Computational Tools for Applying Multi-level Models to Synthetic Biology

Savino

et al. 2023

Preprint

Self Cite

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.

Section: Models Of Biological Complexitymentioning

confidence: 99%

“…Ordinary Differential Equations (ODEs) are common in systems biology [23], while Agent-Based Models (ABMs) and discrete models, though less standardized, offer significant potential in computational biology [24,25,26].…”

Section: Introductionmentioning

confidence: 99%

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

Castrignanò

Savino

et al. 2023

Preprint

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“…The idea of tokens being PN goes back to R. Valk [71] , and NWN approaches are extensively studied in the PN literature [40] , [46] , [63] , [10] . The Nets-Within-Nets (NWN) formalism supports all capabilities of other high-level PN and can face the additional modeling requirements posed by ontogenetic processes [5] , [3] as will be explained in the next sections. Among the different flavors of the NWN formalisms, this paper adopts the one provided by the Renew simulator [41] , [11] , [70] .…”

Section: Introductionmentioning

confidence: 99%

Nets-within-nets for modeling emergent patterns in ontogenetic processes

Computational and Structural Biotechnology Journal

Benso

Politano

et al. 2021

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“…The Nets-Within-Nets (NWNs) formalism [25, 45] supports all capabilities of other high-level PNs and can face the additional modeling requirements posed by ontogenetic processes [4, 2]. As depicted in Figure 1, NWNs can express token colors using the Petri Net formalism.…”

Section: Introductionmentioning

confidence: 99%

“…The idea of tokens being PN goes back to R. Valk [54], and NWN approaches are extensively studied in the PN literature [28, 33, 47, 9]. The Nets-Within-Nets (NWNs) formalism supports all capabilities of other high-level PN and can face the additional modeling requirements posed by ontogenetic processes [4, 2] as will be explained in the next sections. Among the different flavors of the NWN formalisms, this paper adopts the one provided by the Renew simulator [29, 10, 53].…”

Section: Introductionmentioning

confidence: 99%

Nets-within-nets for modeling emergent patterns in ontogenetic processes

Benso

Politano

et al. 2021

Preprint

Self Cite

Ontogenesis is the development of an organism from its earliest stage to maturity, including homeostasis maintenance throughout adulthood despite environmental perturbations. Almost all cells of a multicellular organism share the same genomic information. Nevertheless, phenotypic diversity and complex supra-cellular architectures emerge at every level, starting from tissues and organs. This is possible thanks to a robust and dynamic interplay of regulative mechanisms. To study ontogenesis, it is necessary to consider different levels of regulation, both genetic and epigenetic. Each cell undergoes a specific path across a landscape of possible regulative states affecting both its structure and its functions during development. This paper proposes using the Nets-Within-Nets formalism, which combines Petri Nets' simplicity with the capability to represent and simulate the interplay between different layers of regulation connected by non-trivial and context-dependent hierarchical relations. In particular, this work introduces a modeling strategy based on Nets-Within-Nets that can model several critical processes involved in ontogenesis. Moreover, it presents a case study focusing on the first phase of Vulval Precursor Cells specification in C. Elegans. The case study shows that the proposed model can simulate the emergent morphogenetic pattern corresponding to the observed developmental outcome of that phase, in both the physiological case and different mutations. The model presented in the results section is available online at https://github.com/sysbio-polito/NWN_CElegans_VPC_model