Abstract
Mechanistic explanations appeal to the parts, operations, and organizations of mechanisms to explain the phenomena for which they are responsible. Scientists have developed accounts of myriads of mechanisms thought to be operative in biology, each involving distinctive parts and operations organized in idiosyncratic ways. The focus on specific mechanisms (e.g., those found in a particular cell type in a given model organism) appears opposed to the idea that explanations ought to be generalizable to new instances. Some generalizability can arise from the fact that many biological mechanisms inherit their parts and operations from mechanisms found in ancestral species and one can often identify commonalities in these parts and the operations they perform. But organization seems to be idiosyncratic to specific mechanisms, thwarting attempts to develop generalizations about how mechanisms organized in a specific way will behave. For example, as a result of different genes being expressed, the organizational pattern of interactions between proteins varies in different tissues or in the same tissue in different strains of a species. This poses an even greater problem when it is recognized that many biological mechanisms exhibit non-sequential organization of non-linear operations, making it difficult to use mental simulation to determine the behavior of the mechanism. Instead researchers resort to computational models, resulting in dynamic mechanistic explanations that integrate mathematical modeling with empirically ascertained details of parts and operations. These models, though, appear to be even more idiosyncratic, revealing only the behavior of the specific organization employed in a specific mechanism.
In recent years, however powerful tools have been developed for abstracting from the details of individual networks, providing a basis for informative generalizations about how networks employing the same abstract design will behave. These involve developing graph-theoretic representations of mechanisms and analyzing the properties of classes of graphs. Watts and Strogatz, Barabási, and Sporns have shown that large networks (e.g., neural circuits or gene networks) often exhibit a scale-free, small-world organization capable of efficient, flexible coordination of operations and appeal to these properties to explain behaviors of specific mechanisms. Alon and Tyson, focusing on sub-networks with just two to four nodes, have identified different motifs (distinctive micro-architectures such as feedforward loops and double negative feedback loops) that are specialized for particular types of processing. These tools offer abstract organizational principles to which researchers appeal in their efforts to explain the behaviors generated by the mechanisms in which they are implemented. In this paper I show how these projects provide a basis for developing generalizable accounts of complex mechanisms and their dynamical behavior.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
- 2.
While recognizing that one might integrate them, Woodward (2013) emphasizes the differences between more prototypical mechanistic approaches and those that adopt a more holistic perspective in, for example, explaining the robustness of a biological system or the dynamical behavior of a complex network. Woodward is right to emphasize the difference in research strategies and assumptions of the two approaches. More traditional mechanistic approaches assume modularity of the mechanism and dependence on the fine-tuned details of the system. In developing dynamical models of integrated systems researchers typically abstract away from these details. My reason for emphasizing their integration in dynamic mechanistic explanations is that many contemporary biological explanations draw on both details about the components of the mechanism and the dynamics of the integrated system, attempting to integrate the approaches that on their own make different assumptions and pursue different research strategies.
- 3.
See Issad and Malaterre (2015, this volume) for a discussion of the explanatory force of dynamic mechanistic explanations.
- 4.
Jones (2014) discusses graph representations of the topology of a system that are not intended to be mechanistic but to identify such things as vulnerabilities of a system to disruption. While the diagrams he discusses and those discussed below often abstract from the details of the mechanism and thus provide a different type of understanding than do accounts of the parts and operations of a mechanism, they nonetheless play an important role in understanding how, as a result of its organization, a mechanism behaves either normally or when disrupted.
- 5.
A special case on which extensive research has been conducted are Boolean networks (networks in which the behavior of a node is a Boolean function of the state of other nodes). See Derrida and Pomeau (1986), Flyvbjerg (1988), Kauffman (1993), Luque and Solé (1997), Rohlf and Bornholdt (2002) for analyses of Boolean networks.
- 6.
The one type of task that yields comparable activation in these regions are episodic memory tasks, resulting in the suggestion that while resting quietly in the scanning individuals are remembering events in their lives or planning future events (see Buckner et al. 2008).
- 7.
See also Braillard (2015, this volume) for a discussion of the heuristic and explanatory role of such motifs and more generally of modules in biology.
- 8.
