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Modeling a Class of Naturally Occurring Mechanisms for Use in Synthetic Biology

by BURCICA, CRISTINA IRINA

Abstract (Summary)
In recent years technology has made possible more complex and CPU intensive simulations in the area of biological modeling and biochemical kinetic modeling. Biological systems are stochastic and function in highly noisy surroundings. For a simple system, with a low number of molecules, fluctuations can be large and can play an enormous role in system decisions. Many regulatory proteins and RNA molecules are present in very small numbers, typically on the order of 10 to 100 molecules. But almost all simulation systems are ultimately based on solving either ordinary differential equations (ODEs), partial differential equations (PDEs) or stochastic differential equations (SDEs). Using an ODE approach to model a biological pathway is unrealistic, because spatial phenomena must be considered, discrete events (binding, switching) and non-continuous variables (low copy numbers) must be modeled, and important parameters may be found to be indeterminate. In addition, since proteins and RNA are often present in the cell in small quantities, certain reactions are subject to large statistical fluctuation. Differential equation (DE) methods don?uro;™t easily capture stochasticity or noise (common in biology). The reality is that cellular behavior is not deterministic and the DE approach is not accurate. This thesis discusses an approach involving the chemical kinetics of spatially homogenous systems that is complete and attentive to detail such as the stochastic kinetics formulation (e.g. Gillespie algorithm) applied to a series of synthetic systems. Our knowledge of how synthetic biological systems can be designed would benefit from biophysically realistic models that can make accurate predictions on the time-evolution of molecular events given arbitrary arrangements of genetic regulatory elements. Building blocks for constructing intracellular logic circuits are useful in achieving circuits of significant complexity. Modularization of a GRN (genetic regulatory network) can help us understand the system's complexity and its behavior. This approach is similar to the method that electrical engineers use to understand systems, working their way up from properties of resistors, capacitors and diodes, to simple circuits and finally complex devices. The modular approach facilitates the use of parallel and distributed computing methods to control aspects of cell function and design protocols. This work is focused on constructing models for gene expression from the example of bacteriophage lambda infection of an E. coli bacterium. The lambda-gene expression is a particularly well suited model system because knowledge of how the phage functions is thought to be relatively complete. This thesis addresses two issues in particular. First, we can discuss the deficiencies in the past simulations and measurements of bacteriophage lambda and improve models of gene expression by including noise and stochastic effects. For instance, protein burst size (the average number of proteins synthesized per mRNA transcript) is stochastic. Second, it is possible to design synthetic modules from biological regulatory systems that are easier to understand and that incrementally model the architectures of these systems.
Bibliographical Information:

Advisor:

School:University of Cincinnati

School Location:USA - Ohio

Source Type:Master's Thesis

Keywords:bacteriophage lambda e coli synthetic biology

ISBN:

Date of Publication:01/01/2008

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