Skip to main content

Advertisement

Springer Nature Link
Log in

Data-Theoretical Synthesis of the Early Developmental Process

  • Original Article
  • Published:
Neuroinformatics Aims and scope Submit manuscript

Abstract

Biological development is often described as a dynamic, emergent process. This is evident across a variety of phenomena, from the temporal organization of cell types in the embryo to compounding trends that affect large-scale differentiation. To better understand this, we propose combining quantitative investigations of biological development with theory-building techniques. This provides an alternative to the gene-centric view of development: namely, the view that developmental genes and their expression determine the complexity of the developmental phenotype. Using the model system Caenorhabditis elegans, we examine time-dependent properties of the embryonic phenotype and utilize the unique life-history properties to demonstrate how these emergent properties can be linked together by data analysis and theory-building. We also focus on embryogenetic differentiation processes, and how terminally-differentiated cells contribute to structure and function of the adult phenotype. Examining embryogenetic dynamics from 200 to 400 min post-fertilization provides basic quantitative information on developmental tempo and process. To summarize, theory construction techniques are summarized and proposed as a way to rigorously interpret our data. Our proposed approach to a formal data representation that can provide critical links across life-history, anatomy and function.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  • Alicea, B. (2018). The emergent connectome in Caenorhabditis elegans embryogenesis. BioSystems, 173, 247–255.

    Article  CAS  Google Scholar 

  • Alicea, B. (2020). Raising the connectome: the emergence of neuronal activity and behavior in C. elegans. Frontiers in Cellular Neuroscience. https://doi.org/10.3389/fncel.2020.524791.

  • Alicea, B., & Cibelli, J. B. (2014). Comparing SCNT-derived ESCs and iPSCs. In J. Cibelli, I. Wilmut, R. Jaenisch, J. Gurdon, R. Lanza, M. West, & K. Campbell (Eds.), Principles of cloning (pp. 465–471). Cambridge: Academic Press.

    Chapter  Google Scholar 

  • Alicea, B., & Gordon, R. (2016). Quantifying mosaic development: towards an evo-devo postmodern synthesis of the evolution of development via differentiation trees of embryos. Biology, 5(3), 33.

    Article  Google Scholar 

  • Alicea, B., & Gordon, R. (2018). Cell differentiation processes as spatial networks: identifying four-dimensional structure in embryogenesis. BioSystems, 173, 235–246.

    Article  Google Scholar 

  • Alicea, B., Murthy, S., Keaton, S. A., Cobbett, P., Cibelli, J. B., & Suhr, S. T. (2013). Defining the diversity of phenotypic respecification using multiple cell lines and reprogramming regimens. Stem Cells and Development, 22(19), 2641–2654.

    Article  CAS  Google Scholar 

  • Alicea, B., McGrew, S., Gordon, R., Larson, S., Warrington, T., & Watts, M. (2014). DevoWorm: differentiation waves and computation in C. elegans embryogenesis. bioRxiv. https://doi.org/10.1101/009993.

  • Alsous, J. I., Villoutreix, P., Stoop, N., Shvartsman, S. Y., & Dunkel, J. (2018). Entropic effects in cell lineage tree packings. Nature Physics, 14, 1016–1021.

    Article  Google Scholar 

  • Amini, R., Chartier, N. T., & Labbe, J.-C. (2015). Syncytium biogenesis: it’s all about maintaining good connections. Worm, 4(1), e992665.

    Article  Google Scholar 

  • Azevedo, R. B. R., Lohaus, R., Braun, V., Gumbel, M., Umamaheshwar, M., Agapow, P. M., Houthoofd, W., Platzer, U., Borgonie, G., Meinzer, H. P., & Leroi, A. M. (2005). The simplicity of metazoan cell lineages. Nature, 433, 152–156.

    Article  CAS  Google Scholar 

  • Bao, Z., Murray, J. I., Boyle, T., Ooi, S.-L., Sandel, M. J., & Waterston, R. H. (2006). Automated cell lineage tracing in Caenorhabditis elegans. PNAS, 103(8), 2707–2712.