In his analysis of the neural network in C. elegans that he helped identify (discussed above), White (1985) identified the frequent occurrence of the feedforward loop and considered the case in which the connection from Y to Z was inhibitory. He proposed X would initially elicit a response from Z, but this would be soon be suppressed as a result of the negative connection from Y to Z. He suggests: “The whole system would therefore act as a differentiator, the output from [Z] being proportional to the rate of change of stimulus. As the animal is constantly moving, this reformation is probably of more value to it than an absolute measure of the stimulus.”
- 9.
They report that computational analyses show that dual dyads are particularly effective in promoting zero phase-lag synchrony over long distances, which is important if hubs are to coordinate synchronous activity between brain regions.
- 10.
Ward and Thornton (2007) propose a possible origin of feedforward loops from another motif the authors term the bi-fan array to genome-wide duplication followed by random rewiring. This provides an explanation for their occurrence that doesn’t depend on selection, although Ward and Thornton do not deny that selection may have also favored these motifs.
References
Alon, U. (2007a). An introduction to systems biology: Design principles of biological circuits. Boca Raton: Chapman & Hall/CRC.
Alon, U. (2007b). Network motifs: Theory and experimental approaches. Nature Reviews Genetics, 8, 450–461.
Ankeny, R. A. (2001). Model organisms as models: Understanding the ‘Lingua Franca’ of the human genome project. Philosophy of Science, 68, S251–S261.
Ankeny, R. A., & Leonelli, S. (2011). What’s so special about model organisms? Studies in History and Philosophy of Science Part A, 42, 313–323.
Artzy-Randrup, Y., Fleishman, S. J., Ben-Tal, N., & Stone, L. (2004). Comment on ‘network motifs: Simple building blocks of complex networks’ and ‘superfamilies of evolved and designed networks’. Science, 305, 1107.
Baetu, T. (2015). From mechanisms to mathematical models and back to mechanisms: Quantitative mechanistic explanations. In P.-A. Braillard & C. Malaterre (Eds.), Explanation in biology. An enquiry into the diversity of explanatory patterns in the life sciences (pp. 345–363). Dordrecht: Springer.
Barabási, A.-L., & Albert, R. (1999). Emergence of scaling in random networks. Science, 286, 509–512.
Bechtel, W. (2011). Mechanism and biological explanation. Philosophy of Science, 78, 533–557.
Bechtel, W., & Abrahamsen, A. (2009). Decomposing, recomposing, and situating circadian mechanisms: Three tasks in developing mechanistic explanations. In H. Leitgeb & A. Hieke (Eds.), Reduction and elimination in philosophy of mind and philosophy of neuroscience (pp. 173–186). Frankfurt: Ontos Verlag.
Bechtel, W., & Abrahamsen, A. (2010). Dynamic mechanistic explanation: Computational modeling of circadian rhythms as an exemplar for cognitive science. Studies in History and Philosophy of Science Part A, 41, 321–333.
Bechtel, W., & Abrahamsen, A. (2011). Complex biological mechanisms: Cyclic, oscillatory, and autonomous. In C. A. Hooker (Ed.), Philosophy of complex systems. Handbook of the philosophy of science (Vol. 10, pp. 257–285). New York: Elsevier.
Bechtel, W., & Richardson, R. C. (1993/2010). Discovering complexity: Decomposition and localization as strategies in scientific research. Cambridge, MA: MIT Press. 1993 edition published by Princeton University Press.
Berg, H. C. (2004). E. coli in motion. New York: Springer.
Biswal, B., Yetkin, F. Z., Haughton, V. M., & Hyde, J. S. (1995). Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magnetic Resonance in Medicine, 34, 537–541.
Braillard, P.-A. (2015). Prospect and limits of explaining biological systems in engineering terms. In P.-A. Braillard & C. Malaterre (Eds.), Explanation in biology. An enquiry into the diversity of explanatory patterns in the life sciences (pp. 319–344). Dordrecht: Springer.
Brigandt, I. (2013). Systems biology and the integration of mechanistic explanation and mathematical explanation. Studies in History and Philosophy of Biological and Biomedical Sciences, 44, 477–492.
Buckner, R. L., Andrews-Hanna, J. R., & Schacter, D. L. (2008). The brain’s default network: Anatomy, function, and relevance to disease. Annals of the New York Academy of Sciences, 1124, 1–38.
Bullmore, E., & Sporns, O. (2009). Complex brain networks: Graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience, 10, 186–198.
