Research Article |
Corresponding author: Manfred Drack ( manfred.drack@uni-tuebingen.de ) Academic editor: Ingmar Werneburg
© 2022 Manfred Drack, Oliver Betz.
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Citation:
Drack M, Betz O (2022) A technomorphic conceptualisation of biological ‘constructions’ and their evolution. Vertebrate Zoology 72: 839-855. https://doi.org/10.3897/vz.72.e86968
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Here, we build on earlier work concerning notions of engineering design and investigate their conceptual connection to evolutionary biology. The basis for this work is an engineering design schema covering the central concepts of function, working principle and construction. Its relevance for evolutionary biology is explored by connecting these concepts to the so-called design space that is used in engineering optimisation. This tool makes it possible to distinguish various optima of performance and to visualise their robustness with respect to disturbances or changes in parameters. The robustness of morphological ‘constructions’ with regard to changes of shape is shown by means of examples from engineering and biology. The characteristics of various ‘landscapes’ in the design space is then related to the concept of evolvability, whereby we explore analogies between systems biology and morphology. A general property of phenotypes from the molecular to the organismal level seems to be that their ‘construction’ facilitates both their robustness and their exploration of the design space while maintaining the performance of the relevant functions at a high level.
biomimetics, design space, evolvability, function, morphology, optimisation, working principle, robustness
In a broad sense, the so-called technomorphic approach in biology has a long history, as is indicated by the word ‘organ’, which stems from the ancient Greek ὄργανον for tool or instrument, originally ‘that with which one works’ (
For an understanding of the way that biological ‘constructions’ work, we can sometimes usefully consider organisms heuristically as if they were machines, so that analogies between biological and technical systems can be drawn in various contexts (e.g.
Before we dig deeper, however, a warning that was made by an eminent theoretician and that is still valid today should be considered: “One can say that the machine theory, on the one hand, provides a useful heuristic tool for the study of organic entities, although as a theoretical model, it has had an almost devastating effect on the development of biology: it has caused those researchers who are biased by the mechanistic idea to be left struggling with scholastic pseudo-problems, even to this day, whereas, on the other hand, it has prevented the perception of actual problems in organic nature (
Nevertheless, by keeping in mind that comparisons between biological and engineered systems can also have negative results, i.e. the technical approach does not help us to answer biological questions, we can still legitimately investigate those engineering concepts that can be used in biology without ontologically having to claim that organisms are (nothing but) machines.
In the present contribution, we build on earlier work concerning notions of engineering design and investigate their conceptual connection to evolutionary biology. Being functional morphologists and biomimeticists, we mainly consider the morphological or phenotypic levels of living entities, although the approach seems general enough also to cover other areas of biology on the molecular level.
We have previously introduced a (technomorphic) point of view that mainly conceptualises the field of biomimetics (
We propose that robust optima at a construction level are widespread in living nature. To be able comprehensively to analyse these optima, we develop below a conceptual framework that might be useful for further (comparative) investigations within biology and, additionally, for comparing biological with engineering systems.
First, we briefly describe the engineering design schema together with the central concepts that it deploys (function, working principle, construction). Second, the connection of the core concepts of this schema are related to biological evolution. Third, the concepts of optimisation and performance are introduced by means of connecting function, working principle and construction to the design space. Fourth, characteristics of the design space are investigated with respect to differences between biological and engineering constructions. Thus, the connection between evolvability and robustness is analysed with respect to the way that biological systems can evolve in general without negative influence on their performance. Fifth, the connections of the concepts developed on the basis of the design space are evaluated with regard to possible refinements of related concepts of evolutionary biology.
The conceptual framework developed here is a synthesis based on ideas from various disciplines including engineering design, engineering optimisation, morphology, evolutionary biology and systems biology. Our overall aim is to establish a useful theoretical and conceptual framework for further investigations not only in biology itself, but also in the subject linking biology and engineering, namely biomimetics.
