the focus of the agent-based model on spatial events provides information and new insight, allowing the effects of dysregulation of system components to be explored by facile addition or removal of agents.
Collapse
Affiliation(s)
- Hao Bai
- Department of Computer Science, University of Sheffield, Sheffield, United Kingdom
- * E-mail: (HB); (JG)
| | - Matthew D. Rolfe
- Department of Molecular Biology and Biotechnology, University of Sheffield, Sheffield, United Kingdom
| | - Wenjing Jia
- Department of Molecular Biology and Biotechnology, University of Sheffield, Sheffield, United Kingdom
| | - Simon Coakley
- Department of Computer Science, University of Sheffield, Sheffield, United Kingdom
| | - Robert K. Poole
- Department of Molecular Biology and Biotechnology, University of Sheffield, Sheffield, United Kingdom
| | - Jeffrey Green
- Department of Molecular Biology and Biotechnology, University of Sheffield, Sheffield, United Kingdom
- * E-mail: (HB); (JG)
| | - Mike Holcombe
- Department of Computer Science, University of Sheffield, Sheffield, United Kingdom
| |
Collapse
|
35
|
Abstract
Capturing the dynamism that pervades biological systems requires a computational approach that can accommodate both the continuous features of the system environment as well as the flexible and heterogeneous nature of component interactions. This presents a serious challenge for the more traditional mathematical approaches that assume component homogeneity to relate system observables using mathematical equations. While the homogeneity condition does not lead to loss of accuracy while simulating various continua, it fails to offer detailed solutions when applied to systems with dynamically interacting heterogeneous components. As the functionality and architecture of most biological systems is a product of multi-faceted individual interactions at the sub-system level, continuum models rarely offer much beyond qualitative similarity. Agent-based modelling is a class of algorithmic computational approaches that rely on interactions between Turing-complete finite-state machines--or agents--to simulate, from the bottom-up, macroscopic properties of a system. In recognizing the heterogeneity condition, they offer suitable ontologies to the system components being modelled, thereby succeeding where their continuum counterparts tend to struggle. Furthermore, being inherently hierarchical, they are quite amenable to coupling with other computational paradigms. The integration of any agent-based framework with continuum models is arguably the most elegant and precise way of representing biological systems. Although in its nascence, agent-based modelling has been utilized to model biological complexity across a broad range of biological scales (from cells to societies). In this article, we explore the reasons that make agent-based modelling the most precise approach to model biological systems that tend to be non-linear and complex.
Collapse
|
36
|
Blewitt W, Ushaw G, Morgan G. Applicability of GPGPU Computing to Real-Time AI Solutions in Games. IEEE TRANSACTIONS ON COMPUTATIONAL INTELLIGENCE AND AI IN GAMES 2013. [DOI: 10.1109/tciaig.2013.2258156] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
|
37
|
Kaul H, Cui Z, Ventikos Y. A multi-paradigm modeling framework to simulate dynamic reciprocity in a bioreactor. PLoS One 2013; 8:e59671. [PMID: 23555740 PMCID: PMC3612085 DOI: 10.1371/journal.pone.0059671] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2012] [Accepted: 02/19/2013] [Indexed: 12/28/2022] Open
Abstract
Despite numerous technology advances, bioreactors are still mostly utilized as functional black-boxes where trial and error eventually leads to the desirable cellular outcome. Investigators have applied various computational approaches to understand the impact the internal dynamics of such devices has on overall cell growth, but such models cannot provide a comprehensive perspective regarding the system dynamics, due to limitations inherent to the underlying approaches. In this study, a novel multi-paradigm modeling platform capable of simulating the dynamic bidirectional relationship between cells and their microenvironment is presented. Designing the modeling platform entailed combining and coupling fully an agent-based modeling platform with a transport phenomena computational modeling framework. To demonstrate capability, the platform was used to study the impact of bioreactor parameters on the overall cell population behavior and vice versa. In order to achieve this, virtual bioreactors were constructed and seeded. The virtual cells, guided by a set of rules involving the simulated mass transport inside the bioreactor, as well as cell-related probabilistic parameters, were capable of displaying an array of behaviors such as proliferation, migration, chemotaxis and apoptosis. In this way the platform was shown to capture not only the impact of bioreactor transport processes on cellular behavior but also the influence that cellular activity wields on that very same local mass transport, thereby influencing overall cell growth. The platform was validated by simulating cellular chemotaxis in a virtual direct visualization chamber and comparing the simulation with its experimental analogue. The results presented in this paper are in agreement with published models of similar flavor. The modeling platform can be used as a concept selection tool to optimize bioreactor design specifications.
