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AACIMP 2009 Summer School lecture by Gerhard Wilhelm Weber. "Modern Operational Research and Its Mathematical Methods" course.
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Modelling, Dynamics and Development of Gene-Environment
and Eco-Finance Networks
4th International Summer SchoolAchievements and Applications of Contemporary Informatics, Mathematics and PhysicsNational University of Technology of the UkraineKiev, Ukraine, August 5-16, 2009
and Eco-Finance NetworksGerhard-Wilhelm Weber *, Ba şak Akteke-ÖztürkZeynep Alparslan-Gök, Ömür Uğur, Hakan Öktem
Institute of Applied Mathematics, METU, Ankara, Turkey
Pakize Taylan Dicle University, Diyarbakır, Turkey
Erik Kropat University of Erlangen-Nuremberg, Germany
Özlem Deferli Department of Mathematics, Cankaya University, Ankara, Tu rkey
* Faculty of Economics, Management Science and Law, University of Siegen, GermanyCenter for Research on Optimization and Control, University of Aveiro, Portugal
Bio-Systems
Bio-Systems
Bio-Systems
sustainability
Bio-Systems
sustainability
• Computational Biology, Medicine, Health Care and Environment
• Gene-Environment Networks and Eco-Finance Networks
• Dynamical Systems
• Hybrid and Anticipatory Systems
Outline
• Stability
• Optimization and Control Theory
• Regression and Clustering
• Financial Mathematics and Risk Management
• Regulatory Networks under Uncertainty and Ellipsoidal Calculus
• Conclusion
DNA microarray chip experiments
Comp. Bio. & Med.
prediction of gene patterns based on
with
M.U. Akhmet, H. Öktem M.U. Akhmet, H. Öktem
S.W. Pickl, E. Quek Ming Poh
T. Ergenç, B. Karasözen
J. Gebert, N. Radde
Ö. Uğur, R. Wünschiers
M. Taştan, A. Tezel, P. Taylan
F.B. Yılmaz, B. Akteke-Öztürk
S. Özöğür, Z. Alparslan-Gök
A. Soyler, B. Soyler, M. Çetin
Comp. Bio. & Med.
GENE time 0 9.5 11.5 13.5 15.5 18.5 20.5
'YHR007C' 0.224 0.367 0.312 0.014 -0.003 -1.357 -0.811
'YAL051W' 0.002 0.634 0.31 0.441 0.458 -0.136 0.275
'YAL054C' -1.07 -0.51 -0.22 -0.012 -0.215 1.741 4.239
'YAL056W' 0.09 0.884 0.165 0.199 0.034 0.148 0.935
Ex.: yeast data
'YAL056W' 0.09 0.884 0.165 0.199 0.034 0.148 0.935
'PRS316' -0.046 0.635 0.194 0.291 0.271 0.488 0.533
'KAN-MX' 0.162 0.159 0.609 0.481 0.447 1.541 1.449
'E. COLI #10' -0.013 0.88 -0.009 0.144 -0.001 0.14 0.192
'E. COLI #33' -0.405 0.853 -0.259 -0.124 -1.181 0.095 0.027
http://genome-www5.stanford.edu/
Comp. Bio. & Med.
Comp. Bio. & Med.
least squares – ML
statistical learning
time-contin.Expression data
Gene Patterns Modeling & Prediction
0)0( EE =( ) ( )E M E E C E•
= +
time-discr.
kkk EE M1 =+
∈= )(Μ jik em M
Ex.: Euler, Runge-Kutta
environmental effects
Gene Patterns Modeling & Prediction
( ), ΜkM E
=
E
=
Uğur, W. 2006, W., Taylan, Alparsan-Gök, Özöğür, Akteke-Öztürk 2006
Gene Patterns Modeling & Prediction
( ), ΜkM E
=
E
=
Kropat, W. , Tezel, Özöğür-Akyüz 2008
For which parameters, i.e., for which set M (or: dynamics), is stability guaranteed ?
Gene Patterns Model. & Pred.
