Biological processes rely on interactions that result in self-organization, where complex structures emerge without external direction. This phenomenon is seen in the development of the mammalian embryo, where different types of cells communicate with each other through chemical signals to coordinate their functions. David Brückner and Gašper Tkačik from ISTA have developed a mathematical framework to analyze and predict the self-organization of cells in embryonic development. This framework provides a unified language to describe biological self-organization at various levels, from single cells to multicellular organisms.
Embryonic development is guided by self-organization, allowing a single fertilized cell to develop into a complex multicellular organism with distinct organs and functions. Despite the absence of external signals directing the process, cells communicate with each other to establish patterns and structures. The intrinsic property of self-organization ensures that embryonic patterns are formed reliably even in the presence of unpredictable factors or noise. Recent advancements in the molecular understanding of embryonic development have shed light on the complexities of this process, but a mathematical framework to analyze and quantify its performance was lacking until now.
Information theory serves as a universal language to quantify structure and regularity in statistical ensembles, making it a valuable tool for studying biological systems. Gašper Tkačik, a professor at ISTA with expertise in information theory, has studied information processing in biological systems such as the fly embryo. By bridging Tkačik’s expertise with Brückner’s focus on mammalian embryo development, the researchers were able to develop a framework to analyze and predict the self-organization of cells in embryonic development. This collaboration has provided valuable insights into how cells exchange signals and interact robustly in the face of random fluctuations.
The new framework developed by Brückner and Tkačik has been successful in analyzing developmental models reliant on chemical and mechanical signaling. Through computer simulations of interacting cells, the scientists have explored how systems can maintain stability and consistency despite the introduction of noise. Future work will involve applying the framework to experimental recordings of developmental systems and studying more complex models with additional parameters and dimensions. By teaming up with experimentalists, Brückner and Tkačik aim to further advance our understanding of self-organization in biological processes beyond embryonic development.