Cordes, D., Haughton, V. M., Arfanakis, K., Wendt, G. J., Turski, P. A., Moritz, C. H., Quigley, M. A., & Meyerand, M. E. (2000). Mapping functionally related regions of brain with functional connectivity MR imaging. American Journal of Neuroradiology, 21, 1636–1644.
Craver, C. F. (2007). Explaining the brain: Mechanisms and the mosaic unity of neuroscience. New York: Oxford University Press.
Derrida, B., & Pomeau, Y. (1986). Random networks of automata: A simple annealed approximation. Europhysics Letters, 1, 45–49.
Elowitz, M. B., & Leibler, S. (2000). A synthetic oscillatory network of transcriptional regulators. Nature, 403, 335–338.
Erdös, P., & Rényi, A. (1960). On the evolution of random graphs. Proceedings of the Mathematical Institute of the Hungarian Academy of Sciences, 5, 17–61.
Ermentrout, G. B., & Kopell, N. (1984). Frequency plateaus in a chain of weakly coupled oscillators. 1. Siam Journal on Mathematical Analysis, 15, 215–237.
Felleman, D. J., & van Essen, D. C. (1991). Distributed hierarchical processing in the primate cerebral cortex. Cerebral Cortex, 1, 1–47.
Flyvbjerg, H. (1988). An order parameter for networks of automata. Journal of Physics A: Mathematical and General, 21, L955–L960.
Glennan, S. (1996). Mechanisms and the nature of causation. Erkenntnis, 44, 50–71.
Glennan, S. (2002). Rethinking mechanistic explanation. Philosophy of Science, 69, S342–S353.
Goldbeter, A. (1995). A model for circadian oscillations in the Drosophila period protein (PER). Proceedings of the Royal Society of London B: Biological Sciences, 261, 319–324.
Goodwin, B. C. (1965). Oscillatory behavior in enzymatic control processes. Advances in Enzyme Regulation, 3, 425–428.
Green, S., Levy, A., & Bechtel, W. (2015). Design sans adaptation. European Journal for the Philosophy of Science, 5, 15–29.
Greicius, M. D., Krasnow, B., Reiss, A. L., & Menon, V. (2003). Functional connectivity in the resting brain: A network analysis of the default mode hypothesis. Proceedings of the National Academy of Sciences of the United States of America, 100, 253–258.
Greicius, M. D., Supekar, K., Menon, V., & Dougherty, R. F. (2009). Resting-state functional connectivity reflects structural connectivity in the default mode network. Cerebral Cortex, 19, 72–78.
Hagmann, P., Cammoun, L., Gigandet, X., Meuli, R., Honey, C. J., Wedeen, V. J., & Sporns, O. (2008). Mapping the structural core of human cerebral cortex. PLoS Biology, 6, e159.
Hardin, P. E., Hall, J. C., & Rosbash, M. (1990). Feedback of the Drosophila period gene product on circadian cycling of its messenger RNA levels. Nature, 343, 536–540.
He, Y., Wang, J., Wang, L., Chen, Z. J., Yan, C., Yang, H., Tang, H., Zhu, C., Gong, Q., Zang, Y., & Evans, A. C. (2009). Uncovering intrinsic modular organization of spontaneous brain activity in humans. PLoS One, 4, e5226.
Hubel, D. H., & Wiesel, T. N. (1962). Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. Journal of Physiology, 160, 106–154.
Hubel, D. H., & Wiesel, T. N. (1968). Receptive fields and functional architecture of monkey striate cortex. Journal of Physiology, 195, 215–243.
Issad, T., & Malaterre, C. (2015). Are dynamic mechanistic explanations still mechanistic? In P.-A. Braillard & C. Malaterre (Eds.), Explanation in biology. An enquiry into the diversity of explanatory patterns in the life sciences (pp. 265–292). Dordrecht: Springer.
Jeong, H., Mason, S. P., Barabasi, A. L., & Oltvai, Z. N. (2001). Lethality and centrality in protein networks. Nature, 411, 41–42.
Jones, N. (2014). Bowtie structures, pathway diagrams, and topological explanation. Erkenntnis, 79, 1135–1155.
Jones, N., & Wolkenhauer, O. (2012). Diagrams as locality aids for explanation and model construction in cell biology. Biology and Philosophy, 27, 705–721.
