Abstract
BackgroundBiological networks tend to have high interconnectivity, complex topologies and multiple types of interactions. This renders difficult the identification of sub-networks that are involved in condition- specific responses. In addition, we generally lack scalable methods that can reveal the information flow in gene regulatory and biochemical pathways. Doing so will help us to identify key participants and paths under specific environmental and cellular context.ResultsThis paper introduces the theory of network flooding, which aims to address the problem of network minimization and regulatory information flow in gene regulatory networks. Given a regulatory biological network, a set of source (input) nodes and optionally a set of sink (output) nodes, our task is to find (a) the minimal sub-network that encodes the regulatory program involving all input and output nodes and (b) the information flow from the source to the sink nodes of the network. Here, we describe a novel, scalable, network traversal algorithm and we assess its potential to achieve significant network size reduction in both synthetic and E. coli networks. Scalability and sensitivity analysis show that the proposed method scales well with the size of the network, and is robust to noise and missing data.ConclusionsThe method of network flooding proves to be a useful, practical approach towards information flow analysis in gene regulatory networks. Further extension of the proposed theory has the potential to lead in a unifying framework for the simultaneous network minimization and information flow analysis across various “omics” levels.
Highlights
Biological networks tend to have high interconnectivity, complex topologies and multiple types of interactions
In this paper, we have presented the method of network flooding that aims to minimize regulatory networks in order to capture core regulatory patterns and information flow for specific biological conditions
When network flooding was applied in the reconstructed E. coli regulatory network, it was able to reduce its size producing meaningful and statistical significant results
Summary
Biological networks tend to have high interconnectivity, complex topologies and multiple types of interactions. If no sink nodes are specified, our task is reduced to identify the underlying pathways and nodes that are recipients of the information propagated from the source node(s) In this context, source nodes can be thought as cellular components that are sensitive to environmental fluctuations, and they can propagate this information to their downstream targets. Source nodes can be thought as cellular components that are sensitive to environmental fluctuations, and they can propagate this information to their downstream targets In bacteria, this set includes intracellular and transmembrane receptors that participate in complex behaviors, such as chemotaxis and quorum sensing, transcription factors and response-specific proteins that are (in)activated by external environmental stimuli, and sigma factors that initiate system-wide regulatory responses to environmental changes, such as heat shocks and nutrient limitation. Some examples include enzymes that participate in metabolic reactions, and proteins that are responsible for complex traits, such as stress-response proteins, motility genes, and genes involved in aerobic respiration
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