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Model Organisms as Simulators: The Context of Cross-Species Research and Emergence

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Abstract

Model organisms are a living form of scientific models. Despite the widespread use of model organisms in scientific research, the actual representational relationship between model organisms and their target species is often poorly characterized in the context of cross-species research. Many model organisms do not represent the target species adequately, let alone accurately. This is partly due to the complex and emergent life phenomena in the organism, and partly due to the fact that a model organism is always taken to represent a broad range of diverse organisms. More often than not, model organisms are taken as a reference point for an extrapolation to be made to the unknown characteristics of other species. I propose to view model organisms as analogue simulators which represent the emergent phenomenon in the context of cross-species research. A model organism represents a wide range of species by simulating their molecular microstates which underlie various emergent phenomena. I show that although model organisms represent the target species inadequately at many levels of complexity, they have epistemic values as a simulator in virtue of which the emergent phenomenon can be modeled dynamically, a virtue that is hardly attainable by non-dynamic models.

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Notes

  1. Organisms with highly similar genes might differ in various cellular and molecular characteristics, such as mutation rate, synonymous codon usage, protein expression profiles, RNA regulations, and epigenetics.

  2. Mark Davis warns that although humanized mouse modeling is promising, ‘it should not be assumed that such mice are equivalent to a human immune system in any respect unless it is demonstrated to be so by a variety of objective measures.’ (2008, p. 836).

  3. Biologists are aware of this dilemma of representation. They recognize that the success of a model organism (and an animal model) representation is domain-relative. Lampson (2013) suggests to use an animal model to answer specific questions rather than broad questions in order to make the model more successful. Cheon and Orsulic (2011) claim that an ideal mouse model of human cancer needs to fulfill a list of stringent criteria in various domains, some of which are drug response, chemoresistance, and histopathology.

  4. I thank an anonymous reviewer for pointing me to Rosen’s works. Rosen’s mathematical works in theoretical biology have been embraced and clarified by his student Louie (2014).

  5. It is a common understanding among philosophers of biology to take model organisms as a type of living model rather than a type of simulator. I surmise that one of the main reasons is that model organisms share many characteristics of the models. Another reason could be that the term ‘simulator’ has always been associated with ‘computer simulation’, despite the fact that it can be used to refer to analogue simulators such as a wind tunnel. In the literature, model organisms are always regarded as a material model (see Meunier 2012; Huber and Keuck 2013).

  6. In this paper, I can only provide a cursory explanation of these four reasons. A detailed defense of them requires a separate paper. My aim in this paper is to focus on the representation of the emergent phenomenon in the context of cross-species research.

  7. Though Morrison is speaking in the context of computer simulations, it is applicable in the context of analogue simulations.

  8. There is no reason to claim that only a computer simulation is dynamic but an analogue simulation is not. Engineers and scientists do recognize the dynamic nature of analogue simulations, such as a physical wind tunnel simulation. See Ahmad et al. (2005).

  9. See Bechtel and Abrahamsen (2010) for a philosophical analysis.

  10. In an animal model of clinical research, specific diseases and pathological conditions can be simulated via adroitly implemented procedures. For example, aortic stenosis in many species could be simulated using supracoronary banding of the aorta of an animal model (Gross 2009, p. 207; Yarbrough et al. 2012). This type of procedural simulation involves relatively large equipments as compared to the molecular and cellular approach.

  11. According to Winsberg’s definition of a simulation, what qualifies something as a simulation can be the fact that the object in question is actually functioning as a simulator (e.g., a computer simulation of the big bang), or that the object in question is hoped or believed to function as a simulator. He writes, “Any object that we study because we think or hope it is dynamically similar enough for us to learn about basins of fluid by studying it is a simulation of a basin of fluid.” (2009, p. 836; my emphasis).

  12. My view is similar to Rosen’s idea that the basis for system analogy which underlies modeling is the common function of two systems under comparison (Rosen 2000, p. 280).