We first need to introduce some core terms from engineering as a basis for further considerations of the optimisation and robustness of (evolving) biological systems. A conceptual schema from engineering design (
The concepts of function, working principle and construction are also helpful for understanding biological evolution. For instance,
Apart from overall functions (e.g. “to metabolise food”, “to sense the environment”), novel sub-functions can arise during evolution. For example, the predecessors of birds were at first unable to fly. However, once the forelimbs of these vertebrates had attained the characteristics enabling them to fly, the scope of the evolutionary variation of the ‘construction’ wing became limited (cf. Riedl 2000: 259). Not all constructions were now possible because of the implementation of a working principle, namely that by which wing profiles create a favourable ratio of lift to drag force within an air flow. The wings could thus not deviate far from a certain shape and the muscles, bones, etc. needed to be arranged in certain geometric relationships to each other. Hence, a bundle of constructional traits is strongly interconnected and cannot be changed to any great extent evolutionarily as long as these birds wish to fly. A functional burden (
Any (newly emerged) function channels the underlying morphological structures in a certain way by means of employed working principles. When a function is no longer relevant, its underlying structures may become released from their constraints and might either be reduced or evolve in the context of other functions. An example of such changes in function involves the cone-shaped pointed teeth of dolphins (personal communication, Wolfgang Maier, Tübingen). The phylogenetic ancestors of dolphins possessed, in their upper and lower jaws, teeth (tribosphenic molars) with cutting edges that fulfilled a specific function, namely “to cut up food”. Once this function was no longer needed, the working principle and the associated construction (cutting edges) could be abandoned. Extant dolphins can no longer cut up their food, with their teeth, and apparently no longer need this cutting function.
Functions can be preserved for a long time in phylogenesis, but they can be fulfilled by different working principles. During the pollination of plants, one function can be described as “to transport pollen grains to the target”. In the case of wind-pollinated flowers, this requires the pollen grains to be evenly distributed in the air and to hover until they reach a stigma with a good probability. Accordingly, the pollen grains must be ‘constructed’ following the (working) principles of aerodynamics. In pollination achieved by animal vectors, the same function is fulfilled differently and employs different working principles. The pollen grains no longer fly through the air by themselves, rendering the laws of aerodynamics meaningless for them. Instead, working principles now play a role that, for example, improve their attachment to the body of the animal pollinator. “To adhere to an animal’s body” thus becomes a new sub-function of “to transport pollen grains to the target”. Evolutionarily, both the transition from wind pollination to animal pollination and the reverse pathway are known (
Functions of an organism can, as in the previous examples, relate directly to the external environment, which is the subject of research in the field of ecomorphology (cf.
The consideration of function in the context of evolutionary research, the subject of analogy research (e.g.
In order to link the notions of function, working principle and construction more comprehensively to evolutionary biology, we need to introduce the concept of optimisation as the next step.
The previously introduced concepts of function, working principle and construction can be connected via the design space to resolve optimisation problems. For this purpose, the field of engineering optimisation needs to be explored in some breadth.
Optimisation has been broadly defined as: “the efforts and processes of making a decision, a design, or a system as perfect, effective, or functional as possible” and more narrowly, as “the specific methodology, techniques, and procedures used to decide on the one specific solution in a defined set of possible alternatives that will best satisfy a selected criterion” (
In engineering, we can easily find out what has been optimised and with what objective(s), because these questions have guided the designer. In biological systems (e.g. the shapes of diatoms or the leaves of a tree), difficulties are encountered in determining whether and to what extent the systems have been optimised and the value of their adaptive peak. This requires solid knowledge of the function or biological role (
An instructive example for optimisation in engineering is the optimisation of a roof shape illustrated by
The optimum and optimisation always refer to something particular: a particular objective (function). Moreover, those parameters that can be changed to achieve the objective have to be clear. For a simple case like a roof, this can easily be shown in a 3D diagram. The resulting design space in the roof example can have two axes for the two parameters that can be changed. The third (vertical) axis of the diagram indicates the optimisation objective by means of a quantitative measurement regarding the performance of the objective. In the simple two parameter case, one can consider a landscape (Fig.