Def.: M is stable : ⇔ B : (complex) bounded neighbourhood of∃ ,nΟM :
0 1 1, M ,M ,..., Mkk ΙΝ −∀ ∈ ∈
1 2 0(M M ... M ) .k k− − Β ⊆ Β
Stability Analysis
kB
1kB +
Stability Theorems
Akhmet, Gebert, Pickl, Öktem, W. 2005
Öktem 2005, Akçay 2005
Gebert, Radde, W. 2005
Yılmaz 2004 Yılmaz, Öktem, W. 2005
Uğur, Pickl, Taştan, W. 2005
Weber, Tezel 2006 Uğur, W. 2006
W, Tezel, Taylan, Soyler, Çetin 2007
W, Ugur, Taylan, Tezel 2007
Uğur, Pickl, W, Wünschiers 2007
Analysis with Polytopes
Theorem (Brayton, Tong 1979) :
Given a set M : of m distinct complex matrices.
Then,
M is stable is bounded .
B 0
kk
BB∞
==⇔
0
* U
10 M,...,M −= m
Here, is a bounded neighbourhood of ,0B n0
=:kB H
,mod)1(: mkk −=′ H : convex hull .
,M 1'0
−
∞
= kik
iBU
Here, is a bounded neighbourhood of ,
and for k > 0
where
The ‘‘discrete” power of the algorithm is based on using
polyhedra and focussing on the extremal points
of the sets .
kB
kB
Extremal Points
Theorem 1 : If z is an extremal point of , then
there exists a and an extremal point u of ,
in short : E , such that
z
kB
0ΙΝ∈j
.M uj
k ′=
1−kB)( 1−∈ kBu
Construction Principle
Stopping Criterion
Theorem 2:
Let ( as above,
Then,
).,...,2,1M riuz ijki ∈= ′
.,...,2,1M 11 ri,...,zzzB,...,zz rikkr =∀∈⇔= ′ HH
Construction Principle
=⇒≥=•∞
=
∗
dynamicsmatricesof
boundedBB
kstepatstopping
kkBB ii
kk
/
,
,)(""
)(0
0
00U
stability
⇒/=∂∩∂•∗
stability
unboundedBBB k 00
in
Gebert, Laetsch, Pickl, We. Wünschiers 2004
Ergenc, We. 2004
Ex.:
M = 0 1M , M ,0
1M 0
=
b
a
=
0
10M1 b
Stability Analysis
algorithm
instability
region of stability
)()()()(
)()()()(
)()()()(
)()()()(
34333231
24232221
14131211
04030201
tEtEtEtE
tEtEtEtE
tEtEtEtE
tEtEtEtE
=
080170255
25570180255
050200255
2550250255
Genetic Network
Ex. : , 1,E M E hκ
•= = scalar - valued case
−−
−=
2001
039.02.00
0061.04.0
0000
Mö 4ö 2
ö 0
ö 5
ö 1
ö 3
0123456789
0 2 4 6 8
Time, t
Exp
ress
ion
leve
l, ö
gene2
gene3
gene1
gene4
0.4 x1
0.2 x2 1 x1
Genetic Network
Gene-Environment Networks - Hybrid Systems
( ) ( )( 1) M ( )s k s kE k E k C+ = +( ) : ( ( 1))
1 if ( )( ( )) :
0 else
B
i ii
s k F Q E k
E kQ E k
= −> Ω
=
( ( )) :0 elseiQ E k =
Akhmet, Gebert, Pickl, Öktem, W. 2005
Öktem 2005, Akçay 2005
Gebert, Radde, W. 2005
Yılmaz 2004 Yılmaz, Öktem, W. 2005
Uğur, Pickl, Taştan, W. 2005
Weber, Tezel 2006
( ) ( )( 1) M ( )s k s kE k E k C+ = +
( 1) IM ( )kIE k IE k+ =
Gene-Environment Networks
( ) ( )IE t IM IE t•
= locally
)
( ) ( ) ( )( ) ( ) ( )s t s t s tE t M E t C E t D•
= + +
)
( ) ( ) ( )( 1) M ( ) ( )s k s k s kE k E k C E k D+ = + +
( 1) IM ( )kIE k IE k+ =
Gene-Environment Networks
( ) ( )IE t IM IE t•
= locally
)
( ) ( ) ( )( ) ( ) ( )s t s t s tE t M E t C E t D•
= + +
Gene-Environment Networks
( 1) IM ( )kIE k IE k+ =
modules
( ) ( )IE t IM IE t•
=
Gene-Environment Networks
)))(((:)( tEQFts =
1( ( )) ( ( ( )),..., ( ( )))nQ E t Q E t Q E t=
where
)
( ) ( ) ( )( ) ( ) ( )s t s t s tE t M E t C E t D•
= + +
1( ( )) ( ( ( )),..., ( ( )))nQ E t Q E t Q E t=
1,)( ii tE θ<
2,1, )( iii tE θθ <<
)(, tEidi i<θ
0 for
1 for( ( )) :
...
