Data-driven modeling to infer the function of viral replication in a counting-based decision

Cells use gene regulatory networks, sets of genes connected through a web of biochemical interactions, to select a developmental pathway based on signals from their environment. These processes, called cell-fate decisions, are ubiquitous in biology. Yet efforts to study cell-fate decisions are often stymied by the inherent complexity of organisms. Simple model systems provide attractive alternative platforms to study cell-fate decisions and gain insights which may be broadly applicable. Infection of E. coli by the virus lambda is one such model system. The outcome of this viral infection is dependent on the number of initially coinfecting viruses (multiplicity of infection, or MOI), which the viral regulatory network appears to ‘count’. Yet precisely how the viral regulatory network responds to MOI is still unclear, as is how the system is able to achieve sensitivity to MOI despite viral replication, which quickly obfuscates initial viral copy number. In this thesis, I used mathematical modeling of the network dynamics, calibrated by experimental measurements of viral replication and gene expression during infection, to demonstrate how the network responds to MOI and to show that viral replication actually facilitates, rather than hinders, a counting-based decision. This work provides an example of how complex behaviors can emerge from the interplay between gene/network copy number and gene expression, whose coupling cannot be ignored in developing a predictive description of cellular decision-making