Decoding and Encoding Molecular Information in Distributed Cellular Systems

A fundamental task confronting modern biology is to address how physical and chemical processes in cells are translated into information (Paul Nurse, 2020) and to adequately describe information flows in living systems. To this end, I will discuss our attempts at building a conceptual framework for the processing of noisy molecular information in cells and tissues treated as Distributed Computing Systems. I will illustrate this in two vignettes, both dealing with strategies of optimal local control to achieve global tasks.

First, I will talk about information decoding in tissues, and what are the optimal cellular strategies that ensure accurate inference of spatial position of cells during Morphogenesis. Second, I will discuss information encoding in cells in the context of synthesis of a complex molecular (Glycan) code in the Golgi cisternae, and addressing what are the optimal cellular strategies and operational logic that ensure this. I will end with a discussion of the geometry of these high dimensional inference landscapes with implications for dimensional reduction, redundancy and robustness in overparameterized biological networks.