Team Assembly Mechanisms and the Knowledge Produced in the Mexico's National Institute of Geriatrics: A Network Analysis and Agent-Based Modeling Approach.

Mexico's National Institute of Geriatrics (INGER) is the national research center of reference for matters related to human aging. INGER scientists perform basic, clinical, and demographic research which may imply different scientific cultures working together in the same specialized institution. In this paper, by a combination of text mining, coauthorship network analysis, and agent-based modeling, we analyzed and modeled the team assembly practices and the structure of the knowledge produced by scientists from INGER. Our results showed a weak connection between basic and clinical research and the emergence of a highly connected academic leadership. Importantly, basic and clinical-demographic researchers exhibited different team assembly strategies: basic researchers tended to form larger teams mainly with external collaborators, while clinical and demographic researchers formed smaller teams that very often incorporated internal (INGER) collaborators. We showed how these two different ways to form research teams impacted the organization of knowledge produced at INGER. Following these observations, we modeled, via agent-based modeling, the coexistence of different scientific cultures (basic and clinical research) exhibiting different team assembly strategies in the same institution. Three virtual experiments were run in our agent-based model. The three experiments kept similar values to the collaborating dynamics of INGER in terms of average team size and probabilities of choosing incumbents and external collaborators. The only difference among these experiments was the value of homophily defined as the trend to collaborate with research studies from the same field (14% corresponding to the 46% and 79%). The main result of these experiments is that by modulating just one variable (homophily), we could successfully reproduce the current situation of INGER (homophily of 79%) and simulate alternative scenarios in which interdisciplinary (46%) and transdisciplinary (14%) research could be done.

Modeling the dynamics of chromosomal alteration progression in cervical cancer: A computational model.

Computational modeling has been applied to simulate the heterogeneity of cancer behavior. The development of Cervical Cancer (CC) is a process in which the cell acquires dynamic behavior from non-deleterious and deleterious mutations, exhibiting chromosomal alterations as a manifestation of this dynamic. To further determine the progression of chromosomal alterations in precursor lesions and CC, we introduce a computational model to study the dynamics of deleterious and non-deleterious mutations as an outcome of tumor progression. The analysis of chromosomal alterations mediated by our model reveals that multiple deleterious mutations are more frequent in precursor lesions than in CC. Cells with lethal deleterious mutations would be eliminated, which would mitigate cancer progression; on the other hand, cells with non-deleterious mutations would become dominant, which could predispose them to cancer progression. The study of somatic alterations through computer simulations of cancer progression provides a feasible pathway for insights into the transformation of cell mechanisms in humans. During cancer progression, tumors may acquire new phenotype traits, such as the ability to invade and metastasize or to become clinically important when they develop drug resistance. Non-deleterious chromosomal alterations contribute to this progression.