    Article  CAS  Google Scholar 

  • Bao, Z., Zhao, Z., Boyle, T. J., Murray, J. I., & Waterston, R. H. (2008). Control of cell cycle timing during C. elegans embryogenesis. Developmental Biology, 318(1), 65–72.

    Article  CAS  Google Scholar 

  • Baugh, L. R., Hill, A. A., Slonim, D. K., Brown, E. L., & Hunter, C. P. (2003). Composition and dynamics of the Caenorhabditis elegans early embryonic transcriptome. Development, 130(5), 889–900.

    Article  CAS  Google Scholar 

  • Bolker, J. A. (2000). Modularity in development and why it matters to evo-devo. American Zoologist, 40(5), 770–776.

    Google Scholar 

  • Borsboom, D., van der Maas, H. L. J., Dalege, J., Kievit, R. A., & Haig, B. D. (2020). Theory construction methodology: a practical framework for building theories in Psychology. PsyArXiv. https://doi.org/10.31234/osf.io/w5tp8.

  • Broitman-Maduro, G., Lin, K. T.-H., Hung, W. W. K., & Maduro, M. F. (2006). Specification of the C. elegans MS blastomere by the T-box factor TBX-35. Development, 133(16), 3097–3106.

    Article  CAS  Google Scholar 

  • Carpiano, R. M., & Daley, D. M. (2006). Theory building on the high seas of population health: love boat, mutiny on the bounty, or poseidon adventure? Journal of Epidemiology and Community Health, 60(7), 574–577.

    Article  Google Scholar 

  • Chang, H. (2004). Inventing temperature: Measurement and Scientific progress. Oxford: Oxford University Press.

    Book  Google Scholar 

  • Chisholm, A.D. & Hardin, J. (2005). Epidermal Morphogenesis, WormBook: the online review of C. elegans biology. Accessed 11 Mar 2018. http://wormbook.org/chapters/www_epidermalmorphogenesis/epidermalmorphogenesis.pdf

  • Corley, K. G., & Gioia, D. A. (2011). Building theory about theory building: what constitutes a theoretical contribution? Academy of Management Review, 36(1), 12–32.

    Article  Google Scholar 

  • Delvenne, J.-C. (2019). Category theory for autonomous and networked dynamical systems. Entropy, 21, 302.

    Article  Google Scholar 

  • Duellman, W. E., & Trueb, L. (1994). Biology of Amphibians. Baltimore: Johns Hopkins University Press.

    Google Scholar 

  • Eisenhardt, K. M., & Graebner, M. E. (2007). Theory building from cases: opportunities and challenges. The Academy of Management Journal, 50(1), 25–32.

    Google Scholar 

  • Fay, D. S. (2005). The cell cycle and development: lessons from C. elegans. Seminars in Cell and Developmental Biology, 16, 397–406.

    Article  CAS  Google Scholar 

  • Fickentscher, R., & Weiss, M. (2017). Physical determinants of asymmetric cell divisions in the early development of Caenorhabditis elegans. Scientific Reports, 7, 9369.

    Article  Google Scholar 

  • Glanville, R. (1982). Inside every white box there are two black boxes trying to get out. Behavioral Science, 27.

  • Gordon, R. (1999). The Hierarchical Genome and Differentiation Waves: Novel Unification of Development, Genetics and Evolution. Singapore: World Scientific.

    Book  Google Scholar 

  • Ho, V. W. S., Wong, M.-K., An, X., Guan, D., Shao, J., Ng, H. C. K., Ren, X., He, K., Liao, J., Ang, Y., Chen, L., Huang, X., Yan, B., Xia, Y., Chan, L. L. H., Chow, K. L., Yan, H., & Zhao, Z. (2015). Systems-level quantification of division timing reveals a common genetic architecture controlling asynchrony and fate asymmetry. Molecular Systems Biology, 11, 814.29.