for
i
i
Q E t
d
=
Gene-Environment Networks
)))(((:)( tEQFts =
1( ( )) ( ( ( )),..., ( ( )))nQ E t Q E t Q E t=
where
parameter estimation:
)
( ) ( ) ( )( ) ( ) ( )s t s t s tE t M E t C E t D•
= + +
1( ( )) ( ( ( )),..., ( ( )))nQ E t Q E t Q E t=
1,)( ii tE θ<
2,1, )( iii tE θθ <<
)(, tEidi i<θ
0 for
1 for( ( )) :
...
for
i
i
Q E t
d
=
parameter estimation:
(i) estimation of thresholds
(ii) calculation of matrices and vectorsdescribing the system in between thresholds
)2
1
0
l
M E C E D Eαα ακκ κ
α
∗ −∗ ∗ ∗
= ∞
+ + −∑ &
( ), ( ), ( )ij i im c d∗ ∗ ∗l
Gene-Environment Networks
min
Chebychev (maximum) norm
Gene-Environment Networks
Gene-Environment Networks
if gene j regulates gene i
otherwise
,i iξ ζl
1:
0i jχ =
mixed integer programming
( 1,2,..., )j n=
subject to
min
Gene-Environment Networks
)2
1
0
l
M E C E D Eαα ακκ κ
α
∗ −∗ ∗ ∗
= ∞
+ + −∑ &
( ), ( ), ( ), ( ), ( ), ( )ij i i ij i im c d χ ξ ζ∗ ∗ ∗l l
n
χ α≤∑ ( 1,2,..., )j n=
( 1,2,..., )m=l1
1
1
,min
&
ij ji
n
ii
n
ii
ii im
χ α
ξ β
ζ γ
δ
=
=
=
≤
≤
≤
≥
∑
∑
∑
l l
overall box constraints
( 1,2,..., )i n=
Trehalose UDP-Glucose
Glycogen
Glucose-1-Phosphate
TPS3 GSY2
NTH2
UGP1
GLC3
GPH1
Gene-Environment Networks
Glucose Glucose-6-Phosphate
Glycolysis pathway
HXK1
PGM1
knockoutglycogen metabolism pathway in yeast Saccharomyces cerevisiae
Gene-Environment Networks
min
( ), ( ), ( )ij i im c d∗ ∗ ∗l
( 1, . . . , )( , ) ( )n
i j i j j j np m y yα∗ =≤∑
subject to
)2
1
0
l
M E C E D Eαα ακκ κ
α
∗ −∗ ∗ ∗
= ∞
+ + −∑ &
GSIP relaxation
set of combined environmental effects combined environmental effects combined environmental effects combined environmental effects :
( , ) :Y C D∗ ∗ =
1,..., 1,...,1,...,
( 0, ) ( 0, )i ii n i n
m
c d∗ ∗
= ==
× ∏ ∏l
l
1
1
1
, m in
( 1, . . . , )( , ) ( )
( , ) ( )
&
i j i j ji
n
i ii
n
i ii
i i i
mq c y y
d y y
m
β
ζ γ
δ
=
∗
=
∗
=
=≤
≤
≥
∑
∑
∑
l l ll
o v e ra ll b o x c o n s t ra in ts
( ( , ))y Y C D∗ ∗∈
( 1, . . . , )i n=
General. Semi-Infinite Programming
2C
I, K, L finite
)(τψτ
:),(),( 0C∈⋅⋅⋅ ϕψ∈∀∃ ∃
ψ
GSIP – Structural Stability
Jongen, W.
