Formalizing Phenotypes of Regeneration

Emergent Tissue Shapes from the Regulatory Feedback between Morphogens and Cell Growth

Patterning and morphogenesis in multicellular organisms require precise dynamic coordination between cellular behaviors and mechano-chemical signals. However, the mechanisms underlying the pathways that coordinate and integrate these signals into emergent cellular behaviors and tissue shapes remain poorly understood. Here, we present a cell-centered agent-based mathematical approach to shed light on the feedback mechanisms underlying tissue growth and pattern formation. The model includes cell size dynamics governed by both intercellular diffusible morphogen concentrations and mechanical stress between cells to control their spatial organization, and does not require the use of any superimposed lattice, increasing its applicability and performance. The results show how the precise integration of the feedback loop between cellular behaviors and mechano-chemical signaling is essential for the regulation of shape and spatial patterns across the tissue scale. Furthermore, the regulation of cellular dynamics by patterning processes, such as Turing activator-inhibitor systems, can drive the formation of emergent stable tissue shapes, which, in turn, specify the domain for morphogen patterning-closing the self-regulated loop between tissue shape and morphogenetic signals. Overall, this study highlights the importance of the feedback loop between morphogen patterning and cellular behaviors in regulating tissue growth dynamics and stable shape formation. Moreover, this study establishes a framework for further experiments to understand the regulatory dynamics of whole-body development and regeneration using high spatiotemporal resolution models.

Automatic design of gene regulatory mechanisms for spatial pattern formation

Gene regulatory mechanisms (GRMs) control the formation of spatial and temporal expression patterns that can serve as regulatory signals for the development of complex shapes. Synthetic developmental biology aims to engineer such genetic circuits for understanding and producing desired multicellular spatial patterns. However, designing synthetic GRMs for complex, multi-dimensional spatial patterns is a current challenge due to the nonlinear interactions and feedback loops in genetic circuits. Here we present a methodology to automatically design GRMs that can produce any given two-dimensional spatial pattern. The proposed approach uses two orthogonal morphogen gradients acting as positional information signals in a multicellular tissue area or culture, which constitutes a continuous field of engineered cells implementing the same designed GRM. To efficiently design both the circuit network and the interaction mechanisms-including the number of genes necessary for the formation of the target spatial pattern-we developed an automated algorithm based on high-performance evolutionary computation. The tolerance of the algorithm can be configured to design GRMs that are either simple to produce approximate patterns or complex to produce precise patterns. We demonstrate the approach by automatically designing GRMs that can produce a diverse set of synthetic spatial expression patterns by interpreting just two orthogonal morphogen gradients. The proposed framework offers a versatile approach to systematically design and discover complex genetic circuits producing spatial patterns.

Automatic design of gene regulatory mechanisms for spatial pattern formation

Synthetic developmental biology aims to engineer gene regulatory mechanisms (GRMs) for understanding and producing desired multicellular patterns and shapes. However, designing GRMs for spatial patterns is a current challenge due to the nonlinear interactions and feedback loops in genetic circuits. Here we present a methodology to automatically design GRMs that can produce any given spatial pattern. The proposed approach uses two orthogonal morphogen gradients acting as positional information signals in a multicellular tissue area or culture, which constitutes a continuous field of engineered cells implementing the same designed GRM. To efficiently design both the circuit network and the interaction mechanisms-including the number of genes necessary for the formation of the target pattern-we developed an automated algorithm based on high-performance evolutionary computation. The tolerance of the algorithm can be configured to design GRMs that are either simple to produce approximate patterns or complex to produce precise patterns. We demonstrate the approach by automatically designing GRMs that can produce a diverse set of synthetic spatial expression patterns by interpreting just two orthogonal morphogen gradients. The proposed framework offers a versatile approach to systematically design and discover pattern-producing genetic circuits.