Description: Nature Geoscience 8, 122 (2015). doi:10.1038/ngeo2349 Authors: Hanno Meyer, Thomas Opel, Thomas Laepple, Alexander Yu Dereviagin, Kirstin Hoffmann & Martin Werner Relative to the past 2,000 years, the Arctic region has warmed significantly over the past few decades. However, the evolution of Arctic temperatures during the rest of the Holocene is less clear. Proxy reconstructions, suggest a long-term cooling trend throughout the mid- to late Holocene, whereas climate model simulations show only minor changes or even warming. Here we present a record of the oxygen isotope composition of permafrost ice wedges from the Lena River Delta in the Siberian Arctic. The isotope values, which reflect winter season temperatures, became progressively more enriched over the past 7,000 years, reaching unprecedented levels in the past five decades. This warming trend during the mid- to late Holocene is in opposition to the cooling seen in other proxy records. However, most of these existing proxy records are biased towards summer temperatures. We argue that the opposing trends are related to the seasonally different orbital forcing over this interval. Furthermore, our reconstructed trend as well as the recent maximum are consistent with the greenhouse gas forcing and climate model simulations, thus reconciling differing estimates of Arctic and northern high-latitude temperature evolution during the Holocene.

Description: Background: Accurate estimation of parameters of biochemical models is required to characterize the dynamics of molecular processes. This problem is intimately linked to identifying the most informative experiments for accomplishing such tasks. While significant progress has been made, effective experimental strategies for parameter identification and for distinguishing among alternative network topologies remain unclear. We approached these questions in an unbiased manner using a unique community-based approach in the context of the DREAM initiative (Dialogue for Reverse Engineering Assessment of Methods). We created an in silico test framework under which participants could probe a network with hidden parameters by requesting a range of experimental assays; results of these experiments were simulated according to a model of network dynamics only partially revealed to participants. Results: We proposed two challenges; in the first, participants were given the topology and underlying biochemical structure of a 9-gene regulatory network and were asked to determine its parameter values. In the second challenge, participants were given an incomplete topology with 11 genes and asked to find three missing links in the model. In both challenges, a budget was provided to buy experimental data generated in silico with the model and mimicking the features of different common experimental techniques, such as microarrays and fluorescence microscopy. Data could be bought at any stage, allowing participants to implement an iterative loop of experiments and computation. Conclusions: A total of 19 teams participated in this competition. The results suggest that the combination of state-of-the-art parameter estimation and a varied set of experimental methods using a few datasets, mostly fluorescence imaging data, can accurately determine parameters of biochemical models of gene regulation. However, the task is considerably more difficult if the gene network topology is not completely defined, as in challenge 2. Importantly, we found that aggregating independent parameter predictions and network topology across submissions creates a solution that can be better than the one from the best-performing submission.