Model‑Based Design for Morphogen‑Driven Spatial Patterns in Synthetic Biology

The evolution of multicellular organisms from single cells is a striking example of how cooperation, communication, and spatial coordination give rise to biological complexity. Cells must adhere to one another, exchange biochemical signals, and regulate gene expression in ways that enable specialization and correct positioning within a developing tissue. Morphogenesis, the process by which cells self‑organize into defined shapes, plays a central role in this transformation. Although many of its underlying mechanisms have been studied, the principles that govern how local interactions give rise to global structure remain only partially understood.

Synthetic developmental biology seeks to recreate these processes using engineered, orthogonal gene circuits that generate predictable multicellular patterns. One key component of such systems are morphogens—diffusible signaling molecules that guide cell fate decisions and spatial organization. Building on this concept, we extend the synthetic Notch‑based morphogen circuit introduced by Toda et al. (2020) by incorporating morphogen‑dependent proliferation. This extension enables us to explore how variations in morphogen source geometry, spatial placement, and inhibitor interactions influence the emergence of multicellular structures.

To investigate these questions, we develop a mathematical modeling framework based on partial differential equations that describes morphogen diffusion, cell–cell communication, and circuit dynamics. The model is designed to be simple yet extensible, allowing us to simulate a wide range of synthetic developmental scenarios. By systematically varying circuit parameters and spatial configurations, we aim to identify design principles that enable the formation of predefined tissue architectures. These simulations provide insight into how engineered circuits can be arranged to produce desired spatial patterns and how growth dynamics contribute to pattern formation.

Our work connects to recent advances in automated gene regulatory network design, such as the approach proposed by Mousavi and Lobo (2021), who demonstrated how spatial gene expression patterns can be generated using two orthogonal morphogen gradients. Integrating such ideas into our modeling framework supports the broader goal of developing computational tools for the rational design of synthetic multicellular systems. Ultimately, we aim to bridge the gap between circuit design, spatial modeling, and experimental implementation.

Beyond contributing to a deeper understanding of morphogenesis, this bottom‑up engineering approach opens new possibilities for designing complex tissues with specific shapes and functions. By combining synthetic biology with predictive modeling, we seek to establish a foundation for constructing multicellular structures that can be used in regenerative medicine, tissue engineering, and the study of developmental processes. The long‑term vision is to enable the controlled design of synthetic tissues whose architecture and behavior can be precisely specified in silico and realized experimentally.