Introduction

In this chapter we will be using a different meaning of model than is used in the rest of this book. Here we discuss the use of computational models of mitochondrial DNA (mtDNA) applied to aging research. Computational models have unique advantages for investigating the aging process. With a simulation we can follow the changes over a full human lifetime in a single cell, or in a group of dividing cells. We can do this nondestructively, and we can do this down to the level of single DNA molecules or even to the level of individual genes on each DNA molecule. The computational model should be thought of as a way to determine the consequences of explicit assumptions and hypotheses. Given the long time scales involved in human aging, and the important role of random processes in mtDNA maintenance and mutation, these consequences are often nonintuitive. The primary use of our simulations has not been to calculate exact numerical values. Instead, we are more concerned with understanding the qualitative behavior of the system. Based on this increased understanding of the simulated system we can propose new hypotheses, which can then be tested in the lab, completing the circle between theory and experiment that is fundamental to the scientific method.

Our purpose in this chapter is to discuss the different simulation methods that we have used in our work on mtDNA dynamics (Chinnery and Samuels, 1999; Elson et al., 2001; Chinnery et al., 2002; Capps et al., 2003). We will discuss two general methods of modeling: DNA-molecule level simulations and cell level simulations. Each method has its strengths and weaknesses, and each is suitable for investigating different types of questions about the dynamics of mtDNA.

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