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CLASSIFYING SIGNS OF REGULATORY INTERACTIONS IN GENE NETWORKS Jessica Otah
Rachel Crusius
Introduction
Data
Gene regulatory networks control many life processes. An important problem in systems biology is to reconstruct a model of the network from existing laboratory data. A major challenge is to validate predictions made by these data-driven models. While designing new experiments would be the most desirable, the cost involved is often prohibitive. Gold standard networks (GSNs), which are knowledge-driven models built from existing knowledge, have become necessary tools for validation. Therefore building comprehensive GSNs is imperative.
We also used the following references for targets of the genes listed below. Most experiments were conducted using reporter genes. • • • • •
lin26
tbx-8 / tbx-9
unc120
elt-1
scrt1
Fig. 1: First GSN, presented in [2].
Interactions from Figure 2 proposed to be direct are shown in the figure above along with their signs: • Arrow end = positive • Blunt end = negative All other interactions are indirect and not shown.
Predictions
Methods
Tissue development in C. elegans has been well studied. In [2] the authors presented a preliminary, biological model for the regulatory network based on a wildtype time course data. In [5], the authors extended the model to include interactions from knock-out data; however the authors did not include signs or directedness.
The interactions in Figure 2 were classified according to signs and directedness. Signs and directedness were determined from the matrix above as follows: • • • •
Fig. 2: Second GSN, presented in [5].
Positive regulation was assigned to nodes with a green dot Negative regulation was assigned to nodes with a red dot Direct regulation was assigned to nodes with a square Indirect regulation was assigned to nodes w/o a square
Signs and directedness were determined from the other sources by reading the articles and consulting with an expert [7].
References
nob1
The genes in the network can be categorized as follows: • Blue = maternal genes • Yellow = ectoderm (skin) genes • Grey = mesoderm (muscle) genes • Brown = genes of mixed activity • Green = other
Results
Several sources were used to infer signs and directedness of the interactions. Most inferences were made from the pairwise knockout experiments shown in the matrix [6].
We extended an existing GSN for C. elegans by 1. classifying interactions as either positive or negative (signs). 2. distinguishing between direct and indirect regulation (directedness).
elt-3
Dr. Brandilyn Stigler
1. 2. 3. 4. 5. 6. 7.
J. Ahringer (1997). Development 124. L. R. Baugh et al (2005). Development 132. T. Fukushige and M. Krause (2005). Development 132. R. Pocock et al (2004). Development 131:10. B. Stigler and H. Chamberlin (2012). BMC Systems Biology 6:1. I. Yanai et al (2008). Molecular Systems Biology 4:163. H. Chamberlin, Prof. of Mol. Gen., The Ohio State University.
• • • • •
Maternal gene interactions are all direct and positive. pal–1 only directly regulates hnd–1 and vab–7. Regulation between mesoderm genes is indirect. All negative interactions involve ectoderm genes. Ectoderm genes could be key in negative feedback loops which drive oscillations.
Analysis
Our GSN corroborates several interactions proposed to be direct in [6]: • lin–26 elt–1 • elt–1 lin–26 • lin–26 nhr–25 • nhr–25 elt–1 • pal–1 hnd–1 The distributions of the total degree (shown), in-degree, and out-degree for all interactions (shown) and direct interactions suggest that this network does not exhibit scale-free dynamics, as is expected in gene networks.
Discussion 1. We present a more comprehensive knowledge-driven model for C. elegans tissue development than currently exists. 2. It can improve validation for data-driven models. 3. It can used in building consensus models.
