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In contrast to physics which has a long history and experience in collaborative projects, biology is only recently becoming an evermore collaborative discipline. In this article we explore the effect of a collaboration network on the distribution of players having access to certain amount of resources from other players in the network and the distribution of the strength of interactions among them. We are interested in how they affect each other in the context of a network of scientific collaboration under the idea that while researchers are interested in maximizing their utilities, they also know that it is important to invest in building collaborative relationships.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We implemented two games played simultaneously: one for maximizing individual utility based on the iterated prisoner\u2019s dilemma; the other, a coordination game for maximizing the connection strength between players. We tested our simulation on a biomedical research community network in M\u00e9xico and compared the results with Erd\u00f6s\u2013Reny\u00ed, a Watts\u2013Strogatz small-world and Barab\u00e1si\u2013Albert topologies.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Different topologies display different global utility and global strength of interaction distributions. Moreover, the distribution of utility and strength of interaction in the researchers network is similar to that of Barab\u00e1si\u2013Albert and Watts\u2013Strogatz topologies, respectively.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Data related to Science, from co-authorships to Scientists' movility are increasingly becoming available. We think that the readiness of these sort of data is a great opportunity for scientists interested in the social dynamics of science, especially in the context of computational social science.<\/jats:p><\/jats:sec>","DOI":"10.1186\/s40294-017-0044-0","type":"journal-article","created":{"date-parts":[[2017,5,16]],"date-time":"2017-05-16T13:44:00Z","timestamp":1494942240000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A game-theory modeling approach to utility and strength of interactions dynamics in biomedical research social networks"],"prefix":"10.1186","volume":"5","author":[{"given":"J. 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