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SYSTEMSBIOLOGYMODELING:

PLANTS TRANSPORT PHENOMENA

MR. SARAWUTWONGPHAYAK

BIOINFORMATICS PROGRAM, SCHOOL OF BIORESOURCES AND TECHNOLOGY,AND SCHOOL OF INFORMATION TECHNOLOGY, KING MONGKUT’S UNIVERSITYOF TECHNOLOGY THOBURI.

ADVISOR:DR. ASAWINMEECHAI



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Outline

Systems Biology Modeling

Transport in plants

Example of Fluid Mechanic Theories

MATLAB®

The Language of TechnicalComputing

Application of systems biology modelingin plants transport phenomena



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Systems Biology

An academic field that seeks tointegrate different levels of informationto understand how biological systemsfunction.

Two major and complementary focusesin systems biology:

– Quantitative Systems Biology – Systems Biology Modeling



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focuses on mapping, explaining andpredicting systemic biological processes and

events through the building of computationaland visualization models

Creating comprehensive models that canpredict cellular behaviors is one of the majorgoals of systems biology

This requires the integration of experimental,computational, and theoretical approaches

Molecular Systems Biology 29 March 2005; doi:10.1038/msb4100011



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Comprehensive

models

ExperimentalApproaches

ComputationalApproaches

TheoreticalApproaches


Molecular Systems Biology 29 March 2005; doi:10.1038/msb4100011



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Outline


Transport in plants


MATLAB®


Application of systems biology modelingin plants transport phenomena



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Absorb waternutrients

Absorb light &Exchange gases

systems evolved for long-distance transport that allowed the shoot and

the root to efficiently exchange products of absorption and assimilation



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Transport in plants occurs on

three levels

1- uptake and loss ofwater and solutes by

individual cells 2- short-distance

transport from cell to

cell (sugar loading fromleaves to phloem)

3- long-distance

transport of sap withinxylem and phloem inwhole plant

Lincoln Taiz, Plant Physiology, Third Edition, 2004



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Long-Distance Transport

Xylem – transport of water and nutrients from the

soil to the leaf

Phloem – transport of photosynthates, amino acids,

and electrolytes between various parts ofthe plant




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Xylem and Phloem




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Transport of Xylem Sap

Transpiration

Root pressure




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Translocation of Phloem Sap

Translocation: food/phloem transport

Sugar source: sugar production organ(mature leaves)

Sugar sink: sugar storage organ(growing roots, tips, stems, fruit)

1- loading of sugar into sieve tube atsource reduces water potential inside;

this causes tube to take up water fromsurroundings by osmosis

2- this absorption of water generatespressure that forces sap to flow along

tube 3- pressure gradient in tube is

reinforced by unloading of sugar andconsequent loss of water from tube at

the sink 4- xylem then recycles water from

sink to source Lincoln Taiz, Plant Physiology, Third Edition, 2004



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Outline


Transport in plantsExample of Fluid Mechanic Theories

MATLAB

®


Application of systems biology modeling

in plants transport phenomena



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Diffusion

Fick’s first lawHagen–Poiseuille flow

OsmosisPressure-Flow Hypothesis



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Fluid Forces

Diffusion – The net movement of asubstance from a region of higherconcentration to a region of lowerconcentration until an equilibrium is

reached.



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Fick’s first law

German scientist Adolf Fick (1880s)

The rate of diffusion [mol m –2

s –1

]




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Hagen–Poiseuille flow

Pressure-Driven Bulk Flow DrivesLong-Distance Water Transport

Bulk flow is the concerted movement ofgroups of molecules, most often in

response to a pressure gradient.




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Osmotic Forces

Osmosis is diffusion of water through a differentially

permeable membrane from a region where the water is

more concentrated to a region where it is less concentrated.




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Pressure-Flow Hypothesis

Ernst Münch (1930)

A flow of solution in the sieve elementsis driven by an osmotically generated

pressure gradient between source and

sink ( Δψ p)




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Outline


Transport in plantsExample of Fluid Mechanic Theories

MATLAB® The Language of TechnicalComputing





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a high-performance language fortechnical computing

It integrates computation, visualization,

and programming in an easy-to-useenvironment where problems andsolutions are expressed in familiar

mathematical notation. Typical usesinclude



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Math and computation

Algorithm development

Data acquisition

Modeling, simulation, and prototyping

Data analysis, exploration, andvisualization Scientific and engineering

graphics Application development, includinggraphical user interface building



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Desktop Tools and Development

Environment



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The MATLAB Mathematical

Function Library



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SimBiology Toolbox



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The MATLAB Language



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Graphics



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Outline


Transport in plants


MATLAB® The Language of TechnicalComputing





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Application of a Single-solute Non-steady-state Phloem

Model to the Study of Long-distance Assimilate Transport

Matthew V. Thompson and N. Michele Holbrook

Biological Laboratories 3113, 16 Divinity Avenue, Department ofOrganismic and Evolutionary

Biology, Harvard University, Cambridge, MA 02138, U.S.A.

J. theor. Biol. (2003) 220, 419–455

Data Compilation and



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Data Compilation andCollation

Chemical rxn. Kinetic Data

Sucrose Flux Initial Conc. Final Conc., etc.

Physiological Properties Length

Radius Cross-section area Sieve plate Sieve pore

# sieve tubes/trunk # sieve pores/plate Pressure in sieve tube, etc.

sucrose Mechanisms of

translocation in phloem

Phloem

(sieve elements)

Types of Translocation – Apoplastic (not

considered) – Symplastic

Hypothesis, Theories oflong-distances transport

– OGPF (Münch, 1930) Rate of Translocation,

etc.



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Fig. 1. A schematic of an idealized sieve tube.

J. theor. Biol. (2003) 220, 419 – 455



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Coupled System of PDEs

Fully expanded volume conservationequation

Fully expanded sucrose conservation

equation

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J. theor. Biol. (2003) 220, 419 – 455



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Results and Discussion

Fig. 3. The time evolution over a 24 hr period of pressure p; concentration c; axial volume flux j;

and membrane water flux w in an idealized sieve tube of length L = 5 m, and high spatial( f = 200 nodes m-1) and temporal (Dt = 1 s) resolution.

J. theor. Biol. (2003) 220, 419 – 455



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Other Example Study

ESPR – Environ Sci & Pollut Res 11 (1) 33 – 39 (2004)



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Suggestion

Keys for success in systems biologymodeling – Basis and Modern Theories

– Computational Support

– Experiment Support

Comprehensivemprehensive Modelsdels



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THANK YOU FOR

YOUR ATTENTION

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