title: Driving Cells to the Desired State in a Bimodal Distribution through Manipulation of Internal Noise with Biologically Practicable Approaches.
abstract: The stochastic nature of gene regulatory networks described by Chemical Master Equation (CME) leads to the distribution of proteins. A deterministic bistability is usually reflected as a bimodal distribution in stochastic simulations. Within a certain range of the parameter space, a bistable system exhibits two stable steady states, one at the low end and the other at the high end. Consequently, it appears to have a bimodal distribution with one sub-population (mode) around the low end and the other around the high end. In most cases, only one mode is favorable, and guiding cells to the desired state is valuable. Traditionally, the population was redistributed simply by adjusting the concentration of the inducer or the stimulator. However, this method has limitations; for example, the addition of stimulator cannot drive cells to the desired state in a common bistable system studied in this work. In fact, it pushes cells only to the undesired state. In addition, it causes a position shift of the modes, and this shift could be as large as the value of the mode itself. Such a side effect might damage coordination, and this problem can be avoided by applying a new method presented in this work. We illustrated how to manipulate the intensity of internal noise by using biologically practicable methods and utilized it to prompt the population to the desired mode. As we kept the deterministic behavior untouched, the aforementioned drawback was overcome. Remarkably, more than 96% of cells has been driven to the desired state. This method is genetically applicable to biological systems exhibiting a bimodal distribution resulting from bistability. Moreover, the reaction network studied in this work can easily be extended and applied to many other systems.
authors.affiliation: Department of Chemical Engineering and Biotechnology, National Taipei University of Technology, Taipei City, Taiwan.
authors.forename: Che-Chi
authors.initials: CC
authors.lastname: Shu
authors.affiliation: Department of Chemical Engineering and Biotechnology, National Taipei University of Technology, Taipei City, Taiwan.
authors.forename: Chen-Chao
authors.initials: CC
authors.lastname: Yeh
authors.affiliation: Department of Chemical Engineering and Biotechnology, National Taipei University of Technology, Taipei City, Taiwan.
authors.forename: Wun-Sin
authors.initials: WS
authors.lastname: Jhang
authors.affiliation: Department of Chemical Engineering and Biotechnology, National Taipei University of Technology, Taipei City, Taiwan.
authors.forename: Shih-Chiang
authors.initials: SC
authors.lastname: Lo
mesh_terms: D003198:Computer Simulation; D005786:Gene Expression Regulation / Q000502:physiology*; D053263:Gene Regulatory Networks / Q000502:physiology*; D008954:Models, Biological; D059629:Signal-To-Noise Ratio; D013269:Stochastic Processes