Plant Synthetic Biology: Quantifying the Known Unknowns and Discovering the Unknown Unknowns

Abstract

Our knowledge of plant biology has reached the point where we can begin to rationally engineer plant form and function to meet our needs. From a bioengineer’s or synthetic biologist’s point of view, the goal of studying developmental biology is to generate a predictive model that specifies the molecular circuitry required to move a cell from one state to another. This model could then serve as a guide for harvesting the most useful parts and logic to enable the engineering of novel states and multi-cell behaviors. Among the most critical parts to understand from this perspective are the signaling molecules that enable intra- and intercellular communication. Several biosensors have been developed in recent years to detect plant-specific signals and secondary messengers. Many other general biosensors have been successfully implemented in plant systems. These biosensors, in combination with single cell ‘omics techniques and predictive statistical frameworks, are providing the type of high resolution, quantitative descriptions of cell state that will ultimately make it possible to decode and re-engineer traits associated with higher yields and stress tolerance. Being a plant developmental biologist today can feel like a lot like being a cryptographer piecing together fragmented messages with only a partial knowledge of the cipher. Biological signaling is rife with redundancy, feedback, and feedforward motifs acting to dampen or amplify each signal, and modulate outputs depending on position and cell identity. To crack the code of these complex genetic signal processors, it is important to be able to measure, as well as manipulate, both signals and responses. Recent advances in synthetic biology have provided a means to access such tools. Sensitive, genetically encoded reporters (biosensors), in combination with emerging single-cell transcriptomics approaches, are providing increasingly detailed molecular descriptions of cells undergoing developmental transitions (Moreno-Risueno et al., 2015; Efroni et al., 2016; Ristova et al., 2016; Cao et al., 2017). However, in many cases we are still unable to measure key signaling molecules directly with fine spatiotemporal resolution. Several excellent reviews have been published recently that describe the application of biosensors to plant systems (Goold et al., 2018; Hilleary et al., 2018; Walia et al., 2018). Here, we review the current state of the art in measuring plant signaling, using principles and tools borrowed from and inspired by engineering, as well as efforts to use this knowledge to enable 3 rapid, rational re-engineering of plant development. We have arranged this review as an engineering cycle in which we will cover (i) “Designing” biosensors, (ii) “Building” biosensors, including technologies to facilitate the use of biosensors in plants, (iii) “Testing” biosensors and (iv) “Modeling” signaling and development, including our perspective on integrating biosensors, systems approaches and optimal experimental design to generate minimal predictive models of plant development.