In a recent study published in the journal “Procedings of the National Academy of Sciences,” scientists David Bruckner and Gasper Weaving from the Institute of Science and Technology of Austria (ISTA) introduced a mathematical model that delves into the self-organization process during mammalian embryonic development. The model is able to predict optimal parameters for cell interactions and describe this process with a universal mathematical language.
Self-organization refers to a system’s ability to create new structures independently without external influence, a phenomenon observed in natural occurrences like flocks of birds or fish. In embryonic development, cells communicate through chemical signals to coordinate actions and determine their roles in the body, whether it be eye cells for vision or intestinal cells for digestion.
This cellular communication enables synchronized and coordinated development without the need for centralized control. Each cell responds to signals from its neighbors, leading to the self-organization of cellular groups and ultimately the formation of a multicellular organism from a single fertilized egg.
Theoretical studies into self-organization processes have garnered interest from David Bruckner, a postdoctoral researcher at Nomis and ISTA. Bruckner’s focus lies in embryonic development, a sophisticated process controlled by genetics and cellular communication. Despite unpredictable factors known as “noise,” embryonic structures form reliably and consistently.
Gasper Weaving, an ISTA professor, specializes in studying information processing in biological systems. He highlights the role of information theory as a universal language for quantitatively assessing structure and regularity in statistical ensembles, like those found in developing embryos where functional organisms are reproduced with slight variations.
The new mathematical model developed by Bruckner and Weaving allows for the measurement of optimized cell interactions in the face of noise. Through computer simulations of interacting cells, researchers have explored conditions where stable results can be maintained despite fluctuations.
While the model has shown success in three different development scenarios based on chemical and mechanical signals, further work is needed to apply it to experimental data of developing systems. Future studies plan to delve into more complex models with numerous parameters and measurements to better understand patterns of chemical signals in developing embryos.