Life in cells is inherently stochastic due to low number effects that dominate biochemical processes on small scales. This stochasticity results in significant cell-to-cell variability across genetically identical populations, which shapes many important biological processes ranging from stem cell differentiation, to cancer treatment, to antibiotic resistance.

Using theoretical approaches we try to understand: how are stochastic fluctuations generated, transmitted, and eliminated in complex biochemical reactions networks? The second focus of our research is: how can we exploit naturally occurring stochastic fluctuations to probe cellular dynamics?

Answering these questions requires addressing a fundamental challenge in biology: cellular processes are complex but only sparsely characterized. This makes fluctuations in biology difficult to analyze reliably, because in the absence of general laws such as the Fluctuation-Dissipation-Theorem complex biological systems must be completely described to predict the dynamics of any single part. To overcome this challenge we utilize and develop generalized theoretical approaches to characterize entire classes of systems instead of solving individual models.

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