169 research outputs found
Data from: The architecture of an empirical genotype-phenotype map
No full textRecent advances in high-throughput technologies are bringing the study of empirical genotype-phenotype (GP) maps to the fore. Here, we use data from protein binding microarrays to study an empirical GP map of transcription factor (TF) binding preferences. In this map, each genotype is a DNA sequence. The phenotype of this DNA sequence is its ability to bind one or more TFs. We study this GP map using genotype networks, in which nodes represent genotypes with the same phenotype, and edges connect nodes if their genotypes differ by a single small mutation. We describe the structure and arrangement of genotype networks within the space of all possible binding sites for 525 TFs from three eukaryotic species encompassing three kingdoms of life (animal, plant, and fungi). We thus provide a high-resolution depiction of the architecture of an empirical GP map. Among a number of findings, we show that these genotype networks are “small-world” and assortative, and that they ubiquitously overlap and interface with one another. We also use polymorphism data from Arabidopsis thaliana to show how genotype network structure influences the evolution of TF binding sites in vivo. We discuss our findings in the context of regulatory evolution.,The architecture of an empirical genotype-phenotype mapThis DRYAD package contains files from: Aguilar-Rodríguez, J., Peel, L., Stella, M., Wagner, A., and Payne, J. L. The architecture of an empirical genotype-phenotype map. This package contains the network files in GML format for the genotype space of transcription factor (TF) binding sites ('genotype_space.gml'), 525 genotype networks of TF binding sites, and 66 genotype networks of DNA binding domains. The genotype networks of TF binding sites are classified in three directories according to their species provenance ('Arabidopsis_thaliana', 'Mus_musculus,' and 'Neurospora_crassa'). Each network file is named with the TF name. More information about these networks can be found in Table S1. The genotype networks of DNA binding domains are within a 'domains' sub-folder that can be found inside each of the three species folders. Each file is named with the DNA binding domain class. Each network file has the following vertex attributes: - id: vertex identification number. - sequence: the nucleotide sequence of the binding site. - reversecomplement: the reverse complement of 'sequence.' Genotype network of TF binding sites have the following additional vertex attributes: - Escore: the enrichment score in protein binding microarrays of the sequence. - PartitionSBM: Information about the stochastic block model partition group where the vertex is found: '0', '1', or 'None'. 'None' is for vertices not found in the dominant genotype network. - PartitionBA: Information about the binding affinity partition group where the vertex is found: '0', '1', or 'None'. 'None' is for vertices not found in the dominant genotype network. For questions regarding these data, contact Joshua Payne at [email protected] or Andreas Wagner at [email protected]</span
The architecture of an empirical genotype-phenotype map
Get PDFThis DRYAD package contains files from:
Aguilar-Rodríguez, J., Peel, L., Stella, M., Wagner, A., and Payne, J. L. The architecture of an empirical genotype-phenotype map.
This package contains the network files in GML format for the genotype space of transcription factor (TF) binding sites ('genotype_space.gml'), 525 genotype networks of TF binding sites, and 66 genotype networks of DNA binding domains. The genotype networks of TF binding sites are classified in three directories according to their species provenance ('Arabidopsis_thaliana', 'Mus_musculus,' and 'Neurospora_crassa'). Each network file is named with the TF name. More information about these networks can be found in Table S1. The genotype networks of DNA binding domains are within a 'domains' sub-folder that can be found inside each of the three species folders. Each file is named with the DNA binding domain class.
Each network file has the following vertex attributes:
- id: vertex identification number.
- sequence: the nucleotide sequence of the binding site.
- reversecomplement: the reverse complement of 'sequence.'
Genotype network of TF binding sites have the following additional vertex attributes:
- Escore: the enrichment score in protein binding microarrays of the sequence.
- PartitionSBM: Information about the stochastic block model partition group where the vertex is found: '0', '1', or 'None'. 'None' is for vertices not found in the dominant genotype network.
- PartitionBA: Information about the binding affinity partition group where the vertex is found: '0', '1', or 'None'. 'None' is for vertices not found in the dominant genotype network.
