Core and Periphery in Endogenous Networks

Author(s):  
Daniel A. Hojman ◽  
Adam Szeidl
Keyword(s):  
2003 ◽  
Author(s):  
Giorgio Fagiolo ◽  
Luigi Marengo ◽  
Marco Valente

2006 ◽  
Vol 55 (1) ◽  
pp. 112-130 ◽  
Author(s):  
Daniel A. Hojman ◽  
Adam Szeidl

2012 ◽  
Vol 18 (3) ◽  
pp. 640-663 ◽  
Author(s):  
Lanfang Wang ◽  
Susheng Wang
Keyword(s):  

2014 ◽  
Author(s):  
Lior Nissim ◽  
Samuel D Perli ◽  
Alexandra Fridkin ◽  
Pablo Perez-Pinera ◽  
Timothy Lu

RNA-based regulation, such as RNA interference, and CRISPR/Cas transcription factors (CRISPR-TFs), can enable scalable synthetic gene circuits and the modulation of endogenous networks but have yet to be integrated together. Here, we combined multiple mammalian RNA regulatory strategies, including RNA triple helix structures, introns, microRNAs, and ribozymes, with Cas9-based CRISPR-TFs and Cas6/Csy4-based RNA processing in human cells. We describe three complementary strategies for expressing functional gRNAs from transcripts generated by RNA polymerase II (RNAP II) promoters while allowing the harboring gene to be translated. These architectures enable the multiplexed expression of proteins and multiple gRNAs from a single compact transcript for efficient modulation of synthetic constructs and endogenous human promoters. We used these regulatory tools to implement tunable synthetic gene circuits, including multi-stage transcriptional cascades. Finally, we show that Csy4 can rewire regulatory connections in RNA-dependent gene circuits with multiple outputs and feedback loops to achieve complex functional behaviors. This multiplexable toolkit will be valuable for the construction of scalable gene circuits and the perturbation of natural regulatory networks in human cells for basic biology, therapeutic, and synthetic-biology applications.


Science ◽  
2021 ◽  
Vol 373 (6550) ◽  
pp. eaav0780
Author(s):  
Deepak Mishra ◽  
Tristan Bepler ◽  
Brian Teague ◽  
Bonnie Berger ◽  
Jim Broach ◽  
...  

Synthetic biological networks comprising fast, reversible reactions could enable engineering of new cellular behaviors that are not possible with slower regulation. Here, we created a bistable toggle switch in Saccharomyces cerevisiae using a cross-repression topology comprising 11 protein-protein phosphorylation elements. The toggle is ultrasensitive, can be induced to switch states in seconds, and exhibits long-term bistability. Motivated by our toggle’s architecture and size, we developed a computational framework to search endogenous protein pathways for other large and similar bistable networks. Our framework helped us to identify and experimentally verify five formerly unreported endogenous networks that exhibit bistability. Building synthetic protein-protein networks will enable bioengineers to design fast sensing and processing systems, allow sophisticated regulation of cellular processes, and aid discovery of endogenous networks with particular functions.


Author(s):  
Zhenghui Sha ◽  
Jitesh H. Panchal

There is an emerging class of networks that evolve endogenously based on the local characteristics and behaviors of nodes. Examples of such networks include social, economic, and peer-to-peer communication networks. The node-level behaviors determine the overall structure and performance of these networks. This is in contrast to exogenously designed networks whose structures are directly determined by network designers. To influence the performance of endogenous networks, it is crucial to understand a) what kinds of local behaviors result in the observed network structures and b) how these local behaviors influence the overall performance. The focus in this paper is on the first aspect, where information about the structure of networks is available at different points in time and the goal is to estimate the behavior of nodes that resulted in the observed structures. We use three different approaches to estimate the node-level behaviors. The first approach is based on the generalized preferential attachment model of network evolution. In the second approach, statistical regression-based models are used to estimate the node-level behaviors from consecutive snapshots of the network structure. In the third approach, the nodes are modeled as rational decision-making agents who make linking decisions based on the maximization of their payoffs. Within the decision-making framework, the multinomial logit choice model is adopted to estimate the preferences of decision-making nodes. The autonomous system (AS) level Internet is used as an illustrative example to illustrate and compare the three approaches.


2004 ◽  
Vol 11 (2) ◽  
pp. 121-147 ◽  
Author(s):  
Giorgio Fagiolo ◽  
Luigi Marengo ◽  
Marco Valente

2012 ◽  
Vol 20 (3) ◽  
pp. 316-328 ◽  
Author(s):  
Jon C. Rogowski ◽  
Betsy Sinclair

Identifying causal effects attributable to network membership is a key challenge in empirical studies of social networks. In this article, we examine the consequences of endogeneity for inferences about the effects of networks on network members' behavior. Using the House office lottery (in which newly elected members select their office spaces in a randomly chosen order) as an instrumental variable to estimate the causal impact of legislative networks on roll call behavior and cosponsorship decisions in the 105th–112th Houses, we find no evidence that office proximity affects patterns of legislative behavior. These results contrast with decades of congressional scholarship and recent empirical studies. Our analysis demonstrates the importance of accounting for selection processes and omitted variables in estimating the causal impact of networks.


2012 ◽  
Vol 75 (1) ◽  
pp. 35-52 ◽  
Author(s):  
Siegfried K. Berninghaus ◽  
Karl-Martin Ehrhart ◽  
Marion Ott

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