title: Loads bias genetic and signaling switches in synthetic and natural systems.
abstract: Biological protein interactions networks such as signal transduction or gene transcription networks are often treated as modular, allowing motifs to be analyzed in isolation from the rest of the network. Modularity is also a key assumption in synthetic biology, where it is similarly expected that when network motifs are combined together, they do not lose their essential characteristics. However, the interactions that a network module has with downstream elements change the dynamical equations describing the upstream module and thus may change the dynamic and static properties of the upstream circuit even without explicit feedback. In this work we analyze the behavior of a ubiquitous motif in gene transcription and signal transduction circuits: the switch. We show that adding an additional downstream component to the simple genetic toggle switch changes its dynamical properties by changing the underlying potential energy landscape, and skewing it in favor of the unloaded side, and in some situations adding loads to the genetic switch can also abrogate bistable behavior. We find that an additional positive feedback motif found in naturally occurring toggle switches could tune the potential energy landscape in a desirable manner. We also analyze autocatalytic signal transduction switches and show that a ubiquitous positive feedback switch can lose its switch-like properties when connected to a downstream load. Our analysis underscores the necessity of incorporating the effects of downstream components when understanding the physics of biochemical network motifs, and raises the question as to how these effects are managed in real biological systems. This analysis is particularly important when scaling synthetic networks to more complex organisms.
authors.affiliation: School of Biomedical Engineering, Colorado State University, Fort Collins, Colorado, United States of America.
authors.forename: Samanthe M
authors.initials: SM
authors.lastname: Lyons
authors.affiliation: Chemical and Biological Engineering Department, Colorado State University, Fort Collins, Colorado, United States of America.
authors.forename: Wenlong
authors.initials: W
authors.lastname: Xu
authors.affiliation: Department of Biology, Colorado State University, Fort Collins, Colorado, United States of America.
authors.forename: June
authors.initials: J
authors.lastname: Medford
authors.affiliation: School of Biomedical Engineering, Colorado State University, Fort Collins, Colorado, United States of America; Chemical and Biological Engineering Department, Colorado State University, Fort Collins, Colorado, United States of America.
authors.forename: Ashok
authors.initials: A
authors.lastname: Prasad
mesh_terms: D000465:Algorithms; D000818:Animals; D001426:Bacterial Proteins / Q000378:metabolism; D003198:Computer Simulation; D025461:Feedback, Physiological; D053263:Gene Regulatory Networks; D006801:Humans; D008957:Models, Genetic; D011336:Probability; D015398:Signal Transduction / Q000235:genetics*; D013269:Stochastic Processes; D058615:Synthetic Biology; D018631:ras Proteins / Q000378:metabolism
chemical_list: D001426:Bacterial Proteins; D018631:ras Proteins