Fighting fire with fire: deploying complexity in computational modeling to effectively characterize complex biological systems

Prybutok, Alexis N; Cain, Jason Y; Leonard, Joshua N.; Bagheri, Neda

doi:10.1016/j.copbio.2022.102704

Cited by 5 publications

(3 citation statements)

References 54 publications

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“…The emulation of ABMs is difficult due to the complexity (e.g., number of species and corresponding interactions) and emergent nature of the biological phenomena they are well suited to simulate. As ABMs become increasingly multi-scale and complex (Prybutok et al, 2022a), challenges in emulation will persist and likely magnify. Mitigating these challenges is necessary to mediate the quantity of ABM simulations required to identify patterns, sample high-dimensional spaces, and generate testable hypotheses across emergent dynamics; such simulations can be cost-prohibitive.…”

Section: Discussionmentioning

confidence: 99%

Incorporating temporal information during feature engineering bolsters emulation of spatio-temporal emergence

Cain,

Evarts,

et al. 2024

Preprint

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Motivation: Emergent biological dynamics derive from the evolution of lower-level spatial and temporal processes. A long-standing challenge for scientists and engineers is identifying simple low-level rules that give rise to complex higher-level dynamics. High-resolution biological data acquisition enables this identification and has evolved at a rapid pace for both experimental and computational approaches. Simultaneously harnessing the resolution and managing the expense of emerging technologies—e.g. live cell imaging, scRNAseq, agent-based models—requires a deeper understanding of how spatial and temporal axes impact biological systems. Effective emulation is a promising solution to manage the expense of increasingly complex high-resolution computational models. In this research, we focus on the emulation of a tumor microenvironment agent-based model to examine the relationship between spatial and temporal environment features, and emergent tumor properties. Results: Despite significant feature engineering, we find limited predictive capacity of tumor properties from initial system representations. However, incorporating temporal information derived from intermediate simulation states dramatically improves the predictive performance of machine learning models. We train a deep-learning emulator on intermediate simulation states and observe promising enhancements over emulators trained solely on initial conditions. Our results underscore the importance of incorporating temporal information in the evaluation of spatio-temporal emergent behavior. Nevertheless, the emulators exhibit inconsistent performance, suggesting that the underlying model characterizes unique cell populations dynamics that are not easily replaced. Availability: All source codes for the agent-based model, emulation, and analyses are publicly available at github.com/bagherilab/ARCADE, github.com/bagherilab/emulation, and github.com/bagherilab/ emulation_analysis, respectively.

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Section: Discussionmentioning

confidence: 99%

Incorporating temporal information during feature engineering bolsters emulation of spatio-temporal emergence

Cain,

Evarts,

et al. 2024

Preprint

Self Cite

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“…Computational models are among the well-known tools that systems biologists employ to study the interactions between different biological components at the system level [ 1–4 ]. These mechanistic models can be constructed to simulate temporal and/or spatial dynamics of a network of reactions [ 5 ], a complete cell [ 6 ] or a holistic model of multi-cells [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

“…Once the model is validated, it can be adopted to predict and study various scenarios by feeding it with different parameters and initial conditions (e.g. initial states) [ 1 ].…”

Section: Introductionmentioning

confidence: 99%

Design patterns for the construction of computational biological models

Herajy,

Liu,

Heiner

2024

Briefings in Bioinformatics

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Computational biological models have proven to be an invaluable tool for understanding and predicting the behaviour of many biological systems. While it may not be too challenging for experienced researchers to construct such models from scratch, it is not a straightforward task for early stage researchers. Design patterns are well-known techniques widely applied in software engineering as they provide a set of typical solutions to common problems in software design. In this paper, we collect and discuss common patterns that are usually used during the construction and execution of computational biological models. We adopt Petri nets as a modelling language to provide a visual illustration of each pattern; however, the ideas presented in this paper can also be implemented using other modelling formalisms. We provide two case studies for illustration purposes and show how these models can be built up from the presented smaller modules. We hope that the ideas discussed in this paper will help many researchers in building their own future models.

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Modeling multi-component separation in hydrophobic interaction chromatography with improved parameter-by-parameter estimation method

Yang,

Lin,

Chen

et al. 2024

Journal of Chromatography A

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Fighting fire with fire: deploying complexity in computational modeling to effectively characterize complex biological systems

Cited by 5 publications

References 54 publications

Incorporating temporal information during feature engineering bolsters emulation of spatio-temporal emergence

Incorporating temporal information during feature engineering bolsters emulation of spatio-temporal emergence

Design patterns for the construction of computational biological models

Modeling multi-component separation in hydrophobic interaction chromatography with improved parameter-by-parameter estimation method

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