a maximum principle for single-input boolean control networks
DESCRIPTION
A Maximum Principle for Single-Input Boolean Control Networks. Michael Margaliot School of Electrical Engineering Tel Aviv University, Israel Joint work with Dima Laschov. Layout. Boolean Networks (BNs) Applications of BNs in systems biology Boolean Control Networks (BCNs) - PowerPoint PPT PresentationTRANSCRIPT
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A Maximum Principle for Single-Input Boolean
Control Networks
Michael MargaliotSchool of Electrical EngineeringTel Aviv University, Israel
Joint work with Dima Laschov
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Layout Boolean Networks (BNs) Applications of BNs in systems biology Boolean Control Networks (BCNs) Algebraic representation of BCNs An optimal control problem A maximum principle An example Conclusions
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Boolean Networks (BNs)
A BN is a discrete-time logical dynamical system:
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1 1 1
1
( 1) ( ( ), ( )),
(1)
( 1) ( ( ), ( )),
n
n n n
x k f x k x k
x k f x k x k
1x
or
2x
and
where and is a Boolean function. ( ) {True,False}ix if
→ A finite number of possible states.
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A Brief Review of a Long History
BNs date back to the early days of switching theory, artificial neural networks, and cellular automata.
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BNs in Systems Biology
S. A. Kauffman (1969) suggested using BNs for modeling gene regulation networks.
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gene state-variable
expressed/not expressed True/False
network interactions Boolean functions
Modeling
Analysis
stable genetic state attractor
robustness basin of attraction
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BNs in Systems BiologyBNs have been used for modeling numerous
genetic and cellular networks:
1. Cell-cycle regulatory network of the budding yeast (F. Li et al, PNAS, 2004);
2. Transcriptional network of the yeast (Kauffman et al, PNAS, 2003);
3. Segment polarity genes in Drosophila melanogaster (R. Albert et al, JTB, 2003);
4. ABC network controlling floral organ cell fate in Arabidopsis (C. Espinosa-Soto, Plant Cell, 2004).
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BNs in Systems Biology
5. Signaling network controlling the stomatal closure in plants (Li et al, PLos Biology, 2006);
6. Molecular pathway between dopamine and glutamate receptors (Gupta et al, JTB, 2007);
BNs with control inputs have been used to
design and analyze therapeutic intervention strategies (Datta et al., IEEE MAG. SP, 2010, Liu et al., IET Systems Biol., 2010).
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Single—Input Boolean Control Networks
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1 1 1
1
( 1) ( ( ), ( ), ( )),
( 1) ( ( ), ( ), ( )),
n
n n n
x k f x k x k u k
x k f x k x k u k
where:
is a Boolean function
( ), ( ) {True,False}ix u
if
Useful for modeling biological networks with a
controlled input.
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Algebraic Representation of BCNsState evolution of BCNs:
Daizhan Chen developed an algebraic
representation for BNs using the semi—tensor
product of matrices.
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( ) ... ( ( (0), (0)), (1))...).x k f f f x u u
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Semi—Tensor Product of MatricesDefinition Kronecker product of
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and
p qB R
11 1( ) ( )
1
.n
mp nq
n nn
a B a B
A B R
a B a B
m nA R
m nA R Definition semi-tensor product of and p qB R
/ /( )( )α n α pA B A I B I â
where ( , ).α lcm n p
Let lcm( , )a b
lcm(6,8) 24.denote the least common multiplier of , .a b
For example,
/ /
( / ) ( / ) ( / ) ( / )
( ) ( )α n α p
mα n nα n pα p qα p
A B A I B I
â
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Semi—Tensor Product of Matrices
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A generalization of the standard matrix product to
matrices with arbitrary dimensions.
/ /( )( ).α n α pA B A I B I â
Properties:
( ) ( )A B C A B Câ â â â
( ) ( ) ( )A B C A C B C â â â
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Semi—Tensor Product of Matrices
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/ /( )( ).α n α pA B A I B I â
Example Suppose that0 1
, { , }.1 0
a b
Then
2 1
0
0( )( )
0
0
v vw
v w v w v w vwa b I I
v w v w v w vw
v vw
â â
All the minterms of the two Boolean variables.
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Algebraic Representation of Boolean Functions
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Represent Boolean values as:
1 21 0True , False .
0 1e e
1 1 2( ,..., ) ...n f nf x x M x x x â â â â
Theorem (Cheng & Qi, 2010). Any Boolean function 1 2 1 2:{ , } { , }nf e e e e
may be represented as
where is the structure matrix of .f2 2nxfM R
Proof This is the sum of products representation of .f
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Algebraic Representation of Single-Input BCNs
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( 1) ( ) ( ) x k L u k x k â â
1 1 1
1
( 1) ( ( ), ( ), ( )),
( 1) ( ( ), ( ), ( )),
n
n n n
x k f x k x k u k
x k f x k x k u k
Theorem Any BCN
may be represented as
12 2n n
LRwhere is the transition matrix of the BCN.
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BCNs as Boolean Switched Systems
( 1) ( ) ( )x k L u k x k â â
1( )u k e 2( )u k e
1( 1) ( ) x k L x k â 2( 1) ( ) x k L x k â
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Optimal Control Problem for BCNs Fix an arbitrary and an arbitrary final time
Denote Fix a vector Define a cost-functional:
Problem: find a control that maximizes
Since contains all minterms, any Boolean function of the state at time may be represented as
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0.N
1 2(0), ( 1) , ( ) , .U u u N u i e e
2 .n
r R
( ) ( , ). (4)TJ u r x N u
Uu .J
),( uNx
( , ).Tr x N u
(0)x
N
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Main Result: A Maximum Principle Theorem Let be an optimal control.
