title: Moment-based inference predicts bimodality in transient gene expression.
abstract: Recent computational studies indicate that the molecular noise of a cellular process may be a rich source of information about process dynamics and parameters. However, accessing this source requires stochastic models that are usually difficult to analyze. Therefore, parameter estimation for stochastic systems using distribution measurements, as provided for instance by flow cytometry, currently remains limited to very small and simple systems. Here we propose a new method that makes use of low-order moments of the measured distribution and thereby keeps the essential parts of the provided information, while still staying applicable to systems of realistic size. We demonstrate how cell-to-cell variability can be incorporated into the analysis obviating the need for the ubiquitous assumption that the measurements stem from a homogeneous cell population. We demonstrate the method for a simple example of gene expression using synthetic data generated by stochastic simulation. Subsequently, we use time-lapsed flow cytometry data for the osmo-stress induced transcriptional response in budding yeast to calibrate a stochastic model, which is then used as a basis for predictions. Our results show that measurements of the mean and the variance can be enough to determine the model parameters, even if the measured distributions are not well-characterized by low-order moments only--e.g., if they are bimodal.
authors.affiliation: Automatic Control Laboratory, ETH Zurich, CH-8092 Zurich, Switzerland.
authors.forename: Christoph
authors.initials: C
authors.lastname: Zechner
authors.forename: Jakob
authors.initials: J
authors.lastname: Ruess
authors.forename: Peter
authors.initials: P
authors.lastname: Krenn
authors.forename: Serge
authors.initials: S
authors.lastname: Pelet
authors.forename: Matthias
authors.initials: M
authors.lastname: Peter
authors.forename: John
authors.initials: J
authors.lastname: Lygeros
authors.forename: Heinz
authors.initials: H
authors.lastname: Koeppl
mesh_terms: D003198:Computer Simulation; D005434:Flow Cytometry; D015966:Gene Expression Regulation, Fungal / Q000502:physiology*; D005990:Glycerol / Q000378:metabolism*; D020928:Mitogen-Activated Protein Kinases / Q000235:genetics; D008957:Models, Genetic; D012441:Saccharomyces cerevisiae / Q000235:genetics*; D029701:Saccharomyces cerevisiae Proteins / Q000235:genetics; D015398:Signal Transduction / Q000235:genetics; D013269:Stochastic Processes; D013312:Stress, Physiological / Q000235:genetics*; D014882:Water-Electrolyte Balance / Q000235:genetics*
chemical_list: D029701:Saccharomyces cerevisiae Proteins; C088589:HOG1 protein, S cerevisiae; D020928:Mitogen-Activated Protein Kinases; D005990:Glycerol