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Evolutionary morphogenesis for multi-cellular systems | SpringerLink
very interesting

now you are really getting into the biological metaphor
Evo-Devo  Morphogenesis 
november 2017 by tonyyet
excd.lab – exploring the evolution of cross-cultural diversity
The excd.lab investigates how the startling (yet not infinite!) variety in human culture has evolved. Led by Professor Fiona Jordan and based at the University of Bristol Department of Anthropology & Archaeology, we are interdisciplinary researchers and bring together methods and ideas from across anthropology, biology, and the cognitive and language sciences. Much of the work in the group uses computational evolutionary methods, particularly phylogenetics, but we also use experimental, field, linguistic, and elicitation approaches.

Our current work includes a study of the cross-cultural diversity of human kinship systems, experimental work on accent prestige and cultural transmission, research into the evolution of music endings, a study on the cultural adjustment of international students at the University of Bristol, and much more. For more details, see our research, people, and publication pages.

Why “excd.lab”?

Because we EXplore Cultural Diversity and we use Evolutionary and Cross (X) – Cultural methoDs. It’s pronounced “exceed”.
evolution  Evo-Devo 
september 2017 by tonyyet
The Diversity of Development: Embryos and Evolution - YouTube
How does variation in genes generate the beautiful diversity of animal body shapes that fill the world? UCSD Biologist William McGinnis explains that all animals, whether fish, fowl, or fly, share similar architectural control genes called Homeobox genes. The discovery and study of these genes has led to an understanding of how subtle changes in Homeobox genes can lead to changes in animal form during evolution. Series: "Evolution Matters"
october 2016 by tonyyet
Topslam: Waddington Landscape Recovery for Single Cell Experiments | bioRxiv
We present an approach to estimating the nature of the Waddington (or epigenetic) landscape that underlies a population of individual cells. Through exploiting high resolution single cell transcription experiments we show that cells can be located on a landscape that reflects their differentiated nature. Our approach makes use of probabilistic non-linear dimensionality reduction that respects the topology of our estimated epigenetic landscape. In simulation studies and analyses of real data we show that the approach, known as \manifold, outperforms previous attempts to understand the differentiation landscape. Hereby, the novelty of our approach lies in the correction of distances \emph{before} extracting ordering information. This gives the advantage over other attempts, which have to correct for extracted time lines by post processing or additional data.
epigenetic-landscapes  evo-devo  theoretical-biology  rather-interesting  to-understand  consider:looking-to-see  consider:feature-discovery 
july 2016 by Vaguery
Coalescent models for developmental biology and the spatio-temporal dynamics of growing tissues. | bioRxiv
Development is a process that needs to tightly coordinated in both space and time. Cell tracking and lineage tracing have become important experimental techniques in developmental biology and allow us to map the fate of cells and their progeny in both space and time. A generic feature of developing (as well as homeostatic) tissues that these analyses have revealed is that relatively few cells give rise to the bulk of the cells in a tissue; the lineages of most cells come to an end fairly quickly. This has spurned the interest also of computational and theoretical biologists/physicists who have developed a range of modelling -- perhaps most notably are the agent-based modelling (ABM) --- approaches. These can become computationally prohibitively expensive but seem to capture some of the features observed in experiments. Here we develop a complementary perspective that allows us to understand the dynamics leading to the formation of a tissue (or colony of cells). Borrowing from the rich population genetics literature we develop genealogical models of tissue development that trace the ancestry of cells in a tissue back to their most recent common ancestors. We apply this approach to tissues that grow under confined conditions --- as would, for example, be appropriate for the neural crest --- and unbounded growth --- illustrative of the behaviour of 2D tumours or bacterial colonies. The classical coalescent model from population genetics is readily adapted to capture tissue genealogies for different models of tissue growth and development. We show that simple but universal scaling relationships allow us to establish relationships between the coalescent and different fractal growth models that have been extensively studied in many different contexts, including developmental biology. Using our genealogical perspective we are able to study the statistical properties of the processes that give rise to tissues of cells, without the need for large-scale simulations.
theoretical-biology  developmental-biology  evo-devo  artificial-life  population-biology  self-organization  rather-interesting  morphology  fitness-landscapes  nudge-targets  consider:detailed-reexamination 
july 2015 by Vaguery
[1407.6554] Multi-species network inference improves gene regulatory network reconstruction for early embryonic development in Drosophila