Define the adjoint by:
and the switching function by:
Then
The MP provides a necessary condition for optimality in terms of the switching function
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*u
2: 1,n
N R
( ) ( ( )) ( 1), ( ) , (5)Ts L u s s N r â â
: 0,1 1m N R
*1( ) ( 1) ( ).
1Tm s s L x s
â â â
1
2
, if ( ) 0,*( ) (6)
, if ( ) 0.
e m su s
e m s
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Comments on the Maximum Principle
The MP provides a necessary condition for
optimality.
Structurally similar to the Pontryagin MP:
adjoint, switching function, two-point
boundary value problem.
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1
2
, if ( ) 0,*( ) (6)
, if ( ) 0.
e m su s
e m s
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The Singular Case
Theorem If
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1
2
, if ( ) 0,*( ) (6)
, if ( ) 0.
e m su s
e m s
( ) 0,m s then there exists an
optimal control satisfying
and there exists an optimal control
satisfying
*u 1*( ) ,u s e
2*( ) .w s e
*w
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Proof of the MP: Transition Matrix
More generally,
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( 1) ( ) ( )x k L u k x k â â
is called the transition matrix from
time to time corresponding to the control
Recall
( ; ) ( , ; ) ( ; )x k u C k j u x j u â
( , ; ) ( 1) ( 2) ... ( ).C k j u L u k L u k L u j â â â â â â
kj .u
( , ; )C k j u
so ( 2) ( 1) ( 1) ( 1) ( ) ( ).x k L u k x k L u k L u k x k â â â â â â
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Proof of the MP: Needle Variation
Define
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*u
,( )
*( ), otherwise.
v j pu j
u j
Suppose that is an optimal control. Fix a time{0,1,..., 1}p N and 1 2{ , }.v e e
j
( )u j
p
v
1N 0
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Proof of the MP: Needle Variation
This yields
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*( ) ( , 1; *) *( ) *( )x N C N p u L u p x p â â âThen
( ) ( , 1; ) ( ) ( )
( , 1; *) *( )
x N C N p u L u p x p
C N p u L v x p
â â â
â â â
*( ) ( ) ( , 1; *) ( *( ) ) *( )x N x N C N p u L u p v x p â â âso
( *) ( ) ( *( ) ( ))
( , 1; *) ( *( ) ) *( )
T
T
J u J u r x N x N
r C N p u L u p v x p
â â â
?
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Proof of the MP: Needle Variation
Recall the definition of the adjoint
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so
( *) ( ) ( *( ) ( ))
( , 1; *) ( *( ) ) *( ).
T
T
J u J u r x N x N
r C N p u L u p v x p
â â â
?
( ) ( ( )) ( 1), ( ) , Ts L u s s N r â â
( *) ( ) ( *( ) ( ))
( 1) ( *( ) ) *( ).
T
T
J u J u r x N x N
p L u p v x p
â â â
This provides an expression for the effect of the
needle variation.
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Proof of the MP
Suppose that
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If
( *) ( ) ( 1) ( *( ) ) *( ).TJ u J u p L u p v x p â â â
so
*1( ) ( 1) ( ) 0.
1Tm p p L x p
â â â
1*( ) ,u p e take
2.v e Then 1 0
( *) ( ) ( 1) ( ) *( )0 1
0,
TJ u J u p L x p
â â â
u is also optimal. This proves the result in
the singular case. The proof of the MP is similar.
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An ExampleConsider the BCN
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1 2
2 1 2
( 1) ( ) ( )
( 1) ( ) ( ) ( )
x k u k x k
x k u k x k x k
1 2(0) (0) False.x x
Consider the optimal control problem with
and [1 0 0 0] .Tr
3N
This amounts to finding a control
steering the system to 1 2(3) (3) True.x x
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An ExampleThe algebraic state space form:
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with
( 1) ( ) ( )x k L u k x k â â
(0) [0 0 0 1]Tx
0 1 0 1 0 0 0 0
0 0 0 0 0 0 0 0.
1 0 1 0 0 1 1 1
0 0 0 0 1 0 0 0
L
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An ExampleAnalysis using the MP:
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*
*
*
1(2) (3) (2)
1
1(2)
1
[0 1 0 1] (2).
T
T
m L x
r L x
x
â â â
â â â
â
This means that (2) 0,m so 1*(2) .u e Now*(2) ( (2)) (3)
[0 1 0 1] .
T
T
L u
â
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An ExampleWe can now calculate
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*
*
1(1) (2) (1)
1
[ 1 0 0 0] (1).
Tm L x
x
â â â
â
This means that (1) 0,m so 2*(1) .u e
1 2 1*(2) , *(1) , *(0) .u e u e u e
Proceeding in this way yields
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Conclusions We considered a Mayer –type optimal control
problem for single –input BCNs. We derived a necessary condition for optimality in the
form of an MP. Further research:
(1) analysis of optimal controls in BCNs that model
real biological systems, (2) developing a geometric theory of optimal control for BCNs.
For more information, see
http://www.eng.tau.ac.il/~michaelm/
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