    Article  Google Scholar 

  • Hobert, O., Glenwinkel, L., & White, J. (2016). Revisiting neuronal cell type classification in Caenorhabditis elegans. Current Biology, 26(22), R1197–R1203.

    Article  CAS  Google Scholar 

  • Kitchin, R. (2014). Big Data, new epistemologies and paradigm shifts. Big Data and Society, April–June, 1–12.

  • Krakauer, D. C., Collins, J. P., Erwin, D., Flack, J. C., Fontana, W., Laubichler, M. D., Prohaska, S. J., West, G. B., & Stadler, P. F. (2011). The challenges and scope of theoretical biology. Journal of Theoretical Biology, 276, 269–276.

    Article  Google Scholar 

  • Kuhn, T. (1962). The structure of scientific revolutions. Chicago: University of Chicago Press.

    Google Scholar 

  • Laplane, L., Mantovani, P., Adolphs, R., Chang, H., Mantovani, A., McFall-Ngai, M., Rovelli, C., Sober, E., & Pradeu, T. (2019). Opinion: why science needs philosophy. PNAS, 116(10), 3948–3952.

    Article  Google Scholar 

  • Luo, L., Wen, Q., Ren, J., Hendricks, M., Gershow, M., Qin, Y., Greenwood, J., Soucy, E. R., Klein, M., Smith-Parker, H. K., Calvo, A. C., Colon-Ramos, D. A., Samuel, A. D. T., & Zhang, Y. (2014). Dynamic encoding of perception, memory, and movement in a C. elegans chemotaxis circuit. Neuron, 82(5), 1115–1128.

    Article  CAS  Google Scholar 

  • Meredith, J. (1993). Theory building through conceptual methods. International Journal of Operations and Production Management, 13(5), 3–11.

    Article  Google Scholar 

  • Moss, E. G. (2007). Heterochronic genes and the nature of developmental time. Current Biology, 17(11), R425–R434.

    Article  CAS  Google Scholar 

  • Nakamoto, A., Hester, S. D., Constantinou, S. J., Blaine, W. G., Tewksbury, A. B., Matei, M. T., Nagy, L. M., & Williams, T. A. (2015). Changing cell behaviors during beetle embryogenesis correlates with slowing of segmentation. Nature Communications, 6, 6635.

    Article  CAS  Google Scholar 

  • Naur, P. (1985). Programming as theory building. Microprocessing and Microprogramming, 15, 253–261.

    Article  Google Scholar 

  • Pettersson, S., Forchheimer, R., & Larsson, J.-A. (2013). Meta-Boolean models of asymmetric division patterns in the C. elegans intestinal lineage Implications for the posterior boundary of intestinal twist. Worm, 2(1), e23701.

    Article  Google Scholar 

  • Portereiko, M. F., & Mango, S. E. (2001). Early morphogenesis of the Caenorhabditis elegans pharynx. Developmental Biology, 233, 482–494.

    Article  CAS  Google Scholar 

  • Sammut, M., Cook, S. J., Nguyen, K. C. Q., Felton, T., Hall, D. H., Emmons, S. W., Poole, R. J., & Barrios, A. (2015). Glia-derived neurons are required for sex-specific learning in C. elegans. Nature, 526, 385–390.

    Article  CAS  Google Scholar 

  • Schnabel, R., Hutter, H., Moerman, D., & Schnabel, H. (1997). Assessing normal embryogenesis in Caenorhabditis elegans using a 4D microscope: variability of development and regional specification. Developmental Biology, 184, 234–265.

    Article  CAS  Google Scholar 

  • Shapiro, E., Biezuner, T., & Linnarsson, S. (2013). Single-cell sequencing-based technologies will revolutionize whole-organism science. Nature Reviews Genetics, 14, 618–630.

    Article  CAS  Google Scholar 

  • Smaldino, P. E. (2020). How to translate a verbal theory into a formal model. Social Psychology, 51(4), 207–218.

    Article  Google Scholar 

  • Spivak, D. I. (2014). Category theory for the sciences. Cambridge: MIT Press.

