Christophe Malaterre

Defining Life: conference proceedings.

There is a long tradition of software simulations in theoretical biology to complement pure analytical mathematics which are often limited to reproduce and understand the self-organization phenomena resulting from the non-linear and spatially grounded interactions of the huge number of diverse biological objects. Since John Von Neumann and Alan Turing pioneering works on self-replication and morphogenesis, proponents of artificial life have chosen to resolutely neglecting a lot of materialistic and quantitative information deemed not indispensable and have focused on the rule-based mechanisms making life possible, supposedly neutral with respect to their underlying material embodiment. Minimal life begins at the intersection of a series of processes which need to be isolated, differentiated and duplicated as such in computers. Only software developments and running make possible to understand the way these processes are intimately interconnected in order for life to appear at the crossroad. In this paper, I will attempt to set out the history of life as the disciples of artificial life understand it, by placing these different lessons on a temporal and causal axis, showing which one is indispensable to the appearance of the next and how does it connect to the next. I will discuss the task of artificial life as setting up experimental software platforms where these different lessons, whether taken in isolation or together, are tested, simulated, and, more systematically, analyzed. I will sketch some of these existing software platforms: chemical reaction networks, Varela’s autopoietic cellular automata, Ganti’s chemoton model, whose running delivers interesting take home messages to open-minded biologists.

Organicism and Reductionism in Cancer Research: Towards a Systemic Approach

In recent cancer research, strong and apparently conflicting epistemological stances have been advocated by different research teams in a mist of an ever‐growing body of knowledge ignited by ever‐more perplexing and non‐conclusive experimental facts: in the past few years, an ‘organicist’ approach investigating cancer development at the tissue level has challenged the established and so‐called ‘reductionist’ approach focusing on disentangling the genetic and molecular circuitry of carcinogenesis. This article reviews the ways in which ‘organicism’ and ‘reductionism’ are used and opposed in this context, with an aim at clarifying the debate. Methodological, epistemological and ontological implications of both approaches are discussed. We argue that the ‘organicist/reductionist’ opposition in the present case of carcinogenesis is more a matter of diverging heuristics than a claim about theoretical or ontological (ir)reducibility. As a matter of fact, except for the downward causation claim, which we question, we argue that the organicist arguments are compatible with the reductionist approach. Moreover, we speculate that both approaches, which currently focus on specific entities i.e., genes versus tissues, will need to shift their conceptual frameworks to studying complex arrays of relationships potentially ranging over several levels of entities, as is the case with ‘systems biology’.

Are Dynamic Mechanistic Explanations Still Mechanistic

Mechanistic explanations are one of the major types of explanation in biology. The explanatory force of mechanisms is apparent in such typical cases as the functioning of an ion channel or the molecular activation of a receptor: it includes the specification of a model of mechanism and the rehearsing of a causal story that tells how the explanandum phenomenon is produced by the mechanism. It is however much less clear how mechanisms explain in the case of complex and non-linear biomolecular networks such as those that underlie the action of hormones and the regulation of genes. While dynamic mechanistic explanations have been proposed as an extension of mechanistic explanations, we argue that the former depart from the latter in that they do not draw their explanatory force from a causal story but from the mathematical warrants they give that the explanandum phenomenon follows from a mathematical model. By analyzing the explanatory force of mechanistic explanation and of dynamic mechanistic explanation, we show that the two types of explanations can be construed as limit cases of a more general pattern of explanation – Causally Interpreted Model Explanations – that draws its explanatory force from a model, a causal interpretation that links the model to biological reality, and a mathematical derivation that links the model to the explanandum phenomenon.

On What It is to Fly Can Tell Us Something About What It is to Live

The plurality of definitions of life is often perceived as an unsatisfying situation stemming from still incomplete knowledge about ‘what it is to live’ as well as from the existence of a variety of methods for reaching a definition. For many, such plurality is to be remedied and the search for a unique and fully satisfactory definition of life pursued. In this contribution on the contrary, it is argued that the existence of such a variety of definitions of life undermines the very feasibility of ever reaching a unique unambiguous definition. It is argued that focusing on the definitions of specific types of ‘living systems’—somehow in the same way that one can define specific types of ‘flying systems’—could be more fruitful from a heuristic point of view than looking for ‘the’ right definition of life, and probably more accurate in terms of carving Nature at its joints.

Life as an Emergent Phenomenon: From an Alternative to Vitalism to an Alternative to Reductionism

In this contribution, I investigate the changes of focus in the philosophical concept of emergence in the nineteenth and twentieth century period, especially in connection with the problem of characterizing life and its origins. Since its early philosophical formulation in the nineteenth century, “emergence” has been applied to vital phenomena, but also to chemical compounds and mental states. In each case, the whole is said to be more than the sum of its parts: a higher level of organization appears to exhibit properties that are claimed to be non-deducible, non-predictable or unexplainable on the basis of the properties of its lower level components. In the early twentieth century, the concept of emergence was strongly stimulated by the wish to formulate a philosophical alternative to both vitalism and mechanism. The concept experienced a golden age that proved to be short lived as it encountered several scientific and philosophical setbacks in the mid-twentieth century. The concept somehow re-emerged in the late twentieth century, especially as it became a central topic in philosophy of mind, and as it also received the unexpected support of the science of complex systems. In the first decade of the twenty-first century, benefiting from a growing awareness of the complexity of biological phenomena, the concept of emergence re-emerges as a way of characterizing life and its origin, not so much as an alternative to vitalism, but as an alternative to reductive explanations of life. Its relevance remains a debated topic.

Explanation in Biology: An Introduction

Explanation in biology has long been characterized as being different from explanation in other scientific disciplines, in particular from explanation in physics. One of the reasons was the existence in biology of explanation types that were unheard of in the physical sciences: teleological and functional explanations, historical and evolutionary explanations. More recently, owing in part to the rise of molecular biology, biological explanations have been depicted as mechanisms. This profusion of explanatory patterns is typical of biology. The aim of the present volume Explanation in Biology. An Enquiry into the Diversity of Explanatory Patterns in the Life Sciences is to shed some new light on the diversity of explanation models in biology. In this introductory chapter, we recall the general philosophical context of scientific explanation as it has unfolded in the past seven decades, and highlight the specific issues that models of explanation have faced in biology. We then show how the different essays gathered in this collective volume tackle aspects of this important debate.

The (In)Determinism of Biological Evolution: Where Does the Stochastic Character of Evolutionary Theory Come From?

Evolutionary theory is readily acknowledged to be stochastic in that it only enables one to make probabilistic predictions, for instance regarding changes in genotypic frequencies within given populations. However, the very origin of this stochastic character has been the focus of much philosophical debate. Is it due to an inherent indeterminism? Or rather to epistemic limitations? In this chapter, we review some of the major arguments that have been exchanged on the topic recently. We argue that settling the issue would require first to answer the question of the relative contribution of the different factors of evolution. This leads us to defend a more nuanced vision of the origin of the stochastic character of evolutionary theory.