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      Review and application of group theory to molecular systems biology

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          Abstract

          In this paper we provide a review of selected mathematical ideas that can help us better understand the boundary between living and non-living systems. We focus on group theory and abstract algebra applied to molecular systems biology. Throughout this paper we briefly describe possible open problems. In connection with the genetic code we propose that it may be possible to use perturbation theory to explore the adjacent possibilities in the 64-dimensional space-time manifold of the evolving genome.

          With regards to algebraic graph theory, there are several minor open problems we discuss. In relation to network dynamics and groupoid formalism we suggest that the network graph might not be the main focus for understanding the phenotype but rather the phase space of the network dynamics. We show a simple case of a C 6 network and its phase space network. We envision that the molecular network of a cell is actually a complex network of hypercycles and feedback circuits that could be better represented in a higher-dimensional space. We conjecture that targeting nodes in the molecular network that have key roles in the phase space, as revealed by analysis of the automorphism decomposition, might be a better way to drug discovery and treatment of cancer.

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          Most cited references35

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          Statistical mechanics of complex networks

          Complex networks describe a wide range of systems in nature and society, much quoted examples including the cell, a network of chemicals linked by chemical reactions, or the Internet, a network of routers and computers connected by physical links. While traditionally these systems were modeled as random graphs, it is increasingly recognized that the topology and evolution of real networks is governed by robust organizing principles. Here we review the recent advances in the field of complex networks, focusing on the statistical mechanics of network topology and dynamics. After reviewing the empirical data that motivated the recent interest in networks, we discuss the main models and analytical tools, covering random graphs, small-world and scale-free networks, as well as the interplay between topology and the network's robustness against failures and attacks.
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            Transcriptional regulatory networks in Saccharomyces cerevisiae.

            We have determined how most of the transcriptional regulators encoded in the eukaryote Saccharomyces cerevisiae associate with genes across the genome in living cells. Just as maps of metabolic networks describe the potential pathways that may be used by a cell to accomplish metabolic processes, this network of regulator-gene interactions describes potential pathways yeast cells can use to regulate global gene expression programs. We use this information to identify network motifs, the simplest units of network architecture, and demonstrate that an automated process can use motifs to assemble a transcriptional regulatory network structure. Our results reveal that eukaryotic cellular functions are highly connected through networks of transcriptional regulators that regulate other transcriptional regulators.
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              Bidirectional promoters generate pervasive transcription in yeast.

              Genome-wide pervasive transcription has been reported in many eukaryotic organisms, revealing a highly interleaved transcriptome organization that involves hundreds of previously unknown non-coding RNAs. These recently identified transcripts either exist stably in cells (stable unannotated transcripts, SUTs) or are rapidly degraded by the RNA surveillance pathway (cryptic unstable transcripts, CUTs). One characteristic of pervasive transcription is the extensive overlap of SUTs and CUTs with previously annotated features, which prompts questions regarding how these transcripts are generated, and whether they exert function. Single-gene studies have shown that transcription of SUTs and CUTs can be functional, through mechanisms involving the generated RNAs or their generation itself. So far, a complete transcriptome architecture including SUTs and CUTs has not been described in any organism. Knowledge about the position and genome-wide arrangement of these transcripts will be instrumental in understanding their function. Here we provide a comprehensive analysis of these transcripts in the context of multiple conditions, a mutant of the exosome machinery and different strain backgrounds of Saccharomyces cerevisiae. We show that both SUTs and CUTs display distinct patterns of distribution at specific locations. Most of the newly identified transcripts initiate from nucleosome-free regions (NFRs) associated with the promoters of other transcripts (mostly protein-coding genes), or from NFRs at the 3' ends of protein-coding genes. Likewise, about half of all coding transcripts initiate from NFRs associated with promoters of other transcripts. These data change our view of how a genome is transcribed, indicating that bidirectionality is an inherent feature of promoters. Such an arrangement of divergent and overlapping transcripts may provide a mechanism for local spreading of regulatory signals-that is, coupling the transcriptional regulation of neighbouring genes by means of transcriptional interference or histone modification.
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                Author and article information

                Journal
                Theor Biol Med Model
                Theoretical Biology & Medical Modelling
                BioMed Central
                1742-4682
                2011
                22 June 2011
                : 8
                : 21
                Affiliations
                [1 ]Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
                [2 ]Department of Genetics, Harvard Medical School, Boston, MA, 02115, USA
                [3 ]Department of Systems Biology, Harvard Medical School, Boston, MA, 02115, USA
                [4 ]Department of Experimental Oncology, Cross Cancer Institute, 11560 University Avenue, Edmonton, AB, T6G 1Z2, Canada
                [5 ]Department of Physics, University of Alberta, Edmonton, AB, T6G 2G7, Canada
                [6 ]Center of Cancer Systems Biology, St. Elizabeth's Medical Center, Tufts University School of Medicine, Boston, MA, 02135, USA
                Article
                1742-4682-8-21
                10.1186/1742-4682-8-21
                3149578
                21696623
                83b383ce-69ea-4e81-a43d-e61e1f75f28e
                Copyright ©2011 Rietman et al; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 5 February 2011
                : 22 June 2011
                Categories
                Review

                Quantitative & Systems biology
                Quantitative & Systems biology

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