Networks, systems biology and data analysis

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Network Reduction: Science Signalling
Network Reduction: Science Signalling

In my research, I always seem to be somewhere near the extremes of having too much data or not having enough data to address the questions I am asking. Even in experimental design, there seems to be a similar dichotomy: how to find the answer when you can only realistically use eight microscope slide preparations in an experiment, or how to find the answer from gigabases of DNA sequence (your own or databased) or thousands of microarray spots or hundreds of genotypes.

Modelling the dynamics and responses of gene signalling networks requires analysis of large numbers of multiple and complex interactions. Parts of many network are redundant, and control or feedback can be achieved at multiple points in most biological systems. Our paper in a collaborative project with Kwang-Hyun Cho’s lab and colleagues from the Daejeon, Korea shows how to reduce large and complex signalling networks to much smaller kernels that preserve the dynamic properties of the original networks. These network kernels are amenable to computer simulation, manipulation and perturbation, for example to examine large-scale effects on stability and robustness. They can also be used as a framework around a detailed (non-reduced) network which is being studied, putting it into the broader context of all the interactions and signalling in a system. Although the reduced nodes or vertices and edges do not necessarily correspond to any features in the original network, we were interested to

Simple examples of network reduction
Simple examples of network reduction

find that nodes with obvious relationship to the original network – those which did not undergo a high degree of reduction – were often related to genes that showed high evolutionary conservation and carried out core and less redundant cellular processes.

Our network reduction paper was selected for the cover illustration in Science Signaling, and also an Editor’s Summary: “Reducing Complexity The large and complex nature of the biochemical regulatory networks that govern cell behavior provides a major challenge to the systematic analysis of cell signaling. However, most processes that reduce network complexity fail to reproduce the dynamic properties of the original network. Kim et al. describe an algorithmic approach to network reduction and simplification that preserves the dynamics of the network. They applied their approach to several networks in species from bacteria to humans, producing simplified networks called “kernels.” Examination of the genes represented by the kernel nodes provided insight into the evolution of these core network genes. Furthermore, the genes represented by the kernel nodes were enriched in disease-associated genes and drug targets, suggesting that this type of analysis may be therapeutically beneficial.”

This is the first blog I’ve written largely about one of my own papers; although not primarily using plant examples, I think the implications for modelling of gene networks in plants are enormous. I am looking forward now to trying this approach in analysis of some of the disease resistance pathways in plants: maybe the kernel will help understand the nature and requirements for redundancy and rapid evolution of these pathways. I hope other papers with strong implications for plant science from complementary fields will feature more in AoBBlog.com in the future.

Citation: Kim J-R, Kim J, Kwon Y-K, Lee H-Y, Heslop-Harrison P, Cho K-H. 2011. Reduction of complex signaling networks to a representative kernel. Science Signaling 4, ra35 . Available from: http://dx.doi.org/10.1126/scisignal.2001390

Abstract: The network of biomolecular interactions that occurs within cells is large and complex. When such a network is analyzed, it can be helpful to reduce the complexity of the network to a “kernel” that maintains the essential regulatory functions for the output under consideration. We developed an algorithm to identify such a kernel and showed that the resultant kernel preserves the network dynamics. Using an integrated network of all of the human signaling pathways retrieved from the KEGG (Kyoto Encyclopedia of Genes and Genomes) database, we identified this network’s kernel and compared the properties of the kernel to those of the original network. We found that the percentage of essential genes to the genes encoding nodes outside of the kernel was about 10%, whereas ~32% of the genes encoding nodes within the kernel were essential. In addition, we found that 95% of the kernel nodes corresponded to Mendelian disease genes and that 93% of synthetic lethal pairs associated with the network were contained in the kernel. Genes corresponding to nodes in the kernel had low evolutionary rates, were ubiquitously expressed in various tissues, and were well conserved between species. Furthermore, kernel genes included many drug targets, suggesting that other kernel nodes may be potential drug targets. Owing to the simplification of the entire network, the efficient modeling of a large-scale signaling network and an understanding of the core structure within a complex framework become possible.


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