




Inverse Perturbation for Optimal Intervention in Gene Regulatory Networks
Motivation: Analysis and intervention in the dynamics of gene regulatory
networks is at the heart of emerging efforts in the development of
modern treatment of numerous ailments including cancer. The ultimate
goal is to develop methods to intervene in the function of living
organisms in order to drive cells away from a malignant state into a
benign form. A serious limitation of much of the previous work in
cancer network analysis is the use of external control, which
requires intervention at each time step, for an indefinite time
interval. This is in sharp contrast to the proposed approach, which
relies on the solution of an inverse perturbation problem to
introduce a onetime intervention in the structure of regulatory
networks. This isolated intervention transforms the attractor set of
the dynamic system to a unique attractor characterized by the
desired steadystate distribution.
Results:
We formulate the optimal intervention problem in gene regulatory
networks as a minimalperturbation of the network in order to force
it to converge to a desiredsteadystate distribution of gene
regulation. We cast optimal intervention in gene regulation as a
convex optimization problem, thus providing a globally optimal
solution which can be efficiently computed for networks with
thousands of nodes. The criteria adopted for optimality is chosen to
minimize potential adverse effects as a consequence of the
intervention strategy. We consider a perturbation that minimizes (i)
the overall energy of change between the original and controlled
networks and (ii) the time needed to reach the desired steadystate
of gene regulation. Furthermore, we show that there is an inherent
tradeoff between minimizing the energy of the perturbation and the
convergence rate to the desired distribution. We apply the proposed
control to the Human melanoma gene regulatory network.
MATLAB & MATHEMATICA Code:
DATA: Download the probability transition matrix of the Human melanoma network: P_matrix.xls
CODE: The MATLAB code uses CVX, a package for specifying and solving convex programs. One needs to download and install the CVX package before running the MATLAB program Min_energy_perturbation.m . For CVX installation instructions, please follow the guidelines in http://cvxr.com/cvx/download
Download the MATLAB code: Min_energy_perturbation.m with output the minimalperturbation energy matrix.xls
Download the log file of the code: log file.txt
The code is also displayed below:
0001 %% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
0002 % The program "Min_energy_perturbation.m" reads a square matrix, the
0003 % probability transition matrix of the network, "P_matrix.xls" in excel format and returns the outputs "C", "nu",
0004 % "gamma", and "mu" defined as follows:
0005 % C: the minimal energy perturbation matrix, which forces the network to
0006 % transition from its initial steadystate to the desired steadystate pid.
0007 % nu: the Lagrange multiplier associated with the equality: pid'*(P+C) ==
0008 % pid'
0009 % gamma: the Lagrange multiplier associated with the equality C*OneVec ==
0010 % ZeroVec
0011 % mu: the Lagrange multiplier associated with the inequality P+C >= 0.
0012 %
0013 % This code uses the CVX software. For installation instructions, please
0014 % refer to: http://cvxr.com/cvx/download
0015 %% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
0016
0017
0018 %% Read the probability transition matrix "P_matrix.xls"
0019 [P,Txt,Raw]=xlsread('P_matrix');
0020
0021 %% Global variable definition
0022 N = size(P,1);
0023 OneVec = ones(N,1);
0024 ZeroVec = zeros(N,1);
0025
0026 %% Define the desired steadystate \pi_d
0027 pid1 = 0.015525 * ones(N/2,1);
0028 pid2 = 10^(4) * ones(N/2,1);
0029 pid = [pid1;pid2];
0030
0031 %% The optimization algorithm using the CVX software
0032 cvx_begin
0033 variable C(N,N);
0034 dual variable nu;
0035 dual variable gamma;
0036 dual variable mu;
0037
0038 minimize( norm(C) );
0039 subject to
0040 nu: pid'*(P+C) == pid';
0041 gamma: C*OneVec == ZeroVec;
0042 mu: P+C >= 0;
0043 cvx_end
Download the MATLAB code for Fig. 1: plot_SLEM.m
plot_Norm_C.m
plot_prop5.m
Download the MATLAB code for Fig. 2:
plot_steady_states.m
Download the MATHEMATICA code for Fig. 3:
Matrix_plot.nb






