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Discrete dynamic models: Boolean networks
Published in Karthik Raman, An Introduction to Computational Systems Biology, 2021
While ODE-based dynamic models provide detailed insights into the dynamics of a given system, they also demand large amounts of data for effective parameter estimation, as well as accurate measurements of various variables, e.g. concentrations of metabolites or expression levels of different genes. In many cases, notably in the case of gene regulatory networks, both the available measurements and the required readouts, such as the activity of a gene, may be restricted to discrete values such as high, medium, and low, or, more simply, just ON and OFF. In gene regulatory networks, it is common to capture the activity of a given gene (or regulatory protein) as the net consequence of various effectors/inhibitors. For example, “Gene A is active in the absence of Gene B or Gene C and low levels of metabolite M” is a typical description of regulatory relationships in a biological system.
Graphical Models in Genetics, Genomics, and Metagenomics
Published in Marloes Maathuis, Mathias Drton, Steffen Lauritzen, Martin Wainwright, Handbook of Graphical Models, 2018
As introduced in the preceding Chapter , a biological network consists of a collection of biomolecules and their interactions that correspond to various cellular functional relationships, and is often represented as a graph with directed and/or undirected edges. Throughout the chapter, the word ‘interaction’ is used to denote the presence of an edge between two nodes, which may be directed or undirected and defined experimentally or statistically depending on the context. Examples of important biological networks include gene regulatory networks, whose directed edges represent activation or repression relationships between genes; protein-protein interaction networks, whose nodes are proteins linked together by physical binding events; metabolic networks, whose nodes are metabolites and edges reflect the chemical reactions of metabolism. Other useful networks are gene co-expression networks [49], which are phenotypic networks in which genes are linked if they share similar co-expression patterns.
Expression of Genes in Bacteria, Yeast, and Cultured Mammalian Cells
Published in Jay L. Nadeau, Introduction to Experimental Biophysics, 2017
Some experiments are readily performed by expressing the genes of interest in E. coli. These include studies of gene regulatory networks such as the “repressilator,” where the interactions among mutually repressing or enhancing promoters can be studied. The advantage of working with E. coli is that the cells are easy to grow and transform, and may be produced in large amounts and readily transferred onto any substrate of interest (microscope slides, chips, microfluidics, and so on). Selection for successful transformation is easy and efficient using antibiotics. The genotypes and phenotypes of E. coli are also well characterized for many different strains, so that unexpected interactions between the introduced plasmid and the host can be minimized.
State-flipped control and Q-learning for finite horizon output tracking of Boolean control networks
Published in International Journal of Systems Science, 2023
According to Hu et al. (2019), a goal of investigating the gene regulatory networks is to design effective therapeutic intervention strategies to influence the network dynamics. In the therapeutic intervention of gene regulatory networks, the reference output trajectory may be untrackable by using the traditional control strategy. In this case, we introduce the state-flipped control mechanism, combining with the traditional control strategy, to change the corresponding state trajectory with fewer interventions (Chen et al., 2020).