8 research outputs found
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
Regulatory networks and connected components of the neutral space
The functioning of a living cell is largely determined by the structure of
its regulatory network, comprising non-linear interactions between regulatory
genes. An important factor for the stability and evolvability of such
regulatory systems is neutrality - typically a large number of alternative
network structures give rise to the necessary dynamics. Here we study the
discretized regulatory dynamics of the yeast cell cycle [Li et al., PNAS, 2004]
and the set of networks capable of reproducing it, which we call functional.
Among these, the empirical yeast wildtype network is close to optimal with
respect to sparse wiring. Under point mutations, which establish or delete
single interactions, the neutral space of functional networks is fragmented
into 4.7 * 10^8 components. One of the smaller ones contains the wildtype
network. On average, functional networks reachable from the wildtype by
mutations are sparser, have higher noise resilience and fewer fixed point
attractors as compared with networks outside of this wildtype component.Comment: 6 pages, 5 figure
Adaptive gene regulatory networks
Regulatory interactions between genes show a large amount of cross-species
variability, even when the underlying functions are conserved: There are many
ways to achieve the same function. Here we investigate the ability of
regulatory networks to reproduce given expression levels within a simple model
of gene regulation. We find an exponentially large space of regulatory networks
compatible with a given set of expression levels, giving rise to an extensive
entropy of networks. Typical realisations of regulatory networks are found to
share a bias towards symmetric interactions, in line with empirical evidence.Comment: 5 pages RevTe
A systems pharmacology model for inflammatory bowel disease
<div><p>Motivation</p><p>The literature on complex diseases is abundant but not always quantitative. This is particularly so for Inflammatory Bowel Disease (IBD), where many molecular pathways are qualitatively well described but this information cannot be used in traditional quantitative mathematical models employed in drug development. We propose the elaboration and validation of a logic network for IBD able to capture the information available in the literature that will facilitate the identification/validation of therapeutic targets.</p><p>Results</p><p>In this article, we propose a logic model for Inflammatory Bowel Disease (IBD) which consists of 43 nodes and 298 qualitative interactions. The model presented is able to describe the pathogenic mechanisms of the disorder and qualitatively describes the characteristic chronic inflammation. A perturbation analysis performed on the IBD network indicates that the model is robust. Also, as described in clinical trials, a simulation of anti-TNFα, anti-IL2 and Granulocyte and Monocyte Apheresis showed a decrease in the Metalloproteinases node (MMPs), which means a decrease in tissue damage. In contrast, as clinical trials have demonstrated, a simulation of anti-IL17 and anti-IFNγ or IL10 overexpression therapy did not show any major change in MMPs expression, as corresponds to a failed therapy. The model proved to be a promising <i>in silico</i> tool for the evaluation of potential therapeutic targets, the identification of new IBD biomarkers, the integration of IBD polymorphisms to anticipate responders and non-responders and can be reduced and transformed in quantitative model/s.</p></div