8,459 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
Applications of Biological Cell Models in Robotics
In this paper I present some of the most representative biological models
applied to robotics. In particular, this work represents a survey of some
models inspired, or making use of concepts, by gene regulatory networks (GRNs):
these networks describe the complex interactions that affect gene expression
and, consequently, cell behaviour
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
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
Developmental motifs reveal complex structure in cell lineages
Many natural and technological systems are complex, with organisational structures that exhibit characteristic patterns, but defy concise description. One effective approach to analysing such systems is in terms of repeated topological motifs. Here, we extend the motif concept to characterise the dynamic behaviour of complex systems by introducing developmental motifs, which capture patterns of system growth. As a proof of concept, we use developmental motifs to analyse the developmental cell lineage of the nematode Caenorhabditis elegans, revealing a new perspective on its complex structure. We use a family of computational models to explore how biases arising from the dynamics of the developmental gene network, as well as spatial and temporal constraints acting on development, contribute to this complex organisation
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