A cellular automaton model for hypoxia effects on tumour growth dynamics

Al-Mamun, Mohammad A.; Srisukkham, Worawut; Fall, C.; Bass, Rosemary; Hossain, Alamgir; Farid, Dewan Md.

doi:10.1109/skima.2014.7083562

Cited by 1 publication

(3 citation statements)

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“…Frontiers in Systems Biology frontiersin.org 2012a; Kazmi et al, 2012b). Other studies used a similar ANN architecture to pinpoint effects of hypoxia on tumor growth (Al-Mamun et al, 2014), and explored the efficacy of a chemotherapeutic agent, maspin, on tumor metastasis (Al-Mamun et al, 2013;Al-Mamun et al, 2016). Overall, this design scheme-i.e., "embedding" ANNs into the agent entities of an ABM-illustrates an intriguing and creative type of synergy that is possible when integrating ML and ABM-based approaches.…”

Section: Expert Knowledge-driven Supervised Learning Approaches For Abmsmentioning

confidence: 99%

“…Current experimental methodologies are challenged to create datasets that associate these spatiotemporal features of the environment with cellular behaviors across the broad populations of heterogeneous cells that comprise various tissues; thus, training supervised learning algorithms to predict cellular behavior based on environmental features is not currently a routine possibility for those sorts of systems. However, several ABMs have augmented the lack of data in this area with either expert-knowledge-driven ML (Gerlee and Anderson, 2007;Gerlee and Anderson, 2008;Kazmi et al, 2012a;Kazmi et al, 2012b;Al-Mamun et al, 2013;Al-Mamun et al, 2014;Al-Mamun et al, 2016) or RL algorithms (Zangooei and Habibi, 2017;Wang et al, 2018;Hou et al, 2019) to define cellular behaviors within a multicellular ABM. While these recent approaches are not trained on actual experimental datasets, they do enable each cell to autonomously make decisions based on their local environment, thereby realistically modelling cell-tocell heterogeneity within multicellular systems.…”

Section: Factors Influencing Abm/ml Integration: Data Volume Constraintsmentioning

confidence: 99%

“…Several other epidemiological ABMs have leveraged large datasets to train supervised learning algorithms to determine agent behavior(Day et al, 2013;Abdulkareem et al, 2019;Alexander Jr et al, 2019;Augustijn et al, 2020). The right-hand example references multicellular ABMs that simulate individual cell behaviors in a tissue, with cellular "decisions" being made based on either Q-learning(Zangooei and Habibi, 2017) or ANN approaches(Gerlee and Anderson, 2007, 2008;Kazmi et al, 2012a; Kazmi et al, 2012b;Al-Mamun et al, 2013;Al-Mamun et al, 2014; Al-Mamun et al, 2016;Abdulkareem et al, 2019).…”

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confidence: 99%

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Innovations in integrating machine learning and agent-based modeling of biomedical systems

Sivakumar

Mura²,

Peirce³

2022

Front. Syst. Biol.

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Agent-based modeling (ABM) is a well-established computational paradigm for simulating complex systems in terms of the interactions between individual entities that comprise the system’s population. Machine learning (ML) refers to computational approaches whereby algorithms use statistical methods to “learn” from data on their own, i.e., without imposing any a priori model/theory onto a system or its behavior. Biological systems—ranging from molecules, to cells, to entire organisms, to whole populations and even ecosystems—consist of vast numbers of discrete entities, governed by complex webs of interactions that span various spatiotemporal scales and exhibit nonlinearity, stochasticity, and variable degrees of coupling between entities. For these reasons, the macroscopic properties and collective dynamics of biological systems are generally difficult to accurately model or predict via continuum modeling techniques and mean-field formalisms. ABM takes a “bottom-up” approach that obviates common difficulties of other modeling approaches by enabling one to relatively easily create (or at least propose, for testing) a set of well-defined “rules” to be applied to the individual entities (agents) in a system. Quantitatively evaluating a system and propagating its state over a series of discrete time-steps effectively simulates the system, allowing various observables to be computed and the system’s properties to be analyzed. Because the rules that govern an ABM can be difficult to abstract and formulate from experimental data, at least in an unbiased way, there is a uniquely synergistic opportunity to employ ML to help infer optimal, system-specific ABM rules. Once such rule-sets are devised, running ABM calculations can generate a wealth of data, and ML can be applied in that context too—for example, to generate statistical measures that accurately and meaningfully describe the stochastic outputs of a system and its properties. As an example of synergy in the other direction (from ABM to ML), ABM simulations can generate plausible (realistic) datasets for training ML algorithms (e.g., for regularization, to mitigate overfitting). In these ways, one can envision a variety of synergistic ABM⇄ML loops. After introducing some basic ideas about ABMs and ML, and their limitations, this Review describes examples of how ABM and ML have been integrated in diverse contexts, spanning spatial scales that include multicellular and tissue-scale biology to human population-level epidemiology. In so doing, we have used published studies as a guide to identify ML approaches that are well-suited to particular types of ABM applications, based on the scale of the biological system and the properties of the available data.

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Section: Expert Knowledge-driven Supervised Learning Approaches For Abmsmentioning

confidence: 99%

Section: Factors Influencing Abm/ml Integration: Data Volume Constraintsmentioning

confidence: 99%

mentioning

confidence: 99%

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Innovations in integrating machine learning and agent-based modeling of biomedical systems

Sivakumar

Mura²,

Peirce³

2022

Front. Syst. Biol.

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A cellular automaton model for hypoxia effects on tumour growth dynamics

Cited by 1 publication

References 17 publications

Innovations in integrating machine learning and agent-based modeling of biomedical systems

Innovations in integrating machine learning and agent-based modeling of biomedical systems

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