Genomic Science and its Relation to Soil-Plant-Atmosphere Continuum

Genomic Science and its Relation to Soil-Plant-Atmosphere Continuum Studies S. M. Welch, J. L. Roe, M. B. Kirkham Kansas State University

Third in a Series of Talks • Next Generation Crop Growth Models: Physics, Genomics, Soil Characterization, and Computation – ASA Annual Meeting, Salt Lake City, 1999 • Modeling the Genetic Control of Flowering in Arabidopsis thaliana – ASA Annual Meeting, Minneapolis, 2000 • Genomic Science and its Relation to Soil-Plant. Atmosphere Continuum Studies – ASA Annual Meeting, Charlotte, 2001

A Network Conception of Plants. . . • Plants can be viewed as networks of parts in space that develop and grow with time… • These parts induce, constrain, and modulate a network of matter and energy flows… • Networks of genes manage the system either by direct action or through the establishment of physiological mechanisms Mass Energy fox 3 pdq 7

Resistance/Capacitance Models || Campbell, G. S. 1985. Soil physics with BASIC: Transport models for soil-plant systems. Elsevier.

But, one should be careful. . . • “However, in the case of a living plant, and all the more so in the case of a growing plant, we are in danger of gross oversimplifications. ” (Hillel, 1998) • “On the other hand, every biological organism, whatever its complexity, exists and operates within a physical setting requiring it to interact with its environment in obedience to physical principles. ” (Ibid. )

Network Mathematics || Network Flows Growth/Regulation Storage Capacity Development ABA Storage I/O Physical Eq’ns Biological Eq’ns

Some Basic Vocabulary Protein Synthesis m. RNA Example: Diurnal clock Protein Product General Metabolism Transcription Factors modulate reading DNA Double Helix RNA Polymerase Transcription

Multiple Gene Interactions Transcription Factor RNAP “A” Gene Codons Promoter Region Prot. Syn. DNA RNAP Promoter Region “B” Gene Codons DNA

“B” “A” Expression Rate Modeling Multiple Genes “B” on “B” off Time [A]

ABA Gene Network Some genes influence ABA biosysnthesis in response to exo- or endogenous stimuli while many others modulate plant response to ABA levels; Individual stimuli such as low water potential can alter both; Effects range from short-term physiology (closing stomates) to affecting growth patterns (through cell cycle control? ) to developmental effects (e. g. interaction between ABI 3 and the CO flowering-time gene). [CR, 00] Antagonistic Stimulatory

Relationship to Neural Networks Genetic Neural Networks • Multiple transcription factors (pos & neg w) • Chemical (A, B>0) Hopfield Neural Networks • Multiple neural inputs • Electrical (pos & neg A, B)

Temperature Effects

Modeling Temperature Effects

Time-Dependent Expression LD SD Data from Suarez-Lopez et al. Autoradiograph Radio-labeled complementary DNA RNA Size sorted RNA One sample track

From approximated models… q q (Dong, et al. , 2001) We have demonstrated the utility of genomic information in the prediction of flowering time; Complex interactions between temperature and photoperiod can be explained in terms of a network of nodes with various functions (oscillators, threshold devices, products, etc. ) Elaborate, difficult to parameterize, nonlinear models can, on occasion, be simply approximated; For the first time, specific genes are implicated as underpinning mathematical formalisms (photothermal days, degree-days, etc. ) commonly used to model floral transition times.

Transport in Growing Tissues Elongation Region RC Fescue tiller (Martre, et al. , 2001) Water storage is often ignored in transport models • RC time constants for trees and tomatoes are ca 75 min & 1 min, respectively (Nobel, 1991); Yet storage is key to plant growth. RC

Tissue-Specific Expression Fusion Gene Promoter Region Target Reporter [TK, 01] DNA

Once upon a time at the ASA…

GCM Primitive Equations Although exceptionally complex, all global climate models are, at their cores, formulated around six mathematical field equations. The remainder of each model adapts the equations to the specifics of Earth’s air and ocean circulation. Conservation of Momentum First Law of Thermodynamics Mass Continuity Conservation of Mass The Hydrostatic Equation The Ideal Gas Law

Plant Primitive Equations? Energy Balance Network Flows Storage Capacity Physiology Solute Transport Genetic Control ? Storage I/O Physical Eq’ns Development Biological Eq’ns Needed Eq’ns

NSF Project 2010 • “To exploit the revolution in plant genomics by understanding the function of all genes of a reference species within their cellular, organismal and evolutionary context. ” • “The ultimate expression of our goal is nothing short of a virtual plant which one could observe growing on a computer screen, stopping this process at any point in that development, and with the click of a computer mouse, accessing all the genetic information expressed in any organ or cell under a variety of environmental conditions. ” [www. arabidopsis. org/workshop 1. html]

Computing Issues

Software Organization Issues • • • Sets of equations that change with time Tissue-specific features Spatial structure Efficient numerical methods needed Visualization of large amounts of output Etc.

Integration Testbed (Ruiqing He) Plant parts are modeled as Java objects with internal variables for size, location, rules for developmental, and physiological state. Object methods: – Manage associated ODE systems; – Solve them to compute growth and transpiration; – Instantiate new plant parts in response to development triggers; – Generate 3 D rendering commands for vegetation/circuit images.

A single plant part

Summary • Genomics is bringing to light the ultimate control mechanisms of plants; • Networks of interacting genes can be modeled by sets of ordinary differential equations; • As many physical/physiological processes can be similarly represented, a unified theory of the soil-plant-atmosphere continuum is conceivable and appears computationally tractable; • Whatever theory’s final form, Dr. Campbell’s many contributions will have a visible role.

Microarray Technology • How it works • Can be micro-miniaturized / automated • Gives quantitative responses • Very sensitive (PCR amplification) • “Scatter gun” methodology

Epistasis Experiments A B C Genotype Phenotype Genotype WT WT -A -A -B -B -A, -B, 35 S: : A Phenotype