Comparing stochastic differential equations and agent-based modelling and simulation for early-stage cancer
There is great potential to be explored regarding the use of agent-based modelling and simulation as an alternative paradigm to investigate early-stage cancer interactions with the immune system. It does not suffer from some limitations of ordinary differential equation models, such as the lack of s...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Published: |
Public Library of Science
2014
|
| Subjects: | |
| Online Access: | https://eprints.nottingham.ac.uk/3348/ |
| _version_ | 1848791006444519424 |
|---|---|
| author | Figueredo, Grazziela P. Siebers, Peer-Olaf Owen, Markus R. Reps, Jenna Aickelin, Uwe |
| author_facet | Figueredo, Grazziela P. Siebers, Peer-Olaf Owen, Markus R. Reps, Jenna Aickelin, Uwe |
| author_sort | Figueredo, Grazziela P. |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | There is great potential to be explored regarding the use of agent-based modelling and simulation as an alternative paradigm to investigate early-stage cancer interactions with the immune system. It does not suffer from some limitations of ordinary differential equation models, such as the lack of stochasticity, representation of individual behaviours rather than aggregates and individual memory. In this paper we investigate the potential contribution of agent-based modelling and simulation when contrasted with stochastic versions of ODE models using early-stage cancer examples. We seek answers to the following questions: (1) Does this new stochastic formulation produce similar results to the agent-based version? (2) Can these methods be used interchangeably? (3) Do agent-based models outcomes reveal any benefit when compared to the Gillespie results? To answer these research questions we investigate three well-established mathematical models describing interactions between tumour cells and immune elements. These case studies were re-conceptualised under an agent-based perspective and also converted to the Gillespie algorithm formulation. Our interest in this work, therefore, is to establish a methodological discussion regarding the usability of different simulation approaches, rather than provide further biological insights into the investigated case studies. Our results show that it is possible to obtain equivalent models that implement the same mechanisms; however, the incapacity of the Gillespie algorithm to retain individual memory of past events affects the similarity of some results. Furthermore, the emergent behaviour of ABMS produces extra patters of behaviour in the system, which was not obtained by the Gillespie algorithm. |
| first_indexed | 2025-11-14T18:21:39Z |
| format | Article |
| id | nottingham-3348 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T18:21:39Z |
| publishDate | 2014 |
| publisher | Public Library of Science |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-33482020-05-04T20:15:41Z https://eprints.nottingham.ac.uk/3348/ Comparing stochastic differential equations and agent-based modelling and simulation for early-stage cancer Figueredo, Grazziela P. Siebers, Peer-Olaf Owen, Markus R. Reps, Jenna Aickelin, Uwe There is great potential to be explored regarding the use of agent-based modelling and simulation as an alternative paradigm to investigate early-stage cancer interactions with the immune system. It does not suffer from some limitations of ordinary differential equation models, such as the lack of stochasticity, representation of individual behaviours rather than aggregates and individual memory. In this paper we investigate the potential contribution of agent-based modelling and simulation when contrasted with stochastic versions of ODE models using early-stage cancer examples. We seek answers to the following questions: (1) Does this new stochastic formulation produce similar results to the agent-based version? (2) Can these methods be used interchangeably? (3) Do agent-based models outcomes reveal any benefit when compared to the Gillespie results? To answer these research questions we investigate three well-established mathematical models describing interactions between tumour cells and immune elements. These case studies were re-conceptualised under an agent-based perspective and also converted to the Gillespie algorithm formulation. Our interest in this work, therefore, is to establish a methodological discussion regarding the usability of different simulation approaches, rather than provide further biological insights into the investigated case studies. Our results show that it is possible to obtain equivalent models that implement the same mechanisms; however, the incapacity of the Gillespie algorithm to retain individual memory of past events affects the similarity of some results. Furthermore, the emergent behaviour of ABMS produces extra patters of behaviour in the system, which was not obtained by the Gillespie algorithm. Public Library of Science 2014-01 Article PeerReviewed Figueredo, Grazziela P., Siebers, Peer-Olaf, Owen, Markus R., Reps, Jenna and Aickelin, Uwe (2014) Comparing stochastic differential equations and agent-based modelling and simulation for early-stage cancer. PLoS ONE, 9 (4). e95150. ISSN 1932-6203 Biomedical Informatics Simulation http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0095150 doi:10.1371/journal.pone.0095150 doi:10.1371/journal.pone.0095150 |
| spellingShingle | Biomedical Informatics Simulation Figueredo, Grazziela P. Siebers, Peer-Olaf Owen, Markus R. Reps, Jenna Aickelin, Uwe Comparing stochastic differential equations and agent-based modelling and simulation for early-stage cancer |
| title | Comparing stochastic differential equations and agent-based modelling and simulation for early-stage cancer |
| title_full | Comparing stochastic differential equations and agent-based modelling and simulation for early-stage cancer |
| title_fullStr | Comparing stochastic differential equations and agent-based modelling and simulation for early-stage cancer |
| title_full_unstemmed | Comparing stochastic differential equations and agent-based modelling and simulation for early-stage cancer |
| title_short | Comparing stochastic differential equations and agent-based modelling and simulation for early-stage cancer |
| title_sort | comparing stochastic differential equations and agent-based modelling and simulation for early-stage cancer |
| topic | Biomedical Informatics Simulation |
| url | https://eprints.nottingham.ac.uk/3348/ https://eprints.nottingham.ac.uk/3348/ https://eprints.nottingham.ac.uk/3348/ |