Gene regulatory network inference uses genome-wide transcriptome measurements in response to genetic, environmental or dynamic perturbations to predict causal regulatory influences between genes. We hypothesized that evolution also acts as a suitable network perturbation and that integration of data from multiple closely related species can lead to improved reconstruction of gene regulatory networks. To test this hypothesis, we predicted networks from temporal gene expression data for 3,610 genes measured during early embryonic development in six Drosophila species and compared predicted networks to gold standard networks of ChIP-chip and ChIP-seq interactions for developmental transcription factors in five species. We found that (i) the performance of single-species networks was independent of the species where the gold standard was measured; (ii) differences between predicted networks reflected the known phylogeny and differences in biology between the species; (iii) an integrative consensus network which minimized the total number of edge gains and losses with respect to all single-species networks performed better than any individual network. Our results show that in an evolutionarily conserved system, integration of data from comparable experiments in multiple species improves the inference of gene regulatory networks. They provide a basis for future studies on the numerous multi-species gene expression datasets for other biological processes available in the literature.
bioinformatics  evo-devo  statistics  rather-interesting  data-fusion  genetic-regulatory-networks  inference 
february 2015 by Vaguery
Systematic Imaging Reveals Features of Localized mRNAs and Their Changing Subcellular Destinations in Development | bioRxiv
The asymmetric distribution of cytoplasmic components by mRNA localization is critical for eukaryotic cells and affects large numbers of transcripts. How such global subcellular localization of mRNAs is regulated is still unknown. We combined transcriptomics and systematic imaging to determine tissue-specific expression and subcellular localizations of 5862 mRNAs during Drosophila oogenesis. While the transcriptome is stable and alternative splicing and polyadenylation is rare, cytoplasmic localization of mRNAs is widespread. Localized mRNAs have distinct gene features and diverge in expression level, 3'UTR length and sequence conservation. We show that intracellular localization of mRNAs depends on an intact microtubule cytoskeleton and that specifically the posterior enrichment requires the localization of oskar mRNA to the posterior cortex. Using cross-tissue comparison we revealed that the localization landscape differs substantially between epithelial, germline and embryonic cells and the localization status of mRNAs also changes considerably within the oocyte over the course of oogenesis.
evo-devo  developmental-biology  experiment  molecular-biology  visualization  dynamical-systems  rather-interesting  molecular-design  biological-engineering  self-organization  design-patterns 
november 2014 by Vaguery
[1309.4722] An explanatory evo-devo model for the developmental hourglass
The "developmental hourglass" describes a pattern of increasing morphological divergence towards earlier and later embryonic development, separated by a period of significant conservation across distant species (the "phylotypic stage"). Recent studies have also found evidence in support of the hourglass effect at the genomic level. For instance, the phylotypic stage expresses the oldest and most conserved transcriptomes. However, the regulatory mechanism that causes the hourglass pattern remains an open question. Here, we propose an abstract model of regulatory gene interactions during development, and of their evolution. The model captures how the "functional state" of genes change as development progresses in the form of a hierarchical network. It also captures the evolution of a population under random perturbations in the structure of this regulatory network. The model predicts, under fairly general assumptions, the emergence of an hourglass pattern in terms of the number of state-transitioning genes during development. Additionally, the evolutionary age of those genes also follows an hourglass pattern, with the oldest genes concentrated at the hourglass waist. The key condition behind the hourglass effect is that developmental regulators should have an increasingly specific function as development progresses. We have confirmed the theoretical predictions of the model with gene expression profiles from Drosophila melanogaster and Arabidopsis thaliana.
evo-devo  developmental-biology  artificial-life  simulation  nudge-targets  complexology 
september 2013 by Vaguery
[1309.2614] Morphogenesis at criticality?
Spatial patterns in the early fruit fly embryo emerge from a network of interactions among transcription factors, the gap genes, driven by maternal inputs. Such networks can exhibit many qualitatively different behaviors, separated by critical surfaces. At criticality, we should observe strong correlations in the fluctuations of different genes around their mean expression levels, a slowing of the dynamics along some but not all directions in the space of possible expression levels, correlations of expression fluctuations over long distances in the embryo, and departures from a Gaussian distribution of these fluctuations. Analysis of recent experiments on the gap genes shows that all these signatures are observed, and that the different signatures are related in ways predicted by theory. While there might be other explanations for these individual phenomena, the confluence of evidence suggests that this genetic network is tuned to criticality.
systems-biology  bioinformatics  evo-devo  developmental-biology  simulation  morphogenesis  artificial-life  nudge-targets  consider:pattern-capacity 
september 2013 by Vaguery

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