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Introduction to graph theory
Published in Karthik Raman, An Introduction to Computational Systems Biology, 2021
Amongst the most widely studied biological networks are protein interaction networks, as well as networks of functional associations5. Their popularity is exemplified by the existence of several databases (such as those listed in Appendix C), which catalogue these interactions and functional associations. In all these cases, proteins are the nodes of the network, with edges representing various types of associations, ranging from physical interactions/complexation to co-occurrence in PubMed abstracts as identified via text-mining algorithms, or co-expression in transcriptomes. These networks are generally undirected. While databases such as the STRING (see Figure 2.6) host functional association networks (physical interaction networks are essentially a subset of these), others such as the DIP (Database of Interacting Proteins) [14] host only experimentally determined protein–protein interactions. It is important to note that these are distinct types of networks, as elaborated in Appendix B.
Graphical Models in Molecular Systems Biology
Published in Marloes Maathuis, Mathias Drton, Steffen Lauritzen, Martin Wainwright, Handbook of Graphical Models, 2018
Biological networks can be thought of as structural summaries of biological processes such as gene regulation or protein signaling. These processes have complicated temporal and spatial aspects that we have so far entirely ignored. Furthermore, we have not detailed what the random vector X=(X1,…,Xp)T $ X=(X_1, \ldots , X_p)^{ \mathrm T } $ is intended to model in the underlying physical system. For instance, do the Xj $ X_j $ ’s refer to the concentration of molecules in a single cell or in an aggregate of multiple cells? Such a distinction may be significant, because while the networks that graphical models are intended to represent are usually conceived of at the cellular level, statistical methods are often applied to bulk or aggregate data (i.e. data that are averages over large numbers of cells). In this Section we discuss the temporal and cellular aspects and introduce some graphical models that have been used for time-varying molecular data. For a fuller treatment of dynamical and systems biology perspectives we direct the interested reader to [5] and [3] and for some statistical aspects to [35] and [27].
Kernel Methods
Published in Mark Chang, Artificial Intelligence for Drug Development, Precision Medicine, and Healthcare, 2020
The analysis of biological systems can be carried out by investigating interacting molecules through biological networks. For example, the prediction of interacting proteins to reconstruct the interaction network can be posed as a binary classification problem: given a pair of proteins, do they interact or not? Binary kernel discrimination has thus far mostly been used for distinguishing between active and inactive compounds in the context of virtual screening (Wilton et al. 2006, Harper 2001).
Supportness of the protein complex standards in PPI networks
Published in Journal of Information and Telecommunication, 2022
Milana Grbić, Vukašin Crnogorac, Milan Predojević, Aleksandar Kartelj, Dragan Matić
In recent years, many sophisticated computing methods are developed in order to enable easier processing of biological data. Since biological networks can be considered as mathematical structures – graphs, a lot of problems considered on biological networks can be represented and resolved as computational and mathematical problems on graphs. Nodes in such biological networks are biological elements (like proteins, genes, metabolites) while the edge between two nodes exists if there is some dependency between them, like physical interaction or participating in a particular biochemical process. Biological networks often have a lot of nodes and edges (several thousands or even more). Therefore, identifying functionally related elements or partitioning biological networks into smaller subnetworks is an often used approach for analyzing such networks (Grbić, Kartelj et al., 2020; Grbić, Matić et al., 2020; Hüffner et al., 2013; Liu et al., 2009; Martins, 2016).
A fast community detection algorithm using a local and multi-level label diffusion method in social networks
Published in International Journal of General Systems, 2022
Asgarali Bouyer, Khatereh Azad, Alireza Rouhi
By exploring in the real-world, many complex systems such as social networks, Internet, the World Wide Web (WWW), protein–protein interactions, and several Metabolic networks are found. Such systems can be modeled as complex networks in which entities are represented by nodes and their relationships are called edges or links (Berahmand and Bouyer 2019). For example, the WWW is a network of web pages interconnected by links. Social networks are represented by individuals as their nodes and the underlying interrelationships are represented by edges. In addition, biological networks are represented by biochemical molecules as nodes and the reaction between them by edges.