Plant Biosystems Design Research Roadmap 1.0

Abstract

Human life intimately depends on plants for food, biomaterials, health, energy, and a sustainable environment. Various plants have been genetically improved mostly through breeding, along with limited modification via genetic engineering, yet they are still not able to meet the ever-increasing needs, in terms of both quantity and quality, resulting from the rapid increase in world population and expected standards of living. A step change that may address these challenges would be to expand the potential of plants using biosystems design approaches. This represents a shift in plant science research from relatively simple trial-and-error approaches to innovative strategies based on predictive models of biological systems. Plant biosystems design seeks to accelerate plant genetic improvement using genome editing and genetic circuit engineering or create novel plant systems through de novo synthesis of plant genomes. From this perspective, we present a comprehensive roadmap of plant biosystems design covering theories, principles, and technical methods, along with potential applications in basic and applied plant biology research. We highlight current challenges, future opportunities, and research priorities, along with a framework for international collaboration, towards rapid advancement of this emerging interdisciplinary area of research. Finally, we discuss the importance of social responsibility in utilizing plant biosystems design and suggest strategies for improving public perception, trust, and acceptance.

Figure 1

The scope of plant biosystems design research. Plant biosystems design is an interdisciplinary research field integrating plant systems biology, engineering, chemistry, computer science, bioinformatics, artificial intelligence, physics, and mathematics to redesign natural plant systems and construct new plant systems in a predictable and programmable manner. Plant biosystems design research covers four aspects: theories and principles, methods and toolboxes, applications, and social responsibilities. Some icon made by Pixel perfect from http://www.flaticon.com/ and http://www.pngtree.com/.

Figure 2

The network control theory of plant biosystems design. (a) Dynamic networks of genes and metabolites distributed in spatial (e.g., cell and tissue) and temporal (e.g., cell cycle, circadian time, season, developmental stage, and life cycle) dimensions. (b) A plant gene-metabolite network; arrow-shaped edges represent activation; blunt edges represent inhibition, and edges ending with a solid circle indicate enzymatic catalysis; adapted from Gonçalves et al. [370]. (c) The structure of regulatory/signaling network motifs; arrows indicate positive regulation; T-shape arrows indicate negative regulation; adapted from Gupta and Singh [20].

Figure 3

Mechanistic modeling for plant biosystems design. (a) General steps in reconstructing a genome-scale metabolic network. (b) Mass balance of metabolites in a metabolic network. Equation 1 is a system of ordinary differential equations describing dynamic chemical transformation of metabolites in a metabolic network where $C$ is a metabolite concentration vector, $S$ is a stoichiometric matrix, $r$ is a reaction flux vector, and $\mu$ is the cell growth. (c) Calculation of steady-state flux distributions. Three common methods can be employed to determine metabolic flux distributions including metabolic flux analysis (MFA), flux balance analysis (FBA), and elementary mode analysis (EMA). For a typical metabolic network, a system of homogenous equations [2] is highly underdetermined, resulting in an infinite solution space. MFA determines a physiological state of a cell under a defined condition by calculating $r_u$ based on experimentally measured fluxes $r_m$ that make [2] being determined. Here, $r=\left[r_u{r}_m\right]$, and $S=\left[S_u{S}_m\right]$. FBA also determines a physiological state of a cell by implementing a cellular objective function subject to (s.t.) mass balance and flux bounds. Different from MFA and FBA, EMA unbiasedly seeks to identify all finite admissible fluxes in the solution space by imposing the thermodynamic constraints of reaction direction and pathway nondecomposability. Adapted from [30, 34, 371].

Figure 4

The principles of plant biosystems design. (a) Modularity; M1 and M2 represent two different modules; F1 and F2 represent the processes converting inputs S1 and S2 to outputs O1 and O2, respectively; M1 and M2 are connected, with $\text{O}2=\text{S}1$; redrawn from Grunberg and Del Vecchio [372]. (b) Dynamic programming as exemplified by the expression of regulators (suppressors or activators) programmed in the sequential developmental stages from a vegetative meristem to a floral meristem; redrawn from Kaufmann et al. [56]. (c) Tradeoff between natural selection and artificial selection. (d) Genetic stability, as exemplified by the pathogen-associated molecular pattern- (PAMP-) triggered immunity signaling network, with the inhibitory loops within the network to provide buffering interference (i.e., loss-of-function of some network components releases associated inhibitory loops allowing other components of the network to compensate for the loss); redrawn from Tyler [373]. (e) Upgradability, as exemplified by marker-free systems, in which the selectable marker gene can be excised from the plant genome after transformation, to allow for unlimited rounds of genetic transformation.

Figure 5

Technical approaches for plant biosystems design. In general, plant biosystems design requires iterative cycles of design-build-test-learn. The number of cycles varies with the plant traits to be engineered. LIMS: laboratory information management system.

Figure 6

A biological parts registration and curation module for plant biosystems design. The registration form includes accession number, name, type, description, function, sequence (i.e., DNA and protein sequence), and references (e.g., publications associated with the biological parts). For illustration purpose, the functional items are listed for protein-encoding genes only. CDS: protein-encoding sequence; ncRNA: noncoding RNA sequence, including natural noncoding RNAs and guide RNAs for genome editing.

Figure 7

An automated gene construct design module for plant biosystems design. Redrawn from Nielsen et al. [115].

Figure 8

Strategies for DNA assembly across scales relevant for plant biosystems design. (a) Major platforms and key factors considered for executing large DNA assemblies across scales. (b) Detail of the CCTL method. crRNA sequence is from [123]; overhang is underlined with the last 4 nt being programmable [124]. ^¥Produce scarless assemblies (NEBuilder allows ssDNA oligos in substitution of homologous overlap).^¤May leave scars. ${}^{\ast }$Undesired type IIS restriction sites can be partially masked by oligos [122]. For BRAID systems, see [120]. ^§Hybrid system using golden-gate followed by in vivo homology-based assembly [129]. CX: complex design and execution; PL20: parts size/length beyond 20 kb; RS: repetitive sequences; SS: secondary structures; IRS: internal restriction sites. Recombination-based approaches (e.g., Gateway) were omitted due to limited use for biosystems design.

Figure 9

A computational learning module for plant biosystems design. (a) Integrative analysis of multiomics data; redrawn from Yugi et al. [374]. (b) Metabolic modeling; redrawn from Zampieri et al. [375].

Figure 10

Applications of biosystems design to basic plant biology research. (a) Elucidating plant gene function through genomic mutations created by using genome editing technologies. (b) Manipulation of gene expression using CRISPR interference (CRISPRi) and activation (CRISPRa) and synthetic promoters. (c) Studying the function of gene modules, signaling, or metabolic pathways using synthetic genetic circuits. (d) Understanding the complexity of plant systems using minimal or minimized plant genomes as well as exploring novel function using synthetic genomics.

Figure 11

Applications of biosystems design to applied plant science research. GHG: greenhouse gas. Only representative examples are shown.

Figure 12

Diagram showing concepts and examples of bio-based materials from plants. Interdisciplinary gradient connects biological, engineering, medical, physical, and material sciences. Bioproducts are the building blocks of bio-derived materials, which can be manufactured outside (chemically) or inside (biologically) an organism through biomanufacturing. Each class is color coded to its major constituents (organic, inorganic, and/or living cells) with examples in plants (dark green) and elsewhere (light gray). CNC: cellulose nanocrystals; 3HP: 3 hydroxy-propionic acid; PAA: polyacrylic acid; PHA: polyhydroxyalkanoate.