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Wagner G.P.,Yale Systems Biology Institute
AAAI Fall Symposium - Technical Report | Year: 2010

Theory suggests that biological robustness allows for the maintenance of fitness in the face of mutational change, and to the extent that this mutational change translates to heritable phenotypic change, that biological robustness allows for evolvability. However, empirical demonstrations that robustness promotes evolvability remain scant. This is in part due to the difficulty of defining and measuring both evolvability and robustness in real biological systems. Here we test whether protein structural robustness is associated with the extent of adaptive change a protein experiences. We find this to be the case for two forms of protein robustness - designability and modularity, which we measure via contact density and helix/sheet density, respectively. We interpret this association to be primarily the result of reduced constraints on amino acid substitutions in highly designable and/or modular proteins, resulting in less antagonistic pleiotropy and faster adaptation through natural selection.

Researchers from Yale, Emory, Purdue, and other universities looked at how cells sense the chemical and mechanical cues that determine cell behavior. Two studies with their results—which have potential implications ranging from breast cancer treatment to semiconductor manufacturing—appear the week of Jan. 18 in the journal Proceedings of the National Academy of Sciences. Cells read these signals by sensing the concentration of a chemical and gravitating toward it, as if tracking a scent. "The cells want to find where there's more of this molecule," said Andre Levchenko, the John C. Malone Professor of Biomedical Engineering and director of the Yale Systems Biology Institute, one of the papers' authors. "They use the gradients as directional cues." The studies focused on how well individual cells sense these cues compared to teams of cells. One study addressed this theoretically, while the other combined theory and experiments. The researchers placed together breast cells, which can self-organize into miniature breast tissue. The development of these small organ-like tissues, known as organoids, allowed the scientists to study how cell ensembles of different sizes sense the chemical signal's gradients. Epidermal growth factor (EGF), a substance that stimulates cell growth, was the chemical used in the experiments. When there were very weak gradients of EGF, with only slight differences in molecular concentration, the superiority of collective decision-making among cells became clear. "The single cells could not detect those differences; it was important for the cells to be together," Levchenko said. But the benefits of working together have a limit, he notes. The greater the number of cells that are communicating, the more the group generates its own internal noise—the cells' varying responses to cues—which can significantly jumble the communicated signal. "They need to 'talk' to each other to interpret the signal, but talking is a noisy process," said Levchenko, comparing it to the din of a crowded party. "It's hard to hear what's happening on the other side of the room. Friends can pass on a message for you, but it gets distorted in all the noise, as in a game of 'telephone.' It's like the famous adage, but with a twist: Bigger is better, but only to a degree." How cells communicate is crucial to many biological processes, and could have profound implications for the treatment of breast cancer. Growth factor gradients frequently guide breast cancer cells as they invade surrounding tissues, so understanding the influence of collective cell responses is critical to developing new treatments, said the researchers. Other impacts extend beyond biology. As electronic circuits get smaller and noisier, the semiconductor industry has increasingly become interested in cellular biology, and in how coupled cell circuits can support information processing. The National Science Foundation, National Institutes of Health, and the Semiconductor Research Corporation provided funding for the study. Explore further: How cells know which way to go More information: Limits to the precision of gradient sensing with spatial communication and temporal integration, PNAS, www.pnas.org/cgi/doi/10.1073/pnas.1509597112 Cell–cell communication enhances the capacity of cell ensembles to sense shallow gradients during morphogenesis, PNAS, www.pnas.org/cgi/doi/10.1073/pnas.1516503113

Musser J.M.,Yale Systems Biology Institute | Musser J.M.,Yale University | Wagner G.P.,Yale Systems Biology Institute | Wagner G.P.,Yale University | And 2 more authors.
Journal of Experimental Zoology Part B: Molecular and Developmental Evolution | Year: 2015

