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AMATH 382: Computational Modeling of Cellular Systems Dynamic modelling of biochemical, genetic, and neural networks Introductory Lecture, Jan. 6, 2014
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AMATH 382: Computational Modeling of Cellular Systems Dynamic modelling of biochemical, genetic, and neural networks Introductory Lecture, Jan. 6, 2014.

Jan 02, 2016

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Page 1: AMATH 382: Computational Modeling of Cellular Systems Dynamic modelling of biochemical, genetic, and neural networks Introductory Lecture, Jan. 6, 2014.

AMATH 382:Computational Modeling

of Cellular Systems

Dynamic modelling of biochemical, genetic, and

neural networks

Introductory Lecture, Jan. 6, 2014

Page 2: AMATH 382: Computational Modeling of Cellular Systems Dynamic modelling of biochemical, genetic, and neural networks Introductory Lecture, Jan. 6, 2014.

Dynamic biological systems -- multicellular

http://megaverse.net/chipmunkvideos/

Page 3: AMATH 382: Computational Modeling of Cellular Systems Dynamic modelling of biochemical, genetic, and neural networks Introductory Lecture, Jan. 6, 2014.

Dynamic biological systems -- cellular

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

Neutrophil chasing a bacterium

Page 4: AMATH 382: Computational Modeling of Cellular Systems Dynamic modelling of biochemical, genetic, and neural networks Introductory Lecture, Jan. 6, 2014.

Dynamic biological systems -- intracellular

http://stke.sciencemag.org/cgi/content/full/sigtrans;3/147/tr5/DC1

Calcium waves in astrocytes in rat cerebral cortex

Page 5: AMATH 382: Computational Modeling of Cellular Systems Dynamic modelling of biochemical, genetic, and neural networks Introductory Lecture, Jan. 6, 2014.

Dynamic biological systems -- molecular

Page 6: AMATH 382: Computational Modeling of Cellular Systems Dynamic modelling of biochemical, genetic, and neural networks Introductory Lecture, Jan. 6, 2014.

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: AMATH 382: Computational Modeling of Cellular Systems Dynamic modelling of biochemical, genetic, and neural networks Introductory Lecture, Jan. 6, 2014.

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: AMATH 382: Computational Modeling of Cellular Systems Dynamic modelling of biochemical, genetic, and neural networks Introductory Lecture, Jan. 6, 2014.

• 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: AMATH 382: Computational Modeling of Cellular Systems Dynamic modelling of biochemical, genetic, and neural networks Introductory Lecture, Jan. 6, 2014.

Metabolic Networks

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

Page 10: AMATH 382: Computational Modeling of Cellular Systems Dynamic modelling of biochemical, genetic, and neural networks Introductory Lecture, Jan. 6, 2014.

Enzyme-Catalysed Reactions

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

Page 11: AMATH 382: Computational Modeling of Cellular Systems Dynamic modelling of biochemical, genetic, and neural networks Introductory Lecture, Jan. 6, 2014.

Allosteric Regulation

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

Page 12: AMATH 382: Computational Modeling of Cellular Systems Dynamic modelling of biochemical, genetic, and neural networks Introductory Lecture, Jan. 6, 2014.

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

Page 13: AMATH 382: Computational Modeling of Cellular Systems Dynamic modelling of biochemical, genetic, and neural networks Introductory Lecture, Jan. 6, 2014.

E. Coli metabolism

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

Metabolic Networks

Page 14: AMATH 382: Computational Modeling of Cellular Systems Dynamic modelling of biochemical, genetic, and neural networks Introductory Lecture, Jan. 6, 2014.

• 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: AMATH 382: Computational Modeling of Cellular Systems Dynamic modelling of biochemical, genetic, and neural networks Introductory Lecture, Jan. 6, 2014.

Transmembrane receptors

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

Page 16: AMATH 382: Computational Modeling of Cellular Systems Dynamic modelling of biochemical, genetic, and neural networks Introductory Lecture, Jan. 6, 2014.

Signal Transduction pathway

Page 17: AMATH 382: Computational Modeling of Cellular Systems Dynamic modelling of biochemical, genetic, and neural networks Introductory Lecture, Jan. 6, 2014.

Bacterial Chemotaxis

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

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

Page 18: AMATH 382: Computational Modeling of Cellular Systems Dynamic modelling of biochemical, genetic, and neural networks Introductory Lecture, Jan. 6, 2014.

Apoptotic Signalling pathway

Page 19: AMATH 382: Computational Modeling of Cellular Systems Dynamic modelling of biochemical, genetic, and neural networks Introductory Lecture, Jan. 6, 2014.

• 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: AMATH 382: Computational Modeling of Cellular Systems Dynamic modelling of biochemical, genetic, and neural networks Introductory Lecture, Jan. 6, 2014.

Simple genetic network: lac operon

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

Page 21: AMATH 382: Computational Modeling of Cellular Systems Dynamic modelling of biochemical, genetic, and neural networks Introductory Lecture, Jan. 6, 2014.

