Emerging Perspectives from within the Life Sciences

Introduction: Issues About Robustness in the Practice of Biological Sciences

Robustness has lately become a bridging notion, in particular across the sciences of the natural and the artificial, crucial for prediction and control of natural and artificial systems in recent scientific practice, in biomedicine, neurobiology and engineering, as well as for risk management, planning and policy in ecology, healthcare, markets and economy. From biological, neurological and societal systems, arising by the interplay of self-organizing dynamics and environmental pressures, to the current sophisticated engineering that aims at artificially reproducing the adaptability and resilience of living systems in front of perturbations in man-made devices, robustness seems to hold the key for orchestrating stability and change. This introduction offers a general survey of the contribution that the notion of robustness is providing to reframing major concepts within the life sciences, such as development, evolution, time and environment, and to reframing the relationship between biology and engineering, as well as between biology and physics.

Prolegomena to a History of Robustness

The paper outlines a historical reconstruction of the spread of the concept of robustness across different disciplinary fields, and of the major significant shifts, which comprise the stratigraphy of the semantic expansion of this notion. Starting from the emergence of the modern notion in statistics, which inspired also its actual epistemic instantiations, the paper examines the historical relationship between dynamical systems theory and the notion of robustness, and analyzes the developments that prompted the shift from “modern” to “robust” control theory in engineering. It further deals with the first instantiations of the concept in biology in the 1990s, in order to highlight the turn impressed on the concept by Systems Biology, focusing particularly on its implications as to the relationship between robustness and complexity.

Robustness, Mechanism, and the Counterfactual Attribution of Goals in Biology

The first part of this paper discusses two important meanings of robustness (robustness as stability as against variations in parameter values and robustness as consilience of results from different sources of evidence) and shows their essential connection with the notion of intersubjective reproducibility. As I shall maintain, robustness in both senses of the term is intimately connected with the notion of scientific experiment. This is the important element of truth of the mechanistic systems approach, which explains events as products of robust and regular systems and processes. In the second part of this paper I shall show that the concept of robustness of a mechanism, if applied to biological systems, is one-sided and incomplete without a heuristic˗methodical reference to final causes, even though the assumption of the teleological point of view is justified in biology only to the extent that we use it as a counterfactual artifice, capable of bringing to light causal relations which have a robustly reproducible content. In this way, the reflexive, typically human concept of purposefulness may be employed to investigate living beings scientifically, that is, in an intersubjectively testable and reproducible way, to discover mechanisms in living systems which are robust in both senses of the word.

Multiple Realization and Robustness

Multiple realization has traditionally been characterized as a thesis about the relation between kinds posited by the taxonomic systems of different sciences. In this paper, I argue that there are good reasons to move beyond this framing. I begin by showing how the traditional framing is tied to positivist models of explanation and reduction and proceed to develop an alternate framing that operates instead within causal explanatory frameworks. I draw connections between this account and the notion of functional robustness in biology and neuroscience. I then examine two cases from systems neuroscience that substantiate my account and show how traditional debates fail to track important features of these cases.

Robustness: The Explanatory Picture

Robustness is a pervasive property of living systems, instantiated at all levels of the biological hierarchies (including ecology). As several other usual concepts in evolutionary biology, such as plasticity or dominance, it has been questioned from the viewpoint of its consequences upon evolution as well as from the side of its causes, on an ultimate or proximate viewpoint. It is therefore equally the explanandum for some enquiries in evolution in ecology, and the explanans for some interesting evolutionary phenomena such as evolvability. This epistemological fact instantiates general property of biological evolution that I call “explanatory reversibility”. In this chapter, I attempt to systematize the explanatory projects regarding robustness by distinguishing a set of epistemological questions. Are they the various expressions of one general project with specific key concepts and methods, or very disparate epistemic projects, unified by the mere homonymy of the term “robustness”? More precisely, are there specific kinds of explanations suited to explain robustness? Finally, how does robustness as an explanandum connect with other explananda in which evolutionists have been massively interested recently such as complexity, modularity or evolvability? After having initially explored various meanings of the concept of robustness and surveyed its instances in biology, I will propose a distinction between mechanical and structural explanations of robustness in evolutionary and functional biology. Then, among the latter, I will highlight the class of “topological explanations,” and the subclass of explanations based on networks, as a major explanatory tool to address robustness. Focusing on evolutionary issues, I will eventually address the “explanatory reversibility” of robustness and consider its relation to key evolutionary concepts that are also explanatorily revertible such as modularity, evolvability and complexity.

Robustness and Autonomy in Biological Systems: How Regulatory Mechanisms Enable Functional Integration, Complexity and Minimal Cognition Through the Action of Second-Order Control Constraints

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.

Robustness and Emergent Dynamics in Noisy Biological Systems

The concepts of robustness and stability play a central role in many natural phenomena ranging from Astrophysics up to Life. In this contribution we discuss these concepts by specifically focusing on a biological paradigmatic mathematical model for the nonlinear electrophysiology of clusters of animal beta-cells.

