Abstract
Living systems employ several mechanisms and behaviors to achieve robustness and maintain themselves under changing internal and external conditions. Regulation stands out from them as a specific form of higher-order control, exerted over the basic regime responsible for the production and maintenance of the organism, and provides the system with the capacity to act on its own constitutive dynamics. It consists in the capability to selectively shift between different available regimes of self-production and self-maintenance in response to specific signals and perturbations, due to the action of a dedicated subsystem which is operationally distinct from the regulated ones. The role of regulation, however, is not exhausted by its contribution to maintain a living system’s viability. While enhancing robustness, regulatory mechanisms play a fundamental role in the realization of an autonomous biological organization. Specifically, they are at the basis of the remarkable integration of biological systems, insofar as they coordinate and modulate the activity of distinct functional subsystems. Moreover, by implementing complex and hierarchically organized control architectures, they allow for an increase in structural and organizational complexity while minimizing fragility. Finally, they endow living systems, from their most basic unicellular instances, with the capability to control their own internal dynamics to adaptively respond to specific features of their interaction with the environment, thus providing the basis for the emergence of minimal forms of cognition.
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Notes
- 1.
An alternative way to address biological robustness, closer to engineering approaches, would be to focus on individual behaviours and mechanisms, and on the maintenance of specific functions or performances .
- 2.
As argued by Cornish-Bowden (2006), among others, most engineering approaches “often seem to imply little more than reductionist biology applied on a large scale ” while a “systemic approach to biology ought to put the emphasis on the entire system”. A somehow similar categorization is proposed by Dupré and O’Malley (2005), who distinguish between ‘pragmatic systems biologists’ – who find it useful to refer to some systems properties in terms of interactions and of integration of data – and ‘systems-theoretic biologists’ – who focus on the investigation of general systems principles. An interesting case is that of Robert Rosen, whose contributions include both engineering (Rosen 1967, 1970) and system-oriented approaches (Rosen 1972, 1985, 1991), and who saw the former as inadequate to capture the distinctive features of living organisms as systems (Rosen 1991).
- 3.
For different ways to achieve biological robustness through stability see for example Rosen (1970), a classic textbook in dynamical systems theory.
- 4.
The coupling between these subsystems is realised by means of supply and demand of metabolites necessary for the production of the components in the different subsystems (metabolic complementarity). The subsystems provide the necessary substrates for the internal processes of production taking place in the others and, in turn, consume the metabolites supplied by the others.
- 5.
Dynamic stability is the capability to counterbalance the displacement of the system from a certain initial state, provoked by a perturbation, and end up in the same final state (Rosen 1970). It is a widespread property in the natural world , instantiated by any system whose dynamic behavior is characterized by the presence of at least one stable attractor . When realized collectively, dynamic stability is a distributed property of a whole network of reactions - it cannot be attributed to any single transformation , or to a partial subset of transformations.
- 6.
See Bich et al. (2016) for a discussion of dynamic stability in models of minimal living organizations.
- 7.
These favorable conditions might also include the presence of most of the necessary building blocks for early living systems, as claimed for example by the heterotrophic models of the origin of life (see for example Mansy et al. 2008).
- 8.
It does not imply that different forms of robustness, based respectively on dynamic stability , and modular and hierarchical control mechanisms cannot coexist in the same system or subsystem.
- 9.
This issue is closely related to the debate on the relationship between mechanistic and network descriptions. Whereas mechanisms describe distinguishable parts which play different specific tasks, network descriptions focus on global properties and patterns of connectivity (Moreno et al. 2011). Finding ways to bring together network and mechanistic descriptions, apparently irreducible to each other, is on the of the challenges faced by complex systems theory. An attempt to develop an heuristic s to move between the two descriptive approaches has been proposed by Bechtel (2017a, 2017b). The basic idea is that clusters of interconnections in a network description are possible candidates for the parts of the mechanistic description of the same system. The limit of this approach is that patterns and network configurations derived from numbers of connections do not provide the same kind of information on the system as the identification of different types of contributions (e.g. in metabolism a complex hierarchy involving, metabolites, enzymes acting on metabolites, energy currencies, kinases acting on enzymes, etc.), but a complementary one.