    Google Scholar 

  • Steiner, E. (1988). Methodology of Theory-building. Sydney: Educology Research Associates.

    Google Scholar 

  • Stout, R. F., Verkhratsky, A., & Parpura, V. (2014). Caenorhabditis elegans glia modulate neuronal activity and behavior. Frontiers in Cellular Neuroscience, 8, 67.

    Article  Google Scholar 

  • Sulston, J. E., Schierenberg, E., White, J. G., & Thomson, J. N. (1983). The embryonic cell lineage of the nematode Caenorhabditis elegans. Developmental Biology, 100(1), 64–119.

    Article  CAS  Google Scholar 

  • Thiry, L., Zhao, H., & Hassenforder, M. (2018). Categories for (Big) Data models and optimization. Journal of Big Data, 5, 21.

    Article  Google Scholar 

  • Tintori, S. C., Osborne Nishimura, E., Golden, P. T., Lieb, J. D., & Goldstein, B. (2016). A transcriptional lineage of early C. elegans development. Developmental Cell, 38(4), 430–444.

    Article  CAS  Google Scholar 

  • Tiraihi, A., Tiraihi, M., & Tiraihi, T. (2011). Self-organization of developing embryos using scale-invariant approach. Theoretical Biology and Medical Modeling, 8, 17.

    Article  Google Scholar 

  • Vagero, D. (2006). Where does new theory come from? Journal of Epidemiology and Community Health, 60(7), 573–574.

    Article  Google Scholar 

  • Vasiev, B., Balter, A., Chaplain, M., Glazier, J. A., & Weijer, C. J. (2010). Modeling gastrulation in the chick embryo: formation of the primitive streak. PLoS One, 5(5), e10571.

    Article  Google Scholar 

  • Velarde, N., Gunsalus, K. C., & Piano, F. (2007). Diverse roles of actin in C. elegans early embryogenesis. BMC Developmental Biology, 7, 142.

    Article  Google Scholar 

  • Voit, E. O. (2019). Perspective: dimensions of the scientific method. PLoS Computational Biology, 15(9), e1007279.

    Article  CAS  Google Scholar 

  • Wacker, J. G. (1998). A definition of theory: research guidelines for different theory-building research methods in operations management. Journal of Operations Management, 16, 361–385.

    Article  Google Scholar 

  • Wong, M.-K., Guan, D., Ng, K. H. C., Ho, S. V. W., An, X., Li, R., Ren, X., & Zhao, X. Z. (2016). Timing of tissue-specific cell division requires a differential onset of zygotic transcription during metazoan embryogenesis. Journal of Biological Chemistry, 291(24), 12501–12513.

    Article  CAS  Google Scholar 

  • Yan, C., Gong, B., Wei, Y., & Gao, Y. (2020a). Deep multi-view enhancement hashing for image retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/TPAMI.2020.2975798.

  • Yan, C., Shao, B., Zhao, H., Ning, R., Zhang, Y., & Xu, F. (2020b). 3D room layout estimation from a single RGB image. IEEE Transactions on Multimedia, 27(11), 3014–3024.

    Article  Google Scholar 

  • Yan, C., Li, Z., Zhang, Y., Liu, Y., Ji, X., & Zhang, Y. (2020c). Depth image denoising using nuclear norm and learning graph models. arXiv, 2008.03741.

  • Yanai, I., & Lercher, M. (2020). A hypothesis is a liability. Genome Biology, 21, 231.

    Article  Google Scholar 

  • Zhao, J., Xie, X., Xu, X., & Sun, S. (2017). Multi-view learning overview: recent progress and new challenges. Information Fusion, 38, 43–54.

    Article  Google Scholar 

Download references

Acknowledgments

We would like to acknowledge feedback from the OpenWorm and DevoWorm communities, particularly Drs. Stephen Larson, George Mikhailovsky, and senior contributors at the OpenWorm Foundation.

