14 research outputs found
Evolutionary constraints on the complexity of genetic regulatory networks allow predictions of the total number of genetic interactions
Genetic regulatory networks (GRNs) have been widely studied, yet there is a
lack of understanding with regards to the final size and properties of these
networks, mainly due to no network currently being complete. In this study, we
analyzed the distribution of GRN structural properties across a large set of
distinct prokaryotic organisms and found a set of constrained characteristics
such as network density and number of regulators. Our results allowed us to
estimate the number of interactions that complete networks would have, a
valuable insight that could aid in the daunting task of network curation,
prediction, and validation. Using state-of-the-art statistical approaches, we
also provided new evidence to settle a previously stated controversy that
raised the possibility of complete biological networks being random and
therefore attributing the observed scale-free properties to an artifact
emerging from the sampling process during network discovery. Furthermore, we
identified a set of properties that enabled us to assess the consistency of the
connectivity distribution for various GRNs against different alternative
statistical distributions. Our results favor the hypothesis that highly
connected nodes (hubs) are not a consequence of network incompleteness.
Finally, an interaction coverage computed for the GRNs as a proxy for
completeness revealed that high-throughput based reconstructions of GRNs could
yield biased networks with a low average clustering coefficient, showing that
classical targeted discovery of interactions is still needed.Comment: 28 pages, 5 figures, 12 pages supplementary informatio
Corynebacterium glutamicum regulation beyond transcription: Organizing principles and reconstruction of an extended regulatory network incorporating regulations mediated by small RNA and protein-protein interactions
Corynebacterium glutamicum is a Gram-positive bacterium found in soil where
the condition changes demand plasticity of the regulatory machinery. The study
of such machinery at the global scale has been challenged by the lack of data
integration. Here, we report three regulatory network models for C. glutamicum:
strong (3040 interactions) constructed solely with regulations previously
supported by directed experiments; all evidence (4665 interactions) containing
the strong network, regulations previously supported by non-directed
experiments, and protein-protein interactions with a direct effect on gene
transcription; and sRNA (5222 interactions) containing the all evidence network
and sRNA-mediated regulations. Compared to the previous version (2018), the
strong and all evidence networks increased by 75 and 1225 interactions,
respectively. We analyzed the system-level components of the three networks to
identify how they differ and compared their structures against those for the
networks of more than 40 species. The inclusion of the sRNAs regulations
changed the proportions of the system-level components and increased the number
of modules but decreased their size. The C. glutamicum regulatory structure
contrasted with other bacterial regulatory networks. Finally, we used the
strong networks of three model organisms to provide insights and future
directions of the C. glutamicum regulatory network characterization.Comment: 32 pages, 4 figures, 1 supplementary materia
Functional architecture of Escherichia coli: new insights provided by a natural decomposition approach
The E. coli transcriptional regulatory network is shown to have a nonpyramidal architecture of independent modules governed by transcription factors, whose responses are integrated by intermodular genes
Abasy Atlas: a comprehensive inventory of systems, global network properties and systems-level elements across bacteria
Ibarra-Arellano MA, Campos-Gonzalez AI, Trevino-Quintanilla LG, Tauch A, Freyre-Gonzalez JA. Abasy Atlas: a comprehensive inventory of systems, global network properties and systems-level elements across bacteria. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION. 2016;2016: baw089.The availability of databases electronically encoding curated regulatory networks and of high-throughput technologies and methods to discover regulatory interactions provides an invaluable source of data to understand the principles underpinning the organization and evolution of these networks responsible for cellular regulation. Nevertheless, data on these sources never goes beyond the regulon level despite the fact that regulatory networks are complex hierarchical-modular structures still challenging our understanding. This brings the necessity for an inventory of systems across a large range of organisms, a key step to rendering feasible comparative systems biology approaches. In this work, we take the first step towards a global understanding of the regulatory networks organization by making a cartography of the functional architectures of diverse bacteria. Abasy (Across-bacteria systems) Atlas provides a comprehensive inventory of annotated functional systems, global network properties and systems-level elements (global regulators, modular genes shaping functional systems, basal machinery genes and intermodular genes) predicted by the natural decomposition approach for reconstructed and meta-curated regulatory networks across a large range of bacteria, including pathogenically and biotechnologically relevant organisms. The meta-curation of regulatory datasets provides the most complete and reliable set of regulatory interactions currently available, which can even be projected into subsets by considering the force or weight of evidence supporting them or the systems that they belong to. Besides, Abasy Atlas provides data enabling large-scale comparative systems biology studies aimed at understanding the common principles and particular lifestyle adaptions of systems across bacteria. Abasy Atlas contains systems and system-level elements for 50 regulatory networks comprising 78 649 regulatory interactions covering 42 bacteria in nine taxa, containing 3708 regulons and 1776 systems. All this brings together a large corpus of data that will surely inspire studies to generate hypothesis regarding the principles governing the evolution and organization of systems and the functional architectures controlling them
