Botrytis cinerea has long been recognized as a highly diverse pathogen with natural variation modulating an extreme range of phenotypes. We hypothesize that this genetic polymorphism is central to being a generalist pathogen. We are combining modern genomic analysis with natural variation within Botrytis to identify how the pathogen variation interacts with plant genetic variation from Arabidopsis thaliana and tomato. Conversely, we are focusing on the use of quantitative systems biology to identify networks within the plant that determine generalist pathogen resistance. This is beginning to highlight potential fundamental mechanisms influencing broad host resistance.Part of our interest is to understand how natural variation in gene expression controls differences between individuals within a species. We have been investigating the relationship between natural variation in gene expression and variation in pathogen resistance within Arabidopsis. We are investigating quantitative trait loci that control differential resistance to Botrytis isolates. This shows that we can link global variation in plant defense gene and defense metabolite expression with altered Botrytis virulence. We have extended this analysis into infecting defined Arabidopsis signal transduction mutants with diverse Botrytis isolates. We will present evidence that jasmonic acid signaling is only absolutely required for response to and resistance to a subset of Botrytis genotypes. Understanding what is conserved and variable within a generalist pathogen helps to identify mechanistic targets that will not be easily overcome by standing genetic variation in the pathogen.One complexity to systems biology is the concept that only information from genomics experiments done in the presence of the pathogen will be informative. Given the level of diversity within these pathogens this supposition would create massively expensive experiments. We have begun testing the concept that systems biological approaches to transcriptomic and metabolomic datasets can identify predictive signatures of generalist resistance mechanism in the absence of the pathogen itself. We will present evidence that we can identify genetic mechanisms controlling resistance to pathogens without conducting the expensive systems biology experiments in the presence of a specific pathogen.