Kalir, S., Mangan, S., & Alon, U. (2005). A coherent feed-forward loop with a SUM input function prolongs flagella expression in Escherichia coli. Molecular Systems Biology, 1, 2005.0006.
Kauffman, S. A. (1969). Metabolic stability and epigenesis in randomly constructed genetic nets. Journal of Theoretical Biology, 22(3), 437–467.
Kauffman, S. A. (1974). The large scale structure and dynamics of gene control circuits: An ensemble approach. Journal of Theoretical Biology, 44(1), 167–190.
Kauffman, S. A. (1993). The origins of order: Self-organization and selection in evolution. New York: Oxford University Press.
Konopka, R. J., & Benzer, S. (1971). Clock mutants of Drosophila melanogaster. Proceedings of the National Academy of Sciences of the United States of America, 89, 2112–2116.
Kopell, N., & Ermentrout, G. B. (1988). Coupled oscillators and the design of central pattern generators. Mathematical Biosciences, 90, 87–109.
Kuramoto, Y. (1984). Chemical oscillations, waves, and turbulence. Berlin: Springer.
Leonelli, S., Ramsden, E., Nelson, N., & Ankeny, R. A. (2014). Making organisms model humans: Situated models in alcohol research. Science in Context, 27(3), 485–509.
Levy, A., & Bechtel, W. (2013). Abstraction and the organization of mechanisms. Philosophy of Science, 80, 241–261.
Luque, B., & Solé, R. (1997). Phase transitions in random networks: Simple analytic determination of critical points. Physical Review E, 55, 257–260.
Machamer, P., Darden, L., & Craver, C. F. (2000). Thinking about mechanisms. Philosophy of Science, 67, 1–25.
Macnab, R. M. (2003). How bacteria assemble flagella. Annual Review of Microbiology, 57, 77–100.
Mangan, S., Zaslaver, A., & Alon, U. (2003). The coherent feedforward loop serves as a sign-sensitive delay element in transcription networks. Journal of Molecular Biology, 334, 197–204.
Mangan, S., Itzkovitz, S., Zaslaver, A., & Alon, U. (2006). The incoherent feed-forward loop accelerates the response-time of the gal system of Escherichia coli. Journal of Molecular Biology, 356, 1073–1081.
Mantini, D., Perrucci, M. G., Del Gratta, C., Romani, G. L., & Corbetta, M. (2007). Electrophysiological signatures of resting state networks in the human brain. Proceedings of the National Academy of Sciences, 104, 13170–13175.
Meunier, R. (2012). Stages in the development of a model organism as a platform for mechanistic models in developmental biology: Zebrafish, 1970–2000. Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences, 43, 522–531.
Milo, R., Shen-Orr, S., Itzkovitz, S., Kashtan, N., Chklovskii, D., & Alon, U. (2002). Network motifs: Simple building blocks of complex networks. Science, 298, 824–827.
Minami, Y., Ode, K. L., & Ueda, H. R. (2013). Mammalian circadian clock: The roles of transcriptional repression and delay. Handbook of Experimental Pharmacology, 217, 359–377.
Overton, J. A. (2011). Mechanisms, types, and abstractions. Philosophy of Science, 78, 941–954.
Rohlf, T., & Bornholdt, S. (2002). Criticality in random threshold networks: Annealed approximation and beyond. Physica A: Statistical Mechanics and its Applications, 310, 245–259.
Rosenblueth, A., Wiener, N., & Bigelow, J. (1943). Behavior, purpose, and teleology. Philosophy of Science, 10, 18–24.
Shen-Orr, S. S., Milo, R., Mangan, S., & Alon, U. (2002). Network motifs in the transcriptional regulation network of Escherichia coli. Nature Genetics, 31, 64–68.
Shulman, G. L., Corbetta, M., Buckner, R. L., Fiez, J. A., Miezin, F. M., Raichle, M. E., & Petersen, S. E. (1997). Common blood flow changes across visual tasks: I. increases in subcortical structures and cerebellum but not in nonvisual cortex. Journal of Cognitive Neuroscience, 9, 624–647.
Solé, R. V., & Valverde, S. (2006). Are network motifs the spandrels of cellular complexity? Trends in Ecology and Evolution, 21, 419–422.
Sporns, O., & Kötter, R. (2004). Motifs in brain networks. PLoS Biology, 2, e369.
