ABSTRACT
A variety of biological networks can be modeled as logical or Boolean networks. However, a simplification of the reality to binary states of the nodes does not ease the difficulty of analyzing the dynamics of large, complex networks, such as signal transduction networks, due to the exponential dependence of the state space on the number of nodes. This paper considers a recently introduced method for finding a fairly small subnetwork, representing a collection of nodes that determine the states of most other nodes with a reasonable level of entropy. The subnetwork contains the most determinative nodes that yield the highest information gain. One of the goals of this paper is to propose an algorithm for finding a suitable subnetwork size. The information gain is quantified by the so-called determinative power of the nodes, which is obtained via the mutual information, a concept originating in information theory. We find the most determinative nodes for 36 network models available in the online database Cell Collective (http://cellcollective.org). We provide statistical information that indicates a weak correlation between the subnetwork size and other variables, such as network size, or maximum and average determinative power of nodes. We observe that the proportion represented by the subnetwork in comparison to the whole network shows a weak tendency to decrease for larger networks. The determinative power of nodes is weakly correlated to the number of outputs of a node, and it appears to be independent of other topological measures such as closeness or betweenness centrality. Once the subnetwork of the most determinative nodes is identified, we generate a biological function analysis of its nodes for some of the 36 networks. The analysis shows that a large fraction of the most determinative nodes are essential and involved in crucial biological functions. The biological pathway analysis of the most determinative nodes shows that they are involved in important disease pathways.
ABSTRACT
SAP-1 is a low molecular weight cysteine protease inhibitor (CPI) which belongs to type-2 cystatins family. SAP-1 protein purified from human seminal plasma (HuSP) has been shown to inhibit cysteine and serine proteases and exhibit interesting biological properties, including high temperature and pH stability. Heparin is a naturally occurring glycosaminoglycan (with varied chain length) which interacts with a number of proteins and regulates multiple steps in different biological processes. As an anticoagulant, heparin enhances inhibition of thrombin by the serpin antithrombin III. Therefore, we have employed surface plasmon resonance (SPR) to improve our understanding of the binding interaction between heparin and SAP-1 (protease inhibitor). SPR data suggest that SAP-1 binds to heparin with a significant affinity (KD = 158 nm). SPR solution competition studies using heparin oligosaccharides showed that the binding of SAP-1 to heparin is dependent on chain length. Large oligosaccharides show strong binding affinity for SAP-1. Further to get insight into the structural aspect of interactions between SAP-1 and heparin, we used modelled structure of the SAP-1 and docked with heparin and heparin-derived polysaccharides. The results suggest that a positively charged residue lysine plays important role in these interactions. Such information should improve our understanding of how heparin, present in the reproductive tract, regulates cystatins activity.
