Statistical physics of information processing by cells
MetadataShow full item record
This thesis provides a physics account of the ability of cells to integrate environmental information to make complex decisions, a process commonly known as signaling. It strives to address the following questions: (i) How do cells relate the state of the environment (e.g. presence/absence of specific molecules) to a desired response such as gene expression? (ii) How can cells robustly transfer information? (iii) Is there a biophysical limit to a cells' ability to process information? (iv) Can we use the answers to the above questions to formulate biophysical principles that inform us about the evolution of signaling? Throughout, I borrow techniques from non-equilibrium statistical physics, statistical learning theory, information theory and information geometry to construct biophysical models capable of making quantitative experimental predictions. Finally, I address the connection of energy expenditure and biological efficiency by zeroing in on a process unique to eukaryotic cells-- nuclear transport. The thesis concludes with a discussion of our theory and its implications for synthetic biology.
RightsAttribution-NonCommercial-NoDerivatives 4.0 International