| The sequencing of the human genome revealed that the number of genes does not explain why humans are different from other organisms like mice and dogs. Instead, it is how genes interact with each other and the environment that separates us from other organisms. This motivates the study of genetic networks and, consequently, my research. My work delves into the roles that simple genetic networks play in a cell and explores the biotechnological aspects of how to uncover such genes and their interactions in experimental models.;Next, I explore the genetic underpinnings of two canine models of atrial fibrillation: atrial tachypacing and ventricular tachypacing. Using Affymetrix microarrays, I find that the genetic signatures of these two models are significantly different both in magnitude and in class of genes expressed. The ventricular tachypacing model has thousands of transcripts differentially expressed with little overlap between 24 hours and 2 weeks, suggesting independent mechanisms. The atrial tachypacing model demonstrates an adaptation as the number of genes found changed decreases with increasing time to the point that no genes are changed at 6 weeks. I use higher level analysis to find that extracellular matrix components are among the most changed in ventricular tachypacing and that genes like connective tissue growth factor may be responsible.;Finally, I generalize the main problem of microarray analysis into an evaluation problem of choosing between two competing options based on the scores of many independent judges. In this context, I rediscover the voting paradox and compare two different solutions to this problem: the sum rule and the majority rule. I find that the accuracy of a decision depends on the distribution of the judges' scores. Narrow distributions are better solved with a sum rule, while broad distributions prefer a majority rule. This finding motivates a new algorithm for microarray analysis which outperforms popular existing algorithms on a sample data set and the canine data set examined earlier. A cost analysis reveals that the optimal number of judges depends on the ratio of the cost of a wrong decision to the cost of a judge.;Cells must respond to the extracellular environment to contract, migrate, and live. Cells, however, are subject to stochastic fluctuations in protein concentrations. I investigate how cells make important decisions such as gene transcription based on noisy measurements of the extracellular environment. I propose that genetic networks perform Bayesian inference as a way to consider the probabilistic nature of these measurements and make the best decision. With mathematical models, I show that allosteric repressors and activators can correctly infer the state of the environment despite fluctuating concentrations of molecules. Viewing transcriptional networks as inference modules explains previous experimental data. I also discover that the particular inference problem determines whether repressors or activators are better. |
