Sensitivity analysis of agent-based simulation utilizing massively parallel computation and interactive data visualization

Niida, Atsushi; Hasegawa, Takanori; Miyano, Satoru

doi:10.1101/510057

Cited by 8 publications

(11 citation statements)

References 14 publications

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“…In both the driver and driver-d models, we do not consider spatial information. However, it should be noted that, by simulating tumor growth on a one-dimensional lattice, we demonstrated that the spatial bias of a resource necessary for cell divisions could prompt the driver-branching process (Niida et al, 2019).…”

Section: /21mentioning

confidence: 81%

“…Recently reported studies have shown that spatial structures regulate evolutionary dynamics in tumors (Noble et al, 2019;West et al, 2019). We also determined that resource bias prompts the driver-branching process, by simulating tumor growth on a one-dimensional lattice (Niida et al, 2019). Moreover, Iwasaki and Innan 2017 -Giner et al, 2015).…”

Section: Discussionmentioning

confidence: 94%

“…The MASSIVE viewer also displays single-cell mutation profiles from 5 of the 50 trial with the same parameter setting. For details, please refer to our methodological report (Niida et al, 2019). All the results in this study can be interactively explored in the MASSIVE viewer on our website (https://www.hgc.jp/˜niiyan/canevosim).…”

Section: Sensitivity Analysis Based On Massivementioning

confidence: 99%

“…To examine the manner in which each parameter affects the evolutionary dynamics of the simulation model, we performed a sensitivity analysis utilizing MASSIVE (Niida et al, 2019), for which we employed a supercomputer. MASSIVE first performs a very large number of agent-based simulations with a broad range of parameter settings.…”

Section: Driver Modelmentioning

confidence: 99%

“…In agentbased simulation models, each cell in a tumor correspond to an agent; the cells can divide to produce new cells, die, or migrate, and each cell's behavior can be stochastically determined from its own state and/or the environment surrounding the cell. By applying sensitivity analysis to the simulation models, (i.e., examining the simulation results while changing the parameters of the models), it is possible to identify the factors affecting the cancer evolutionary dynamics (Niida et al, 2019). However, to the best of our knowledge, there exists no simulation work aiming to reproduce and analyze the four above-stated evolutionary processes in a unified manner.…”

Section: Introductionmentioning

confidence: 99%

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Peer Review #1 of "A unified simulation model for understanding the diversity of cancer evolution (v0.1)"

2020

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Because cancer evolution underlies the therapeutic difficulties of cancer, it is clinically important to understand the evolutionary dynamics of cancer. Thus far, a number of evolutionary processes have been proposed to be working in cancer evolution. However, there exists no simulation model that can describe the different evolutionary processes in a unified manner. In this study, we constructed a unified simulation model for describing the different evolutionary processes and performed sensitivity analysis on the model to determine the conditions in which cancer growth is driven by each of the different evolutionary processes. Our sensitivity analysis has successfully provided a series of novel insights into the evolutionary dynamics of cancer. For example, we found that, while a high neutral mutation rate shapes neutral intratumor heterogeneity (ITH) characterized by a fractal-like pattern, a stem cell hierarchy can also contribute to shaping neutral ITH by apparently increasing the mutation rate. Although It has been reported that the evolutionary principle shaping ITH shifts from selection to accumulation of neutral mutations during colorectal tumorigenesis, our simulation revealed the possibility that this evolutionary shift is triggered by drastic evolutionary events that occur in a a short time and confer a marked fitness increase on one or a few cells. This result helps us understand that each process works not separately but simultaneously and continuously as a series of phases of cancer evolution. Collectively, this study serves as a basis to understand in greater depth the diversity of cancer evolution.

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Section: /21mentioning

confidence: 81%

Section: Discussionmentioning

confidence: 94%

Section: Sensitivity Analysis Based On Massivementioning

confidence: 99%

Section: Driver Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Peer Review #1 of "A unified simulation model for understanding the diversity of cancer evolution (v0.1)"

2020

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A vector-agent approach to (spatiotemporal) movement modelling and reasoning

Rahimi

Moore

Whigham

2022

Sci Rep

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Modelling a complex system of autonomous individuals moving through space and time essentially entails understanding the (heterogeneous) spatiotemporal context, interactions with other individuals, their internal states and making any underlying causal interrelationships explicit, a task for which agents (including vector-agents) are specifically well-suited. Building on a conceptual model of agent space–time and reasoning behaviour, a design guideline for an implemented vector-agent model is presented. The movement of football players was chosen as it is appropriately constrained in space, time and individual actions. Sensitivity-variability analysis was applied to measure the performance of different configurations of system components on the emergent movement patterns. The model output varied more when the condition of the contextual actors (players’ role-areas) was manipulated. The current study shows how agent-based modelling can contribute to our understanding of movement and how causally relevant evidence can be produced, illustrated through a spatiotemporally constrained football case-study.

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Using Paired Agent-Based Simulations To Test Strategies For Limiting The Effects Of Epidemics

Armstrong

Fc²

2019

Preprint

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Agent-based simulations are widely used nowadays in public health research for comparing different strategies for mitigating epidemics and for planning appropriate responses in the aftermath of crises in large urban areas because they can capture fine scale heterogeneities that may have important non-linear effects on the results. Given the costs of implementing strategies, decision-makers have to be convinced that the proposed treatment/procedure leads to a statistically significant improvement. This paper presents an innovative method for constructing paired agent-based simulations where exactly the same set of random effects is applied to simulations with and without the treatment/procedure. Statistical Analysis of Variance distinguishes the sum of squares between groups (BSS) from the sum of squares within groups (WSS). Our aim was to filter out the within sum of squares (WSS) leaving only the sum of squares between the control group and the treatment group (BSS). We propose to filter out the WSS by constructing paired simulations because as is well known, when paired t-tests can be used, they are much more powerful than ordinary t-tests. Pearson's Chi-squared goodness of fit, the Kolmogorov-Smirnov statistic and the Kullback-Leibler Divergence are then used to test whether the effect is statistically significant. This procedure has been tested on a case-study on the propagation of the Zika epidemic in Rio de Janeiro in 2015.

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Sensitivity analysis of agent-based simulation utilizing massively parallel computation and interactive data visualization

Cited by 8 publications

References 14 publications

Peer Review #1 of "A unified simulation model for understanding the diversity of cancer evolution (v0.1)"

Peer Review #1 of "A unified simulation model for understanding the diversity of cancer evolution (v0.1)"

A vector-agent approach to (spatiotemporal) movement modelling and reasoning

Using Paired Agent-Based Simulations To Test Strategies For Limiting The Effects Of Epidemics

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