Object Spaces: An Organizing Strategy for Biological Theorizing

Abstract

A classic analytic approach to biological phenomena seeks to refine definitions until classes are sufficiently homogenous to support prediction and explanation, but this approach founders on cases where a single process produces objects with similar forms but heterogeneous behaviors. I introduce object spaces as a tool to tackle this challenging diversity of biological objects in terms of causal processes with well-defined formal properties. Object spaces have three primary components: (1) a combinatorial biological process such as protein synthesis that generates objects with parts that are modular, independent, and organized according to an invariant syntax; (2) a notion of “distance” that relates the objects according to rules of change over time as found in nature or useful for algorithms; (3) mapping functions defined on the space that map its objects to other spaces or apply an evaluative criterion to measure an important quality, such as parsimony or biochemical function. Once defined, an object space can be used to represent and simulate the dynamics of phenomena on multiple scales; it can also be used as a tool for predicting higher-order properties of the objects, including stitching together series of causal processes. Object spaces are the basis for a strategy of theorizing, discovery, and analysis in biology: as heuristic idealizations of biology, they help us transform inchoate, intractable problems into articulated, well-structured ones. Developing an object space is a research strategy with a long, successful history under many other names, and it offers a unifying but not overreaching approach to biological theory.

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References

  1. Brakefield PM, Roskam JC (2006) Exploring evolutionary constraints is a task for an integrative biology. American Naturalist 168: S4–S13.

    Article  Google Scholar 

  2. Crick FH (1958) On protein synthesis. Symposia of the Society for Experimental Biology 12: 138–163.

    Google Scholar 

  3. Griffiths PE, Gray RD (1994) Developmental systems and evolutionary explanation. Journal of Philosophy 91: 277–304.

    Article  Google Scholar 

  4. Kauffman SA (1993) The Origins of Order: Self-Organization and Selection in Evolution. New York: Oxford University Press.

    Google Scholar 

  5. Maynard Smith J (1970) Natural selection and the concept of a protein space. Nature 225: 563–564.

    Article  Google Scholar 

  6. Mitteroecker P, Huttegger SM (2009) The concept of morphospaces in evolutionary and developmental biology: Mathematics and metaphors. Biological Theory 4: 54–67.

    Article  Google Scholar 

  7. Oyama S (2000) The Ontogeny of Information: Developmental Systems and Evolution, 2nd ed. Durham, NC: Duke University Press.

    Google Scholar 

  8. Raup DM (1966) Geometric analysis of shell coiling: General problems. Journal of Paleontology 40: 1178–1190.

    Google Scholar 

  9. Raup DM, Gould SJ (1974) Stochastic simulation and evolution of morphology: Towards a nomothetic paleontology. Systematic Zoology 23: 305–322.

    Article  Google Scholar 

  10. Simon HA (1973) The structure of ill-structured problems. Artificial Intelligence 4: 181–201.

    Article  Google Scholar 

  11. Stadler BMR, Stadler PF, Wagner GP, Fontana W (2001) The topology of the possible: Formal spaces underlying patterns of evolutionary change. Journal of Theoretical Biology 213: 241–274.

    Article  Google Scholar 

  12. Stein LD (2008) Towards a cyber infrastructure for the biological sciences: Progress, visions and challenges. Nature Review Genetics 9: 678–688.

    Article  Google Scholar 

  13. Stolyar S, Van Dien S, Hillesland KL, Pinel N, Lie TJ, Leigh JA, Stahl DA (2007) Metabolic modeling of a mutualistic microbial community. Molecular Systems Biology 3: 92.

    Article  Google Scholar 

  14. Wimsatt WC (2007) Re-Engineering Philosophy for Limited Beings: Piece-wise Approximations to Reality. Cambridge, MA: Harvard University Press.

    Google Scholar 

  15. Zuckerkandl E, Pauling L (1965) Divergence and convergence in proteins. In: Evolving Genes and Protein (Bryson V, Vogel HJ eds), 97–166. New York: Academic Press.

    Google Scholar 

Download references

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Correspondence to Beckett Sterner.

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Sterner, B. Object Spaces: An Organizing Strategy for Biological Theorizing. Biol Theory 4, 280–286 (2009). https://doi.org/10.1162/biot.2009.4.3.280

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Keywords

  • causal process
  • combinatorics
  • fitness landscape
  • heuristics
  • morphospace
  • sequence space