Perspectives on Nuclear Medicine for Molecular Diagnosis ...
Perspectives of Molecular Life Sciences on the Crossroads
Transcript of Perspectives of Molecular Life Sciences on the Crossroads
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Perspectives of Molecular Life Sciences on the Crossroads
of Mathematics, Computation, and Biology
Institut für Theoretische Chemie, Universität Wien, Austria and
The Santa Fe Institute, Santa Fe, New Mexico, USA
Workshop „Bridging Mathematics and Life Sciences“
CEMM-RICAM, Wien, 16.02.2012
Peter Schuster
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Web-Page for further information:
http://www.tbi.univie.ac.at/~pks
Peter Schuster. Is there a Newton of the blade of grass? The complex relation between mathematics, physics, and biology. Complexity 16/6: 5-9, 2011. Peter Schuster. Mathematical modeling of evolution. Solved and open problems. Theory in Biosciences 130:71-89, 2011
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1. Prologue: Mathematics and biology
2. Modeling specific biological systems
3. Networks and evolution
4. Perspectives
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1. Prologue: Mathematics and biology
2. Modeling specific biological systems
3. Networks and evolution
4. Perspectives
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Immanuel Kant. 1786. Metaphysische Anfangsgründe der Naturwissenschaft:
…….Ich behaupte nur, daß in jeder besonderen Naturlehre nur so viel eigentliche Wissenschaft angetroffen werden könnte, als darin Mathematik anzutreffen ist. ……
Immanuel Kant. 1786. Metaphysical Foundations of Science: … I maintain only that in every special doctrine of nature only so much science proper can be found as there is mathematics in it. …
Immanuel Kant, 1724-1804
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Immanuel Kant. 1790. Kritik der Urteilskraft, Kap.85:
…….und zwar so gewiß, daß man dreist sagen kann, es ist für Menschen ungereimt …… zu hoffen, daß noch etwa dereinst ein Newton aufstehen könnte, der auch nur die Erzeugung eines Grashalms nach Natur-gesetzen, die keine Absicht geordnet hat, begreiflich machen werde, sondern man muß diese Einsicht den Menschen schlechterdings absprechen. ……
Immanuel Kant. 1790. Critique of Judgment, chapter 85: … there will never be a Newton of the blade of grass, because human science will never be able to explain how a living being can originate from inanimate matter. …
Immanuel Kant, 1724-1804
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1953 molecular biology
1958 protein sequencing
1977 DNA sequencing
1978 bioinformatics
1986 genomics
1997 proteomics
1997 functional genomics
2000 systems biology
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synthetic biology and biotechnology 1828
synthesis of urea from ammonium
cyanate
1996
Ian Wilmut and Keith campbell
Dolly, the sheep 1996 – 2003
gave birth to
Bonnie, Sally & Rosie,
Lucy & Darcy & Cotton
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2007
synthetic biology and biotechnology
Yiannis N. Kaznessis. Models for synthetic biology. BMC Systems Biology 1:e47, 2007.
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Sir Peter Brian Medawar, 1915 - 1987
…… no new principle will declare itself from below a heap of facts. ……
Torbjörn Fagerström, Peter Jagers, Peter Schuster, and Eörs Szathmáry. Biologists put on mathematical glasses. Science 271:2039-240, 1996.
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Sydney Brenner, 1927 -
…… the prime intellectual task of the future lies in constructing an appropriate theoretical framework for biology …… theoretical biology has a bad name because of its past …… I have decided to forget and forgive the past and call it –the badly required new discipline – theoretical biology.
Sydney Brenner. Theoretical biology in the third millenium. Phil.Trans.Roy.Soc.London B 354:1963-1965, 1999
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Theodosius Dobzhansky, 1900 - 1975
Nothing makes sense in biology
except in the light of evolution, …
Theodosius Dobzhansky. Biology, molecular and organismic. American Zoologist 4:443-452, 1974.