- 10.
- 11.
Given a particular thermodynamics process P, a molecular configuration C acts as a constraint upon P if: (1) at a time scale characteristic of P, C is locally unaffected by P; (2) at this time scale C exerts a causal role on P, i.e. there is some observable difference between free P, and P under the influence of C (Mossio et al. 2013, 164. A more detailed characterisation can be found in Montévil and Mossio 2015).
- 12.
- 13.
See for example Mossio et al. 2016 for a characterisation of the role of mRNA as a constraint.
- 14.
- 15.
The requirement for a structure to be a constraint is to be locally unaffected by the process they are harnessing. But a constraint can be affected by other interactions in the system.
- 16.
For example, the effects described by the law of mass action.
- 17.
For a more detailed discussion of biological regulation as second-order control see Bich et al. (2016).
- 18.
As argued by William Bechtel, “Although stoichiometric linkages between reactions are effective for insuring linkages between operations, they do not provide a means for varying the reactions independently. Such independent control can only be achieved by a property not directly linked to the critical stoichiometry of the system” (Bechtel 2007, 229).
- 19.
The concentration of repressor proteins is usually low (proportional to the number of copies of the promoter sequence it regulates) and does not undergo variation in concentrations to bring forth its regulatory effect. The lac-operon system, a more complex example of genetic regulation, which coordinates the metabolism of two sugars (lactose and glucose) through the second-order control of the transcription step, follows the same logic (see Bich et al. 2016 for an analysis of regulatory decoupling in this latter case).
- 20.
Specifically, the example relies on the analysis of glycogen synthesis in the rat gastrocnemius muscle provided in Schafer et al. (2005).
- 21.
See also J. S. Hofmeyr and Cornish-Bowden (2000) for an analysis of this type of stoichiometrically dependent responses.
- 22.
One of the effects of the release of insulin is the rapid change of the phosphorylation state of the enzyme glycogen synthase (GSase), which alters its kinetics. It is a clear case of second order control interaction, in which a regulatory subsystem acts on a first order control constraints (GSase) by activating (or inhibiting) it.
- 23.
Another way to express the idea is that systems can be alive under very special stable conditions, without regulatory mechanisms , by relying only on the constitutive network. Regulation would then become necessary only when the system is immersed in changing environments and develops a higher internal differentiation.
- 24.
Different subsystems might present different internal norms of operation, and need to be coordinated to ensure both their compatibility and their joint functional contribution to the maintenance of the system. Another relevant feature of biological cells is that they cannot synthesise all the possible molecules at the same time, due to energetic and spatial limits. Therefore, they have to exert some control over biosynthetic processes in order to produce the necessary components at the right times. Other types of functional resources need coordination as well. Let us think of the interplay between metabolic regimes relying on different carbon sources, such as lactose and glucose. Without the proper regulation (realised for example by the lac-operon subsystem) these regimes would compete for basic catalytic resources in the cell.
- 25.
The idea of improving adaptivity and robustness by increasing the degrees of freedom available to the systems’ dynamics at the expense of distributed stability of the network can be found already in Piaget’s school (Meyer 1967). For a recent discussion of the interplay between organisation and variation in biology see instead Montévil et al. (2016).
- 26.
See Bich and Moreno (2016) for a more detailed discussion of minimal cognition in relation to biological regulation.
- 27.
See Godfrey-Smith (2016) for a critical discussion of this account of minimal cognition focused on sensory-motor activity.
- 28.
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This project has received funding from: the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme – grant agreement n° 637647 – IDEM; from the Ministerio de Economia, Industria y Competitividad (MINECO), Spain (‘Ramon y Cajal’ Programme RYC-2016-19798 and research project FFI2014-52173-P); and from the Basque Government (Project: IT 590-13).
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Bich, L. (2018). Robustness and Autonomy in Biological Systems: How Regulatory Mechanisms Enable Functional Integration, Complexity and Minimal Cognition Through the Action of Second-Order Control Constraints. In: Bertolaso, M., Caianiello, S., Serrelli, E. (eds) Biological Robustness. History, Philosophy and Theory of the Life Sciences, vol 23. Springer, Cham. https://doi.org/10.1007/978-3-030-01198-7_6
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