Biology subjects

Giannantoni, L.

Publications and source records attributed to Giannantoni, L..

3 recordsLinked to original sources

bioRxiv2024

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

BackgroundThe 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. ResultsBiSDLs effectiveness is showcased through three case studies on complex multicellular systems: a bacterial consortium, a synthetic morphogen system and a conjugative plasmid transfer process. 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. ConclusionsBiSDL represents a significant step forward in computational languages for synthetic biology, providing a sophisticated yet user-friendly tool for designing and simulating 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.

synthetic biology↗

bioRxiv2023

Optimization of synthetic oscillatory biological networks through Reinforcement Learning

In the expanding realm of computational biology, Reinforcement Learning (RL) emerges as a novel and promising approach, especially for designing and optimizing complex synthetic biological circuits. This study explores the application of RL in controlling Hopf bifurcations within ODE-based systems, particularly under the influence of molecular noise. Through two case studies, we demonstrate RLs capabilities in navigating biological systems inherent non-linearity and high dimensionality. Our findings reveal that RL effectively identifies the onset of Hopf bifurcations and preserves biological plausibility within the optimized networks. However, challenges were encountered in achieving persistent oscillations and matching traditional algorithms computational speed. Despite these limitations, the study highlights RLs significant potential as an instrumental tool in computational biology, offering a novel perspective for exploring and optimizing oscillatory dynamics within complex biological systems. Our research establishes RL as a promising strategy for manipulating and designing intricate behaviors in biological networks.

bioengineering↗

bioRxiv2022

A methodology for co-simulation-based optimization of biofabrication protocols

Biofabrication processes are complex and often unsatisfactory. Trial-and-error methods are costly and yield only incremental innovation, starting from sub-optimal and poorly represented existing processes. Although computational techniques might support efficient process design to find optimal process configurations, intelligent computational approaches must comprise biological complexity to provide meaningful insights. This paper proposes a novel co-simulation-based optimization methodology for the systematic design of protocols for cell culture and biofabrication. The proposed strategy integrates evolutionary computation and simulation for efficient design space exploration and assessment of candidate protocols. A generic library supports the modular and flexible composition of multiscale and multidomain co-simulation scenarios. The feasibility of the presented approach was demonstrated in the automatic generation of protocols for the biofabrication of an epithelial cell monolayer. The results are twofold. First, the prototype co-simulation library helps build flexible, loosely coupled simulation scenarios. Second, the in-silico experimentation on the use case shows that the proposed approach is a viable first step towards standard and automated design in biofabrication.

bioinformatics↗