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1. Prologue: Mathematics and biology
2. Modeling specific biological systems
3. Networks and evolution
4. Perspectives
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Turing patterns in embryological morphogenesis:
„…… although reaction-diffusion theory provides a very elegant mechanism for segmentation, nature seems to have chosen a much less elegant way of doing it.“
Philip Maini, 1959 - Alan M.Turing, 1912 - 1954
Philip K. Maini, Kevin J. Painter, and Helene Nguyen Phong Chau. Spatial pattern formation in chemical and biological systems.
J.Chem.Soc., Fraday Trans. 93:3602-3610, 1997.
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Synthetic biology
Analysis of synthetic genetic network
The repressilator
Michael B. Elowitz and Stanislas Leibler. A synthetic oscillatory network of transcriptional regulators. Nature 403:335-338, 2000.
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Stefan Müller, Josef Hofbauer, Lukas Endler, Christoph Flamm, Stefanie Widder and Peter Schuster. A generalized model of the repressilator. J. Math. Biol. 53:335-338, 2006.
An example analyzed and simulated by MiniCellSim
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Increasing inhibitor strength
Stable stationary state
Limit cycle oscillations
Fading oscillations caused by a stable heteroclinic orbit
Hopf bifurcation
Bifurcation to May-Leonhard system
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0 1e+ 07 2e+ 07 3e+ 07 4e+ 07 5e+ 070
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Proteins
0 1e+ 07 2e+ 07 3e+ 07 4e+ 07 5e+ 070
0.02
0.04
0.06
0.08
0 1e+ 07 2e+ 07 3e+ 07 4e+ 07 5e+ 070
0.1
0.2
0.3
0.4
0.5
0.6
0.7
mRNAs
0 1e+ 07 2e+ 07 3e+ 07 4e+ 07 5e+ 070
0.05
0.1
0.15
0.2
0.25
0.3
The repressilator limit cycle
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P1
P2 P3
start
start
The repressilator limit cycle
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0 2e+ 08 4e+ 08 6e+ 08 8e+ 080
0.2
0.4
0.6
0.8
1
Proteins
0 2e+ 08 4e+ 08 6e+ 08 8e+ 080
0.05
0.1
0.15
0.2
0.25
0.3
0 2e+ 08 4e+ 08 6e+ 08 8e+ 080
0.2
0.4
0.6
0.8
1
mRNAs
0 2e+ 08 4e+ 08 6e+ 08 8e+ 080
0.05
0.1
0.15
0.2
0.25
0.3
The repressilator heteroclinic orbit
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The repressilator heteroclinic orbit
P1
P2
P2
P2P3
Stable heteroclinic orbit
Unstable heteroclinic orbit
1
1
2
2
2<0
2>0
2=0
Bifurcation from limit cycleto stable heteroclinic orbit
at
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A single neuron signaling to a muscle fiber
Neurobiology
Propagation of a nerve pulse along the axon
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Christof Koch, Biophysics of Computation. Information Processing in single neurons. Oxford University Press, New York 1999.
A B
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Simulation of space independent Hodgkin-Huxley equations: Voltage clamp and constant current
)()()(1 43llKKNaNa
M
VVgVVngVVhmgICtd
Vd−−−−−−=
mmdtdm
mm βα −−= )1(
hhdtdh
hh βα −−= )1(
nndtdn
nn βα −−= )1(
Hodgkin-Huxley ODEs
Alan L. Hodgkin, Andrew F. Huxley. A Quantitative Description of Membrane Current and its Application to Conduction and Excitation in Nerve. Journal of Physiology 117:500–544, 1952.