Hypothetical design space example valid for both engineering and biology illustrating the optimisation of two parameters x1 and x2 of a construction. The parameters can be of a different kind, including length measurements, material properties, optical properties, etc. The third, vertical axis shows a measurable performance. The two parameters x1 and x2 can be changed, with each combination resulting in a different performance value (red dots). With two parameter dimensions, a surface results from different parameter settings that can be depicted as a ‘landscape’. Optimisation is the process indicated by the arrows. By changing the parameters, an ever better performance can be achieved, leading to an optimum at the top of a ‘hill’.
In general, optimisation is the determination of the parameter settings that correspond to a well-optimised system. This landscape depiction is useful when only two parameters are involved. In instances of more than two parameters, which is usually the case, the design space becomes a hyperspace. Here, we can usefully consider a space with clouds of greater or lesser density (high density representing high performance), with optimisation algorithms searching for the area of highest density.
From a biologist’s perspective, the question arises as to whether the engineering concepts of optimisation can be adapted to biological optimisation problems in real organisms. Cases of optimisation towards a single performance measure are difficult to find in biology. One example used by
Another important issue in engineering is multi-criteria (= multi-objective) optimisation (
Since the engineering design schema (Fig.
On the level of the working principle (Fig.
The levels of both the function and working principle provide the performance criterion and the parameter dimensions of the design space. The working principle determines those parameters that have to be considered and altered during the optimisation process, whereas the function indirectly determines a measure for the performance that needs to be optimised.
Finally, at the construction level, the parameters determined by the working principles are set to specific values represented by a particular point in the design space. In the final construction, the parameters are set in such a way that performance is high, i.e. the construction fulfils the function well. Hence, the concept of optimisation via the design space can be seen as connecting the three levels of the scheme, namely function, working principle and construction (Fig.
In this context, we should distinguish between the concepts of effectiveness and efficiency. Effectiveness refers to a quality, whereas efficiency can be quantified and can therefore, in specific cases, serve as a performance measure (cf. Fig.
Constructions using other working principles require the consideration of other parameters. For instance, the dimensions in the design space for the parameters that need to be optimised are different in the case of the steam turbine compared with photovoltaic construction. However, if the function stays the same, then the performance measure (in this case energy efficiency) also stays the same. Note that efficiency is one type of performance measure, others can be visual acuity, jumping hight, etc.
The described concept of effectiveness can also be applied to biological evolution in which, in theory, one working principle should be selected that allows the fulfilment of a function with high performance. Once this working principle is in action, its underlying parameter values are under selective pressure to further improve (optimise) the overall performance of the system employing the given working principle. The selection of a certain working principle in evolution might be attributable to chance (whereby exaptation (
For our understanding of macroevolution, we will find it instructive to analyse to what extent performance differences between clades have resulted from the selection of differently effective working principles or from the adjustment of more efficient parameters within the underlying working principles. For example, in predatory Stenus rove beetles (Coleoptera, Staphylinidae), a shift has occurred from the ancestral way of prey-capture by means of direct seizure with the mandibles to a novel (derived) prey-capture apparatus that involves the combination of new working principles such as the catapult-like protrusion of the elongated labium and the firm adhesive attachment of the prey to sticky cushions at the tip of the labium (reviewed in
Biological evolution can lead to burdens or constraints (
In engineering, as previously mentioned, a set of parameters of a construction is optimised towards multiple criteria during multi-criteria optimisation. All dimensions for the parameters stay the same but have to be optimised with respect to other (conflicting or concurrent) criteria. In biological evolution, another case of optimisation might also be relevant, here referred to as entangled optimisation (Fig.
General illustration of the optimisation problem, here called entangled optimisation, aimed at achieving different performance measures with partly shared parameters. Two performance measures A and B are considered, in addition to the three parameter dimensions. Parameter x1 is relevant for both performances, whereas x1 and x3 can be changed each only affecting one performance. The white arrow indicates that not both optima can be reached simultaneously. For further explanations, see text.
