title: Steady state statistical correlations predict bistability in reaction motifs.
abstract: Various cellular decision making processes are regulated by bistable switches that take graded input signals and convert them to binary all-or-none responses. Traditionally, a bistable switch generated by a positive feedback loop is characterized either by a hysteretic signal response curve with two distinct signaling thresholds or by characterizing the bimodality of the response distribution in the bistable region. To identify the intrinsic bistability of a feedback regulated network, here we propose that bistability can be determined by correlating higher order moments and cumulants (≥2) of the joint steady state distributions of two components connected in a positive feedback loop. We performed stochastic simulations of four feedback regulated models with intrinsic bistability and we show that for a bistable switch with variation of the signal dose, the steady state variance vs. covariance adopts a signatory cusp-shaped curve. Further, we find that the (n + 1)th order cross-cumulant vs. nth order cross-cumulant adopts a closed loop structure for at least n = 3. We also propose that our method is capable of identifying systems without intrinsic bistability even though the system may show bimodality in the marginal response distribution. The proposed method can be used to analyze single cell protein data measured at steady state from experiments such as flow cytometry.
authors.affiliation: School of Chemistry, University of Hyderabad, Central University P.O., Hyderabad, 500046, Telangana, India. dbariksc@uohyd.ac.in.
authors.forename: Suchana
authors.initials: S
authors.lastname: Chakravarty
authors.affiliation: School of Chemistry, University of Hyderabad, Central University P.O., Hyderabad, 500046, Telangana, India. dbariksc@uohyd.ac.in.
authors.forename: Debashis
authors.identifier: http://orcid.org/0000-0003-1681-1273
authors.initials: D
authors.lastname: Barik
mesh_terms: D000465:Algorithms; D002468:Cell Physiological Phenomena; D025461:Feedback, Physiological; D008954:Models, Biological; D015233:Models, Statistical; D015398:Signal Transduction