POLINFO SIGNED

Project "POLINFO" data sheet

The following table provides information about the project.

 Partnership

Take a look of project's partnership.

Map

 Project objective

Data acquisition and mining is currently the principal driving force of technological advances. Massive expansion of the field requires implementation of new solutions in data storing and retrieving, where targeted solutions need not only be resource-sustainable and recyclable, but also have to posses high information-encoding density to decrease space allocated for storage. Polymers represent natural candidates for the task, as nucleic acids already efficiently encode for vast biological complexity. However, the synthesis of long sequence-ordered polymers in vitro has proven to be challenging and remains an open question. Our project aims to address whether conditions of spontaneous sequence ordering can be achieved under non-equilibrium conditions. To that extent, we build a computational model that bridges a coarse-grained representation of polymer with stochastic kinetics modelling of polymerization and depolymerization reaction. The model is a novel approach to exploring information encoding to polymers, as it is not limited by master equation solution feasibility, and allows, for the first time, to interpret the informational content of the generated sequence through its effect on polymer structure. We explore novel theoretical ideas on non-equilibrium systems that have recently emerged, suggesting that spontaneous ordering of systems takes the path of maximum heat dissipation, which we plan to test for the first time in the context of information generation. In addition, we aim to understand the timescales pertinent to sequence ordering under non-equilibrium conditions, where specifically the concentration number of monomer types is varied in time. Finally, we wish to understand the conditions which allow for creation of autocatalytic self-replicative sequence sets, with emphasis on timescales relevant to this process. Overall, we aim to identify timescales pertinent to generation of ordered sequences, which could pave way to new experimental strategies.