In biology and engineering, certain parameters can be subject to neutral variation, i.e. certain values do not affect the final performance of the system, as has been introduced above in the context of many-to-one mapping. One example involves the branching patterns of blood vessels at the aortic arch (
To conclude, optimisation is a problem that is relevant for both engineering and biology. The engineer has to choose one or more objective(s) in accordance with a desired function and even has to consider multi-criteria optimisation problems that involve trade-off solutions. Hence, the envisaged optimum and the optimisation process are subject to the technical aims of the designer. For a biologist, the determination of the particular ‘objectives’ in the evolution of a particular structure can be challenging. Continuous adaptation to the environment is often important and might be indicated by the performance measures that can be investigated by biologists. For instance, the hook shapes on burs (geometric parameters) are well adapted enabling the wide dispersal of seeds. This links performance to fitness.
So far, we have dealt with optimisation and the design space in a general way. Design spaces and their ‘landscapes’ can, however, be diverse, extending from very narrow parameter areas with high performance to broader areas with neutral variation. These areas of neutral variation are related to the concept of robustness, which will be explored next.
Based on the approach of connecting the engineering design schema with the design space, we will now explore the relationship between the design space and the concept of robustness. Design spaces can have different characteristics depending on the deployed working principles (Fig.
Various hypothetical design spaces for two parameters x1 and x2. A Single optimum peak in the design space. For two parameters, only one particular narrow setting leads to a high performance. B Ridge-like optimum. When the two parameters are concurrently changed, an extended optimum can be achieved, but not when only one is changed. C Flat optimum with a high plateau. Both the parameters can be changed within wide boundaries, still leading to an optimum. This indicates the robustness of performance with respect to parameter changes. D A ‘corridor’ (arrow) that links two flat optima. Performance might decrease a little, but still be comparatively high. Interlinked areas of high performance facilitate evolution in various ways, increasing evolvability.
To illustrate the various design spaces in the following, we use simple examples from engineering and compare them with biological structures. Dovetail slides (Fig.
For this simplified engineering dovetail slide example (Fig.
Now imagine a case in which x1A and x1B can be optimised together in the dovetail slides. When these parameters are changed simultaneously, several optimal solutions are possible. In the design space, this would resemble a ridge (Fig.
Similar ‘constructions’ to the dovetail slide can be found in the mouthparts of various insects, for example, in the sucking mouthparts of true bugs (e.g.
A broad optimum would, of course, be much better than a small peak or a narrow ridge in the design space. To illustrate this, first consider a piston pump in engineering (Fig.
Schematic drawings of oscillating pumps in both engineering and biology. A Piston pump in engineering can, for instance, be used to pump water. B Salivary pump in Hemiptera. a: piston retracted; b: piston position with relaxed muscle. Cu cupula, Dia diaphragma, P pistill, PG efferent salivary duct, SpG afferent salivary duct, V1 V2 valves (from
The vertebrate heart (Fig.
A comparison of technical scissors and cutting edges in biological teeth serves as another example. The two cutting edges of scissors are rigidly connected via a screw that does not allow other than one degree of freedom to work properly. If any of the geometric parameters changes (e.g. caused by a loose connection or the deformation of the blades), the cutting performance deteriorates. Analogous to technical scissors, many vertebrates have teeth with edges, whereby the edges of two opposing teeth are used to cut up food (
In general, a high flat elevation in a design space with two parameters (x1, x2) indicates an optimal area that is robust (Fig.
Examples for robustness are not limited to morphological features but are probably widespread in biology. For example, robustness plays an important role in the field of systems biology in which dynamic behaviours are investigated, mainly on the molecular scale (e.g.
In an even more general perspective, going beyond morphological structures and systems biology, the approach developed here may turn out to be useful for the broader debate on robustness. Up to now, no commonly accepted definition exists for robustness. The illustration via the design space (Fig.