For questions regarding these data, contact Joshua Payne at [email protected] or Andreas Wagner at [email protected]
Individual perception dynamics in drunk games
No full textWe study the effects of individual perceptions of payoffs in two-player games. In particular we consider the setting in which individuals' perceptions of the game are influenced by their previous experiences and outcomes. Accordingly, we introduce a framework based on evolutionary games where individuals have the capacity to perceive their interactions in different ways. Starting from the narrative of social behaviors in a pub as an illustration, we first study the combination of the Prisoner's Dilemma and Harmony Game as two alternative perceptions of the same situation. Considering a selection of game pairs, our results show that the interplay between perception dynamics and game payoffs gives rise to nonlinear phenomena unexpected in each of the games separately, such as catastrophic phase transitions in the cooperation basin of attraction, Hopf bifurcations and cycles of cooperation and defection. Combining analytical techniques with multiagent simulations, we also show how introducing individual perceptions can cause nontrivial dynamical behaviors to emerge, which cannot be obtained by analyzing the system at a macroscopic level. Specifically, initial perception heterogeneities at the microscopic level can yield a polarization effect that is unpredictable at the macroscopic level. This framework opens the door to the exploration of new ways of understanding the link between the emergence of cooperation and individual preferences and perceptions, with potential applications beyond social interactions.</p
Network constraints on the mixing patterns of binary node metadata
Get PDFWe consider the network constraints on the bounds of the assortativity coefficient, which aims toquantify the tendency of nodes with the same attribute values to be connected. The assortativitycoefficient can be considered as the Pearson’s correlation coefficient of node metadata values acrossnetwork edges and lies in the interval [−1, 1]. However, properties of the network, such as degreedistribution and the distribution of node metadata values place constraints upon the attainablevalues of the assortativity coefficient. This is important as a particular value of assortativity may sayas much about the network topology as about how the metadata are distributed over the network –a fact often overlooked in literature where the interpretation tends to focus simply on the propensityof similar nodes to link to each other, without any regard on the constraints posed by the topology.In this paper we quantify the effect that the topology has on the assortativity coefficient in the caseof binary node metadata. Specifically we look at the effect that the degree distribution, or the fulltopology, and the proportion of each metadata value has on the extremal values of the assortativitycoefficient. We provide the means for obtaining bounds on the extremal values of assortativity fordifferent settings and demonstrate that under certain conditions the maximum and minimum valuesof assortativity are severely limited, which may present issues in interpretation when these boundsare not considered
Graph-based semi-supervised learning for relational networks
No full textWe address the problem of semi-supervised learning in relational networks, networks in which nodes are entities and links are the relationships or interactions between them. Typically this problem is confounded with the problem of graph-based semi-supervised learning (GSSL), because both problems represent the data as a graph and predict the missing class labels of nodes. However, not all graphs are created equally. In GSSL a graph is constructed, often from independent data, based on similarity. As such, edges tend to connect instances with the same class label. Relational networks, however, can be more heterogeneous and edges do not always indicate similarity. For instance, instead of links tending to connect nodes with the same class label, they may tend to connect nodes with different class labels (link-heterogeneity). Or having the same class label might not imply the same type of connectivity across the whole network (class-heterogeneity), e.g. in a network of sexual interactions we may observe links between opposite genders in some parts of the graph and links between the same genders in others. Performing classification in networks with different types of heterogeneity is a hard problem that is made harder still by the fact we do not know a-priori the type or level of heterogeneity. In this work we present two scalable approaches for graph-based semi-supervised learning for the more general case of relational networks. We demonstrate these approaches on synthetic and real-world networks that display different link patterns within and between classes. Compared to state-of-the-art baseline approaches, ours give better classification performance and do so without prior knowledge of how classes interact. In particular, our two-step label propagation algorithm gives consistently good accuracy and precision, while also being highly efficient and can perform classification in networks of over 1.6 million nodes and 30 million edges in around 12 seconds.</p
Of Leto: a staged concert reading
No full textabstract: Of Leto: a staged concert reading is a new work development created by Alexander Tom and Daniel Oberhaus focusing on collegiate collaboration, production process, and creative intuition. An original story was adapted by Daniel Oberhaus into a working libretto. Alexander Tom created a two-act musical-drama and utilized the colleges on the Arizona State University \u2014 Tempe campus: Barrett, the Honors College, W.P. Carey School of Business, the College of Liberal Arts and Sciences, and the Herberger Institute for Design and the Arts: School of Music and School of Theatre, Film and Dance. This cross-discipline staged concert reading was comprised of a libretto by Daniel Oberhaus, music, additional lyrics and orchestrations by Alexander Tom, and orchestrations by Drew Nichols. The performance included a thirteen-piece orchestra and fourteen vocalists in undergraduate and graduate programs. This paper includes research on Benjamin Britten and Myfanwy Piper's Death in Venice and Stephen Sondheim and Hugh Wheeler's Sweeney Todd, the Demon Barber of Fleet Street. Its purpose is to impart a comparative analysis on the process of collaboration in opera, musical theatre, and the newly determined "musical-drama" \u2014 the genre in which Of Leto resides. Use of historical research will expound on the evolution of musical theatre along with each team's collaborative processes in relation to the music (lyrics and melody respectively), the libretto, and the production. The research permits conclusions regarding the possible practices to utilize in creating new student works like Of Leto
Statistical inference links data and theory in network science
Get PDFThe number of network science applications across many different fields has been rapidly increasing. Surprisingly, the development of theory and domain-specific applications often occur in isolation, risking an effective disconnect between theoretical and methodological advances and the way network science is employed in practice. Here we address this risk constructively, discussing good practices to guarantee more successful applications and reproducible results. We endorse designing statistically grounded methodologies to address challenges in network science. This approach allows one to explain observational data in terms of generative models, naturally deal with intrinsic uncertainties, and strengthen the link between theory and applications.</p
Active discovery of network roles for predicting the classes of network nodes
No full textNodes in real world networks often have class labels, or underlying attributes, that are related to the way in which they connect to other nodes. Sometimes this relationship is simple, for instance nodes of the same class may be more likely to be connected. In other cases, however, this is not true, and the way that nodes link in a network exhibits a different, more complex relationship to their attributes. Here, we consider networks in which we know how the nodes are connected, but we do not know the class labels of the nodes or how class labels relate to the network links. We wish to identify the best subset of nodes to label to learn this relationship between node attributes and network links. We can then use this discovered relationship to accurately predict the class labels of the rest of the network nodes. We present a model that identifies groups of nodes with similar link patterns, which we call network roles, using a generative blockmodel. The model then predicts class labels by learning the mapping from network roles to class labels using a maximum margin classifier. We choose a subset of nodes to label according to an iterative margin-based active learning strategy. By integrating the discovery of network roles with the classifier optimization, the active learning process can adapt the network roles to better represent the network for node classification. We demonstrate the model by exploring a selection of real world networks, including a marine food web and a network of English words. We show that, in contrast to other network classifiers, this model achieves good classification accuracy for a range of networks with different relationships between class labels and network links
- …
