Reduction of Complex Signaling Networks to a Representative Kernel

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.

Aggregation Algorithm Towards Large-Scale Boolean Network Analysis

“…, s, an input-state cycles A i k , is obtained and (8) holds, and the composition of A i k 's is equal to A. Hence, all the attractors of the Boolean network (2), can be revealed by (7). ⇤ As the attractors of the Boolean network (2), cannot be longer than 2 n , their projections onto each subnetwork are shorter than or equal to 2 n as well.…”

“…Besides the genetic regulatory networks, Boolean networks can be also used to model other biological interactions, such as biomolecular signaling pathways [3]. Although Boolean networks are not as detailed as continuous models given in the form of differential equations [4], they have been widely and successfully used in Systems Biology [5]- [7]. Unlike continuous models that usually involve several parameters, which are difficult or even impossible to be estimated or inferred, Boolean networks are parameter free models.…”

Predicting Physical Interactions between Protein Complexes

“…Capturing a higher order perspective of the complex molecular networks in a cell has been demonstrated previously to offer valuable new insights (38). It has also been suggested that cells could be interpreted as higher order networks of protein molecular machines, transforming and passing information to each other (5).…”

Improved Feed-Forward Command Governor Strategies for Constrained Discrete-Time Linear Systems

“…Conditions (20), (21) are crucial because they allow one to guarantee constraints fulfilment by exploiting only the knowledge of the sequence of past applied commands in (5). However, the storage of such a sequence can be avoided by introducing the translated state that satisfies (23) under the assumption . By using such a definition and remembering that , one may rewrite the sufficient condition (21) as (24) Finally, we can denote (25) as the set of all admissible FF-CG commands for a given sequence .…”