Stephan Guttinger

In recent years philosophers of biology have made renewed efforts to develop and defend a process ontology. These efforts have often focused on the example of living systems, which provide a strong case for a processual view of biological entities. Here I will analyze a different kind of biological entity, namely macromolecules. Looking at protein biology, I will show that contemporary theories in this field present us with a substance-like picture of macromolecules. Whilst this poses a challenge for existing process accounts, I will argue that the challenge can be overcome if metaphysicians abandon their focus on theory and follow a practice-informed scientific metaphysics. Turning to the practice of protein biology, and in particular the use of what I will refer to as ‘energy-level management’ practices, will suggest that macromolecules are processes, much like organisms.

Discussions about a replicability crisis in science have been driven by the normative claim that all of science should be replicable and the empirical claim that most of it isn’t. Recently, such crisis talk has been challenged by a new localism, which argues a) that serious problems with replicability are not a general occurrence in science and b) that replicability itself should not be treated as a universal standard. The goal of this article is to introduce this emerging strand of the debate and to discuss some of its implications and limitations. I will in particular highlight the issue of demarcation that localist accounts have to address, i.e. the question of how we can distinguish replicable science from disciplines where replicability does not apply.

The reported birth of genetically modified twins in late 2018 has given new fuel to debates about the ethics of germline genome editing (GGE). There is a broad consensus among stakeholders that clinical uses of GGE should be temporarily banned as the technology is not yet deemed safe for use in humans. However, the idea of a complete ban is dismissed by many based on the expectation that more research will eventually allow scientists to make the technology safe without having to put humans at risk first. In this article, I will analyse this assumption and argue that it is undermined by recent developments in the postgenomic life sciences. In particular, I will argue that in a postgenomic view of germline editing a complete ban on specific uses of the technology is warranted, because the research needed to assess the safety of these interventions would not be morally defensible.

The life sciences are said to be in the midst of a replication crisis because (1) a majority of published results are irreproducible, and (2) scientists rarely replicate existing data. Here I argue that point 2 of this assessment is flawed because there is a hitherto unidentified form of replication in the experimental life sciences, which I call ‘microreplications’ (MRs). Using a case study from biochemistry, I illustrate how MRs depend on a key element of experimentation, namely, experimental controls. I end by reflecting on what MRs mean for the broader debate about the replication crisis.

This chapter argues (a) that macromolecules are fundamentally relational entities and (b) that only a process ontology can account for them. The basis for the argument is the ecological model proposed in 1981 by Charles Birch and John B. Cobb, which states that all entities, from atoms to populations, are ecosystems and hence fundamentally relational in character. The chapter first discusses how Birch and Cobb use the concept of internal relations to argue that ecosystems are processual in nature. It then shows that their account fails when it comes to macromolecules, as it does not offer an understanding of macromolecular behaviour in terms of internal relations. Following this, two case studies—symbiosis and enzyme function—are used for developing a fully relational account of molecular behaviour. This enables the expansion of Birch and Cobb’s ecological model into a process framework that also applies to macromolecules.

Scientists often rely on proxies when they identify or measure new or complex phenomena. However, these tools are frequently seen as epistemologically inferior because they are indirect and make it difficult to properly control for confounding factors. This view implies two methodological norms. First, if possible, proxies should be replaced with more direct and better-controlled tools. Second, if proxies cannot be replaced, they should be improved by increasing control over confounding factors. We evaluate this view by examining functional genomics, a field in which proxies are abundant. Analysing work from the largest contemporary initiative in functional genomics (ENCODE), we observe that researchers do not follow these methodological norms. Instead, they continue using the same proxies and even combine them to produce novel insights or measurements. This potentially paradoxical finding can be explained if we recognise that proxies have a “generativity” that is appreciated and leveraged by the researchers working with them. Proxies in functional genomics form a dynamic toolkit that equips researchers to discover unexpected insights and ask new questions. Our analysis thereby contributes to a better understanding of the epistemic roles that proxies play in scientific practice.

