Engineering Simulations for Cancer Systems Biology

Bown, James L.; Andrews, Paul S.; Deeni, Yusuf Y.; Goltsov, Alexey; Idowu, Michael A.; Polack, Fiona; Sampson, Adam T.; Shovman, Mark; Stepney, Susan

doi:10.2174/138945012803530071

Cited by 13 publications

(18 citation statements)

References 73 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As such, they are a low-cost way for hypothesis generation (of the real-world system) and directing experimental design to test these (within the abstracted computational model). The ABMs thus become a scientific instrument for investigation [80], and therefore require a principled approach to their use, which is akin to the scientific method, to ensure they are fit-for-purpose.…”

Section: Discussionmentioning

confidence: 99%

Lessons learned on development and application of agent-based models of complex dynamical systems

Williams

2018

Simulation Modelling Practice and Theory

View full text Add to dashboard Cite

The field of agent-based modelling (ABM) has gained a significant following in recent years, and it is often marketed as an excellent introduction to modelling for the novice modeller or non-programmer. The typical objective of developing an agent-based model is to either increase our mechanistic understanding of a real-world system, or to predict how the dynamics of the real-world system are likely to be affected by changes to internal or external factors. Although there are some excellent ABMs that have been used in a predictive capacity across a number of domains, we believe that the promotion of ABM as an 'accessible to all' approach, could potentially lead to models being published that are flawed and therefore generate inaccurate predictions of real-world systems. The purpose of this article is to use our experiences in modelling complex dynamical systems, to reinforce the view that agent-based models can be useful for answering questions of the real-world domain through predictive modelling, but also to emphasise that all modellers, expert and novice alike, must make a concerted effort to adopt robust methods and techniques for constructing, validating and analysing their models, if the result is to be meaningful and grounded in the system of interest.

show abstract

Section: Discussionmentioning

confidence: 99%

Lessons learned on development and application of agent-based models of complex dynamical systems

Williams

2018

Simulation Modelling Practice and Theory

View full text Add to dashboard Cite

show abstract

“…The construction of simulations which demonstrably capture biological systems has received recent attention [29]. This resulted in a process through which assumptions underpinning the abstraction of key biological components and processes into a conceptual model and thereafter a software implementation are explicitly captured [30].…”

Section: Discussionmentioning

confidence: 99%

Leukocyte Motility Models Assessed through Simulation and Multi-objective Optimization-Based Model Selection

et al. 2016

View full text Add to dashboard Cite

The advent of two-photon microscopy now reveals unprecedented, detailed spatio-temporal data on cellular motility and interactions in vivo. Understanding cellular motility patterns is key to gaining insight into the development and possible manipulation of the immune response. Computational simulation has become an established technique for understanding immune processes and evaluating hypotheses in the context of experimental data, and there is clear scope to integrate microscopy-informed motility dynamics. However, determining which motility model best reflects in vivo motility is non-trivial: 3D motility is an intricate process requiring several metrics to characterize. This complicates model selection and parameterization, which must be performed against several metrics simultaneously. Here we evaluate Brownian motion, Lévy walk and several correlated random walks (CRWs) against the motility dynamics of neutrophils and lymph node T cells under inflammatory conditions by simultaneously considering cellular translational and turn speeds, and meandering indices. Heterogeneous cells exhibiting a continuum of inherent translational speeds and directionalities comprise both datasets, a feature significantly improving capture of in vivo motility when simulated as a CRW. Furthermore, translational and turn speeds are inversely correlated, and the corresponding CRW simulation again improves capture of our in vivo data, albeit to a lesser extent. In contrast, Brownian motion poorly reflects our data. Lévy walk is competitive in capturing some aspects of neutrophil motility, but T cell directional persistence only, therein highlighting the importance of evaluating models against several motility metrics simultaneously. This we achieve through novel application of multi-objective optimization, wherein each model is independently implemented and then parameterized to identify optimal trade-offs in performance against each metric. The resultant Pareto fronts of optimal solutions are directly contrasted to identify models best capturing in vivo dynamics, a technique that can aid model selection more generally. Our technique robustly determines our cell populations’ motility strategies, and paves the way for simulations that incorporate accurate immune cell motility dynamics.

show abstract

“…It has been used to explore the mechanisms through which splenectomy, the removal of the spleen, a primary immune organ, exacerbates disease severity and predict the outcome of T-cell interaction-blocking drugs [18]. It was conceived through a collaboration of immunologists and computer scientists, and developed through a principled approach focusing on documenting how biological concepts are translated into computer code: the CoSMoS process [25]. It is written in the Java programming language.…”

Section: A Test Bed For Calibrating Biological Simulationsmentioning

confidence: 99%

Automated multi-objective calibration of biological agent-based simulations

Read

Alden

Rose

et al. 2016

J. R. Soc. Interface.

View full text Add to dashboard Cite

Computational agent-based simulation (ABS) is increasingly used to complement laboratory techniques in advancing our understanding of biological systems. Calibration, the identification of parameter values that align simulation with biological behaviours, becomes challenging as increasingly complex biological domains are simulated. Complex domains cannot be characterized by single metrics alone, rendering simulation calibration a fundamentally multi-metric optimization problem that typical calibration techniques cannot handle. Yet calibration is an essential activity in simulation-based science; the baseline calibration forms a control for subsequent experimentation and hence is fundamental in the interpretation of results. Here, we develop and showcase a method, built around multi-objective optimization, for calibrating ABSs against complex target behaviours requiring several metrics (termed objectives) to characterize. Multi-objective calibration (MOC) delivers those sets of parameter values representing optimal trade-offs in simulation performance against each metric, in the form of a Pareto front. We use MOC to calibrate a well-understood immunological simulation against both established a priori and previously unestablished target behaviours. Furthermore, we show that simulation-borne conclusions are broadly, but not entirely, robust to adopting baseline parameter values from different extremes of the Pareto front, highlighting the importance of MOC's identification of numerous calibration solutions. We devise a method for detecting overfitting in a multi-objective context, not previously possible, used to save computational effort by terminating MOC when no improved solutions will be found. MOC can significantly impact biological simulation, adding rigour to and speeding up an otherwise time-consuming calibration process and highlighting inappropriate biological capture by simulations that cannot be well calibrated. As such, it produces more accurate simulations that generate more informative biological predictions.

show abstract

Engineering Simulations for Cancer Systems Biology

Cited by 13 publications

References 73 publications

Lessons learned on development and application of agent-based models of complex dynamical systems

Lessons learned on development and application of agent-based models of complex dynamical systems

Leukocyte Motility Models Assessed through Simulation and Multi-objective Optimization-Based Model Selection

Automated multi-objective calibration of biological agent-based simulations

Contact Info

Product

Resources

About