@article{162,
  abstract     = {Facial shape is the basis for facial recognition and categorization. Facial features reflect the underlying geometry of the skeletal structures. Here, we reveal that cartilaginous nasal capsule (corresponding to upper jaw and face) is shaped by signals generated by neural structures: brain and olfactory epithelium. Brain-derived Sonic Hedgehog (SHH) enables the induction of nasal septum and posterior nasal capsule, whereas the formation of a capsule roof is controlled by signals from the olfactory epithelium. Unexpectedly, the cartilage of the nasal capsule turned out to be important for shaping membranous facial bones during development. This suggests that conserved neurosensory structures could benefit from protection and have evolved signals inducing cranial cartilages encasing them. Experiments with mutant mice revealed that the genomic regulatory regions controlling production of SHH in the nervous system contribute to facial cartilage morphogenesis, which might be a mechanism responsible for the adaptive evolution of animal faces and snouts.},
  author       = {Kaucka, Marketa and Petersen, Julian and Tesarova, Marketa and Szarowska, Bara and Kastriti, Maria and Xie, Meng and Kicheva, Anna and Annusver, Karl and Kasper, Maria and Symmons, Orsolya and Pan, Leslie and Spitz, Francois and Kaiser, Jozef and Hovorakova, Maria and Zikmund, Tomas and Sunadome, Kazunori and Matise, Michael P and Wang, Hui and Marklund, Ulrika and Abdo, Hind and Ernfors, Patrik and Maire, Pascal and Wurmser, Maud and Chagin, Andrei S and Fried, Kaj and Adameyko, Igor},
  journal      = {eLife},
  publisher    = {eLife Sciences Publications},
  title        = {{Signals from the brain and olfactory epithelium control shaping of the mammalian nasal capsule cartilage}},
  doi          = {10.7554/eLife.34465},
  volume       = {7},
  year         = {2018},
}

@misc{9838,
  abstract     = {Facial shape is the basis for facial recognition and categorization. Facial features reflect the underlying geometry of the skeletal structures. Here we reveal that cartilaginous nasal capsule (corresponding to upper jaw and face) is shaped by signals generated by neural structures: brain and olfactory epithelium. Brain-derived Sonic Hedgehog (SHH) enables the induction of nasal septum and posterior nasal capsule, whereas the formation of a capsule roof is controlled by signals from the olfactory epithelium. Unexpectedly, the cartilage of the nasal capsule turned out to be important for shaping membranous facial bones during development. This suggests that conserved neurosensory structures could benefit from protection and have evolved signals inducing cranial cartilages encasing them. Experiments with mutant mice revealed that the genomic regulatory regions controlling production of SHH in the nervous system contribute to facial cartilage morphogenesis, which might be a mechanism responsible for the adaptive evolution of animal faces and snouts.},
  author       = {Kaucka, Marketa and Petersen, Julian and Tesarova, Marketa and Szarowska, Bara and Kastriti, Maria Eleni and Xie, Meng and Kicheva, Anna and Annusver, Karl and Kasper, Maria and Symmons, Orsolya and Pan, Leslie and Spitz, Francois and Kaiser, Jozef and Hovorakova, Maria and Zikmund, Tomas and Sunadome, Kazunori and Matise, Michael P and Wang, Hui and Marklund, Ulrika and Abdo, Hind and Ernfors, Patrik and Maire, Pascal and Wurmser, Maud and Chagin, Andrei S and Fried, Kaj and Adameyko, Igor},
  publisher    = {Dryad},
  title        = {{Data from: Signals from the brain and olfactory epithelium control shaping of the mammalian nasal capsule cartilage}},
  doi          = {10.5061/dryad.f1s76f2},
  year         = {2018},
}

