Reconstruction of protein complexes by analyzing protein interaction networks
My final year research project is about identifying biologically relevant community structures in the Protein Interaction Networks. The intuition here is that networks and complex systems from different disciplines share architectural features to a higher degree (Barabasi and Oltvai, 2004). We exploit this universality by bringing insights and techniques from other paradigms such as World Wide Web (WWW) and Online Social Networks (OSN) to Protein Interaction Networks in order to effectively identify biologically relevant communities which are commonly known as protein complexes.
Protein Interaction Networks
Our study is primarily based on analyzing protein interaction networks (PIN). Interactions between proteins are modeled as a network by representing proteins as nodes and the interactions as the links. The hypothesis is that accurately identified community structures in these networks might represent protein complexes.
Protein Interaction Networks
Our study is primarily based on analyzing protein interaction networks (PIN). Interactions between proteins are modeled as a network by representing proteins as nodes and the interactions as the links. The hypothesis is that accurately identified community structures in these networks might represent protein complexes.
Some of the Protein Interaction Networks; (a) A consolidated protein interaction network constructed using two experimentally derived protein-protein interaction datasets (Gavin et al., 2006; Krogan et al., 2006) ; (b) A reliable protein interaction network constructed by applying ICD reliability scoring scheme; (c) A reliable protein interaction network constructed with PE scoring scheme
Community Detection Algorithms
Detection of community structures in PINs plays a major role in my study. Most of the traditional community detection algorithms were based on the hypothesis that members of a potential community in a network are tightly connected with each other rather than the members outside the community. Owing to the incompleteness of the PINs, these traditional approaches may fail to capture the sparse protein communities (complexes), leading to lower performances. Incorporation of biological information with algorithms has become a viable approach to address this problem and a number of techniques has been devised on that basis as well. But the lower performance values of the techniques in general suggest developing more effective and efficient computational methods to detect protein complexes by analyzing PINs.
We extend our study beyond the traditional methods by combining multiple forms of biological knowledge with the community detection algorithms to detect biologically relevant protein communities (protein complexes).
References
Albert-Laszlo Barabasi and Zoltan N Oltvai. Network biology: understanding the cell's functional organization. Nature Reviews Genetics, 5(2):110-113, 2004.
Anne-Claude Gavin, Patrick Aloy, Paola Grandi, Roland Krause, Markus Boesche, Martina Marzioch, Christina Rau, Lars Juhl Jensen, Sonja Bastuck, Birgit Dumpelfeld, et al. Proteome survey reveals modularity of the yeast cell machinery. Nature, 440(7084):631-636, 2006.
Nevan J Krogan, Gerard Cagney, Haiyuan Yu, Gouqing Zhong, Xinghua Guo, Alexandr Ignatchenko, Joyce Li, Shuye Pu, Nira Datta, Aaron P Tikuisis, et al. Global landscape of protein complexes in the yeast saccharomyces cerevisiae. Nature, 440(7084):637-643, 2006.
Detection of community structures in PINs plays a major role in my study. Most of the traditional community detection algorithms were based on the hypothesis that members of a potential community in a network are tightly connected with each other rather than the members outside the community. Owing to the incompleteness of the PINs, these traditional approaches may fail to capture the sparse protein communities (complexes), leading to lower performances. Incorporation of biological information with algorithms has become a viable approach to address this problem and a number of techniques has been devised on that basis as well. But the lower performance values of the techniques in general suggest developing more effective and efficient computational methods to detect protein complexes by analyzing PINs.
We extend our study beyond the traditional methods by combining multiple forms of biological knowledge with the community detection algorithms to detect biologically relevant protein communities (protein complexes).
References
Albert-Laszlo Barabasi and Zoltan N Oltvai. Network biology: understanding the cell's functional organization. Nature Reviews Genetics, 5(2):110-113, 2004.
Anne-Claude Gavin, Patrick Aloy, Paola Grandi, Roland Krause, Markus Boesche, Martina Marzioch, Christina Rau, Lars Juhl Jensen, Sonja Bastuck, Birgit Dumpelfeld, et al. Proteome survey reveals modularity of the yeast cell machinery. Nature, 440(7084):631-636, 2006.
Nevan J Krogan, Gerard Cagney, Haiyuan Yu, Gouqing Zhong, Xinghua Guo, Alexandr Ignatchenko, Joyce Li, Shuye Pu, Nira Datta, Aaron P Tikuisis, et al. Global landscape of protein complexes in the yeast saccharomyces cerevisiae. Nature, 440(7084):637-643, 2006.
Application of game theory in peer-to-peer networks
Peer-To-Peer (P2P) networks have emerged as an alternative to traditional client-server distributed systems. Characteristics such as decentralized authority and scalability have caused P2P networks to become highly popular among user communities with a common goal. P2P Systems are based on voluntary participation and contribution of peers. This causes the resource availability of P2P systems to be dependent on the rationality of peers. System designers incorporate incentive mechanisms with P2P networks to increase peer contribution and to boost the performance. To analyse the rational behavior of peers and to design robust P2P systems, concepts from game theory could be of great use. Game theory is a branch in mathematics which captures and extensively analyses the behavior of rational agents. In a game theoretically modeled P2P network, underlying game model can analyse the eeffectiveness of incentive mechanisms and ways to improve it. In this study I have surveyed how the concepts from game theory are applied in the literature to design and analyse incentive compatible P2P systems to mitigate free-rider problem thus improving the overall performance.