Partition Quantitative Assessment (PQA): A quantitative methodology to assess the embedded noise in clustered omics and systems biology data
Identifying groups that share common features among datasets through
clustering analysis is a typical problem in many fields of science,
particularly in post-omics and systems biology research. In respect of this,
quantifying how a measure can cluster or organize intrinsic groups is important
since currently there is no statistical evaluation of how ordered is, or how
much noise is embedded in the resulting clustered vector. Many of the
literature focuses on how well the clustering algorithm orders the data, with
several measures regarding external and internal statistical measures; but none
measure has been developed to statistically quantify the noise in an arranged
vector posterior a clustering algorithm, i.e., how much of the clustering is
due to randomness. Here, we present a quantitative methodology, based on
autocorrelation, to assess this problem.Comment: 9 pages, 6 figure
Lessons from the modular organization of the transcriptional regulatory network of Bacillus subtilis
Functional architecture and global properties of the Corynebacterium glutamicum regulatory network: Novel insights from a dataset with a high genomic coverage
Freyre-González JA, Tauch A. Functional architecture and global properties of the Corynebacterium glutamicum regulatory network: Novel insights from a dataset with a high genomic coverage. Journal of Biotechnology. 2017;257:199-210.Corynebacterium glutamicum is a Gram-positive, anaerobic, rod-shaped soil bacterium able to grow on a diversity of carbon sources like sugars and organic acids. It is a biotechnological relevant organism because of its highly efficient ability to biosynthesize amino acids, such as l-glutamic acid and l-lysine. Here, we reconstructed the most complete C. glutamicum regulatory network to date and comprehensively analyzed its global organizational properties, systems-level features and functional architecture. Our analyses show the tremendous power of Abasy Atlas to study the functional organization of regulatory networks. We created two models of the C. glutamicum regulatory network: all-evidences (containing both weak and strong supported interactions, genomic coverage = 73%) and strongly-supported (only accounting for strongly supported evidences, genomic coverage = 71%). Using state-of-the-art methodologies, we prove that power-law behaviors truly govern the connectivity and clustering coefficient distributions. We found a non-previously reported circuit motif that we named complex feed-forward motif. We highlighted the importance of feedback loops for the functional architecture, beyond whether they are statistically over-represented or not in the network. We show that the previously reported top-down approach is inadequate to infer the hierarchy governing a regulatory network because feedback bridges different hierarchical layers, and the top-down approach disregards the presence of intermodular genes shaping the integration layer. Our findings all together further support a diamond-shaped, three-layered hierarchy exhibiting some feedback between processing and coordination layers, which is shaped by four classes of systems-level elements: global regulators, locally autonomous modules, basal machinery and intermodular genes
Identification of network topological units coordinating the global expression response to glucose in <it>Bacillus subtilis </it>and its comparison to <it>Escherichia coli</it>
<p>Abstract</p> <p>Background</p> <p>Glucose is the preferred carbon and energy source for <it>Bacillus subtilis </it>and <it>Escherichia coli</it>. A complex regulatory network coordinates gene expression, transport and enzymatic activities, in response to the presence of this sugar. We present a comparison of the cellular response to glucose in these two model organisms, using an approach combining global transcriptome and regulatory network analyses.</p> <p>Results</p> <p>Transcriptome data from strains grown in Luria-Bertani medium (LB) or LB+glucose (LB+G) were analyzed, in order to identify differentially transcribed genes in <it>B. subtilis</it>. We detected 503 genes in <it>B. subtilis </it>that change their relative transcript levels in the presence of glucose. A similar previous study identified 380 genes in <it>E. coli</it>, which respond to glucose. Catabolic repression was detected in the case of transport and metabolic interconversion activities for both bacteria in LB+G. We detected an increased capacity for <it>de novo </it>synthesis of nucleotides, amino acids and proteins. A comparison between orthologous genes revealed that global regulatory functions such as transcription, translation, replication and genes relating to the central carbon metabolism, presented similar changes in their levels of expression. An analysis of the regulatory network of a subset of genes in both organisms revealed that the set of regulatory proteins responsible for similar physiological responses observed in the transcriptome analysis are not orthologous. An example of this observation is that of transcription factors mediating catabolic repression for most of the genes that displayed reduced transcript levels in the case of both organisms. In terms of topological functional units in both these bacteria, we found interconnected modules that cluster together genes relating to heat shock, respiratory functions, carbon and peroxide metabolism. Interestingly, <it>B. subtilis </it>functions not found in <it>E. coli</it>, such as sporulation and competence were shown to be interconnected, forming modules subject to catabolic repression at the level of transcription.</p> <p>Conclusion</p> <p>Our results demonstrate that the response to glucose is partially conserved in model organisms <it>E. coli </it>and <it>B. subtilis</it>, including genes encoding basic functions such as transcription, translation, replication and genes involved in the central carbon metabolism.</p