@inbook{37,
  abstract     = {Developmental processes are inherently dynamic and understanding them requires quantitative measurements of gene and protein expression levels in space and time. While live imaging is a powerful approach for obtaining such data, it is still a challenge to apply it over long periods of time to large tissues, such as the embryonic spinal cord in mouse and chick. Nevertheless, dynamics of gene expression and signaling activity patterns in this organ can be studied by collecting tissue sections at different developmental stages. In combination with immunohistochemistry, this allows for measuring the levels of multiple developmental regulators in a quantitative manner with high spatiotemporal resolution. The mean protein expression levels over time, as well as embryo-to-embryo variability can be analyzed. A key aspect of the approach is the ability to compare protein levels across different samples. This requires a number of considerations in sample preparation, imaging and data analysis. Here we present a protocol for obtaining time course data of dorsoventral expression patterns from mouse and chick neural tube in the first 3 days of neural tube development. The described workflow starts from embryo dissection and ends with a processed dataset. Software scripts for data analysis are included. The protocol is adaptable and instructions that allow the user to modify different steps are provided. Thus, the procedure can be altered for analysis of time-lapse images and applied to systems other than the neural tube.},
  author       = {Zagórski, Marcin P and Kicheva, Anna},
  booktitle    = {Morphogen Gradients },
  isbn         = {978-1-4939-8771-9},
  issn         = {1064-3745},
  pages        = {47 -- 63},
  publisher    = {Springer Nature},
  title        = {{Measuring dorsoventral pattern and morphogen signaling profiles in the growing neural tube}},
  doi          = {10.1007/978-1-4939-8772-6_4},
  volume       = {1863},
  year         = {2018},
}

@article{943,
  abstract     = {Like many developing tissues, the vertebrate neural tube is patterned by antiparallel morphogen gradients. To understand how these inputs are interpreted, we measured morphogen signaling and target gene expression in mouse embryos and chick ex vivo assays. From these data, we derived and validated a characteristic decoding map that relates morphogen input to the positional identity of neural progenitors. Analysis of the observed responses indicates that the underlying interpretation strategy minimizes patterning errors in response to the joint input of noisy opposing gradients. We reverse-engineered a transcriptional network that provides a mechanistic basis for the observed cell fate decisions and accounts for the precision and dynamics of pattern formation. Together, our data link opposing gradient dynamics in a growing tissue to precise pattern formation.},
  author       = {Zagórski, Marcin P and Tabata, Yoji and Brandenberg, Nathalie and Lutolf, Matthias and Tkacik, Gasper and Bollenbach, Tobias and Briscoe, James and Kicheva, Anna},
  issn         = {0036-8075},
  journal      = {Science},
  number       = {6345},
  pages        = {1379 -- 1383},
  publisher    = {American Association for the Advancement of Science},
  title        = {{Decoding of position in the developing neural tube from antiparallel morphogen gradients}},
  doi          = {10.1126/science.aam5887},
  volume       = {356},
  year         = {2017},
}

@article{654,
  abstract     = {In November 2016, developmental biologists, synthetic biologists and engineers gathered in Paris for a meeting called ‘Engineering the embryo’. The participants shared an interest in exploring how synthetic systems can reveal new principles of embryonic development, and how the in vitro manipulation and modeling of development using stem cells can be used to integrate ideas and expertise from physics, developmental biology and tissue engineering. As we review here, the conference pinpointed some of the challenges arising at the intersection of these fields, along with great enthusiasm for finding new approaches and collaborations.},
  author       = {Kicheva, Anna and Rivron, Nicolas},
  issn         = {0950-1991},
  journal      = {Development},
  number       = {5},
  pages        = {733 -- 736},
  publisher    = {Company of Biologists},
  title        = {{Creating to understand – developmental biology meets engineering in Paris}},
  doi          = {10.1242/dev.144915},
  volume       = {144},
  year         = {2017},
}

@article{685,
  abstract     = {By applying methods and principles from the physical sciences to biological problems, D'Arcy Thompson's On Growth and Form demonstrated how mathematical reasoning reveals elegant, simple explanations for seemingly complex processes. This has had a profound influence on subsequent generations of developmental biologists. We discuss how this influence can be traced through twentieth century morphologists, embryologists and theoreticians to current research that explores the molecular and cellular mechanisms of tissue growth and patterning, including our own studies of the vertebrate neural tube.},
  author       = {Briscoe, James and Kicheva, Anna},
  issn         = {0925-4773},
  journal      = {Mechanisms of Development},
  pages        = {26 -- 31},
  publisher    = {Elsevier},
  title        = {{The physics of development 100 years after D'Arcy Thompson's “on growth and form”}},
  doi          = {10.1016/j.mod.2017.03.005},
  volume       = {145},
  year         = {2017},
}

