Abstract
BackgroundInteractions between genes and their products give rise to complex circuits known as gene regulatory networks (GRN) that enable cells to process information and respond to external stimuli. Several important processes for life, depend of an accurate and context-specific regulation of gene expression, such as the cell cycle, which can be analyzed through its GRN, where deregulation can lead to cancer in animals or a directed regulation could be applied for biotechnological processes using yeast. An approach to study the robustness of GRN is through the neutral space. In this paper, we explore the neutral space of a Schizosaccharomyces pombe (fission yeast) cell cycle network through an evolution strategy to generate a neutral graph, composed of Boolean regulatory networks that share the same state sequences of the fission yeast cell cycle.ResultsThrough simulations it was found that in the generated neutral graph, the functional networks that are not in the wildtype connected component have in general a Hamming distance more than 3 with the wildtype, and more than 10 between the other disconnected functional networks. Significant differences were found between the functional networks in the connected component of the wildtype network and the rest of the network, not only at a topological level, but also at the state space level, where significant differences in the distribution of the basin of attraction for the G1 fixed point was found for deterministic updating schemes.ConclusionsIn general, functional networks in the wildtype network connected component, can mutate up to no more than 3 times, then they reach a point of no return where the networks leave the connected component of the wildtype. The proposed method to construct a neutral graph is general and can be used to explore the neutral space of other biologically interesting networks, and also formulate new biological hypotheses studying the functional networks in the wildtype network connected component.
Highlights
Interactions between genes and their products give rise to complex circuits known as gene regulatory networks (GRN) that enable cells to process information and respond to external stimuli
An evolution strategy was developed to construct a neutral graph of Boolean regulatory networks that share the same state trajectory of the fission yeast cell cycle network [10]
Through simulations it was found that in the generated neutral graph, the functional networks that are not in the wildtype connected component have in general a Hamming distance more than 3 with the wildtype, and more than 10 between the other disconnected functional networks
Summary
Interactions between genes and their products give rise to complex circuits known as gene regulatory networks (GRN) that enable cells to process information and respond to external stimuli. An important part of this machinery, which determines the downstream information, are the gene regulatory networks (GRN). Because the molecular interactions within a gene regulatory network are very complex, the functional integration of the network cannot be understood only by intuitive reasoning, the need to incorporate mathematical models for its study arises. Boolean networks give a first idea of the qualitative dynamics of a gene regulatory network represented by the temporal evolution of the protein states. The dynamics of the network is computed by a set of Boolean functions for each node and an updating scheme (synchronous or asynchronous). Fixed point attractors are invariant to changes or the selection of the updating scheme, limit cycles and the size of basins of attractions may change drastically
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