Stefan Wuchty

The Increasing Dominance of Teams in Production of Knowledge

We have used 19.9 million papers over 5 decades and 2.1 million patents to demonstrate that teams increasingly dominate solo authors in the production of knowledge. Research is increasingly done in teams across nearly all fields. Teams typically produce more frequently cited research than individuals do, and this advantage has been increasing over time. Teams now also produce the exceptionally high-impact research, even where that distinction was once the domain of solo authors. These results are detailed for sciences and engineering, social sciences, arts and humanities, and patents, suggesting that the process of knowledge creation has fundamentally changed.

Multi-University Research Teams: Shifting Impact, Geography, and Stratification in Science

This paper demonstrates that teamwork in science increasingly spans university boundaries, a dramatic shift in knowledge production that generalizes across virtually all fields of science, engineering, and social science. Moreover, elite universities play a dominant role in this shift. By examining 4.2 million papers published over three decades, we found that multi-university collaborations (i) are the fastest growing type of authorship structure, (ii) produce the highest-impact papers when they include a top-tier university, and (iii) are increasingly stratified by in-group university rank. Despite the rising frequency of research that crosses university boundaries, the intensification of social stratification in multi-university collaborations suggests a concentration of the production of scientific knowledge in fewer rather than more centers of high-impact science.

Evolutionary conservation of motif constituents in the yeast protein interaction network

Understanding why some cellular components are conserved across species but others evolve rapidly is a key question of modern biology. Here we show that in Saccharomyces cerevisiae, proteins organized in cohesive patterns of interactions are conserved to a substantially higher degree than those that do not participate in such motifs. We find that the conservation of proteins in distinct topological motifs correlates with the interconnectedness and function of that motif and also depends on the structure of the overall interactome topology. These findings indicate that motifs may represent evolutionary conserved topological units of cellular networks molded in accordance with the specific biological function in which they participate.

Identifying Causal Genes and Dysregulated Pathways in Complex Diseases

In complex diseases, various combinations of genomic perturbations often lead to the same phenotype. On a molecular level, combinations of genomic perturbations are assumed to dys-regulate the same cellular pathways. Such a pathway-centric perspective is fundamental to understanding the mechanisms of complex diseases and the identification of potential drug targets. In order to provide an integrated perspective on complex disease mechanisms, we developed a novel computational method to simultaneously identify causal genes and dys-regulated pathways. First, we identified a representative set of genes that are differentially expressed in cancer compared to non-tumor control cases. Assuming that disease-associated gene expression changes are caused by genomic alterations, we determined potential paths from such genomic causes to target genes through a network of molecular interactions. Applying our method to sets of genomic alterations and gene expression profiles of 158 Glioblastoma multiforme (GBM) patients we uncovered candidate causal genes and causal paths that are potentially responsible for the altered expression of disease genes. We discovered a set of putative causal genes that potentially play a role in the disease. Combining an expression Quantitative Trait Loci (eQTL) analysis with pathway information, our approach allowed us not only to identify potential causal genes but also to find intermediate nodes and pathways mediating the information flow between causal and target genes. Our results indicate that different genomic perturbations indeed dys-regulate the same functional pathways, supporting a pathway-centric perspective of cancer. While copy number alterations and gene expression data of glioblastoma patients provided opportunities to test our approach, our method can be applied to any disease system where genetic variations play a fundamental causal role.

The binary protein-protein interaction landscape of Escherichia coli

Efforts to map the Escherichia coli interactome have identified several hundred macromolecular complexes, but direct binary protein-protein interactions (PPIs) have not been surveyed on a large scale. Here we performed yeast two-hybrid screens of 3,305 baits against 3,606 preys (∼70% of the E. coli proteome) in duplicate to generate a map of 2,234 interactions, which approximately doubles the number of known binary PPIs in E. coli. Integration of binary PPI and genetic-interaction data revealed functional dependencies among components involved in cellular processes, including envelope integrity, flagellum assembly and protein quality control. Many of the binary interactions that we could map in multiprotein complexes were informative regarding internal topology of complexes and indicated that interactions in complexes are substantially more conserved than those interactions connecting different complexes. This resource will be useful for inferring bacterial gene function and provides a draft reference of the basic physical wiring network of this evolutionarily important model microbe.