Author information

Authors and Affiliations

  1. OpenWorm Foundation, Boston, MA, USA

    Bradly Alicea & Thomas E. Portegys

  2. Orthogonal Research Lab, Champaign, IL, USA

    Bradly Alicea

  3. Gulf Marine Specimen Laboratory, Panacea, FL, USA

    Richard Gordon

  4. C.S. Mott Center for Human Growth and Development, Department of Obstetrics and Gynecology, Wayne State University, Detroit, MI, USA

    Richard Gordon

  5. Dialectek LLC, LaSalle, IL, USA

    Thomas E. Portegys

Authors
  1. Bradly Alicea
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Richard Gordon
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Thomas E. Portegys
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Manuscript was written by B.A., and revised by R.G. and T.E.P. Figures and tables were created and assembled by B.A., with input from R.G. Analysis was conducted by B.A. Ideas and conception for manuscript were done by B.A., R.G., and T.E.P. Data archival was done by B.A.

Corresponding author

Correspondence to Bradly Alicea.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Supplemental Figure 1

A comparison of developmental and terminally-differentiated cell counts for 50-min intervals. Data collected from embryos raised at 25 °C. (PNG 31 kb)

High Resolution Image (TIF 2674 kb)

Supplemental Figure 2

The number of developmental cells alive for each sublineage (AB, MS, C, D, and E) at 20 min time intervals for 200–400 min of embryogenesis. Panel A shows AB and MS, while panel B shows C, D, and E. Data collected from embryos raised at 25 °C. (PNG 116 kb)

High Resolution Image (TIF 6002 kb)

Supplemental Figure 3

Heat map showing the emergence of terminally differentiated cells in C. elegans from 200 to 400 min of embryogenesis. Emergence of Terminally Differentiated Cells in each cell type class, relative to the 400 min of embryogenesis. The 200 to 300 min period is sampled at 5-min intervals, while the 300 to 400 min period is sampled at 30-min intervals. Bottom of the figure is labeled with the corresponding developmental stages and images of the embryo during select times. Data collected from embryos raised at 25 °C. (PNG 681 kb)

High Resolution Image (TIF 103573 kb)

Supplemental Figure 4

Histogram containing counts of types of cells born during a specific time interval (bins of size 10 except where noted). Figure 5A: Interneurons (blue) vs. Neurons (red), Fig. 5B: Interneurons (blue) vs. Hypodermal cells (red). Gray region denotes bins of size 50. Data collected from embryos raised at 25 °C. (PNG 47 kb)

High Resolution Image (TIF 8743 kb)

Supplemental Figure 5

Information content for each terminally-differentiated cell family, based on a hierarchical clustering analysis. Information content (blue bars, left axis) is compared to the number of cells in each family (red bars, right axis). Data collected from embryos raised at 25 °C. (PNG 26 kb)

High Resolution Image (TIF 3359 kb)

Supplemental Figure 6

A time-series of CAST coefficients for 200 to 400 min of C. elegans embryogenesis. Time intervals from 200 to 300 min are five minutes in length; time intervals from 300 to 400 min are fifty minutes in length (denoted within gray region). Data collected from embryos raised at 25 °C. (PNG 85 kb)

High Resolution Image (TIF 12707 kb)

Supplemental File 1

Terminally-differentiated cell nomenclature identities and annotations by developmental birth time (min). (XLSX 121 kb)

Supplemental File 2

Table of number of cells born at a specific developmental birth time sampling point (min) for five distinct somatic cell types. (XLSX 8 kb)

Supplemental File 3

Table of somatic cell types by family, class, and developmental birth time (min). (XLSX 24 kb)

Supplemental File 4

Table of cell families by number of family members and average developmental birth time (min). (XLSX 12 kb)

Supplemental File 5

Pairwise alignments (per pairs of birth time sampling points) and calculation of alignment scores for CAST analysis. (XLSX 152 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Alicea, B., Gordon, R. & Portegys, T.E. Data-Theoretical Synthesis of the Early Developmental Process. Neuroinform 20, 7–23 (2022). https://doi.org/10.1007/s12021-020-09508-1

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12021-020-09508-1

Keywords

Search

Navigation