Tarja Knuuttila

Synthetic biology is often understood in terms of the pursuit for well-characterized biological parts to create synthetic wholes. Accordingly, it has typically been conceived of as an engineering dominated and application oriented field. We argue that the relationship of synthetic biology to engineering is far more nuanced than that and involves a sophisticated epistemic dimension, as shown by the recent practice of synthetic modeling. Synthetic models are engineered genetic networks that are implanted in a natural cell environment. Their construction is typically combined with experiments on model organisms as well as mathematical modeling and simulation. What is especially interesting about this combinational modeling practice is that, apart from greater integration between these different epistemic activities, it has also led to the questioning of some central assumptions and notions on which synthetic biology is based. As a result synthetic biology is in the process of becoming more “biology inspired.”

In synthetic biology the use of engineering metaphors to describe biological organisms and their behavior has become a common practice. The concept of noise provides one of the most compelling examples of such transfer. But this notion is also confusing: While in engineering noise is a destructive force perturbing artificial systems, in synthetic biology it has acquired an additional functional meaning. It has been found out that noise is an important factor in driving biological processes such as gene regulation, development, and evolution. How did noise acquire this dual meaning in the field of synthetic biology? In this paper we study the emergence of the functional meaning of noise in relation to synthetic modeling. We will pay particular attention to the interdisciplinary aspects of this process highlighting the way borrowed concepts, analogical reasoning and the use of cross-disciplinary computational templates were entwined in it.

New mechanical philosophy posits that explanations in the life sciences involve the decomposition of a system into its entities and their respective activities and organization that are responsible for the explanandum phenomenon. This mechanistic account of explanation has proven problematic in its application to mathematical models, leading the mechanists to suggest different ways of aligning abstract models with the mechanist program. Initially, the discussion centered on whether the Hodgkin-Huxley model is explanatory. Network models provided another complication, as they apply to a wide number of materially diverse systems. In this article, we examine the various attempts to integrate abstract models within the mechanist program, also presenting a further challenge: the Heimburg-Jackson model, which was introduced as an alternative to the Hodgkin-Huxley model. We argue that although the notion of abstraction as the omission of irrelevant mechanistic details appears to give a mechanistic solution for accommodating abstract models, this notion does not suit models whose epistemic strategy is not decompositional. As a result, the mechanist has to choose whether to dilute the mechanistic approach nearly beyond recognition or to claim that many, if not most, abstract theoretical models do not deliver mechanistic explanations, or qualify as explanatory at all.

Natalia Carrillo and Tarja Knuuttila claim that there are two traditions of thinking about idealization offering almost opposite views on their functioning and epistemic status. While one tradition views idealizations as epistemic deficiencies, the other one highlights the epistemic benefits of idealization. Both of them treat idealizations as deliberate misrepresentations, however. They then argue for an artifactual account of idealization, comparing it to the traditional accounts of idealization, and exemplifying it through the Hodgkin and Huxley model of the nerve impulse. From the artifactual perspective, the epistemic benefits and deficiencies introduced by idealization frequently come in a package due to the way idealization draws together different resources in model construction. Accordingly, idealization tends to be holistic in that it is not often easily attributable to some specific parts of the model. They conclude that the artifactual approach offers a unifying view into idealization in that it is able to recover several basic philosophical insights motivating both the deficiency and epistemic benefit accounts, while being simultaneously detached from the idea of distortion by misrepresentation.

This paper examines how scientific understanding is enhanced by virtual entities, focusing on the case of the synthetic cell. Comparing it to other virtual entities and environments in science, we argue that the synthetic cell has a virtual dimension, in that it is functionally similar to living cells, though it does not mimic any particular naturally evolved cell (nor is it constructed to do so). In being cell-like at most, the synthetic cell is akin to many other virtual objects as it is selective and only partially implemented. However, there is one important difference: it is constructed by using the same materials and, to some extent, the same kind of processes as its natural counterparts. In contrast to virtual reality, especially to that of digital entities and environments, the details of its implementation is what matters for the scientific understanding generated by the synthetic cell. We conclude by arguing for the close connection between the virtual and the artifactual.

New mechanical philosophy posits that explanations in the life sciences involve the decomposition of a system into its entities and their respective activities and organization that are responsible for the explanandum phenomenon. This mechanistic account of explanation has proven problematic in its application to mathematical models, leading the mechanists to suggest different ways of aligning abstract models with the mechanist program. Initially, the discussion centered on whether the Hodgkin-Huxley model is explanatory. Network models provided another complication, as they apply to a wide number of materially diverse systems. In this article, we examine the various attempts to integrate abstract models within the mechanist program, also presenting a further challenge: the Heimburg-Jackson model, which was introduced as an alternative to the Hodgkin-Huxley model. We argue that although the notion of abstraction as the omission of irrelevant mechanistic details appears to give a mechanistic solution for accommodating abstract models, this notion does not suit models whose epistemic strategy is not decompositional. As a result, the mechanist has to choose whether to dilute the mechanistic approach nearly beyond recognition or to claim that many, if not most, abstract theoretical models do not deliver mechanistic explanations, or qualify as explanatory at all.

Although the interdisciplinary nature of contemporary biological sciences has been addressed by philosophers, historians, and sociologists of science, the different ways in which engineering concepts and methods have been applied in biology have been somewhat neglected. We examine - using the mechanistic philosophy of science as an analytic springboard - the transfer of network methods from engineering to biology through the cases of two biology laboratories operating at the California Institute of Technology. The two laboratories study gene regulatory networks, but in remarkably different ways. The research strategy of the Davidson lab fits squarely into the traditional mechanist philosophy in its aim to decompose and reconstruct, in detail, gene regulatory networks of a chosen model organism. In contrast, the Elowitz lab constructs minimal models that do not attempt to represent any particular naturally evolved genetic circuits. Instead, it studies the principles of gene regulation through a template-based approach that is applicable to any kinds of networks, whether biological or not. We call for the mechanists to consider whether the latter approach can be accommodated by the mechanistic approach, and what kinds of modifications it would imply for the mechanistic paradigm of explanation, if it were to address modelling more generally.