Gaussian process emulation of an individual-based model simulation of microbial communities

Oyebamiji, Oluwole; Wilkinson, Darren J.; Jayathilake, Pahala Gedara; Curtis, Thomas P.; Rushton, Steve; Li, B.; Gupta, Prashant

doi:10.1016/j.jocs.2017.08.006

Cited by 31 publications

(38 citation statements)

References 43 publications

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“…Predominantly a lack of comprehending of the chemistry implicated is considered as part of the issue (Fig. 3) (Oyebamiji et al, 2017). The scale transition from a bacterium or cellular level (microscale -< micrometer size) to the floc and biofilm aggregates (mesoscale -millimeter size) to the macroscopic bulk WWTP operation as well as floc and biofilm interactions (macroscalemeter size).…”

Section: Exploring the Challenges And Needsmentioning

confidence: 99%

“…The scale transition from a bacterium or cellular level (microscale -< micrometer size) to the floc and biofilm aggregates (mesoscale -millimeter size) to the macroscopic bulk WWTP operation as well as floc and biofilm interactions (macroscalemeter size). The emulator is linking the microscopic (bacterium/cell) to the mesoscopic (biofilm/floc) and, ultimately, to the macroscopic bulk operational parameters (Oyebamiji et al, 2017) …”

Section: Exploring the Challenges And Needsmentioning

confidence: 99%

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Applying big data in water treatment industry: A new era of advance

Ghernaout

2018

Int. j. adv. appl. sci.

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Section: Exploring the Challenges And Needsmentioning

confidence: 99%

Section: Exploring the Challenges And Needsmentioning

confidence: 99%

Applying big data in water treatment industry: A new era of advance

Ghernaout

2018

Int. j. adv. appl. sci.

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“…To understand the morphological dynamics of microbial systems, characteristics such 379 as biofilm average height, biofilm surface roughness, floc equivalent diameter and floc 380 fractal dimension can be measured during simulation. These aggregated characteristics 381 are essential factors to study and design microbial system [39]. Parallelisation of the 382 characteristics measurements is supported by NUFEB and based on domain 383 decomposition.…”

mentioning

confidence: 99%

“…In [17], we studied 527 the influence of nutrient gradients on biofilm structure formation, and the influence of 528 shear flow on growing biofilm. In [39] and [47], we used micro-scale NUFEB simulations 529 to emulate the behaviour of microbial communities in the macro-scale. An emulation 530 strategy for parameter calibration of NUFEB against iDynoMiCS has been applied 531 in [48].…”

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confidence: 99%

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NUFEB: A Massively Parallel Simulator for Individual-based Modelling of Microbial Communities

Taniguchi

Jayathilake

et al. 2019

Preprint

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We present NUFEB, a flexible, efficient, and open source software for simulating the 3D dynamics of microbial communities. The tool is based on the Individual-based Modelling (IbM) approach, where microbes are represented as discrete units and their behaviour changes over time due to a variety of processes. This approach allows us to study population behaviours that emerge from the interaction between individuals and their environment. NUFEB is built on top of the classical molecular dynamics simulator LAMMPS, which we extended with IbM features. A wide range of biological, physical and chemical processes are implemented to explicitly model microbial systems. NUFEB is fully parallelised and allows for the simulation of large numbers of microbes (10 7 individuals and beyond). The parallelisation is based on a domain decomposition scheme that divides the domain into multiple sub-domains which are distributed to different processors. NUFEB also offers a collection of post-processing routines for the visualisation and analysis of simulation output. In this article, we give an overview of NUFEB's functionalities and implementation details. We provide examples that illustrate the type of microbial systems NUFEB can be used to model and simulate. Author summaryIndividual-based Models (IbM) are one of the most promising frameworks to study microbial communities, as they can explicitly describe the behaviour of each cell. The development of a general-purpose IbM solver should focus on efficiency and flexibility due to the unique characteristics of microbial systems. However, available tools for these purposes present significant limitations. Most of them only facilitate serial computing for single simulation, or only focus on biological processes, but do not model mechanical and chemical processes in detail. In this work, we introduce the IbM solver NUFEB May 17, 2019 1/19 that addresses these shortcoming. The tool facilitates the modelling of much needed biological, chemical, physical and individual microbes in detail, and offers the flexibility of model extension and customisation. NUFEB is also fully parallelised and allows for the simulation of large complex microbial system. In this paper, we first give an overview of NUFEB's functionalities and implementation details. Then, we use NUFEB to model and simulate a biofilm system with fluid dynamics, and a large and complex biofilm system with multiple microbial functional groups and multiple nutrients. 1 This is a PLOS Computational Biology Software paper. 28 biological, chemical and physical processes, or sometimes it may be a simple model that 29 describes mono-functional group or focuses on a few processes. Thus, it is important for 30 the solver to be highly customisable (for building IbM) and extendible (with new IbM 31 features). Second, the solver should be scalable. Simulation of large microbial 32 communities is difficult since they contain a very high number of individuals. Different 33 modelling strategies have been proposed to overcome this limitation, inclu...

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Global sensitivity analysis based on Gaussian‐process metamodelling for complex biomechanical problems

Wirthl

Brandstaeter

Nitzler

et al. 2023

Numer Methods Biomed Eng

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Biomechanical models often need to describe very complex systems, organs or diseases, and hence also include a large number of parameters. One of the attractive features of physics-based models is that in those models (most) parameters have a clear physical meaning. Nevertheless, the determination of these parameters is often very elaborate and costly and shows a large scatter within the population. Hence, it is essential to identify the most important

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Gaussian process emulation of an individual-based model simulation of microbial communities

Cited by 31 publications

References 43 publications

Applying big data in water treatment industry: A new era of advance

Applying big data in water treatment industry: A new era of advance

NUFEB: A Massively Parallel Simulator for Individual-based Modelling of Microbial Communities

Global sensitivity analysis based on Gaussian‐process metamodelling for complex biomechanical problems

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