Evolutionary cores of domain co-occurrence networks

BackgroundThe modeling of complex systems, as disparate as the World Wide Web and the cellular metabolism, as networks has recently uncovered a set of generic organizing principles: Most of these systems are scale-free while at the same time modular, resulting in a hierarchical architecture. The structure of the protein domain network, where individual domains correspond to nodes and their co-occurrences in a protein are interpreted as links, also falls into this category, suggesting that domains involved in the maintenance of increasingly developed, multicellular organisms accumulate links. Here, we take the next step by studying link based properties of the protein domain co-occurrence networks of the eukaryotes S. cerevisiae, C. elegans, D. melanogaster, M. musculus and H. sapiens.ResultsWe construct the protein domain co-occurrence networks from the PFAM database and analyze them by applying a k-core decomposition method that isolates the globally central (highly connected domains in the central cores) from the locally central (highly connected domains in the peripheral cores) protein domains through an iterative peeling process. Furthermore, we compare the subnetworks thus obtained to the physical domain interaction network of S. cerevisiae. We find that the innermost cores of the domain co-occurrence networks gradually grow with increasing degree of evolutionary development in going from single cellular to multicellular eukaryotes. The comparison of the cores across all the organisms under consideration uncovers patterns of domain combinations that are predominately involved in protein functions such as cell-cell contacts and signal transduction. Analyzing a weighted interaction network of PFAM domains of Yeast, we find that domains having only a few partners frequently interact with these, while the converse is true for domains with a multitude of partners. Combining domain co-occurrence and interaction information, we observe that the co-occurrence of domains in the innermost cores (globally central domains) strongly coincides with physical interaction. The comparison of the multicellular eukaryotic domain co-occurrence networks with the single celled of S. cerevisiae (the overlap network) uncovers small, connected network patterns.ConclusionWe hypothesize that these patterns, consisting of the domains and links preserved through evolution, may constitute nucleation kernels for the evolutionary increase in proteome complexity. Combining co-occurrence and physical interaction data we argue that the driving force behind domain fusions is a collective effect caused by the number of interactions and not the individual interaction frequency.

Controllability in protein interaction networks

Significance In human and yeast protein interaction datasets we determined minimum dominating sets (MDSets), proteins that play a role in the control of the underlying interaction webs. Such proteins are defined as optimized subsets from where each remaining protein can be immediately reached. Notably, MDSet proteins were enriched with cancer-related and virus-targeted genes. Furthermore, MDSet proteins have a higher impact on network resilience than hub proteins. Indicating their relevance for the controllability of biological networks, we also found a strong involvement in bottleneck interactions, regulatory and phosphorylation events as well as genetic interactions. Recently, the focus of network research shifted to network controllability, prompting us to determine proteins that are important for the control of the underlying interaction webs. In particular, we determined minimum dominating sets of proteins (MDSets) in human and yeast protein interaction networks. Such groups of proteins were defined as optimized subsets where each non-MDSet protein can be reached by an interaction from an MDSet protein. Notably, we found that MDSet proteins were enriched with essential, cancer-related, and virus-targeted genes. Their central position allowed MDSet proteins to connect protein complexes and to have a higher impact on network resilience than hub proteins. As for their involvement in regulatory functions, MDSet proteins were enriched with transcription factors and protein kinases and were significantly involved in bottleneck interactions, regulatory links, phosphorylation events, and genetic interactions.

Interaction and domain networks of yeast.

Data of currently available protein‐protein interaction sets and protein domain sets of yeast are used to set up protein and domain interaction and domain sequence networks. All of them are far from being random or regular networks. In fact, they turn out to be sparse and locally well clustered indicating so‐called scale‐free and partially small‐world topology. These subtle topologies display considerable indirect properties which are measured with a newly introduced transitivity coefficient. Fairly small sets of highly connected proteins and domains shape the topologies of the underlying networks, emphasizing a kind of backbone the nets are based on. The biological nature of these particular nodes is further investigated. Since highly connected proteins and domains accumulated a significant higher number of links by their important involvement in certain cellular aspects, their mutational effect on the cell is considered by a perturbation analysis. In comparison to domains of yeast, what factors force domains to accumulate links to other domains in protein sequences of higher eukaryotes are investigated.

The Architecture of Biological Networks

An ambitious goal of contemporary biological research is the elucidation of the structure and functions of networks that constitute cells and organisms. In biological systems, networks appear in many different disguises, ranging from protein interactions to metabolic networks. The emergence of these networks is driven by self-organizing processes that are governed by simple but generic laws. While unraveling the complex and interwoven systems of different interacting units, it has become clear that the topology of networks of different biological origin share the same characteristics on the large scale. In this chapter, we survey the most prominent characteristics of biological networks, focusing on the emergence of the scale-free architecture and the hierarchical arrangement of modules.

Regulatory Hotspots in the Malaria Parasite Genome Dictate Transcriptional Variation

The determinants of transcriptional regulation in malaria parasites remain elusive. The presence of a well-characterized gene expression cascade shared by different Plasmodium falciparum strains could imply that transcriptional regulation and its natural variation do not contribute significantly to the evolution of parasite drug resistance. To clarify the role of transcriptional variation as a source of stain-specific diversity in the most deadly malaria species and to find genetic loci that dictate variations in gene expression, we examined genome-wide expression level polymorphisms (ELPs) in a genetic cross between phenotypically distinct parasite clones. Significant variation in gene expression is observed through direct co-hybridizations of RNA from different P. falciparum clones. Nearly 18% of genes were regulated by a significant expression quantitative trait locus. The genetic determinants of most of these ELPs resided in hotspots that are physically distant from their targets. The most prominent regulatory locus, influencing 269 transcripts, coincided with a Chromosome 5 amplification event carrying the drug resistance gene, pfmdr1, and 13 other genes. Drug selection pressure in the Dd2 parental clone lineage led not only to a copy number change in the pfmdr1 gene but also to an increased copy number of putative neighboring regulatory factors that, in turn, broadly influence the transcriptional network. Previously unrecognized transcriptional variation, controlled by polymorphic regulatory genes and possibly master regulators within large copy number variants, contributes to sweeping phenotypic evolution in drug-resistant malaria parasites.