Before revision:  We formulate strain identification as an inverse problem that aims at simultaneously estimating a binary matrix (encoding presence or absence of mutations  ) and a real-valued vector (representing the mixture of strains) such that their product is approximately equal to the measured data vector.
After revision:  We formulate strain identification as an inverse problem that aims at simultaneously estimating a binary matrix (encoding presence or absence of mutations in each strain ) and a real-valued vector (representing the mixture of strains) such that their product is approximately equal to the measured data vector.