8,291 research outputs found
Control of complex networks requires both structure and dynamics
The study of network structure has uncovered signatures of the organization
of complex systems. However, there is also a need to understand how to control
them; for example, identifying strategies to revert a diseased cell to a
healthy state, or a mature cell to a pluripotent state. Two recent
methodologies suggest that the controllability of complex systems can be
predicted solely from the graph of interactions between variables, without
considering their dynamics: structural controllability and minimum dominating
sets. We demonstrate that such structure-only methods fail to characterize
controllability when dynamics are introduced. We study Boolean network
ensembles of network motifs as well as three models of biochemical regulation:
the segment polarity network in Drosophila melanogaster, the cell cycle of
budding yeast Saccharomyces cerevisiae, and the floral organ arrangement in
Arabidopsis thaliana. We demonstrate that structure-only methods both
undershoot and overshoot the number and which sets of critical variables best
control the dynamics of these models, highlighting the importance of the actual
system dynamics in determining control. Our analysis further shows that the
logic of automata transition functions, namely how canalizing they are, plays
an important role in the extent to which structure predicts dynamics.Comment: 15 pages, 6 figure
Buffered Qualitative Stability explains the robustness and evolvability of transcriptional networks
The gene regulatory network (GRN) is the central decisionâmaking module of the cell. We have developed a theory called Buffered Qualitative Stability (BQS) based on the hypothesis that GRNs are organised so that they remain robust in the face of unpredictable environmental and evolutionary changes. BQS makes strong and diverse predictions about the network features that allow stable responses under arbitrary perturbations, including the random addition of new connections. We show that the GRNs of E. coli, M. tuberculosis, P. aeruginosa, yeast, mouse, and human all verify the predictions of BQS. BQS explains many of the small- and largeâscale properties of GRNs, provides conditions for evolvable robustness, and highlights general features of transcriptional response. BQS is severely compromised in a human cancer cell line, suggesting that loss of BQS might underlie the phenotypic plasticity of cancer cells, and highlighting a possible sequence of GRN alterations concomitant with cancer initiation. DOI: http://dx.doi.org/10.7554/eLife.02863.00
Graph Theory and Networks in Biology
In this paper, we present a survey of the use of graph theoretical techniques
in Biology. In particular, we discuss recent work on identifying and modelling
the structure of bio-molecular networks, as well as the application of
centrality measures to interaction networks and research on the hierarchical
structure of such networks and network motifs. Work on the link between
structural network properties and dynamics is also described, with emphasis on
synchronization and disease propagation.Comment: 52 pages, 5 figures, Survey Pape
Controllability of protein-protein interaction phosphorylation-based networks: Participation of the hub 14-3-3 protein family
Posttranslational regulation of protein function is an ubiquitous mechanism in eukaryotic cells. Here, we analyzed biological properties of nodes and edges of a human protein-protein interaction phosphorylation-based network, especially of those nodes critical for the network controllability. We found that the minimal number of critical nodes needed to control the whole network is 29%, which is considerably lower compared to other real networks. These critical nodes are more regulated by posttranslational modifications and contain more binding domains to these modifications than other kinds of nodes in the network, suggesting an intra-group fast regulation. Also, when we analyzed the edges characteristics that connect critical and non-critical nodes, we found that the former are enriched in domain-to-eukaryotic linear motif interactions, whereas the later are enriched in domain-domain interactions. Our findings suggest a possible structure for protein-protein interaction networks with a densely interconnected and self-regulated central core, composed of critical nodes with a high participation in the controllability of the full network, and less regulated peripheral nodes. Our study offers a deeper understanding of complex network control and bridges the controllability theorems for complex networks and biological protein-protein interaction phosphorylation-based networked systems.Fil: Uhart, Marina. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas. Centro CientĂfico TecnolĂłgico Conicet - Mendoza. Instituto de HistologĂa y EmbriologĂa de Mendoza Dr. Mario H. Burgos. Universidad Nacional de Cuyo. Facultad de Cienicas MĂ©dicas. Instituto de HistologĂa y EmbriologĂa de Mendoza Dr. Mario H. Burgos; ArgentinaFil: Flores, Gabriel. Eventioz/eventbrite Company; ArgentinaFil: Bustos, Diego Martin. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas. Centro CientĂfico TecnolĂłgico Conicet - Mendoza. Instituto de HistologĂa y EmbriologĂa de Mendoza Dr. Mario H. Burgos. Universidad Nacional de Cuyo. Facultad de Cienicas MĂ©dicas. Instituto de HistologĂa y EmbriologĂa de Mendoza Dr. Mario H. Burgos; Argentin
Large-scale inference and graph theoretical analysis of gene-regulatory networks in B. stubtilis
We present the methods and results of a two-stage modeling process that
generates candidate gene-regulatory networks of the bacterium B. subtilis from
experimentally obtained, yet mathematically underdetermined microchip array
data. By employing a computational, linear correlative procedure to generate
these networks, and by analyzing the networks from a graph theoretical
perspective, we are able to verify the biological viability of our inferred
networks, and we demonstrate that our networks' graph theoretical properties
are remarkably similar to those of other biological systems. In addition, by
comparing our inferred networks to those of a previous, noisier implementation
of the linear inference process [17], we are able to identify trends in graph
theoretical behavior that occur both in our networks as well as in their
perturbed counterparts. These commonalities in behavior at multiple levels of
complexity allow us to ascertain the level of complexity to which our process
is robust to noise.Comment: 22 pages, 4 figures, accepted for publication in Physica A (2006
Computational methods in cancer gene networking
In the past few years, many high-throughput techniques have been developed and applied to biological studies. These techniques such as “next generation” genome sequencing, chip-on-chip, microarray and so on can be used to measure gene expression and gene regulatory elements in a genome-wide scale. Moreover, as these technologies become more affordable and accessible, they have become a driving force in modern biology. As a result, huge amount biological data have been produced, with the expectation of increasing number of such datasets to be generated in the future. High-throughput data are more comprehensive and unbiased, but ‘real signals’ or biological insights, molecular mechanisms and biological principles are buried in the flood of data. In current biological studies, the bottleneck is no longer a lack of data, but the lack of ingenuity and computational means to extract biological insights and principles by integrating knowledge and high-throughput data. 

Here I am reviewing the concepts and principles of network biology and the computational methods which can be applied to cancer research. Furthermore, I am providing a practical guide for computational analysis of cancer gene networks
Prediction of lethal and synthetically lethal knock-outs in regulatory networks
The complex interactions involved in regulation of a cell's function are
captured by its interaction graph. More often than not, detailed knowledge
about enhancing or suppressive regulatory influences and cooperative effects is
lacking and merely the presence or absence of directed interactions is known.
Here we investigate to which extent such reduced information allows to forecast
the effect of a knock-out or a combination of knock-outs. Specifically we ask
in how far the lethality of eliminating nodes may be predicted by their network
centrality, such as degree and betweenness, without knowing the function of the
system. The function is taken as the ability to reproduce a fixed point under a
discrete Boolean dynamics. We investigate two types of stochastically generated
networks: fully random networks and structures grown with a mechanism of node
duplication and subsequent divergence of interactions. On all networks we find
that the out-degree is a good predictor of the lethality of a single node
knock-out. For knock-outs of node pairs, the fraction of successors shared
between the two knocked-out nodes (out-overlap) is a good predictor of
synthetic lethality. Out-degree and out-overlap are locally defined and
computationally simple centrality measures that provide a predictive power
close to the optimal predictor.Comment: published version, 10 pages, 6 figures, 2 tables; supplement at
http://www.bioinf.uni-leipzig.de/publications/supplements/11-01
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