A Methodology for Co-simulation-Based Optimization of Biofabrication Protocols

Giannantoni, Leonardo; Bardini, Roberta; Carlo, Stefano Di

doi:10.1007/978-3-031-07802-6_16

Cited by 5 publications

(9 citation statements)

References 25 publications

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“…This work presents a computational DSE approach to optimize the simulated biofabrication of epithelial sheets and to generate optimal biofabrication protocols based on a variant of the A2C algorithm. Starting from the idea proposed in [22], it aims to generate biofabrication protocols for the simulated growth of epithelial cells under compression stimuli to validate its capabilities.…”

Section: Discussionmentioning

confidence: 99%

“…A previous work [22] exemplifies a model-based OvS approach combining evolutionary computation and discrete models simulation to generate protocols for the biofabrication of human epithelial monolayers computationally. Simulation relies on discrete models whose high abstraction level can express more detailed intracellular April 24, 2023 6/41 dynamics while still simulating hundreds of cells.…”

Section: Model-based Design Space Explorationmentioning

confidence: 99%

“…Previous work provided a model-based Optimization via Simulation (OvS) approach [20], where optimization relied on a Genetic Algorithm (GA) [21] and orchestrated the white-box simulation of a discrete model of the biofabrication of epithelial sheets [22]. This work continues the exploration of different DSE and white-box simulation approaches, with the application of Machine Learning (ML) and the simulation of white-box models to optimize biofabrication.…”

Section: Introductionmentioning

confidence: 99%

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A methodology combining reinforcement learning and simulation to optimize thein silicoculture of epithelial sheets

Castrignanò

Bardini

Savino

et al. 2023

Preprint

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Tissue Engineering and Regenerative Medicine aim to replicate and replace tissues for curing disease. However, full tissue integration and homeostasis are still far from reach. Biofabrication is an emerging field that identifies the processes required for generating biologically functional products with the desired structural organization and functionality and can potentially revolutionize the regenerative medicine domain, which aims to use patients' cells to restore the structure and function of damaged tissues and organs. However, biofabrication still has limitations in the quality of processes and products. Biofabrication processes are often improved empirically, but this is slow, costly, and provides partial results. Computational approaches can tap into biofabrication underused potential, supporting analysis, modeling, design, and optimization of biofabrication processes, speeding up their improvement towards a higher quality of products and subsequent higher clinical relevance. This work proposes a reinforcement learning-based computational design space exploration methodology to generate optimal protocols for the simulated fabrication of epithelial sheets. The optimization strategy relies on a deep reinforcement learning algorithm, the Advantage-Actor Critic, which relies on a neural network model for learning. In contrast, simulations rely on the PalaCell2D simulation framework. Validation demonstrates the proposed approach on two protocol generation targets: maximizing the final number of obtained cells and optimizing the spatial organization of the cell aggregate.Advantage-Actor Critic, which relies on a neural network model for learning. In contrast, simulations rely on the PalaCell2D simulation framework. Validation demonstrates the proposed approach on two protocol generation targets: maximizing the final number of obtained cells and optimizing the spatial organization of the cell aggregate.

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Section: Discussionmentioning

confidence: 99%

Section: Model-based Design Space Explorationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A methodology combining reinforcement learning and simulation to optimize thein silicoculture of epithelial sheets

Castrignanò

Bardini

Savino

et al. 2023

Preprint

Self Cite

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“…In [132], the authors employ a model of tissue growth inside 3D scaffolds in a perfusion bioreactor and a multi-objective optimization strategy to find the most cost-effective medium refreshment strategy for maximizing tissue growth while minimizing experimental cost. Authors in [133] combine evolutionary computation and simulation of intra- and extracellular processes for efficient DSE of process designs for the biofabrication of human epithelial monolayers.…”

Section: Computational Methods For Intelligent Biofabricationmentioning

confidence: 99%

Computational Methods for Biofabrication in Tissue Engineering and Regenerative Medicine - a literature review

Bardini

Carlo

2023

Preprint

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Biofabrication is the generation of biologically functional products from living cells and biomaterials through bioprinting and subsequent maturation processes. Technological and scientific domains are underlying biofabrication ranging from biology to automated manufacturing and culture systems. Among its application domains, Tissue Engineering and Regenerative Medicine poses strict quality requirements for biofabrication, requiring fast and disruptive innovation for processes and products to comply. Innovation in biofabrication is slow and incremental, relying on R&D processes that are inefficient (slow, risky, and costly), ineffective (products have sub-optimal quality), and challenging to replicate. While automation and digitalization support process execution, design, and innovation, they often work as tools for empirical, problem-specific, and operator-dependent approaches. On the other hand, computational approaches support biofabrication process modeling, design, and optimization of experimental activity to improve the quality of processes and products. To express their full potential, computational methods must factor in the biological complexity underlying biofabrication. This review analyzes computational approaches to biofabrication process modeling, design, and optimization with a focus on solutions explicitly accounting for biological complexity.

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“…Computational methods have a key role in the analysis [7], [8], modeling [9], [10], design [1] and optimization [11], [12], [13] of complex biological processes. In particular, for the analysis and prediction of multicellular synthetic systems dynamics, computational tools must offer instruments for modeling and simulation, accounting for multiple spatial and temporal scales [14].…”

Section: Introductionmentioning

confidence: 99%

Biology System Description Language (BiSDL): a modeling language for the design of multicellular synthetic biological systems

Giannantoni,

Bardini,

Savino

et al. 2024

Preprint

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Background: The Biology System Description Language (BiSDL) is an accessible, easy-to-use computational language for multicellular synthetic biology. It allows synthetic biologists to represent spatiality and multi-level cellular dynamics inherent to multicellular designs, filling a gap in the state of the art. Developed for designing and simulating spatial, multicellular synthetic biological systems, BiSDL integrates high-level conceptual design with detailed low-level modeling, fostering collaboration in the Design-Build-Test-Learn cycle. BiSDL descriptions directly compile into Nets-Within-Nets (NWNs) models, offering a unique approach to spatial and hierarchical modeling in biological systems. Results: BiSDL's effectiveness is showcased through two case studies on complex multicellular systems: a bacterial consortium and a synthetic morphogen system. These studies highlight the BiSDL proficiency in representing spatial interactions and multi-level cellular dynamics. The language facilitates the compilation of conceptual designs into detailed, simulatable models, leveraging the NWNs formalism. This enables intuitive modeling of complex biological systems, making advanced computational tools more accessible to a broader range of researchers. Conclusions: BiSDL represents a significant step forward in computational languages for synthetic biology, providing a sophisticated yet user-friendly tool for the design and simulation of complex biological systems with an emphasis on spatiality and cellular dynamics. Its introduction has the potential to transform research and development in synthetic biology, allowing for deeper insights and novel applications in understanding and manipulating multicellular systems.

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A Methodology for Co-simulation-Based Optimization of Biofabrication Protocols

Cited by 5 publications

References 25 publications

A methodology combining reinforcement learning and simulation to optimize thein silicoculture of epithelial sheets

A methodology combining reinforcement learning and simulation to optimize thein silicoculture of epithelial sheets

Computational Methods for Biofabrication in Tissue Engineering and Regenerative Medicine - a literature review

Biology System Description Language (BiSDL): a modeling language for the design of multicellular synthetic biological systems

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