Phage Lambda

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

Page 22: AMATH 382: Computational Modeling of Cellular Systems Dynamic modelling of biochemical, genetic, and neural networks Introductory Lecture, Jan. 6, 2014.

Lysis/Lysogeny Switch

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

Page 23: AMATH 382: Computational Modeling of Cellular Systems Dynamic modelling of biochemical, genetic, and neural networks Introductory Lecture, Jan. 6, 2014.

Circadian Rhythm

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

Page 24: AMATH 382: Computational Modeling of Cellular Systems Dynamic modelling of biochemical, genetic, and neural networks Introductory Lecture, Jan. 6, 2014.

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

Large Scale Genetic Network

Page 25: AMATH 382: Computational Modeling of Cellular Systems Dynamic modelling of biochemical, genetic, and neural networks Introductory Lecture, Jan. 6, 2014.

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: AMATH 382: Computational Modeling of Cellular Systems Dynamic modelling of biochemical, genetic, and neural networks Introductory Lecture, Jan. 6, 2014.

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

Page 27: AMATH 382: Computational Modeling of Cellular Systems Dynamic modelling of biochemical, genetic, and neural networks Introductory Lecture, Jan. 6, 2014.

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: AMATH 382: Computational Modeling of Cellular Systems Dynamic modelling of biochemical, genetic, and neural networks Introductory Lecture, Jan. 6, 2014.

• 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: AMATH 382: Computational Modeling of Cellular Systems Dynamic modelling of biochemical, genetic, and neural networks Introductory Lecture, Jan. 6, 2014.

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: AMATH 382: Computational Modeling of Cellular Systems Dynamic modelling of biochemical, genetic, and neural networks Introductory Lecture, Jan. 6, 2014.

Measuring Ion Channel Activity: Patch Clamp

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

Page 31: AMATH 382: Computational Modeling of Cellular Systems Dynamic modelling of biochemical, genetic, and neural networks Introductory Lecture, Jan. 6, 2014.

Measuring Ion Channel Activity: Voltage Clamp

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

Page 32: AMATH 382: Computational Modeling of Cellular Systems Dynamic modelling of biochemical, genetic, and neural networks Introductory Lecture, Jan. 6, 2014.

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: AMATH 382: Computational Modeling of Cellular Systems Dynamic modelling of biochemical, genetic, and neural networks Introductory Lecture, Jan. 6, 2014.

Hodgkin-Huxley Model

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

Page 35: AMATH 382: Computational Modeling of Cellular Systems Dynamic modelling of biochemical, genetic, and neural networks Introductory Lecture, Jan. 6, 2014.

Neural Computation

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

Page 36: AMATH 382: Computational Modeling of Cellular Systems Dynamic modelling of biochemical, genetic, and neural networks Introductory Lecture, Jan. 6, 2014.

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: AMATH 382: Computational Modeling of Cellular Systems Dynamic modelling of biochemical, genetic, and neural networks Introductory Lecture, Jan. 6, 2014.

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: AMATH 382: Computational Modeling of Cellular Systems Dynamic modelling of biochemical, genetic, and neural networks Introductory Lecture, Jan. 6, 2014.

Differential Equation Modelling

Page 39: AMATH 382: Computational Modeling of Cellular Systems Dynamic modelling of biochemical, genetic, and neural networks Introductory Lecture, Jan. 6, 2014.

Differential Equation Modelling: Numerical Simulation

Page 40: AMATH 382: Computational Modeling of Cellular Systems Dynamic modelling of biochemical, genetic, and neural networks Introductory Lecture, Jan. 6, 2014.

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: AMATH 382: Computational Modeling of Cellular Systems Dynamic modelling of biochemical, genetic, and neural networks Introductory Lecture, Jan. 6, 2014.

complete sensitivity analysis:

Page 42: AMATH 382: Computational Modeling of Cellular Systems Dynamic modelling of biochemical, genetic, and neural networks Introductory Lecture, Jan. 6, 2014.

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: AMATH 382: Computational Modeling of Cellular Systems Dynamic modelling of biochemical, genetic, and neural networks Introductory Lecture, Jan. 6, 2014.

unstable

stable

Page 44: AMATH 382: Computational Modeling of Cellular Systems Dynamic modelling of biochemical, genetic, and neural networks Introductory Lecture, Jan. 6, 2014.

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: AMATH 382: Computational Modeling of Cellular Systems Dynamic modelling of biochemical, genetic, and neural networks Introductory Lecture, Jan. 6, 2014.
Page 46: AMATH 382: Computational Modeling of Cellular Systems Dynamic modelling of biochemical, genetic, and neural networks Introductory Lecture, Jan. 6, 2014.

allows construction of falsifiable models

in silico experiments

gain insight into dynamic behaviour of complex networks

Why dynamic modelling?