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Despite our expanding knowledge about the biochemistry of gene regulation involved in host–pathogen interactions, a quantitative understanding of this process at a transcriptional level is still limited. We devise and assess a computational framework that can address this question. This framework is founded on a mixture model-based likelihood, equipped with functionality to cluster genes per dynamic and functional changes of gene expression within an interconnected system composed of the host and pathogen. If genes from the host and pathogen are clustered in the same group due to a similar pattern of dynamic profiles, they are likely to be reciprocally co-evolving. If genes from the two organisms are clustered in different groups, this means that they experience strong host–pathogen interactions. The framework can test the rates of change for individual gene clusters during pathogenic infection and quantify their impacts on host–pathogen interactions. The framework was validated by a pathological study of poplar leaves infected by fungal  Marssonina brunnea  in which co-evolving and interactive genes that determine poplar–fungus interactions are identified. The new framework should find its wide application to studying host–pathogen interactions for any other interconnected systems.

A quantitative model of transcriptional differentiation driving host–pathogen interactions