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Gating functions of the Hodgkin-Huxley equations
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Temperature dependence of the Hodgkin-Huxley equations
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LrVVgVVngVVhmgtVC
xV
R llKKNaNa π2)()()(1 432
2
−+−+−+∂∂
=∂∂
mmtm
mm βα −−=∂∂ )1(
hhth
hh βα −−=∂∂ )1(
nntn
nn βα −−=∂∂ )1(
Hodgkin-Huxley PDEquations
Travelling pulse solution: V(x,t) = V() with
= x + t
Hodgkin-Huxley equations describing pulse propagation along nerve fibers
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Hodgkin-Huxley PDEquations
Travelling pulse solution: V(x,t) = V() with
= x + t
Hodgkin-Huxley equations describing pulse propagation along nerve fibers
[ ] LrVVgVVngVVhmgd
VdCd
VdR llKKNaNaM π
ξθ
ξ2)()()(1 43
2
2
−+−+−+=
mmd
mdmm βα
ξθ −−= )1(
hhd
hdhh βα
ξθ −−= )1(
nnd
ndnn βα
ξθ −−= )1(
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T = 18.5 C; θ = 1873.33 cm / sec
50
0
-50
100
1 2 3 4 5 6 [cm]
V [
mV
]
Paul E. Phillipson and Peter Schuster. Analytical dynamics of neuron pulse propagation. International Journal of Bifurcation and Chaos 16:3605-3616, 2006.
- ″ - , - ″ - . A comparative study of the Hodgkin-Huxley and the Fitzhugh-Nagumo models of neuron pulse propagation.
International Journal of Bifurcation and Chaos 15:3851-3866, 2005.
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T = 18.5 C; θ = 1873.3324514717698 cm / sec
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T = 18.5 C; θ = 1873.3324514717697 cm / sec
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T = 18.5 C; θ = 544.070 cm / sec
-10
0
10
20
30
40
V
[mV
]
6 8 10 12 14 16 18 [cm]
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Propagating wave solutions of the Hodgkin-Huxley equations
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1. Prologue: Mathematics and biology
2. Modeling specific biological systems
3. Networks and evolution
4. Perspectives
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Sewall Wrights fitness landscape as metaphor for Darwinian evolution
Sewall Wright. 1932. The roles of mutation, inbreeding, crossbreeding and selection in evolution. In: D.F.Jones, ed. Int. Proceedings of the Sixth International Congress on Genetics. Vol.1, 356-366. Ithaca, NY.
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The paradigm of structural biology
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RNA sequence
RNA structure of minimal free
energy
RNA folding:
Structural biology, spectroscopy of biomolecules, understanding
molecular function Empirical parameters
Biophysical chemistry: thermodynamics and
kinetics
Sequence, structure, and design
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RNA sequence
RNA structure of minimal free
energy
RNA folding:
Structural biology, spectroscopy of biomolecules, understanding
molecular function
Inverse folding of RNA:
Biotechnology, design of biomolecules
with predefined structures and functions
Inverse Folding Algorithm
Iterative determination of a sequence for the
given secondary structure
Sequence, structure, and design
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? ? ?
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Degree of neutrality of neutral networks and the connectivity threshold
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Roger D. Kouyos, Gabriel E. Leventhal, Trevor Hinkley, Mojgdan Haddad, Jeannette M. Whitcomb, Christos J. Petropoulos, and Sebastian
Bonhoeffer. Exploring the complexity of the HIV-1 fitness landscape. PLoS Genetics, 2012, in press.
Conclusions concerning realistic fitness landscapes:
1. Adaptations take place in high-dimensional spaces,
2. fitness landscapes are rugged, and
3. fitness landscapes show a substantial degree of neutrality.
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The simplified mapping from genotypes into function
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Manfred Eigen 1927 -
∑∑
∑
==
=
=
=−=
n
i in
i ii
jin
i jij
xxfΦ
njΦxxWx
11
1,,2,1;
dtd
Mutation and (correct) replication as parallel chemical reactions
M. Eigen. 1971. Naturwissenschaften 58:465, M. Eigen & P. Schuster.1977. Naturwissenschaften 64:541, 65:7 und 65:341
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The single peak model landscape for all sequences with chain lengths n = 10
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quasispecies
The error threshold in replication and mutation
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„Realistic“ fitness landscapes with scattered fitness values
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Quasispecies with phase transitions
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Strong quasispecies
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Most probable fitness distribution in sequence space
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Condition for the occurence of a strong quasispecies
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Motoo Kimuras population genetics of neutral evolution.
