Scientists often talk about the value of intuition in guiding them toward the big questions. But when it comes to figuring
out how all the pieces of a complex system fit togetherwith hordes of uniquely behaving components interacting in nonlinear pathwayssystems biologists prefer to rely on computers. Theoretical analysis has increasingly been applied to understanding protein, gene, and biochemical networks in cells. Most cell network models either generate dynamic,
quantitative descriptions (specifying the timing and kinetics of interactions) of well-defined pathways but use relatively few components, or present static maps of proteinDNA or proteinprotein interactions that cover the entire genome but lack quantitative and dynamic information.
In a new study, Song Li, Sarah M. Assmann, and Rka Albert create a dynamic model, based on a wide range of
experimental observations that describes a complex signaling network in plants that is initiated by a plant hormone called abscisic acid (ABA). They circumvent a lack of quantitative data, especially concerning interaction strengths and reaction rates, of components in ABA signaling by using a computational technique that implicitly incorporates a range of possible quantitative parameters. Their model describes the regulation of over 40 network components, demonstrates the networks response to a range of perturbations through simulations, and makes novel, testable predictions about the sensitivity of the signaling pathway.
Plant growth and survival depend on the regulation of stomatal pores, which allow both carbon dioxide uptake for photosynthesis and water release through evapo-transpiration. Signaling pathways triggered by ABA (a stress hormone secreted by the roots and synthesized by guard cells surrounding the pores), inhibit stomatal opening and promote closure, allowing the plant to conserve water during drought. ABA signaling triggers changes in cytosolic calcium through intermediate messengers that regulate the release of calcium from internal stores or the import of extracellular calcium; it also triggers the increase of cytosolic pH, and modulates a number of enzymes and cellular metabolites. As a result, membrane-localized ion channels open, releasing potassium ions and the negatively charged chloride and malate ions, leading to stomatal closure.
To shed light on ABA signaling dynamics, Li et al. synthesized published experimental data, mostly from Arabidopsis thaliana studies, about the components and processes of ABA signaling into a theoretical network. Experimental evidence described either direct interactions between components (from biochemical data on enzymatic activity or proteinprotein interactions) or inferences about pathway activity (from genetic mutations or pharmacological interventions). These annotated components formed nodes (and intermediate nodes, representing unknown mediators) in the network, and the annotated processes provided the basis for writing algorithms describing possible interactions between the components and constructing the paths of the network.
Li et al. determined all the paths that nodes participated in and simulated experimental perturbations to individual nodes to predict how the network structure responds to such disturbances. They found several independent paths between the ABA input and the stomatal closure output. For example, the path involving pH-induced anion efflux does not overlap with paths regulating calcium levels, which can in turn be elevated through several independent paths. This independence, the researchers argue, indicates a remarkable topological resilience in which functionally redundant paths maintain ABA sensitivity in the face of disruptions in other pathways.
The path analysis describes routes from input to output, but cant reveal synergism between nonoverlapping paths. To do this, Li et al. used a dynamic modeling technique based on binary (active/on or inactive/off) assumption