Optimal Control of Gene Regulatory Networks with Unknown Cost Function Conference Paper

abstract

• 2018 AACC. Most of the existing methodologies for control of Gene Regulatory Networks (GRNs) assume that the immediate cost function at each state and time point is fully known. In this paper, we introduce an optimal control strategy for control of GRNs with unknown or partially-known immediate cost function. Toward this, we adopt a partially-observed Boolean dynamical system (POBDS) model for the GRN and propose an Inverse Reinforcement Learning (IRL) methodology for quantifying the imperfect behavior of experts, obtained via prior biological knowledge or experimental data. The constructed cost function then is used in finding the optimal infinite-horizon control strategy for the POBDS. The application of the proposed method using a single sequence of experimental data is investigated through numerical experiments using a melanoma gene regulatory network.

name of conference

• 2018 Annual American Control Conference (ACC)

authors

• Braga Neto, Ulisses

published proceedings

• 2018 Annual American Control Conference (ACC)

author list (cited authors)

• Imani, .., & Braga-Neto, U.

citation count

• 12

complete list of authors

• Braga-Neto, Ulisses

publication date

• January 2018

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

published in

• Proceedings of the ... American Control Conference. American Control Conference  Journal

keywords

• Cost Effectiveness Research
• Genetics

Digital Object Identifier (DOI)

• 10.23919/acc.2018.8431514

International Standard Book Number (ISBN) 13

• 9781538654286

start page

• 3939

end page

• 3944

volume

• 00

URL

• http://dx.doi.org/10.23919/acc.2018.8431514