The Robustness/Sensitivity Paradox: An Essay on the Importance of Phase Separation

Considering biological systems at different levels of organization as complex networks in which nodes (genes, proteins, metabolites…) are each other connected by (co-expression, physical interactions) is a very natural way of reasoning. Network approach allows scientist to make sense of the intricacies of biological regulation and, for the same mathematical nature of graphs, to obtain a multilevel description linking single node and whole network topological features. This paradigm allows for the detection of a clear signature of robustness: the ability of a system to keep separate different scales of response to environmental stimuli. A case study on the immune system allows for an immediate appreciation of this point.

Can Engineering Principles Help Us Understand Nervous System Robustness?

Nervous systems are formidably complex networks of nonlinear interacting components that self organise and continually adapt to enable flexible behaviour. Robust and reliable function is therefore non-trivial to achieve and requires a number of dynamic mechanisms and design principles that are the subject of current research in neuroscience. A striking feature of these principles is that they resemble engineering solutions, albeit at a greater level of complexity and layered organisation than any artificial system. I will draw on these observations to argue that biological robustness in the nervous system remains a deep scientific puzzle, but not one that demands radically new concepts.

Robustness vs. Control in Distributed Systems

Understanding and controlling the behavior of dynamical distributed systems, especially biological ones, represents a challenging task. Such systems, in fact, are characterized by a complex web of interactions among their composing elements or subsystems. A typical pattern observed in these systems is the emergence of complex behaviors, in spite of the local nature of the interaction among elements in close spatial proximity. Yet, we point out that each element is a proper system, with its inputs, its outputs and its internal behavior. Moreover, such elements tend to implement feedback control or regulation strategies, where the outputs of a subsystem A are fed as inputs to another subsystem B and so on until, eventually, A itself is influenced. Such complex feedback loops are understood only by considering, at the same time, low- and high-level perspectives, i.e., by regarding such systems as a collection of systems and as a whole, emerging entity. In particular, dynamical distributed systems show nontrivial robustness properties, which are, from one side, inherent to the each subsystem and, from another, depend on the complex web of interactions. In this chapter, therefore, we aim at characterizing the robustness of dynamical distributed systems by using two coexisting levels of abstraction: first, we discuss and review the main concepts related to the robustness of systems, and the relation between robustness, model and control; then, we decline these concepts in the case of dynamical distributed systems as a whole, highlighting similarities and differences with standard systems. We conclude the chapter with a case study related to the chemotaxis of a colony of E. Coli bacteria. We point out that the very reason of existence of this chapter is to make accessible to a vast and not necessarily technical audience the main concepts related to control and robustness of dynamical systems, both traditional and distributed ones.

The Robustness of Musical Language: A Perspective from Complex Systems Theory

Within the field of systems theory, the term robustness has typically been applied to different contexts such as automatic control, genetic networks, metabolic pathways, morphogenesis, and ecosystems. All these systems involve either man-made machines, or living organisms. In this chapter, we will consider music as a peculiar complex system, involving both the realm of machines (the musical instrument) and the realm of biology (the player and the listeners). We will discuss some of the properties of music experience in terms of different attributes of robustness, focusing in particular on stability, the property enabling a complex system to maintain its function against a wide range of external and internal changes. We will provide examples of the human ability of isolating and maintaining stable information within the perceptual flow and despite changes in the external world that reach our perceptions, leading towards a characterization of robustness in music perception as referred both to the search for regularities and to the range of tolerance that perception admits to regularities. Finally, we will list four multiple interaction cycles that typically characterize music experience and that involve both internal properties of the organism and the environment.

Dynamical Rearrangement of Symmetry and Robustness in Physics and Biology

The mechanism of the dynamical rearrangement of symmetry in quantum field theory underlies the phenomenon of coherent boson condensation in the vacuum state. Coherent states appear to be related to fractal self-similarity. The dynamical paradigm of coherence opens the way to an integrated vision of natural phenomena and it may possibly rule morphogenetic processes. Robustness properties of physical systems, such as dynamical and functional robustness, topological robustness, multilevel and semantic robustness may find their root in coherence. Possible extension to biology and neuroscience is discussed.

Difference and Robustness: An Aristotelian Approach

The paper starts by recalling the ordinary and etymological sense of the word “robustness”, for placing it then in the context of the current systemic view. Then I focus discussion on systems robustness in the paradigmatic case of living organisms. We discover that the notion of robustness is closely linked, in the case of organism, with the notion of difference, given that organisms arise precisely by a differentiation process (Sect. 13.1). So, in order to understand the ontology of robustness, we need to explore the ontology of difference; and in order to do so, we must distinguish between constitutive and comparative difference (Sect. 13.2.1). Then I deal with the problem of unity of the constitutive differences: I wonder if it is possible to unify the many differences that an organism exhibits in a single difference. It is an important question, given that unity is one of most essential characteristics of the organism (Sect. 13.2.2). And the answer to this question raises immediately a query for the intelligibility of this final and unique constitutive difference (Sect. 13.2.3). Such intellibility is possible thanks to the formal nature of the final difference, but it requires also a pluralistic approach. Besides that, we have to sketch the ontological and epistemological relationships between difference, identity and similarity (Sect. 13.3), which will be crucial for intelligibility of the difference, since according to a certain tradition, intelligibility depends on identity.