  13. Not to discount the fact that model organisms are similar to some target species, despite they are not (but hoped to) similar to a wide gamut of others.

  14. I thank an anonymous reviewer for pointing out that a simulator needs to be a model of the target system to warrant the structural congruence between both systems.

  15. Dardashti et al. (2017) enumerate a list of prospective cases of analogue simulations in physics. I do not embrace their conditions for an analogue simulation because their account requires mathematical accuracy and syntactic isomorphism between the simulator and the target system, which is inappropriate for the case of model organisms as a simulation of target species.

  16. Although Paul Humphreys’s paper argues for computer simulations, it is applicable to analogue simulations as well.

  17. I contend that the scientific insight provided by model organisms is more important than the exact result that can be extrapolated to the target species. Like a climate simulation model, the significance of model organisms as a simulator lies in the insights learned from the experimentation rather than in the practical solution provided.

  18. Though the finding garnered from model organism studies may not be generalizable to all respects of life phenomena and to all species, it is still critical in providing useful insights into many domains. Besides, it is not always necessary to specifically experiment on a particular organism to learn about it when such insight can be obtained reliably from model organism studies.

  19. This is evidenced in an investigation of the regulation of DNA replication timing during mammalian development. The researchers used a mouse model to simulate the replication mechanism in mammals. The similarity between the mouse model and other species, which is a characteristic feature of simulation, is revealed in the following paragraphs: “Furthermore, changes in replication time are linked to changes in sub-nuclear organization and domain-wide transcriptional potential, and tissue-specific replication timing profiles are conserved from mouse to human, suggesting that the program has developmental significance. Hence, these studies have provided a solid foundation for linking megabase level chromosome structure to function […]” (Pope et al. 2010, p. 127; my emphasis).

  20. It is analogous to the view that although two physical objects (say, water and ice) consist of the same material (i.e., atoms), they may possess different chemical properties due to the different configuration of the atoms, therefore renders them ontologically distinct at the chemical level. This is similar to my claim that a model organism and its target species are distinct ontologically at the emergent level, for the same molecular and cellular components in both organisms have different configurations when giving rise to phenotypes. After all, all objects are made up of the same material—atoms. It is the configuration of the material that matters.

  21. For example, Weber (1999) holds that simulations can be employed to explore the putative routes of emergence of the immune system.

  22. Simulation studies in protein folding are indispensable because experimental studies are unpromising in virtue of the low-resolution structural data yielded with sufficient temporal resolution (Freddolino et al. 2010). In addition, it was reported that physical models of protein folding are lagging behind simulations and bioinformatics approaches (Dill et al. 2007). In characterizing the emergent nature of protein folding, Thirumalai et al. (2010) conclude that both theoretical framework and simulations (which employ a variety of coarse-grained models) are significant in making testable predictions for protein folding.

  23. Johannes Lenhard (2007) argues that simulation takes a form of exploratory cooperation between experimentation and theoretical modeling, in the sense that simulation models are determined by the data and dependent on the right fundamental equations of theoretical models. Simulation is set to run through a process of iterative reciprocal comparison between experiment and model.

  24. Bedau argues against the traditionally stronger notion of emergence according to which a strong form of downward causation is involved. He maintains that this type of emergence is irrelevant in science (Bedau 1997).

  25. Model organisms are artificially constructed in the laboratory through various genetic and molecular manipulations. In terms of the artificiality of model organisms, emergent phenomena observed in model organisms are comparable to Bedau’s view that simulations “produce artificial examples of weak emergence” (Bedau 2012, p. 91).

  26. It is not uncommon that philosophers treat organisms as machines. See Nicholson (2014) and Holm & Powell (2013).

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  1. Xiamen University Malaysia, Jalan Sunsuria, 43900, Sepang, Selangor, Malaysia

    Sim-Hui Tee

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Tee, SH. Model Organisms as Simulators: The Context of Cross-Species Research and Emergence. Axiomathes 29, 363–382 (2019). https://doi.org/10.1007/s10516-019-09415-4

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