Code-driven Simulation of Bacterial Cellulose Growth for Material Innovation and Eco-intelligent Architecture

Bacterial cellulose, with its self-organizing fiber networks, offers a promising model for bio-inspired, sustainable material design. This study explores the computational growth of bacterial cellulose fiber networks using the diffusion-limited aggregation (DLA) algorithm, aiming to decode how these natural patterns can inform ecological and regenerative design practices. By simulating the formation of these networks, the potential of DLA to replicate the complexity and adaptability inherent in bacterial cellulose’s growth process is investigated. The results demonstrate that DLA not only mimics the structural organization of bacterial cellulose but also offers new insights into optimizing material properties for ecological applications for a guided growth. Through this code-driven computational approach, this research aims to contribute to the growing body of work on ecological intelligence in design, providing a framework for developing biomimetic materials that promote sustainability and resilience. This research bridges microbial processes with computational design, advancing the application of ecological intelligence in material innovation and regenerative architecture.