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Inverse perturbation for optimal intervention in gene regulatory networks
Nidhal Bouaynaya1, Roman Shterenberg, Dan Schonfeld
1Department of Systems Engineering, University of Arkansas at Little Rock, Little Rock, AR 72204, USA. nxbouaynaya@ualr.edu
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 one-time intervention in the structure of regulatory networks. This isolated intervention transforms the steady-state distribution of the dynamic system to the desired steady-state distribution.
Results:
We formulate the optimal intervention problem in gene regulatory networks as a minimal perturbation of the network in order to force it to converge to a desired steady-state 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 using standard toolboxes for convex optimization. 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 steady-state distribution of gene regulation. Furthermore, we show that there is an inherent trade-off 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.
Availability:
The MATLAB code for optimal intervention in gene regulatory networks can be found online: http://syen.ualr.edu/nxbouaynaya/Bioinformatics2010.html.
Insights
This study introduces a novel one-time intervention method for gene regulatory networks to combat cancer. By solving an inverse perturbation problem, it efficiently shifts cells from malignant to benign states with minimal network changes.
Area of Science:
- Computational Biology
- Systems Biology
- Bioinformatics
Background:
- Gene regulatory networks (GRNs) are crucial in understanding and treating diseases like cancer.
- Current cancer treatment strategies often rely on external control, requiring continuous intervention.
- A significant limitation is the need for time-step interventions, contrasting with the proposed one-time approach.
Purpose of the Study:
- To develop a method for one-time intervention in GRNs to shift malignant cells to a benign state.
- To address the limitations of external control in cancer network analysis.
- To transform the steady-state distribution of a dynamic system via a single intervention.
Main Methods:
- Formulated optimal intervention as a minimal perturbation problem for GRNs.
- Cast the problem as a convex optimization problem for efficient, globally optimal solutions.
- Utilized standard convex optimization toolboxes for computation.
Main Results:
- Achieved a globally optimal solution for intervention in gene regulation.
- Minimized adverse effects by reducing network energy change and convergence time.
- Demonstrated a trade-off between perturbation energy and convergence rate.
- Applied the control strategy to the human melanoma gene regulatory network.
Conclusions:
- The proposed method offers an efficient and globally optimal approach for one-time intervention in GRNs.
- The convex optimization framework allows for minimizing intervention impact and accelerating convergence.
- This strategy holds promise for developing novel cancer treatments by reprogramming cellular states.
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