The ongoing debate about a “replication crisis” has put scientific failure in the spotlight, not only in psychological research and the social sciences but also in the life sciences. However, despite this increased salience of failure in research, the concept itself has so far received little attention in the literature (for an exception, see Ref. 1). The lack of a systematic perspective on scientific failure—a daily experience for researchers—hampers our understanding of this complex phenomenon and the development of efficient policies and measures to address it. Without a better grasp of the multiple dimensions of scientific failure, there is a risk that necessary measures will be neglected or that inadequate policies will be adopted because different kinds of failures require different responses. Developing a basic taxonomy of scientific failure will help to identify connections between different types of failures and benefit the formulation of policy measures for improving replicability.

Discussions about a replicability crisis in science have been driven by the normative claim that all of science should be replicable and the empirical claim that most of it isn’t. Recently, such crisis talk has been challenged by a new localism, which argues a) that serious problems with replicability are not a general occurrence in science and b) that replicability itself should not be treated as a universal standard. The goal of this article is to introduce this emerging strand of the debate and to discuss some of its implications and limitations. I will in particular highlight the issue of demarcation that localist accounts have to address, i.e. the question of how we can distinguish replicable science from disciplines where replicability does not apply.

The life sciences are said to be in the midst of a replication crisis because a majority of published results are irreproducible, and scientists rarely replicate existing data. Here I argue that point 2 of this assessment is flawed because there is a hitherto unidentified form of replication in the experimental life sciences, which I call ‘microreplications’. Using a case study from biochemistry, I illustrate how MRs depend on a key element of experimentation, namely, experimental controls. I end by reflecting on what MRs mean for the broader debate about the replication crisis.

In 2015 scientists called for a partial ban on genome editing in human germline cells. This call was a response to the rapid development of the CRISPR–Cas9 system, a molecular tool that allows researchers to modify genomic DNA in living organisms with high precision and ease of use. Importantly, the ban was meant to be a trust-building exercise that promises a ‘prudent’ way forward. The goal of this paper is to analyse whether the ban can deliver on this promise. To do so the focus will be put on the precedent on which the current ban is modelled, namely the Asilomar ban on recombinant DNA technology. The analysis of this case will show (a) that the Asilomar ban was successful because of a specific two-step containment strategy it employed and (b) that this two-step approach is also key to making the current ban work. It will be argued, however, that the Asilomar strategy cannot be transferred to human genome editing and that the current ban therefore fails to deliver on its promise. The paper will close with a reflection on the reasons for this failure and on what can be learned from it about the regulation of novel molecular tools.

The reported birth of genetically modified twins in late 2018 has given new fuel to debates about the ethics of germline genome editing (GGE). There is a broad consensus among stakeholders that clinical uses of GGE should be temporarily banned as the technology is not yet deemed safe for use in humans. However, the idea of a complete ban is dismissed by many based on the expectation that more research will eventually allow scientists to make the technology safe without having to put humans at risk first. In this article, I will analyse this assumption and argue that it is undermined by recent developments in the postgenomic life sciences. In particular, I will argue that in a postgenomic view of germline editing a complete ban on specific uses of the technology is warranted, because the research needed to assess the safety of these interventions would not be morally defensible.

The parts-based engineering approach in synthetic biology aims to create pre-characterised biological parts that can be used for the rational design of novel functional systems. Given the context-sensitivity of biological entities, a key question synthetic biologists have to address is what properties these parts should have so that they give a predictable output even when they are used in different contexts. In the first part of this paper I will analyse some of the answers that synthetic biologists have given to this question and claim that the focus of these answers on parts and their properties does not allow us to tackle the problem of context-sensitivity. In the second part of the paper, I will argue that we might have to abandon the notions of parts and their properties in order to understand how independence in biology could be achieved. Using Robert Cummins’ account of functional analysis, I will then develop the notion of a capacity and its condition space and show how these notions can help to tackle the problem of context-sensitivity in biology.