**#461 Analyzing MEETUP as an Affiliation Network and as a Multilayer Network, Using Big Data Technologies**

*Author: Camelia Elena Ciolac*This poster aims to present an empirical study of MeetUp, which is a platform of affiliation networks combining online and in-person activity.

Besides its open data queriable using a REST API (http://www.meetup.com/meetup_api/), MeetUp is an interesting case study for its hierarchical organization in the form of category - group - event - participant where participant is member of group.

We start by analyzing the 2-mode affiliation network participant - event in which we neglect the higher levels of the hierachy. This approach allows us to properly identify the strength of the connection between individuals, regardless of the type of event (e.g. technology, sport, cultural) that facilitates their encounter.

The analysis becomes more complex when we take into account the third level of the hierarchy. What we obtain is a multilayer network where layers correspond to MeetUp groups. One option in the analysis is to let MeetUp events constitute the elementary layers, following Kivela et al.(2014) definition, i.e. in the role of aspects we take the events' rank in the chronological order - first, second, etc., and MeetUp groups be the layers. Among others, this approach allows measuring coupling between subsequent events organized within groups.

Inspired by the particular case of MeetUp, we introduce a new concept: multilayer affiliation networks. Each layer corresponds to a MeetUp group in our case and within each layer we have an affiliation network. Some preliminary ideas are proposed in this respect.

The implementation of all data storage and analysis is carried out using Big Data technologies.

**#462 Complexity and Structural Properties in SmallWorld Networks**

*Authors: Yesid Madrid, Carlos Gershenson and Nelson Fernandez*

The most characteristic features for the study of structure in complex networks are the degree of nodes and their distribution, clustering coefficient, and average path length. In spite of the value of these properties, measuring complexity in networks is desirable. Recently, measures of emergence, self-organization, complexity, and relative complexity based on information theory have been developed, and their usefulness can be evaluated (Fernandez et al.,2014). In this work we analyse the association of topological structural indicators like the number of nodes, clustering coefficient and average path length with formal measures of emergence, self-organization, complexity, the relative complexity of small world random networks. Networks were simulated using the Erdõs-Rényi model implemented in SocNetV software (Kalamaras D.,2015). In total 310 random networks of the following number of nodes were generated: 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 60, 70, 80, 90, 100, 125, 150, 175, 200, 225, 250, 300, 350, 400, 450, 500, 600, 700, 800, 900, and 1000. Multivariate techniques (Le and Worch,2015) were applied to facilitate the analysis of the relationships found between structural properties and complexity. Results show that complexity of networks is associated with self-organization and relative complexity with emergence. The increasing in the clustering coefficient suggests that some complexity could be gained. The increase in the number of nodes and edges are positively correlated with the average path length. Networks with nodes between 5 to 20 nodes are more complex, self-organized and have a high clustering coefficient. Networks between 25 to 450 nodes are more emergent and have more relative complexity. Networks between 500 and 1000 nodes have the highest average path length. These first results invite to explore the complexity of small networks deeply as an additional way of characterising and describe their structure. Dynamical aspects might be explored in future work.

**#463 Frequency and Range Complexity in GTPase Amino-acid Sequences**

*Authors: Andrea Lopez, Carlos Angarita, Carlos Gershenson, Nelson Fernández and Freddy Sandoval*

Proteins are largest biomolecules that perform a vast array of functions in a living organism. In the evolutive time, the same protein can variate some parts of its structure but maintaining its function. Using formal measures of complexity (Fernández et al., 2014) and concepts as Zipf's law, it is possible to analyze how this structural variation takes place at amino-acid scale. In this work, we apply information measures of Emergence, Self-Organization, and Complexity to GTPase amino-acid sequences. We are focusing on the characterization of two structural characteristics: the frequency and the diversity of rank (Cocho et al., 2015) of the amino acid composition. To distinguish these two properties, we named these: frequency complexity and rank complexity. We examine the structural arrangement of the GTPasa protein in 118 species corresponding to the phylum Arthropoda, Nematoda, Mollusca, Cnidaria and some Chordata. We obtained the emergence, self-organization, and complexity for the frequency of amino acids of GTPase. Tryptophan (W), was the most regular (self-organized) amino acid in all protein sequences. Alanine (A), Aspartic Acid (D), Arginine (R), and Serine (S) were the most changing amino-acids. That is, A; D; R; and S have a variable proportion of the 118 species. The remaining amino acids reach high values of complexity due to balance between their regularity and change of their proportion occurrence in the protein sequence. Based on complexity measures we show how is possible to characterize the evolutive change of the GTPase' structure, among various species.

**#464 Measuring Protein Structural and Dynamical Complexity**

*Authors: Jorge Sanchéz, Freddy Sandoval, Carlos Gershenson and Nelson Fernández*

Protein sequences are regularly used for molecular phylogenetics to analyze hereditary differences and to gain information on an organism’s evolutionary relationships (WenHsiung et al., 1997). Currently, we can quantify the complexity of DNA or amino acid sequences using formal information measures of emergence, self-organization, and complexity based on information theory (Gershenson and Fernández, 2012). In this work, these measures were implemented to characterize the structural and dynamical complexity to protein sequences of 118 species. Since complexity can be estimated as a product of self-organization (order) and emergence (change), in a genomic sequence our complexity measure can inform about the degree of adaptability of DNA. Also, using this measure of complexity in amino-acid sequences, we can estimate about the complexity structure or dynamical complexity at genomic and evolutive scales. Thus, we can have formal indicators of structural and dynamical complexity. Values of complexity were ordered in chronological species’ appearance to observe the dynamics of complexity by evolutive time. Also, spectral analyses were carried out to determinate of the periods and frequency of the species complexity, and the periods of cumulative complexity gain or loss. Furthermore, a relation between the complexity of previous species (Xn) vs. the next species (Xn + 1) that appears in the evolutive time was performed. With this relationship, we are focusing on the establishment dependencies of complexity between of more recent species with the more ancestral species. This work is the first step in characterizing the functional complexity as a combination of structure and dynamics. We are confident that measuring complexity at genomic scale can contribute to the understanding of guided self-organization in life.

**#466 Spontaneous Breaking of Left/Right Symmetry in Chemical Oscillators**

*Authors: Evangelia Panagakou, Nate Tompkins, Seth Fraden and Irving Epstein*We study the synchronization of rings of chemical oscillators that exhibit spontaneous left/right symmetry breaking.

Each oscillator in the ring is a 100 micron scale aqueous-in-oil emulsion drop of the Belousov-Zhabotinsky (BZ) reaction confined in an etched silicon chip. Each chip has annuluses of varying circumference (with a constant difference between inner and out radius) to create rings of varying numbers of oscillators. We are interested in the phase synchronization among the droplets of each ring and specifically the spontaneous breaking of left/right symmetry.

We use the Vanag-Epstein (VE) model of BZ to numerically simulate the experiments including calculating the phase response curve (PRC) and Poincare maps. Here we explore the application of control algorithms to lead and track the symmetry breaking with the end goal of experimentally realizing an algorithm that will allow for specific control of the attractor state.

The next step is to create networks of such rings and study the synchronization among the droplets in the network to compare this function with neural algorithms. In the long term we want to combine this chemo-mechanical gels to create controllable physical movement from purely chemical ingredients.

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