Chimpanzees organize their social relationships like humans.

Human relationships are structured in a set of layers, ordered from higher (intimate relationships) to lower (acquaintances) emotional and cognitive intensity. This structure arises from the limits of our cognitive capacity and the different amounts of resources required by different relationships. However, it is unknown whether nonhuman primate species organize their affiliative relationships following the same pattern. We here show that the time chimpanzees devote to grooming other individuals is well described by the same model used for human relationships, supporting the existence of similar social signatures for both humans and chimpanzees. Furthermore, the relationship structure depends on group size as predicted by the model, the proportion of high-intensity connections being larger for smaller groups.

Evolution of social relationships between first-year students at middle school: from cliques to circles.

People organize their social relationships under a restriction on the number that a single individual can maintain simultaneously (the so-called Dunbar's number, ~150). Additionally, personal networks show a characteristic layered structure where each layer corresponds to relationships of different emotional closeness. This structure, referred to as Dunbar's circles, has mostly been considered from a static viewpoint, and their structure and evolution is largely unexplored. Here we study the issue of the evolution of the structure of positive and negative relationships in early adolescence by using data from students in their first year at middle school obtained from surveys conducted in class in two different waves separated by several months. Our results show that, initially, students have a lower number of total relationships but the majority are more intense and over time they report a higher number of total relationships, but the more intense relationships appear in a lower proportion. We have also found differences in the structure of communities at both temporal moments. While in the first instance the communities that appeared are mixed, made up of both boys and girls, in the second they changed so that they were separated primarily by gender. In addition, the size of each community was stabilized around 15 people, which coincides with the size of the second Dunbar's circle, known as the sympathy group in social psychology. As a consequence, in groups with around 20 students of the same gender, they tend to split in two separate communities of about 10 each, below the second Dunbar's circle threshold. On the other hand, groups with more stable community structure appear to go through the inverse process of friendship evolution, becoming more focused on their best relationships. All these results suggest how the layered structure of the personal network, as well as the community structure of the social network, emerge directly from the union of both positive and negative relationships. Thus, we provide a new perspective about its temporal evolution that may have relevant applications to improve school life and student performance.

Drug treatment efficiency depends on the initial state of activation in nonlinear pathways.

An accurate prediction of the outcome of a given drug treatment requires quantitative values for all parameters and concentrations involved as well as a detailed characterization of the network of interactions where the target molecule is embedded. Here, we present a high-throughput in silico screening of all potential networks of three interacting nodes to study the effect of the initial conditions of the network in the efficiency of drug inhibition. Our study shows that most network topologies can induce multiple dose-response curves, where the treatment has an enhanced, reduced or even no effect depending on the initial conditions. The type of dual response observed depends on how the potential bistable regimes interplay with the inhibition of one of the nodes inside a nonlinear pathway architecture. We propose that this dependence of the strength of the drug on the initial state of activation of the pathway may be affecting the outcome and the reproducibility of drug studies and clinical trials.

Drug Resistance Mechanisms in Colorectal Cancer Dissected with Cell Type-Specific Dynamic Logic Models.

Genomic features are used as biomarkers of sensitivity to kinase inhibitors used widely to treat human cancer, but effective patient stratification based on these principles remains limited in impact. Insofar as kinase inhibitors interfere with signaling dynamics, and, in turn, signaling dynamics affects inhibitor responses, we investigated associations in this study between cell-specific dynamic signaling pathways and drug sensitivity. Specifically, we measured 14 phosphoproteins under 43 different perturbed conditions (combinations of 5 stimuli and 7 inhibitors) in 14 colorectal cancer cell lines, building cell line-specific dynamic logic models of underlying signaling networks. Model parameters representing pathway dynamics were used as features to predict sensitivity to a panel of 27 drugs. Specific parameters of signaling dynamics correlated strongly with drug sensitivity for 14 of the drugs, 9 of which had no genomic biomarker. Following one of these associations, we validated a drug combination predicted to overcome resistance to MEK inhibitors by coblockade of GSK3, which was not found based on associations with genomic data. These results suggest that to better understand the cancer resistance and move toward personalized medicine, it is essential to consider signaling network dynamics that cannot be inferred from static genotypes. Cancer Res; 77(12); 3364-75. ©2017 AACR.

Synergistic interaction between selective drugs in cell populations models.

The design of selective drugs and combinatorial drug treatments are two of the main focuses in modern pharmacology. In this study we use a mathematical model of chimeric ligand-receptor interaction to show that the combination of selective drugs is synergistic in nature, providing a way to gain optimal selective potential at reduced doses compared to the same drugs when applied individually. We use a cell population model of proliferating cells expressing two different amounts of a target protein to show that both selectivity and synergism are robust against variability and heritability in the cell population. The reduction in the total drug administered due to the synergistic performance of the selective drugs can potentially result in reduced toxicity and off-target interactions, providing a mechanism to improve the treatment of cell-based diseases caused by aberrant gene overexpression, such as cancer and diabetes.