Spatial Organization of Metabolic and Signalling Systems

Abstract

Single cells utilize spatial organization through different mechanisms: localizing proteins in compartments, in subdomains in the compartments, or by creating concentration gradients. It generates an intricate web of connections in time and space, allowing living creatures to adapt to different environments. Understanding the effect of spatial organization requires a systematic assessment of the interplay of pathways and spatial organization. In this work we analyze both metabolic and signalling systems, starting with their constituent building blocks. A systematic assessment of spatial localization and compartmentalization with its impact on metabolic networks was evaluated. (i) The effect of localization of enzymes and substrates on a variety of metabolic motifs was assessed. (ii) The impact of spatial localization on standard metabolic control coefficients was evaluated and complemented with the inclusion of newly defined spatial control coefficients. (iii) The use of localization for rewiring metabolic networks and the unraveling of various trade-offs therein. (iv) The effect of localization in a concrete exemplar case was evaluated. Then for signalling systems, both enzymatic cascades and substrate modification were evaluated by studying compartmental systems and examining the effect of transport in the spatially. Basic motifs and network motifs were evaluated, while studying the effect of distribution in multiple compartments. Lastly, spatial feedbacks, wherein species either activated or inhibited their transport were defined and evaluated. Our results provide a number of new insights. Common recurring conclusions include the fact that spatial organization can rewire/alter reactions and networks, open up trade-offs depending on the choice of compartmentalized entities, and even generate non-trivial dynamic behaviours. All of this has shown that considering the spatial aspect adds an interesting and intricate twist to the behaviour of biological systems providing a powerful new way to engineer them. These insights can be utilized to optimize, design and engineer robust systems for clear objectives.