The connection between robustness and evolvability plays an important role in evolutionary biology (
Importantly, one central difference exists between engineering optimisation and adaptiogenesis in biological evolution. Whereas the dimensions of the design space are given and do not change in conventional engineering optimisation problems, new dimensions can emerge during phylogenesis. If, for example, morphological novelties evolve, the dimensions of the design space increase. Alternatively, if organisms reduce certain structures (for example, during vestigialization), these dimensions decrease, which means that no fixed, unalterable and pre-defined design space exists in which organisms evolve (
Natural selection as “any consistent difference in fitness among phenotypically different classes [each with several individuals] of biological entities” (
Applying the introduced design schema to Darwinian evolution, we might ask whether selection during adaptiogenesis primarily acts on the task, the function, the working principle, the construction or the overarching system (Fig.
Our design schema can also be related to evolutionary concepts such as adaptation, the adaptive landscape or the adaptive peak. For example, the concept of the adaptive landscape has been widely used to map phenotypes on fitness (e.g.
The design space, however, is not about fitness but about the performance of a particular construction from an engineering perspective. A biological analogue of the design space could be a “performance space” in which performance capabilities in an adaptive landscape are mapped onto (morphological) phenotypes (
Although all technomorphic approaches have their limitations, because they can never grasp all of the complexity existing in living nature, our approach indicates that they can be useful in some respects. The overall aim of this contribution has been to establish a theoretical and conceptual framework for the analysis of morphological structures in more detail from an engineering point of view. Such analyses might also be useful for further investigations in biology and facilitate the exchange between biology and engineering, i.e. biomimetics. Core concepts from engineering design, namely function, working principle and construction, can be described and used to analyse morphological structures in biology. These central concepts are, furthermore, linked to each other via the so-called design space, whereby the function indicates a quantifiable performance measure and the working principles determine the parameters that can and need to be altered in order finally to achieve an optimised construction. In evolutionary biology, the design space can be used to analyse phenomena such as adaptation and robustness. Constructions that are especially robust can be identified in the design space as flat optima.
Our conceptual framework can be useful for further (comparative) investigations within (evolutionary) biology by means of precise conceptual tools and specified levels of investigation in order to understand the way in which morphological structures work and to compare biological and engineering constructions. Such comparisons might provide new insights in biology possibly leading to technical products based on biological research. Via the recognition and definition of the basic working principles and parameters underlying technical and biological functions, ‘constructional characters’ and ‘constructional traits’ can be understood in greater detail and on a heuristic level. Once the employed working principles enabling a certain function are known, the dimensions in the design space can be determined and the effect of the combination of the parameter values for the working principles analysed with respect to engineering questions of optimisation and biological questions of adaptation. In biology, changes in the parameter values in the design space (theoretically, experimentally or empirically by comparisons between individuals, populations or species) can improve our understanding of the adaptiogenesis of living organisms and of the kind of basic working principles that have been taken up during evolution.
The flat optima that allow for sloppy parameter settings seem not only to be present in molecular signal transduction pathways (investigated by systems biology), but can also be found on a morphological level. Our examples show that biological constructions can be more robust than comparable engineering constructions; such findings might be of great interest within the applied sciences (engineering, biomimetics), possibly leading to the design of more robust devices and machines. Flat optima might also facilitate the evolvability of phenotypes when connected by corridors of relatively high performance. In this latter case, pathways or even networks through the design space at high performance levels might open up possibilities for evolution in various directions. The research question as to whether multidimensional design spaces show such characteristics from the molecular to the morphological level remains open.
We thank Wolfgang Maier for numerous informal discussions on topics including those dealt with in this article and for sharing his ideas and extensive knowledge on so many aspects of evolutionary biology with us. We also thank the editors of this commemorative volume, Ingmar Werneburg and Irina Ruf, for inviting us to write a contribution and for their editorial work. We thank Ludger Jansen for philosophical clarifications. Fig.