Collapse
Affiliation(s)
- Himanshu Kaul
- Institute of Biomedical Engineering and Department of Engineering Science, University of Oxford, Oxford, United Kingdom
| | - Zhanfeng Cui
- Institute of Biomedical Engineering and Department of Engineering Science, University of Oxford, Oxford, United Kingdom
| | - Yiannis Ventikos
- Institute of Biomedical Engineering and Department of Engineering Science, University of Oxford, Oxford, United Kingdom
| |
Collapse
|
38
|
Korth MJ, Tchitchek N, Benecke AG, Katze MG. Systems approaches to influenza-virus host interactions and the pathogenesis of highly virulent and pandemic viruses. Semin Immunol 2012; 25:228-39. [PMID: 23218769 PMCID: PMC3596458 DOI: 10.1016/j.smim.2012.11.001] [Citation(s) in RCA: 43] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/20/2012] [Accepted: 11/08/2012] [Indexed: 12/14/2022]
Abstract
Influenza virus research has recently undergone a shift from a virus-centric perspective to one that embraces the full spectrum of virus-host interactions and cellular signaling events that determine disease outcome. This change has been brought about by the increasing use and expanding scope of high-throughput molecular profiling and computational biology, which together fuel discovery in systems biology. In this review, we show how these approaches have revealed an uncontrolled inflammatory response as a contributor to the extreme virulence of the 1918 pandemic and avian H5N1 viruses, and how this response differs from that induced by the 2009 H1N1 viruses responsible for the most recent influenza pandemic. We also discuss how new animal models, such as the Collaborative Cross mouse systems genetics platform, are key to the necessary systematic investigation of the impact of host genetics on infection outcome, how genome-wide RNAi screens have identified hundreds of cellular factors involved in viral replication, and how systems biology approaches are making possible the rational design of new drugs and vaccines against an ever-evolving respiratory virus.
Collapse
Affiliation(s)
- Marcus J Korth
- Department of Microbiology, School of Medicine, and Washington National Primate Research Center, University of Washington, Seattle, WA 98195-8070, USA
| | | | | | | |
Collapse
|
39
|
Dematté L. Smoldyn on graphics processing units: massively parallel Brownian dynamics simulations. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2012; 9:655-667. [PMID: 21788675 DOI: 10.1109/tcbb.2011.106] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
Space is a very important aspect in the simulation of biochemical systems; recently, the need for simulation algorithms able to cope with space is becoming more and more compelling. Complex and detailed models of biochemical systems need to deal with the movement of single molecules and particles, taking into consideration localized fluctuations, transportation phenomena, and diffusion. A common drawback of spatial models lies in their complexity: models can become very large, and their simulation could be time consuming, especially if we want to capture the systems behavior in a reliable way using stochastic methods in conjunction with a high spatial resolution. In order to deliver the promise done by systems biology to be able to understand a system as whole, we need to scale up the size of models we are able to simulate, moving from sequential to parallel simulation algorithms. In this paper, we analyze Smoldyn, a widely diffused algorithm for stochastic simulation of chemical reactions with spatial resolution and single molecule detail, and we propose an alternative, innovative implementation that exploits the parallelism of Graphics Processing Units (GPUs). The implementation executes the most computational demanding steps (computation of diffusion, unimolecular, and bimolecular reaction, as well as the most common cases of molecule-surface interaction) on the GPU, computing them in parallel on each molecule of the system. The implementation offers good speed-ups and real time, high quality graphics output
Collapse
Affiliation(s)
- Lorenzo Dematté
- Center for Computational and Systems Biology, Microsoft Research-University of Trento, Vicolo del Capitolo 3, Trento 38122, Italy.
| |
Collapse
|
40
|
Burkitt M, Walker D, Romano DM, Fazeli A. Computational modelling of maternal interactions with spermatozoa: potentials and prospects. Reprod Fertil Dev 2012; 23:976-89. [PMID: 22127003 DOI: 10.1071/rd11032] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2011] [Accepted: 07/12/2011] [Indexed: 12/20/2022] Open
Abstract
Understanding the complex interactions between gametes, embryos and the maternal tract is required knowledge for combating infertility and developing new methods of contraception. Here we present some main aspects of spermatozoa interactions with the mammalian oviduct before fertilisation and discuss how computational modelling can be used as an invaluable aid to experimental investigation in this field. A complete predictive computational model of gamete and embryo interactions with the female reproductive tract is a long way off. However, the enormity of this task should not discourage us from working towards it. Computational modelling allows us to investigate aspects of maternal communication with gametes and embryos, which are financially, ethically or practically difficult to look at experimentally. In silico models of maternal communication with gametes and embryos can be used as tools to complement in vivo experiments, in the same way as in vitro and in situ models.