: structurally stable
global local global
)(⋅εnIR
asymptotic
effect
homeom.
⇔
ψ
),( τϕ ⋅
Thm. (W. 1999/2003, 2006):
⇔
ξ
GSIP – Structural Stability
GSIP – Structural Stability
Spline Approximation
( )22
1, , 1, ,
1 1 1
2
= ( )U
L
n n n Ei j i jj EE
i= i= =
f E E dEλ α
ααα α α α
α ∞
′′
∑∑ ∑ ∫:penalty term
21
=0
( ,C,D) = ( ) C( ) D( )l
PRSS M M E E E E E E−
κ κ κ κ κ κκ ∞
+ + −
+
∑)
min :
penalty term
.
Tikhonov regularization (ridge regression)
( )
( )
222, , 2, ,
1 1 1
223, 3,
1 1
( )
( )
U
L
U
L
n m n Ei i
E Ei= = =
n n Ei i
EEi= =
f E E dE
f E dE
µ
ς
α
α α
α
αα
α α α αα ∞
α α α αα ∞
′′+
′′+
∑∑ ∑ ∫
∑ ∑ ∫
l l
l
l
)
Spline Approximation
( )22
1, , 1, ,
1 1 1
2
= ( )U
L
n n n Ei j i jj EE
i= i= =
f E E dEλ α
ααα α α α
α ∞
′′
∑∑ ∑ ∫:penalty term
21
=0
( ,C,D) = ( ) C( ) D( )l
PRSS M M E E E E E E−
κ κ κ κ κ κκ ∞
+ + −
+
∑)
min :
penalty term
.
conic quadratic programming
interior points methods
21 2 3 2
21
22
23
( )
( )
( )
( )
0
2
i j i j
2
i i
2
i i
t
U , , t
V M
W N
Z R
t
θ θ θ
θ
θ
θ
∞
α α∞
α α∞
α α∞
≤
≤
≤
≤
≤
l l
m in
s .t.( )
( )
222, , 2, ,
1 1 1
223, 3,
1 1
( )
( )
U
L
U
L
n m n Ei i
E Ei= = =
n n Ei i
EEi= =
f E E dE
f E dE
µ
ς
α
α α
α
αα
α α α αα ∞
α α α αα ∞
′′+
′′+
∑∑ ∑ ∫
∑ ∑ ∫
l l
l
l
)
Spline Approximation
21
=0
( ,C,D) = ( ) C( ) D( )l
PRSS M M E E E E E E−
κ κ κ κ κ κκ ∞
+ + −
+
∑)
min :
penalty term
.
conic quadratic programming
interior points methods
21 2 3 2
21
22
23
( )
( )
( )
( )
0
2
i j i j
2
i i
2
i i
t
U , , t
V M
W N
Z R
t
θ θ θ
θ
θ
θ
∞
α α∞
α α∞
α α∞
≤
≤
≤
≤
≤
l l
m in
s .t.
TEM Model
TEM Model
Example of stabilityArticle 2, Kyoto Protocol
( 1) ( ) ( )
( )
k k k
kM+
=
E E EM M M
Dynamics and Control in CO 2-Emission Reduction
( 1) ( ) ( )0
k k k+ = +
E E E
( +1) ( ) ( ) IE IM IEk k k=
( )( )
0kkM
u
= +
E E EM M M
( 1) ( ) ( )
( )
k k k
kM+
=
E E EM M M
Dynamics and Control in CO 2-Emission Reduction
( 1) ( ) ( )0
k k k+ = +
E E E
( +1) ( ) ( ) IE IM IEk k k=
( )( )
0kkM
u
= +
E E EM M M
( ) ( ) ( ) 2( ) ( ) ( ) ( ) ( ) ( )
( ) ( )
( )1E (E , ) (E , ) ( )(E , ) 1
2
k k kk k k k k ki i
i i i i ik k
W Wa t b t b'b t
h h
∆ ∆≈ + + −
( ) ( ) ( ) ( )E (E , ) (E , )t t t ti i i id a t dt b t dW= +
Gene-Environmental and Financial Dynamics
.