Sporns, O., & Zwi, J. D. (2004). The small world of the cerebral cortex. Neuroinformatics, 2, 145–162.
Sporns, O., Tononi, G., & Kötter, R. (2005). The human connectome: A structural description of the human brain. PLoS Computational Biology, 1, e42.
Stricker, J., Cookson, S., Bennett, M. R., Mather, W. H., Tsimring, L. S., & Hasty, J. (2008). A fast, robust and tunable synthetic gene oscillator. Nature, 456, 516–519.
Théry, F. (2015). Explaining in contemporary molecular biology: Beyond mechanisms. In P.-A. Braillard & C. Malaterre (Eds.), Explanation in biology. An enquiry into the diversity of explanatory patterns in the life sciences (pp. 113–133). Dordrecht: Springer.
Tyson, J. J., & Novák, B. (2010). Functional motifs in biochemical reaction networks. Annual Review of Physical Chemistry, 61, 219–240.
Ueda, H. R., Hayashi, S., Chen, W., Sano, M., Machida, M., Shigeyoshi, Y., Iino, M., & Hashimoto, S. (2005). System-level identification of transcriptional circuits underlying mammalian circadian clocks. Nature Genetics, 37, 187–192.
van den Heuvel, M. P., Mandl, R. C. W., Kahn, R. S., & Pol, H. E. H. (2009). Functionally linked resting-state networks reflect the underlying structural connectivity architecture of the human brain. Human Brain Mapping, 30, 3127–3141.
Ward, J. J., & Thornton, J. M. (2007). Evolutionary models for formation of metwork motifs and modularity in the Saccharomyces transcription factor network. PLoS Computational Biology, 3, e198.
Watts, D., & Strogratz, S. (1998). Collective dynamics of small worlds. Nature, 393, 440–442.
White, J. G. (1985). Neuronal connectivity in Caenorhabditis elegans. Trends in Neurosciences, 8, 277–283.
White, J. G., Southgate, E., Thomson, J. N., & Brenner, S. (1986). The structure of the nervous system of the nematode Caenorhabditis elegans. Philosophical Transactions of the Royal Society of London. B Biological Sciences, 314, 1–340.
Winfree, A. T. (1967). Biological rhythms and the behavior of populations of coupled oscillators. Journal of Theoretical Biology, 16, 15–42.
Woodward, J. (2013). II—Mechanistic explanation: Its scope and limits. Aristotelian Society Supplementary Volume, 87, 39–65.
Young, M. P. (1993). The organization of neural systems in the primate cerebral cortex. Proceedings of the Royal Society of London. Series B: Biological Sciences, 252, 13–18.
Zhang, E. E., & Kay, S. A. (2010). Clocks not winding down: Unravelling circadian networks. Nature Reviews Molecular and Cell Biology, 11, 764–776.
Acknowledgment
Initial research on this project began when I was a Fellow at the Institute for Advanced Studies at Hebrew University. I thank the members of the group for productive discussions and especially Arnon Levy for introducing me to the work of Uri Alon and facilitating a meeting with him. Subsequently I have benefited from many further discussions with Arnon and with Sara Green. I presented much of the material here at colloquia at the University of California, Irvine and the University of Cincinnati, at workshop at the University of Wollongong, and to the reunion conference of the research group at the Institute for Advanced Studies. I thank the audiences at these various forums for very helpful comments. I also thank members of the WORGODS research group (Adele Abrahamsen, Daniel Burnston, and Benjamin Sheredos) at the University of California, San Diego for valuable discussion of the diagrammatic representations of circadian clock mechanisms. Thanks as well to Marta Halina and to the editors of this volume, Pierre-Alain Braillard and Christophe Malaterre, for valuable comments and suggestions.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2015 Springer Science+Business Media Dordrecht
About this chapter
Cite this chapter
Bechtel, W. (2015). Generalizing Mechanistic Explanations Using Graph-Theoretic Representations. In: Explanation in Biology. History, Philosophy and Theory of the Life Sciences, vol 11. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-9822-8_9
Download citation
DOI: https://doi.org/10.1007/978-94-017-9822-8_9
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-017-9821-1
Online ISBN: 978-94-017-9822-8
eBook Packages: Humanities, Social Sciences and LawPhilosophy and Religion (R0)