systems biology intro

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AMATH 900: Mathematical Cell Biology Dynamic modelling of biochemical, genetic, and neuronal networks

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Page 1: Systems Biology Intro

AMATH 900:

Mathematical Cell Biology

Dynamic modelling of biochemical, genetic, and

neuronal networks

Page 2: Systems Biology Intro

Dynamic biological systems -- multicellular

http://megaverse.net/chipmunkvideos/

Page 3: Systems Biology Intro

Dynamic biological systems -- cellular

http://astro.temple.edu/~jbs/courses/204lectures/neutrophil-js.html

Neutrophil chasing a bacterium

Page 4: Systems Biology Intro

Dynamic biological systems -- intracellular

http://www.bio.davidson.edu/courses/movies.html

Calcium Waves in Retinal Glia

Page 5: Systems Biology Intro

Dynamic biological systems -- molecular

Page 6: Systems Biology Intro

Our interest: intracellular dynamics

• Metabolism: chemical reaction networks, enzyme-catalysed reactions, allosteric regulation

• Signal Transduction: G protein signalling, MAPK signalling cascade, bacterial chemotaxis, calcium oscillations.

• Genetic Networks: switches (lac operon, phage lambda lysis/lysogeny switch, engineered toggle switch), oscillators (Goodwin oscillator, circadian rhythms, cell cycle, repressilator), computation

• Electrophysiology: voltage-gated ion channels, Nernst potential, Morris-Lecar model, intercellular communication (gap junctions, synaptic transmission, neuronal circuits)

Page 7: Systems Biology Intro

Our tools: dynamic mathematical models

• Differential Equations: models from kinetic network description, describes dynamic (not usually spatial) phenomena, numerical simulations

• Sensitivity Analysis: dependence of steady-state behaviour on internal and external conditions

• Stability Analysis: phase plane analysis, characterizing long-term behaviour (bistability, oscillations)

• Bifurcation Analysis: dependence of system dynamics on internal and external conditions

Page 8: Systems Biology Intro

• Metabolism: chemical reaction networks, enzyme-catalysed reactions, allosteric regulation

• Signal Transduction: G protein signalling, MAPK signalling

cascade, bacterial chemotaxis, calcium oscillations.

• Genetic Networks: switches (lac operon, phage lambda lysis/lysogeny switch, engineered toggle switch), oscillators (Goodwin oscillator, circadian rhythms, cell cycle, repressilator), computation

• Electrophysiology: voltage-gated ion channels, Nernst

potential, Morris-Lecar model, intercellular communication (gap junctions, synaptic transmission, neuronal circuits)

Page 9: Systems Biology Intro

Metabolic Networks

http://www.chemengr.ucsb.edu/~gadkar/images/network_ecoli.jpg

Page 10: Systems Biology Intro

Enzyme-Catalysed Reactions

http://www.uyseg.org/catalysis/principles/images/enzyme_substrate.gif

Page 11: Systems Biology Intro

Allosteric Regulation

http://courses.washington.edu/conj/protein/allosteric.gif

Page 12: Systems Biology Intro

http://www.cm.utexas.edu/academic/courses/Spring2002/CH339K/Robertus/overheads-3/ch15_reg-glycolysis.jpg

Page 13: Systems Biology Intro

E. Coli metabolism

KEGG: Kyoto Encyclopedia of Genes and Genomes (http://www.genome.ad.jp/kegg/kegg.html)

Metabolic Networks

Page 14: Systems Biology Intro

• Metabolism: chemical reaction networks, enzyme-catalysed reactions, allosteric regulation

• Signal Transduction: G protein signalling, MAPK signalling

cascade, bacterial chemotaxis, calcium oscillations.

• Genetic Networks: switches (lac operon, phage lambda lysis/lysogeny switch, engineered toggle switch), oscillators (Goodwin oscillator, circadian rhythms, cell cycle, repressilator), computation

• Electrophysiology: voltage-gated ion channels, Nernst

potential, Morris-Lecar model, intercellular communication (gap junctions, synaptic transmission, neuronal circuits)

Page 15: Systems Biology Intro

Transmembrane receptors

http://fig.cox.miami.edu/~cmallery/150/memb/fig11x7.jpg

Page 16: Systems Biology Intro

Signal Transduction pathway

Page 17: Systems Biology Intro

Bacterial Chemotaxis

http://www.aip.org/pt/jan00/images/berg4.jpg

http://www.life.uiuc.edu/crofts/bioph354/flag_labels.jpg

Page 18: Systems Biology Intro

Apoptotic Signalling pathway

Page 19: Systems Biology Intro

• Metabolism: chemical reaction networks, enzyme-catalysed reactions, allosteric regulation

• Signal Transduction: G protein signalling, MAPK signalling

cascade, bacterial chemotaxis, calcium oscillations.