( +1) ( ) ( ) IE IM IEk k k=
( ) ( )2i i i ik kh h
Modeling
Testing
Prediction
Stability
•
•
•
•
( ) ( ) ( ) 2( ) ( ) ( ) ( ) ( ) ( )
( ) ( )
( )1E (E , ) (E , ) ( )(E , ) 1
2
k k kk k k k k ki i
i i i i ik k
W Wa t b t b'b t
h h
∆ ∆≈ + + −
( ) ( ) ( ) ( )E (E , ) (E , )t t t ti i i id a t dt b t dW= +
Gene-Environmental and Financial Dynamics
.
( +1) ( ) ( ) IE IM IEk k k=
( ) ( )2i i i ik kh h
Modeling
Testing
Prediction
Stability
•
•
•
•
Regulatory Networks: Errors and Uncertainty
Errors uncorrelated Errors correlated Fuzzy values
Interval arithmetics Ellipsoidal calculus Fuzzy arithme tics
θθ11
θθ22
Regulatory Networks and and
Ellipsoidal Calculus
Assumption:Interacting groups (clusters) of genetic and environmental variables
Clustered variables (errors) are correlated••
Regulatory Networks ― Ellipsoidal Calculus
How can we model the time-discrete dynamics of the ellipsoidal states of clusters?
1. Clustering (Groups of genes / groups of environmental items)
2. Assign ellipsoids(Center = measurement value, configuration matrix = covariances)
3. Regulatory system
Regulatory Networks ― Ellipsoidal Calculus
3. Regulatory system(Interaction of clusters defined by affine-linear coupling rules)
4. Parameter identification
1) Clustering
Identify groups (clusters) of jointly acting genetic and environmental variables
Regulatory Networks ― Ellipsoidal Calculus
disjoint
overlapping
2) Interaction of Genetic Clusters
Regulatory Networks ― Ellipsoidal Calculus
3) Interaction of Environmental Clusters
Regulatory Networks ― Ellipsoidal Calculus
3) Interaction of Genetic & Environmental Clusters
Regulatory Networks ― Ellipsoidal Calculus
Determine the degree of connectivity⇒⇒⇒⇒
Task:
Identify and analyze highly interconnected systems of clusters ofgenetic and environmental data based on ellipsoidal measurement data.
Calculate predictions of the ellipsoidal states.
••
Regulatory Networks ― Ellipsoidal Calculus
Assume: Affine-linear coupling rules.
••
⇒⇒⇒⇒ Ellipsoidal Calculus
Clusters and Ellipsoids:
Genetic clusters: C1,C2,…,CREnvironmental clusters: D1,D2,…,DS
Genetic ellipsoids: X1,X2,…,XR Xi = E (µi,Σi) Environmental ellipsoids: E1,E2,…,ES, Ej = E (ρj,Πj)
Regulatory Networks ― Ellipsoidal Calculus
Environmental ellipsoids: E1,E2,…,ES, Ej = E (ρj,Πj)
Regulatory Networks ― Ellipsoidal Calculus
Regulatory Networks ― Ellipsoidal Calculus
r=1
The Regression Problem:
Regulatory Networks ― Ellipsoidal Calculus
Maximize (overlap of ellipsoids)
∑ ∑ ∑= = =
∩+∩T R
r
R
rrrrr EEXX
1 1 1
)()()()( ˆˆκ
κκκκ
measurement
prediction
Measures for the size of intersection:
• Volume (→ ellipsoid matrix determinant)
• Sum of squares of semiaxes (→ trace of configuration matrix)
• Length of largest semiaxes (→ eigenvalues of configuration matrix)
Regulatory Networks ― Ellipsoidal Calculus
( )rr Π,µE
rµ
Thank you very much for your attention!
References:
http://www3.iam.metu.edu.tr/iam/images/7/73/Willi-CV.pdf