Biological systems are increasingly being studied by high throughput profiling of molecular data over time. Determining the set of time points to sample in studies that profile several different types of molecular data is still challenging. Here we present the Time Point Selection (TPS) method that solves this combinatorial problem in a principled and practical way. TPS utilizes expression data from a small set of genes sampled at a high rate. As we show by applying TPS to study mouse lung development, the points selected by TPS can be used to reconstruct an accurate representation for the expression values of the non selected points. Further, even though the selection is only based on gene expression, these points are also appropriate for representing a much larger set of protein, miRNA and DNA methylation changes over time. TPS can thus serve as a key design strategy for high throughput time series experiments. Supporting Website: www.sb.cs.cmu.edu/TPSDOI: http://dx.doi.org/10.7554/eLife.18541.001

An important experimental design question for high throughout time series studies is the number of replicates required for accurate reconstruction of the profiles. Due to budget and sample availability constraints, more replicates imply fewer time points and vice versa. We analyze the performance of dense and replicate sampling by developing a theoretical framework that focuses on a restricted yet expressive set of possible curves over a wide range of noise levels and by analyzing real expression data. For both the theoretical analysis and experimental data we observe that under reasonable noise levels, autocorrelations in the time series data allow dense sampling to better determine the correct levels of non-sampled points when compared to replicate sampling. A Java implementation of our framework can be used to determine the best replicate strategy given the expected noise. These results provide theoretical support to the large number of high throughput time series experiments that do not use replicates.

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Background: Understanding the evolution of biological networks can provide insight into how their modular structure arises and how they are affected by environmental changes. One approach to studying the evolution of these networks is to reconstruct plausible common ancestors of present-day networks, allowing us to analyze how the topological properties change over time and to posit mechanisms that drive the networks' evolution. Further, putative ancestral networks can be used to help solve other difficult problems in computational biology, such as network alignment. Results: We introduce a combinatorial framework for encoding network histories, and we give a fast procedure that, given a set of gene duplication histories, in practice finds network histories with close to the minimum number of interaction gain or loss events to explain the observed present-day networks. In contrast to previous studies, our method does not require knowing the relative ordering of unrelated duplication events. Results on simulated histories and real biological networks both suggest that common ancestral networks can be accurately reconstructed using this parsimony approach. A software package implementing our method is available under the Apache 2.0 license at http://cbcb.umd.edu/kingsford-group/parana. Conclusions: Our parsimony-based approach to ancestral network reconstruction is both efficient and accurate. We show that considering a larger set of potential ancestral interactions by not assuming a relative ordering of unrelated duplication events can lead to improved ancestral network inference.