Collapse
Affiliation(s)
- Mark Burkitt
- The Department of Computer Science, University of Sheffield, Sheffield, Regent Court, 211 Portobello, Sheffield S1 4DP, UK
| | | | | | | |
Collapse
|
41
|
McGuire MF, Iyengar MS, Mercer DW. Computational approaches for translational clinical research in disease progression. J Investig Med 2012; 59:893-903. [PMID: 21712727 DOI: 10.2310/jim.0b013e318224d8cc] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Today, there is an ever-increasing amount of biological and clinical data available that could be used to enhance a systems-based understanding of disease progression through innovative computational analysis. In this article, we review a selection of published research regarding computational methods, primarily from systems biology, which support translational research from the molecular level to the bedside, with a focus on applications in trauma and critical care. Trauma is the leading cause of mortality in Americans younger than 45 years, and its rapid progression offers both opportunities and challenges for computational analysis of trends in molecular patterns associated with outcomes and therapeutic interventions.This review presents methods and domain-specific examples that may inspire the development of new algorithms and computational methods that use both molecular and clinical data for diagnosis, prognosis, and therapy in disease progression.
Collapse
Affiliation(s)
- Mary F McGuire
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, TX 77030, USA.
| | | | | |
Collapse
|
42
|
Democratic population decisions result in robust policy-gradient learning: a parametric study with GPU simulations. PLoS One 2011; 6:e18539. [PMID: 21572529 PMCID: PMC3087717 DOI: 10.1371/journal.pone.0018539] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2010] [Accepted: 03/03/2011] [Indexed: 11/28/2022] Open
Abstract
High performance computing on the Graphics Processing Unit (GPU) is an emerging
field driven by the promise of high computational power at a low cost. However,
GPU programming is a non-trivial task and moreover architectural limitations
raise the question of whether investing effort in this direction may be
worthwhile. In this work, we use GPU programming to simulate a two-layer network
of Integrate-and-Fire neurons with varying degrees of recurrent connectivity and
investigate its ability to learn a simplified navigation task using a
policy-gradient learning rule stemming from Reinforcement Learning. The purpose
of this paper is twofold. First, we want to support the use of GPUs in the field
of Computational Neuroscience. Second, using GPU computing power, we investigate
the conditions under which the said architecture and learning rule demonstrate
best performance. Our work indicates that networks featuring strong
Mexican-Hat-shaped recurrent connections in the top layer, where decision making
is governed by the formation of a stable activity bump in the neural population
(a “non-democratic” mechanism), achieve mediocre learning results at
best. In absence of recurrent connections, where all neurons “vote”
independently (“democratic”) for a decision via population vector
readout, the task is generally learned better and more robustly. Our study would
have been extremely difficult on a desktop computer without the use of GPU
programming. We present the routines developed for this purpose and show that a
speed improvement of 5x up to 42x is provided versus optimised Python code. The
higher speed is achieved when we exploit the parallelism of the GPU in the
search of learning parameters. This suggests that efficient GPU programming can
significantly reduce the time needed for simulating networks of spiking neurons,
particularly when multiple parameter configurations are investigated.
