Negative feedback control is essential to make biological systems stable to internal and external perturbations, just as homes need thermostats to maintain a fixed room temperature. In cells feedback is used to regulate everything from gene expression to chromosome replication, and its failure causes a range of human disorders. But our understanding of feedback in biology is very incomplete. Most theoretical approaches use mathematical frameworks that are poorly suited to describe cells because they ignore a main source of perturbations: chemical reactions involve molecules in low numbers and since each individual reaction is probabilistic, fluctuations in concentrations arise spontaneously. Unlike human-designed systems, where the control systems (e.g. thermostats) are fundamentally different from what they control (temperature fluctuations), the control systems in cells are similar to the processes they control. This makes it very difficult for cells to suppress perturbations, and also makes it very difficult for researchers to analyze the control system design. Experimentally, very few systems allow both accurate measurements of concentrations in single cells and systematic modifications of the control system to analyze how they affect the system. Analyses can also focus so closely on the specific details of one specific system that general guiding principles are overlooked or misinterpreted. We propose to address these problems by developing new mathematical approaches and systematically applying our novel experimental assays to simple model systems, bacterial plasmids. Our preliminary theory demonstrates hard limits on the ability of negative feedback to suppress fluctuations in cells. It also suggests creative and counter-intuitive mechanisms that minimize these problems. Remarkably enough, we have found examples of these in plasmid gene clusters that we know are under strong selection to suppress noise. In addition to the usefulness of these systems to understand feedback control, bacterial plasmids are also very relevant medically, since they cause the majority of drug resistance cases in hospitals, a problem that leads to millions of serious illnesses and tens of thousands of deaths annually in the US alone. They are also key tools for the biotechnology industry, where the fluctuation suppression properties we study are a significant nuisance. The unmatched experimental tractability of plasmids allows us to systematically vary control properties and rigorously test the mathematical descriptions experimentally, leading to a deeper understanding of feedback mechanisms and also an increase in useful knowledge about plasmid behavior.
Drug resistance in bacteria poses a great threat to human health and is mostly spread by gene clusters called plasmids, which maintain themselves in cells using negative feedback control that up-regulates the plasmid level when it drops to low values. Feedback control is also common in the human body where compromised control causes a whole range of human disorders due to an inability to maintain constant conditions. In this grant we develop mathematical theory and experimental methods to study the principles of feedback control, primarily as illustrated by plasmids.
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