Evolutionary rate at the molecular level. Nature 217: 624-626, 1955.
The Neutral Theory of Molecular Evolution. Cambridge University Press. Cambridge, UK, 1983.
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Motoo Kimura
Is the Kimura scenario correct for frequent mutations?
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Pairs of neutral sequences in replication networks P. Schuster, J. Swetina. 1988. Bull. Math. Biol. 50:635-650
5.0)()(lim 210 ==→ pxpxp
dH = 1
)1(1)(lim
)1()(lim
20
10
α
αα
+=
+=
→
→
px
px
p
p
dH = 2
Random fixation in the sense of Motoo Kimura
dH 3
1)(lim,0)(lim
or0)(lim,1)(lim
2010
2010
==
==
→→
→→
pxpxpxpx
pp
pp
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Neutral network: Individual sequences
n = 10, = 1.1, d = 0.5
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Two neutral sequences with Hamming distances dH=1 and dH=2 can be detected in the consensus sequence of the population.
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1. Prologue: Mathematics and biology
2. Modeling specific biological systems
3. Networks and evolution
4. Perspectives
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…… theory cannot remove complexity, but it shows what kind of „regular“ behavior can be expected and what
experiments have to be done to get a grasp on the irregularities.
Manfred Eigen. Preface to E. Domingo, C.R. Parrish, J.J.Holland, eds. Origin and Evolution of Viruses. Academic Press 2008
Manfred Eigen, 1927 -
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The molecular view has a clear advantage over phenomenology.
Analysis by methods of biochemical kinetics is indispesible for an understanding of cellular dynamics. Automation of kinetic analysis will come in the near future and this will change the situation completely.
Complexity in biology has one origin among others: Evolution does neither care for elegance nor for simplicity nor for intelligibility, the only thing that counts is efficiency.
It sounds commonplace but progress in theoretical biology will be very limited unless new approaches for handling networks and dealing with complexity will be developed.
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Max Planck, 1859 - 1947
„Application without knowledge is
dangerous“
„Anwendung ohne Wissen ist gefährlich.“
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Coworkers Peter Stadler, Bärbel M. Stadler, Universität Leipzig, GE
Paul E. Phillipson, University of Colorado at Boulder, CO
Heinz Engl, Philipp Kügler, James Lu, Stefan Müller, RICAM Linz, AT
Jord Nagel, Kees Pleij, Universiteit Leiden, NL
Walter Fontana, Harvard Medical School, MA
Martin Nowak, Harvard University, MA
Christian Reidys, University of Southern Denmark, Odense, Denmark
Christian Forst, Los Alamos National Laboratory, NM
Thomas Wiehe, Ulrike Göbel, Walter Grüner, Stefan Kopp, Jaqueline Weber,
Institut für Molekulare Biotechnologie, Jena, GE
Ivo L.Hofacker, Christoph Flamm, Andreas Svrček-Seiler, Universität Wien, AT
Kurt Grünberger, Michael Kospach , Andreas Wernitznig, Stefanie Widder, Stefan Wuchty, Jan Cupal, Stefan Bernhart, Lukas Endler, Ulrike Langhammer,
Rainer Machne, Ulrike Mückstein, Erich Bornberg-Bauer, Universität Wien, AT
Universität Wien
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Universität Wien
Acknowledgement of support
Fonds zur Förderung der wissenschaftlichen Forschung (FWF) Projects No. 09942, 10578, 11065, 13093
13887, and 14898
Wiener Wissenschafts-, Forschungs- und Technologiefonds (WWTF) Project No. Mat05
Jubiläumsfonds der Österreichischen Nationalbank
Project No. Nat-7813
European Commission: Contracts No. 98-0189, 12835 (NEST)
Austrian Genome Research Program – GEN-AU: Bioinformatics Network (BIN)
Österreichische Akademie der Wissenschaften
Siemens AG, Austria
Universität Wien and the Santa Fe Institute
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Thank you for your attention!
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Web-Page for further information:
http://www.tbi.univie.ac.at/~pks
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