UC San Diego

In different environments, bacteria are known to allocate their proteome differently and achieve different growth rates. Extensive quantitative studies on proteomic allocations in steady states have been done. Yet it’s still not clear how bacteria manage to adjust their proteome responding to different environments through thousands of reactions and eventually reach different growth rates. In order to gain better understanding on the adaptation strategy and mechanism, in my dissertation, I studied the kinetic behavior of E.coli during various environmental changes. Mathematical modeling were used to quantitatively capture the proteomic re-allocation and growth rate adaptation during transitions. Further studies on the kinetics of Guanosine tetraphosphate (ppGpp) and translational elongation rate during growth transition revealed the molecular basis of well-known ribosomal ‘growth law’. In Chapter 2 and 3, we studied the growth transitions from ‘rich media’, a condition with ample amino acids or/and other nutrient supplies. We started with the case of methionine depletion in Chapter 2. MetE were found to be the major bottleneck in met downshift. By adapting the flux-controlled regulatory model established for carbon shift, we quantitatively captured the relationship between pre-shift MetE reserves and lag time before growth recovery. In Chapter 3, we studied the proteomic allocation during all AAs depletion. Quantitative proteomic analysis revealed a linear relationship between the onset time of enzymes across AAs biosynthesis pathways and their fractional reserve in pre-shift condition, indicating a ‘as-needed’ gene expression strategy during all AAs downshift. Combining flux-controlled global regulation and pathway specific end-production inhibition, we successfully captured the proteome recovery kinetics using only data collected in the pre- and post-shift steady-states. Chapter 4 is focused on bacterial kinetic response to sub-lethal chloramphenicol (an antibiotic inhibiting translation) treatment. By using translational elongation rate as a flux sensor and the key signaling variable, we accurately predicted the kinetics of biomass accumulation and gene expressions. In Chapter 5, we show that translational elongation rate is inversely proportional to ppGpp, an essential molecule regulating translational machinery, during transient and steady-states. We established ppGpp rate-sensing strategy and thus closed a key regulatory circuit linking ppGpp, growth rate, ribosomal content and translational rate together.