Synthetic biology is currently used to develop advanced medical treatments using engineered bacterial consortia. Bacteria are able to exquisitely fine-tune their growth rate to their environment, responding efficiently to sudden environmental changes. Abundance of ribosomes in a cell correlates tightly with the growth rate, and growth rate is controlled by the abundance of ribosomes that translate mRNA into protein. This biopolymerization process is influenced by an interconnected web of both direct and indirect feedbacks that depend on both transcription of genes and translation of ribosomal mRNA. Transcription and translation rates play a key role in response time of the microbe to changing conditions. This research seeks to develop a comprehensive mathematical model to describe ribosome assembly and abundance control in bacteria. The project emphasizes the integration of a broad knowledge of molecular biology, principles of physics, mathematical modeling and sensitivity analysis of a complex biological system. Both undergraduate and graduate students will be trained to use tools from mathematics and physics within the broad field of systems biology, thereby contributing mathematicians with interdisciplinary skills towards the national goal of STEM workforce development.
A new continuum model will have the flexibility to describe three biopolymerization processes: transcription of ribosomal RNA, transcription of mRNA of ribosomal proteins and the translation of ribosomal proteins. The model for each process is referred to as an individual compartment within the full assembly model. The prototypical compartment is a generalization of vehicular motion models, and it provides a mechanism for including inflow and outflow phenomena as well as torque-assisted transcription when appropriate. The full ribosome assembly model is described by a system of ordinary differential equations whose variables are related to the outputs of the individual compartments. The full model also includes a set of feedbacks that regulate initiation and translocation rates governing the dynamics occurring within each compartment. Ribosome production rate is the key determinant of bacterial growth rate, and it depends on several fundamental quantities including the response time after a nutritional up-shift or down-shift, the combined time of transcription and translation, and the time to produce a ribosome. Several system parameters exhibit inherent uncertainties due to the stochastic nature of availability of various enzymes. The project includes a sensitivity analysis effort to quantify effects of the most crucial system parameters on each fundamental quantity. Examining these effects and their variability allows one to determine the average community response of bacteria to its environment. The variability in microbial community response to antibiotic application is often an important determinant of antibiotic effectiveness.
This award is funded jointly by the MPS Division of Mathematical Sciences (DMS) through the Mathematical Biology Program and BIO/MCB through the Program of Genetic Mechanisms.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
