This paper seeks to tease out the systemic character of a body of work that elsewhere in both the primary and secondary literature tends to be described, discussed and applied in fragmented and reductionist terms. The origins of “autopoietic theory” may be traced back to experimental work in cellular biology and neuro-physiology and to the concept of “autopoiesis” (a theory of living systems) itself. From there, it has extended its coverage into a wide range of diverse areas including cognition, perception, emotion, evolution, language, culture, epistemology, the philosophy of science and ethics. Against this background, the paper seeks to outline a high-level systemic interpretation of autopoietic theory; specifically one that integrates its various biological, social and epistemological components and which shows that it is best evaluated and understood as an explanatory whole and not in a reductionist manner.

Key words: Autopoiesis, autopoietic theory, systems, living systems, biology, epistemology

Big data biology – bioinformatics, computational biology, systems biology (including “omics”), and synthetic biology – raises a number of issues for the philosophy of science. This article deals with several, such as: Is data-intensive biology a new kind of science, presumably post-reductionistic? To what extent is big data biology data-driven? Can data “speak for themselves?” I discuss these issues by way of a reflection on Carl Woese’s worry that “a society that permits biology to become an engineering discipline, that allows science to slip into the role of changing the living world without trying to understand it, is a danger to itself.” And I argue that scientific perspectivism, a philosophical stance represented prominently by Giere, Van Fraassen, and Wimsatt, according to which science cannot as a matter of principle transcend our human perspective, provides the best resources currently at our disposal to tackle many of the philosophical issues implied in the modeling of complex, multilevel/multiscale phenomena. Relevance: Many interesting things can be learned about the irreducibly human nature of scientific knowledge in a perspectivist stance (“view from somewhere”) while avoiding futile constructivism vs. realism debates. Qua perspectivists, constructive empiricists à la Van Fraassen and constructive realists à la Giere can cooperate in a profitable way.

Summary: Based on the book, the overall impression is that systems biology struggles with the limits of first-order cybernetics and tries to overcome it by mixing bottom up and top down methods from classical approaches such as genetics, molecular biology and enzymology. However, the contributors avoid the step from first-order to second-order cybernetics.

This article analyses the work of Robert Rosen on an interpretation of metabolic networks that he called (M, R) systems. His main contribution was an attempt to prove that metabolic closure (or metabolic circularity) could be explained in purely formal terms, but his work remains very obscure and we try to clarify his line of thought. In particular, we clarify the algebraic formulation of (M, R) systems in terms of mappings and sets of mappings, which is grounded in the metaphor of metabolism as a mathematical mapping. We define Rosen’s central result as the mathematical expression in which metabolism appears as a mapping f that is the solution to a fixed-point functional equation. Crucially, our analysis reveals the nature of the mapping, and shows that to have a solution the set of admissible functions representing a metabolism must be drastically smaller than Rosen’s own analysis suggested that it needed to be. For the first time, we provide a mathematical example of an (M, R) system with organizational invariance, and we analyse a minimal (three-step) autocatalytic set in the context of (M, R) systems. In addition, by extending Rosen’s construction, we show how one might generate self-referential objects f with the remarkable property f(f)=f, where f acts in turn as function, argument and result. We conclude that Rosen’s insight, although not yet in an easily workable form, represents a valuable tool for understanding metabolic networks.

Key words: (M, R) systems, Metabolic network, Metabolic closure, Infinite regress, Systems biology

Reductionism has largely influenced the development of science, culminating in its application to molecular biology. An increasing number of novel research findings have, however, shattered this view, showing how the molecular-reductionist approach cannot entirely handle the complexity of biological systems. Within this framework, the advent of systems biology as a new and more integrative field of research is described, along with the form which has taken on the debate of reductionism versus holism. Such an issue occupies a central position in systems biology; nonetheless it is not always clearly delineated. This partly occurs because different dimensions (ontological, epistemological, methodological) are involved, and yet the concerned ones often remain unspecified. Besides, within systems biology different streams can be distinguished depending on the degree of commitment to embrace genuine systemic principles. Some useful insights into the future development of this discipline might be gained from the tradition of complexity and self-organization. This is especially true with regard to the idea of self-reference, which incorporated into the organizational scheme is able to generate autonomy as an emergent property of the biological whole. Relevance: It is asserted that systems biology has developed basically within the boundaries of first-order cybernetics. To better integrate the idea of complexity into the development of its framework, insights from the tradition of second-order cybernetics and self-organization need to be taken into consideration more fully. It advances the notion of self-reference as being especially significant.