• Genetic Networks: switches (lac operon, phage lambda lysis/lysogeny switch, engineered toggle switch), oscillators (Goodwin oscillator, circadian rhythms, cell cycle, repressilator), computation

• Electrophysiology: voltage-gated ion channels, Nernst

potential, Morris-Lecar model, intercellular communication (gap junctions, synaptic transmission, neuronal circuits)

Page 20: Systems Biology Intro

Simple genetic network: lac operon

• www.accessexcellence.org/ AB/GG/induction.html

Page 21: Systems Biology Intro

Phage Lambda

http://de.wikipedia.org/wiki/Bild:T4-phage.jpg http://fig.cox.miami.edu/Faculty/Dana/phage.jpg

Page 22: Systems Biology Intro

Lysis/Lysogeny Switch

http://opbs.okstate.edu/~Blair/Bioch4113/LAC-OPERON/LAMBDA%20PHAGE.GIF

Page 23: Systems Biology Intro

Circadian Rhythm

http://www.molbio.princeton.edu/courses/mb427/2001/projects/03/circadian%20pathway.jpg

Page 24: Systems Biology Intro

Eric Davidson's Lab at Caltech (http://sugp.caltech.edu/endomes/)

Large Scale Genetic Network

Page 25: Systems Biology Intro

Genetic Toggle Switch

http://www.cellbioed.org/articles/vol4no1/i1536-7509-4-1-19-f02.jpg

Gardner, T.S., Cantor, C.R., and Collins, J.J. (2000).

Construction of a genetic toggle switch in Escherichia coli. Nature 403, 339–342.

Page 26: Systems Biology Intro

http://www.nature.com/cgi-taf/DynaPage.taf?file=/nature/journal/v420/n6912/full/nature01257_r.html

Page 27: Systems Biology Intro

Construction of computational elements (logic gates) and cell-cell

communication

http://www.molbio.princeton.edu/research_facultymember.php?id=62

Genetic circuit building blocks for cellular computation, communications, and signal processing, Weiss, Basu, Hooshangi, Kalmbach, Karig, Mehreja, Netravali.

Natural Computing. 2003. Vol. 2, 47-84.

Page 28: Systems Biology Intro

• Metabolism: chemical reaction networks, enzyme-catalysed reactions, allosteric regulation

• Signal Transduction: G protein signalling, MAPK signalling

cascade, bacterial chemotaxis, calcium oscillations.

• Genetic Networks: switches (lac operon, phage lambda lysis/lysogeny switch, engineered toggle switch), oscillators (Goodwin oscillator, circadian rhythms, cell cycle, repressilator), computation

• Electrophysiology: voltage-gated ion channels, Nernst

potential, Morris-Lecar model, intercellular communication (gap junctions, synaptic transmission, neuronal circuits)

Page 29: Systems Biology Intro

Excitable Cells

http://users.rcn.com/jkimball.ma.ultranet/BiologyPages/E/

ExcitableCells.html

Resting potential

Ion Channel

http://campus.lakeforest.edu/

~light/ion%20channel.jpg

Page 30: Systems Biology Intro

Measuring Ion Channel Activity: Patch Clamp

http://www.ipmc.cnrs.fr/~duprat/neurophysiology/patch.htm

Page 31: Systems Biology Intro

Measuring Ion Channel Activity: Voltage Clamp

http://soma.npa.uiuc.edu/courses/physl341/Lec3.html

Page 32: Systems Biology Intro

Action Potentials

http://users.rcn.com/jkimball.ma.ultranet/BiologyPages/E/

ExcitableCells.html

http://content.answers.com/main/content/wp/en/thumb/0/02/300px-Action-potential.png

Page 34: Systems Biology Intro

Hodgkin-Huxley Model

http://www.amath.washington.edu/~qian/talks/talk5/

Page 35: Systems Biology Intro

Neural Computation

http://www.dna.caltech.edu/courses/cns187/

Page 36: Systems Biology Intro

Our tools: dynamic mathematical models

• Differential Equations: models from kinetic network description, models dynamic but not spatial phenomena, numerical simulations

• Sensitivity Analysis: dependence of steady-state behaviour on internal and external conditions

• Stability Analysis: phase plane analysis, characterizing long-term behaviour (bistability, oscillations)

• Bifurcation Analysis: dependence of system dynamics on internal and external conditions

Page 37: Systems Biology Intro

Differential Equation Modelling

From Chen, Tyson, Novak Mol. Biol Cell 2000. pp. 369-391

rate of change of concentration

rate of production

rate of degradation

Page 38: Systems Biology Intro

Differential Equation Modelling

Page 39: Systems Biology Intro

Differential Equation Modelling: Numerical Simulation

Page 40: Systems Biology Intro

Our tools: dynamic mathematical models

• Differential Equations: models from kinetic network description, numerical simulations

• Sensitivity Analysis: dependence of steady-state behaviour on internal and external conditions

• Stability Analysis: phase plane analysis, characterizing long-term behaviour (bistability, oscillations)

• Bifurcation Analysis: dependence of system dynamics on internal and external conditions

Page 41: Systems Biology Intro

complete sensitivity analysis:

Page 42: Systems Biology Intro

Our tools: dynamic mathematical models

• Differential Equations: models from kinetic network description, numerical simulations

• Sensitivity Analysis: dependence of steady-state behaviour on internal and external conditions

• Stability Analysis: phase plane analysis, characterizing long-term behaviour (bistability, oscillations)

• Bifurcation Analysis: dependence of system dynamics on internal and external conditions

Page 43: Systems Biology Intro

unstable

stable

Page 44: Systems Biology Intro

Our tools: dynamic mathematical models

• Differential Equations: models from kinetic network description, numerical simulations

• Sensitivity Analysis: dependence of steady-state behaviour on internal and external conditions

• Stability Analysis: phase plane analysis, characterizing long-term behaviour (bistability, oscillations)

• Bifurcation Analysis: dependence of system dynamics on internal and external conditions

Page 45: Systems Biology Intro
Page 46: Systems Biology Intro

allows construction of falsifiable models

in silico experiments

gain insight into dynamic behaviour of complex networks

Why dynamic modelling?