Collapse
|
43
|
Dada JO, Mendes P. Multi-scale modelling and simulation in systems biology. Integr Biol (Camb) 2011; 3:86-96. [DOI: 10.1039/c0ib00075b] [Citation(s) in RCA: 129] [Impact Index Per Article: 9.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
|
44
|
Vouzis PD, Sahinidis NV. GPU-BLAST: using graphics processors to accelerate protein sequence alignment. Bioinformatics 2010; 27:182-8. [PMID: 21088027 PMCID: PMC3018811 DOI: 10.1093/bioinformatics/btq644] [Citation(s) in RCA: 160] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Motivation: The Basic Local Alignment Search Tool (BLAST) is one of the most widely used bioinformatics tools. The widespread impact of BLAST is reflected in over 53 000 citations that this software has received in the past two decades, and the use of the word ‘blast’ as a verb referring to biological sequence comparison. Any improvement in the execution speed of BLAST would be of great importance in the practice of bioinformatics, and facilitate coping with ever increasing sizes of biomolecular databases. Results: Using a general-purpose graphics processing unit (GPU), we have developed GPU-BLAST, an accelerated version of the popular NCBI-BLAST. The implementation is based on the source code of NCBI-BLAST, thus maintaining the same input and output interface while producing identical results. In comparison to the sequential NCBI-BLAST, the speedups achieved by GPU-BLAST range mostly between 3 and 4. Availability: The source code of GPU-BLAST is freely available at http://archimedes.cheme.cmu.edu/biosoftware.html. Contact:sahinidis@cmu.edu Supplementary information:Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Panagiotis D Vouzis
- Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | | |
Collapse
|
45
|
Christley S, Lee B, Dai X, Nie Q. Integrative multicellular biological modeling: a case study of 3D epidermal development using GPU algorithms. BMC SYSTEMS BIOLOGY 2010; 4:107. [PMID: 20696053 PMCID: PMC2936904 DOI: 10.1186/1752-0509-4-107] [Citation(s) in RCA: 56] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/28/2010] [Accepted: 08/09/2010] [Indexed: 12/18/2022]
Abstract
BACKGROUND Simulation of sophisticated biological models requires considerable computational power. These models typically integrate together numerous biological phenomena such as spatially-explicit heterogeneous cells, cell-cell interactions, cell-environment interactions and intracellular gene networks. The recent advent of programming for graphical processing units (GPU) opens up the possibility of developing more integrative, detailed and predictive biological models while at the same time decreasing the computational cost to simulate those models. RESULTS We construct a 3D model of epidermal development and provide a set of GPU algorithms that executes significantly faster than sequential central processing unit (CPU) code. We provide a parallel implementation of the subcellular element method for individual cells residing in a lattice-free spatial environment. Each cell in our epidermal model includes an internal gene network, which integrates cellular interaction of Notch signaling together with environmental interaction of basement membrane adhesion, to specify cellular state and behaviors such as growth and division. We take a pedagogical approach to describing how modeling methods are efficiently implemented on the GPU including memory layout of data structures and functional decomposition. We discuss various programmatic issues and provide a set of design guidelines for GPU programming that are instructive to avoid common pitfalls as well as to extract performance from the GPU architecture. CONCLUSIONS We demonstrate that GPU algorithms represent a significant technological advance for the simulation of complex biological models. We further demonstrate with our epidermal model that the integration of multiple complex modeling methods for heterogeneous multicellular biological processes is both feasible and computationally tractable using this new technology. We hope that the provided algorithms and source code will be a starting point for modelers to develop their own GPU implementations, and encourage others to implement their modeling methods on the GPU and to make that code available to the wider community.
Collapse
Affiliation(s)
- Scott Christley
- Department of Mathematics, University of California, Irvine, CA 92697, USA.
| | | | | | | |
Collapse
|
46
|
Azuaje F. Computational discrete models of tissue growth and regeneration. Brief Bioinform 2010; 12:64-77. [PMID: 20513669 DOI: 10.1093/bib/bbq017] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Tissue growth and regeneration are fundamental processes underpinning crucial physiological and pathological conditions: ranging from normal blood vessel network development, response to stem cells therapy and cancers. Modelling of such biological phenomena has been addressed through mathematical and algorithmic approaches. The former implements continuous representations based on differential equations. The latter exploit operational descriptions in the form of computing programs to represent and execute the models. Within this area, models that define the cell as the fundamental unit of model development, as well as discrete representations of different model entities, are important to plan in vitro experiments and to generate new testable hypotheses. This article reviews the application of algorithmic discrete models, with a focus on tissue growth and regeneration phenomena in the context of health and disease. The review begins with an overview of basic concepts, problems and approaches of computational discrete models. This will include a discussion of basic assumptions and design principles. An overview of key cell-driven approaches and examples of applications in tissue growth and regeneration is provided. The specification, implementation and analysis of a model are illustrated with a hypothetical example, which mimics the branching and sprouting patterns observed in blood vessel network development. The article concludes with a discussion of current challenges and recommendations.
Collapse
|