Reconstructing dynamical networks via feature ranking

Empirical data on real complex systems are becoming increasingly available. Parallel to this is the need for new methods of reconstructing (inferring) the topology of networks from time-resolved observations of their node-dynamics. The methods based on physical insights often rely on strong assumptions about the properties and dynamics of the scrutinized network. Here, we use the insights from machine learning to design a new method of network reconstruction that essentially makes no such assumptions. Specifically, we interpret the available trajectories (data) as features, and use two independent feature ranking approaches -- Random forest and RReliefF -- to rank the importance of each node for predicting the value of each other node, which yields the reconstructed adjacency matrix. We show that our method is fairly robust to coupling strength, system size, trajectory length and noise. We also find that the reconstruction quality strongly depends on the dynamical regime

Eugene – A Domain Specific Language for Specifying and Constraining Synthetic Biological Parts, Devices, and Systems

BACKGROUND: Synthetic biological systems are currently created by an ad-hoc, iterative process of specification, design, and assembly. These systems would greatly benefit from a more formalized and rigorous specification of the desired system components as well as constraints on their composition. Therefore, the creation of robust and efficient design flows and tools is imperative. We present a human readable language (Eugene) that allows for the specification of synthetic biological designs based on biological parts, as well as provides a very expressive constraint system to drive the automatic creation of composite Parts (Devices) from a collection of individual Parts. RESULTS: We illustrate Eugene's capabilities in three different areas: Device specification, design space exploration, and assembly and simulation integration. These results highlight Eugene's ability to create combinatorial design spaces and prune these spaces for simulation or physical assembly. Eugene creates functional designs quickly and cost-effectively. CONCLUSIONS: Eugene is intended for forward engineering of DNA-based devices, and through its data types and execution semantics, reflects the desired abstraction hierarchy in synthetic biology. Eugene provides a powerful constraint system which can be used to drive the creation of new devices at runtime. It accomplishes all of this while being part of a larger tool chain which includes support for design, simulation, and physical device assembly

Rickettsial Disease in the Peruvian Amazon Basin.

Using a large, passive, clinic-based surveillance program in Iquitos, Peru, we characterized the prevalence of rickettsial infections among undifferentiated febrile cases and obtained evidence of pathogen transmission in potential domestic reservoir contacts and their ectoparasites. Blood specimens from humans and animals were assayed for spotted fever group rickettsiae (SFGR) and typhus group rickettsiae (TGR) by ELISA and/or PCR; ectoparasites were screened by PCR. Logistic regression was used to determine associations between patient history, demographic characteristics of participants and symptoms, clinical findings and outcome of rickettsial infection. Of the 2,054 enrolled participants, almost 2% showed evidence of seroconversion or a 4-fold rise in antibody titers specific for rickettsiae between acute and convalescent blood samples. Of 190 fleas (Ctenocephalides felis) and 60 ticks (Rhipicephalus sanguineus) tested, 185 (97.4%) and 3 (5%), respectively, were positive for Rickettsia spp. Candidatus Rickettsia asemboensis was identified in 100% and 33% of the fleas and ticks tested, respectively. Collectively, our serologic data indicates that human pathogenic SFGR are present in the Peruvian Amazon and pose a significant risk of infection to individuals exposed to wild, domestic and peri-domestic animals and their ectoparasites

Principles of genetic circuit design

Cells navigate environments, communicate and build complex patterns by initiating gene expression in response to specific signals. Engineers seek to harness this capability to program cells to perform tasks or create chemicals and materials that match the complexity seen in nature. This Review describes new tools that aid the construction of genetic circuits. Circuit dynamics can be influenced by the choice of regulators and changed with expression 'tuning knobs'. We collate the failure modes encountered when assembling circuits, quantify their impact on performance and review mitigation efforts. Finally, we discuss the constraints that arise from circuits having to operate within a living cell. Collectively, better tools, well-characterized parts and a comprehensive understanding of how to compose circuits are leading to a breakthrough in the ability to program living cells for advanced applications, from living therapeutics to the atomic manufacturing of functional materials.National Institute of General Medical Sciences (U.S.) (Grant P50 GM098792)National Institute of General Medical Sciences (U.S.) (Grant R01 GM095765)National Science Foundation (U.S.). Synthetic Biology Engineering Research Center (EEC0540879)Life Technologies, Inc. (A114510)National Science Foundation (U.S.). Graduate Research FellowshipUnited States. Office of Naval Research. Multidisciplinary University Research Initiative (Grant 4500000552

Intestinal parasites of the grey fox (Pseudalopex culpaeus) in the central Peruvian Andes

This study was funded in part by NIH grant no.U01A135894-01 and funds by CONCYTEC, Lima, Peru.The authors thank J.D. Moro, N. Perez-Palma, J.B. Phu, D.Sara, Amanda Chavez and Eva Casas for editorial help.We also thank Armando Gonzales, Cesar Gavidia, NestorFalcon for helpful comments and M. Gamarra and R.Zarate from Tupac Amaru for their cooperation.Consejo Nacional de Ciencia, Tecnología e Innovación Tecnológica - Concyte