title: Closed-loop learning control of bio-networks.
abstract: A general goal of systems biology is to acquire a detailed quantitative understanding of the life-sustaining interactions between genes and proteins. There arises an interesting question of whether these network dynamics can be controlled externally. In the open-loop approach to experimental biology, a control design would be chosen based on a desired target response and modeling with all the available knowledge about the system. If the system is not completely understood or disturbances occur, then unexpected deviations from the desired response can arise. A means to circumvent this difficulty is to optimize the controls in a closed-loop operation by modifying successive input controls based on the performance of previous controls. This paper presents a simulation of closed-loop learning control applied to biological systems in order to generate a desired response. The most powerful advantage of this technique is that the controls are deduced based on experimental results and the process can operate without a model for the underlying biochemical network. This feature eliminates the problem of faulty predictions as well as the need for a detailed understanding of the underlying molecular pathways, suggesting that biological systems can be controlled even before the post-systems biology era.
authors.affiliation: Department of Chemistry, Princeton University, Princeton, NJ 08544, USA.
authors.forename: Jason
authors.initials: J
authors.lastname: Ku
authors.forename: Xiao-Jiang
authors.initials: XJ
authors.lastname: Feng
authors.forename: Herschel
authors.initials: H
authors.lastname: Rabitz
mesh_terms: D001185:Artificial Intelligence; D002453:Cell Cycle; D019295:Computational Biology; D008954:Models, Biological; D014176:Protein Biosynthesis; D012343:RNA, Transfer / Q000235:genetics / Q000378:metabolism; D012441:Saccharomyces cerevisiae / Q000166:cytology; D049490:Systems Biology
chemical_list: D012343:RNA, Transfer