The inverse correlation between robustness and sensitivity to autoregulation in two-component systems.

Area of Science:

• Microbiology
• Systems Biology
• Biochemistry

Background:

• Two-component systems (TCS) are crucial signal transduction pathways in bacteria and other organisms.
• They involve a histidine kinase and a response regulator to relay environmental signals to genetic components.
• Positive autoregulation is a common mechanism where response regulators modulate their own gene expression.

Purpose of the Study:

• To formally model the relationship between robustness and sensitivity to autoregulation in TCS.
• To investigate how TCS evolve to balance these two properties.
• To understand the trade-offs influencing TCS regulatory dynamics.

Main Methods:

• Development of a formal mathematical model for TCS.
• Analysis of the inverse correlation between robustness and sensitivity to autoregulation.
• Comparison of model predictions with existing experimental data.

Main Results:

• TCS operate on a spectrum with an inverse correlation between robustness and sensitivity to autoregulation.
• Loss of robustness, such as through histidine kinase dephosphorylation, enables positive autoregulation for wider response ranges and scaled amplification.
• Strictly robust TCS exhibit diminished amplification by autoregulation.

Conclusions:

• TCS exhibit a trade-off between robustness and positive autoregulation during evolution.
• This trade-off allows TCS to achieve a balance between precise signal transduction and adaptable response ranges.
• The findings provide insights into the design principles of biological regulatory networks.