Cellular evolution underlies cancer initiation and metastasis as well as the emergence of drug resistance in microbial and cancer cell populations. To tackle these problems, we must understand the underlying cellular evolutionary mechanisms. This requires the development of predictive quantitative models that can be experimentally validated. Unfortunately, the multiscale complexity of cellular evolution poses challenges that have been difficult to overcome. We propose that synthetic biology may offer ways to address this challenge, enabling the creation of model systems whose evolutionary behavior becomes computationally predictable. To illustrate these ideas, I will focus on synthetic gene circuits integrated into the genomes of yeast and cancer cells. Such model systems should deepen our understanding of eukaryotic cellular evolution, and someday may even enable us to control the evolution of cancer cells and infectious microbes, opening new ways to treat diseases more efficiently.
Back to Regulatory and Epigenetic Stochasticity in Development and Disease