Uncertainty is an intrinsic phenomenon in control of gene regulatory networks (GRNs). The presence of uncertainty is related to impreciseness of GRN models due to: (1) Errors caused by imperfection of measurement devices and (2) Models' inability to fully capture a complex structure of the GRN. Consequently, there is a discrepancy between actual behaviour of the GRN and what is predicted by its mathematical model. This can result in false control signals, which can drive a cell to an undesirable state. To address the problem of control under uncertainties, a risk-sensitive control paradigm is proposed. Robustness is accomplished by minimisation of the mean exponential cost as opposed to, for instance, minimisation of the mean square cost by risk-neutral controllers. The authors derive an optimal risk-sensitive controller when a GRN is modelled by a context-sensitive probabilistic Boolean network (CSPBN). By using a relation between the relative entropy and free-energy, a relative stability of the cost achieved by the risk-sensitive controller is demonstrated when the distribution of the CSPBN attractors is perturbed, as opposed to the cost of the risk-neutral controller that exhibits increase. The use of the relation between the relative entropy and free-energy to analyse the influence of a particular attractor on the robustness of the controller is studied. The efficiency of the risk-sensitive controller is tested for the CSPBN obtained from the study of malignant melanoma.
Description
Welcome to IEEE Xplore 2.0: Robust control of uncertain context-sensitive probabilistic Boolean networks
%0 Journal Article
%1 5174556
%A Denic, S.Z.
%A Vasic, B.
%A Charalambous, C.D.
%A Palanivelu, R.
%D 2009
%J Systems Biology, IET
%K boolean network
%N 4
%P 279-295
%R 10.1049/iet-syb.2008.0121
%T Robust control of uncertain context-sensitive probabilistic Boolean networks
%U http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?isnumber=5174550&arnumber=5174556
%V 3
%X Uncertainty is an intrinsic phenomenon in control of gene regulatory networks (GRNs). The presence of uncertainty is related to impreciseness of GRN models due to: (1) Errors caused by imperfection of measurement devices and (2) Models' inability to fully capture a complex structure of the GRN. Consequently, there is a discrepancy between actual behaviour of the GRN and what is predicted by its mathematical model. This can result in false control signals, which can drive a cell to an undesirable state. To address the problem of control under uncertainties, a risk-sensitive control paradigm is proposed. Robustness is accomplished by minimisation of the mean exponential cost as opposed to, for instance, minimisation of the mean square cost by risk-neutral controllers. The authors derive an optimal risk-sensitive controller when a GRN is modelled by a context-sensitive probabilistic Boolean network (CSPBN). By using a relation between the relative entropy and free-energy, a relative stability of the cost achieved by the risk-sensitive controller is demonstrated when the distribution of the CSPBN attractors is perturbed, as opposed to the cost of the risk-neutral controller that exhibits increase. The use of the relation between the relative entropy and free-energy to analyse the influence of a particular attractor on the robustness of the controller is studied. The efficiency of the risk-sensitive controller is tested for the CSPBN obtained from the study of malignant melanoma.
@article{5174556,
abstract = {Uncertainty is an intrinsic phenomenon in control of gene regulatory networks (GRNs). The presence of uncertainty is related to impreciseness of GRN models due to: (1) Errors caused by imperfection of measurement devices and (2) Models' inability to fully capture a complex structure of the GRN. Consequently, there is a discrepancy between actual behaviour of the GRN and what is predicted by its mathematical model. This can result in false control signals, which can drive a cell to an undesirable state. To address the problem of control under uncertainties, a risk-sensitive control paradigm is proposed. Robustness is accomplished by minimisation of the mean exponential cost as opposed to, for instance, minimisation of the mean square cost by risk-neutral controllers. The authors derive an optimal risk-sensitive controller when a GRN is modelled by a context-sensitive probabilistic Boolean network (CSPBN). By using a relation between the relative entropy and free-energy, a relative stability of the cost achieved by the risk-sensitive controller is demonstrated when the distribution of the CSPBN attractors is perturbed, as opposed to the cost of the risk-neutral controller that exhibits increase. The use of the relation between the relative entropy and free-energy to analyse the influence of a particular attractor on the robustness of the controller is studied. The efficiency of the risk-sensitive controller is tested for the CSPBN obtained from the study of malignant melanoma.},
added-at = {2009-10-02T22:59:23.000+0200},
author = {Denic, S.Z. and Vasic, B. and Charalambous, C.D. and Palanivelu, R.},
biburl = {https://www.bibsonomy.org/bibtex/2138a70b4292400fab36deaf8d3947f70/wnpxrz},
description = {Welcome to IEEE Xplore 2.0: Robust control of uncertain context-sensitive probabilistic Boolean networks},
doi = {10.1049/iet-syb.2008.0121},
interhash = {26b4edf0d9f5c566dbf2458ff99cdb53},
intrahash = {138a70b4292400fab36deaf8d3947f70},
issn = {1751-8849},
journal = {Systems Biology, IET},
keywords = {boolean network},
month = {July },
number = 4,
pages = {279-295},
timestamp = {2009-10-02T22:59:23.000+0200},
title = {Robust control of uncertain context-sensitive probabilistic Boolean networks},
url = {http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?isnumber=5174550&arnumber=5174556},
volume = 3,
year = 2009
}