We elaborate a framework for investigating the evolutionary history of morphological characters. We argue that morphological character trees generated by phylogenetic analysis of transcriptomes provide a useful tool for identifying causal gene expression differences underlying the development and evolution of morphological characters. They also enable rigorous testing of different models of morphological character evolution and origination, including the hypothesis that characters originate via divergence of repeated ancestral characters. Finally, morphological character trees provide evidence that character transcriptomes undergo concerted evolution. We argue that concerted evolution of transcriptomes can explain the so-called "species signal" found in several recent comparative transcriptome studies. The species signal is the phenomenon that transcriptomes cluster by species rather than character type, even though the characters are older than the respective species. We suggest the species signal is a natural consequence of concerted gene expression evolution resulting from mutations that alter gene regulatory network interactions shared by the characters under comparison. Thus, character trees generated from transcriptomes allow us to investigate the variational independence, or individuation, of morphological characters at the level of genetic programs. © 2015 Wiley Periodicals, Inc.

Rorick M.M.,Yale University | Rorick M.M.,Yale Systems Biology Institute | Wagner G.P.,Yale Systems Biology Institute | Wagner G.P.,Yale University
Genome Biology and Evolution | Year: 2011

Theory suggests that biological modularity and robustness allow for maintenance of fitness under mutational change, and when this change is adaptive, for evolvability. Empirical demonstrations that these traits promote evolvability in nature remain scant however. This is in part because modularity, robustness, and evolvability are difficult to define and measure in real biological systems. Here, we address whether structural modularity and/or robustness confer evolvability at the level of proteins by looking for associations between indices of protein structural modularity, structural robustness, and evolvability. We propose a novel index for protein structural modularity: the number of regular secondary structure elements (helices and strands) divided by the number of residues in the structure. We index protein evolvability as the proportion of sites with evidence of being under positive selection multiplied by the average rate of adaptive evolution at these sites, and we measure this as an average over a phylogeny of 25 mammalian species. We use contact density as an index of protein designability, and thus, structural robustness. We find that protein evolvability is positively associated with structural modularity as well as structural robustness and that the effect of structural modularity on evolvability is independent of the structural robustness index. We interpret these associations to be the result of reduced constraints on amino acid substitutions in highly modular and robust protein structures, which results in faster adaptation through natural selection. © The Author(s) 2010.

Wagner G.P.,Yale Systems Biology Institute | Kin K.,Yale Systems Biology Institute | Lynch V.J.,Yale University
Theory in Biosciences | Year: 2013

The power of deep sequencing technology to reliably detect single RNA reads leads to a paradoxical problem of high sensitivity. In hybridization or PCR based methods for RNA quantification, the concern is low sensitivity, i.e., the problem that the signal from truly expressed genes might not be distinguishable from noise. In contrast, the problem with RNA-seq is that it is not clear whether genes with very low read counts are from low expressed genes or merely transcriptional noise. The frequency distribution for read counts does not show a clear separation in two classes of genes, which makes the decision whether a gene is to be considered expressed or not seemingly arbitrary. Here we address this problem by suggesting a statistical model that considers the number of transcripts detected in a RNA-seq study as a mixture of two distributions: one is a exponential distribution for transcripts from inactive genes, and a negative binomial distribution for actively transcribed genes. We apply this model to a number of RNA-seq data sets and find that the model fits the data very well. The calculated criteria for distinguishing between expressed and non-expressed gene is remarkably consistent among data sets, suggesting genes with more than two transcripts per million transcripts (TPM) are highly likely from actively transcribed genes. This criterion is consistent with the criterion of 1 RPKM proposed by Hebenstreit et al. Mol Sys Biol 7:497 (2011), based on chromatin modification and per cell RNA expression data. Hence, the regression model correctly identifies the not actively expressed class of genes and thus, provides an operational criterion for classifying genes in expressed and non-expressed sets, facilitating the interpretation of RNA-seq data. © 2013 Springer-Verlag Berlin Heidelberg.

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