@article{1167,
  abstract     = {Evolutionary pathways describe trajectories of biological evolution in the space of different variants of organisms (genotypes). The probability of existence and the number of evolutionary pathways that lead from a given genotype to a better-adapted genotype are important measures of accessibility of local fitness optima and the reproducibility of evolution. Both quantities have been studied in simple mathematical models where genotypes are represented as binary sequences of two types of basic units, and the network of permitted mutations between the genotypes is a hypercube graph. However, it is unclear how these results translate to the biologically relevant case in which genotypes are represented by sequences of more than two units, for example four nucleotides (DNA) or 20 amino acids (proteins), and the mutational graph is not the hypercube. Here we investigate accessibility of the best-adapted genotype in the general case of K > 2 units. Using computer generated and experimental fitness landscapes we show that accessibility of the global fitness maximum increases with K and can be much higher than for binary sequences. The increase in accessibility comes from the increase in the number of indirect trajectories exploited by evolution for higher K. As one of the consequences, the fraction of genotypes that are accessible increases by three orders of magnitude when the number of units K increases from 2 to 16 for landscapes of size N ∼ 106genotypes. This suggests that evolution can follow many different trajectories on such landscapes and the reconstruction of evolutionary pathways from experimental data might be an extremely difficult task.},
  author       = {Zagórski, Marcin P and Burda, Zdzisław and Wacław, Bartłomiej},
  journal      = {PLoS Computational Biology},
  number       = {12},
  publisher    = {Public Library of Science},
  title        = {{Beyond the hypercube evolutionary accessibility of fitness landscapes with realistic mutational networks}},
  doi          = {10.1371/journal.pcbi.1005218},
  volume       = {12},
  year         = {2016},
}

@article{1371,
  abstract     = {Living cells can maintain their internal states, react to changing environments, grow, differentiate, divide, etc. All these processes are tightly controlled by what can be called a regulatory program. The logic of the underlying control can sometimes be guessed at by examining the network of influences amongst genetic components. Some associated gene regulatory networks have been studied in prokaryotes and eukaryotes, unveiling various structural features ranging from broad distributions of out-degrees to recurrent "motifs", that is small subgraphs having a specific pattern of interactions. To understand what factors may be driving such structuring, a number of groups have introduced frameworks to model the dynamics of gene regulatory networks. In that context, we review here such in silico approaches and show how selection for phenotypes, i.e., network function, can shape network structure.},
  author       = {Martin, Olivier and Krzywicki, André and Zagórski, Marcin P},
  journal      = {Physics of Life Reviews},
  pages        = {124 -- 158},
  publisher    = {Elsevier},
  title        = {{Drivers of structural features in gene regulatory networks: From biophysical constraints to biological function}},
  doi          = {10.1016/j.plrev.2016.06.002},
  volume       = {17},
  year         = {2016},
}

@article{1373,
  author       = {Martin, Olivier and Zagórski, Marcin P},
  journal      = {Physics of Life Reviews},
  pages        = {168 -- 171},
  publisher    = {Elsevier},
  title        = {{Network architectures and operating principles. Reply to comments on "Drivers of structural features in gene regulatory networks: From biophysical constraints to biological function"}},
  doi          = {10.1016/j.plrev.2016.06.006},
  volume       = {17},
  year         = {2016},
}

@misc{9866,
  author       = {Zagórski, Marcin P and Burda, Zdzisław and Wacław, Bartłomiej},
  publisher    = {Public Library of Science},
  title        = {{ZIP-archived directory containing all data and computer programs}},
  doi          = {10.1371/journal.pcbi.1005218.s009},
  year         = {2016},
}