Models of' and 'Models for': On the Relation Between Mechanistic Models and Experimental Strategies in Molecular Biology

Molecular biologists exploit information conveyed by mechanistic models for experimental purposes. In this contribution, I make sense of this aspect of biological practice by developing Keller’s idea of the distinction between ‘models of’ and ‘models for’. ‘Models of (phenomena)’ should be understood as models representing phenomena and they are valuable if they explain phenomena. ‘Models for (manipulating phenomena)’ suggest new types of material manipulations and they are important not because of their explanatory force, but because of the interventionist strategies they suggest. This is a distinction between aspects of the same model; in molecular biology, models may be treated either as ‘models of’ or as ‘models for’. By analyzing the discovery and characterization of restriction-modification systems and their exploitation for DNA cloning and mapping, I identify the differences between treating a model as a ‘model of’ or as a ‘model for’. In particular, I claim that the evaluation and development of models as either ‘models of’ or ‘models for’ is grounded in different cognitive dispositions, which prescribe different virtues for models

Models of' and 'Models for': On the Relation Between Mechanistic Models and Experimental Strategies in Molecular Biology

Molecular biologists exploit information conveyed by mechanistic models for experimental purposes. In this contribution, I make sense of this aspect of biological practice by developing Keller’s idea of the distinction between ‘models of’ and ‘models for’. ‘Models of (phenomena)’ should be understood as models representing phenomena and they are valuable if they explain phenomena. ‘Models for (manipulating phenomena)’ suggest new types of material manipulations and they are important not because of their explanatory force, but because of the interventionist strategies they suggest. This is a distinction between aspects of the same model; in molecular biology, models may be treated either as ‘models of’ or as ‘models for’. By analyzing the discovery and characterization of restriction-modification systems and their exploitation for DNA cloning and mapping, I identify the differences between treating a model as a ‘model of’ or as a ‘model for’. In particular, I claim that the evaluation and development of models as either ‘models of’ or ‘models for’ is grounded in different cognitive dispositions, which prescribe different virtues for models

Models of' and 'Models for': On the Relation Between Mechanistic Models and Experimental Strategies in Molecular Biology

Molecular biologists exploit information conveyed by mechanistic models for experimental purposes. In this contribution, I make sense of this aspect of biological practice by developing Keller’s idea of the distinction between ‘models of’ and ‘models for’. ‘Models of (phenomena)’ should be understood as models representing phenomena and they are valuable if they explain phenomena. ‘Models for (manipulating phenomena)’ suggest new types of material manipulations and they are important not because of their explanatory force, but because of the interventionist strategies they afford. This is a distinction between aspects of the same model; in molecular biology, models may be treated either as ‘models of’ or as ‘models for’. By analyzing the discovery and characterization of restriction-modification systems and their exploitation for DNA cloning and mapping, I identify the differences between treating a model as a ‘model of’ or as a ‘model for’. These lie in a cognitive disposition of the modeler towards the model. A modeler will look at a model as a ‘model of’ if he/she is interested in its explanatory force, or as a ‘model for’ if the interest is in the material manipulations it can possibly afford

Models of' and 'Models for': On the Relation Between Mechanistic Models and Experimental Strategies in Molecular Biology

Molecular biologists exploit information conveyed by mechanistic models for experimental purposes. In this contribution, I make sense of this aspect of biological practice by developing Keller’s idea of the distinction between ‘models of’ and ‘models for’. ‘Models of (phenomena)’ should be understood as models representing phenomena and they are valuable if they explain phenomena. ‘Models for (manipulating phenomena)’ suggest new types of material manipulations and they are important not because of their explanatory force, but because of the interventionist strategies they afford. This is a distinction between aspects of the same model; in molecular biology, models may be treated either as ‘models of’ or as ‘models for’. By analyzing the discovery and characterization of restriction-modification systems and their exploitation for DNA cloning and mapping, I identify the differences between treating a model as a ‘model of’ or as a ‘model for’. These lie in a cognitive disposition of the modeler towards the model. A modeler will look at a model as a ‘model of’ if he/she is interested in its explanatory force, or as a ‘model for’ if the interest is in the material manipulations it can possibly afford

Big Data in the Experimental Life Sciences

Book review of Strasser's book Collecting Experiments: Making Big Data Biology, Chicago: The University of Chicago Pres

What Kind of Novelties Can Machine Learning Possibly Generate? The Case of Genomics

Machine learning (ML) has been praised as a tool that can advance science and knowledge in radical ways. However, it is not clear exactly how radical are the novelties that ML generates. In this article, I argue that this question can only be answered contextually, because outputs generated by ML have to be evaluated on the basis of the theory of the science to which ML is applied. In particular, I analyze the problem of novelty of ML outputs in the context of molecular biology. In order to do this, I first clarify the nature of the models generated by ML. Next, I distinguish three ways in which a model can be novel (from the weakest to the strongest). Third, I dissect the way ML algorithms work and generate models in molecular biology and genomics. On these bases, I argue that ML is either a tool to identify instances of knowledge already present and codified, or to generate models that are novel in a weak sense. The notable contribution of ML to scientific discovery in the context of biology is that it can aid humans in overcoming potential bias by exploring more systematically the space of possible hypotheses implied by a theory

Phronesis and Automated Science: The Case of Machine Learning and Biology

The applications of machine learning (ML) and deep learning to the natural sciences has fostered the idea that the automated nature of algorithmic analysis will gradually dispense human beings from scientific work. In this paper, I will show that this view is problematic, at least when ML is applied to biology. In particular, I will claim that ML is not independent of human beings and cannot form the basis of automated science. Computer scientists conceive their work as being a case of Aristotle’s poiesis perfected by techne, which can be reduced to a number of straightforward rules and technical knowledge. I will show a number of concrete cases where at each level of computational analysis, more is required to ML than just poiesis and techne, and that the work of ML practitioners in biology needs also the cultivation of something analogous to phronesis, which cannot be automated. But even if we knew how to frame phronesis into rules (which is inconsistent with its own definition), still this virtue is deeply entrenched in our biological constitution, which computers lack. Whether computers can fully perform scientific practice (which is the result of the way we are cognitively and biologically) independently of humans (and their cognitive and biological specificities) is an ill-posed question

Mechanistic Models and the Explanatory Limits of Machine Learning

We argue that mechanistic models elaborated by machine learning cannot be explanatory by discussing the relation between mechanistic models, explanation and the notion of intelligibility of models. We show that the ability of biologists to understand the model that they work with (i.e. intelligibility) severely constrains their capacity of turning the model into an explanatory model. The more a mechanistic model is complex (i.e. it includes an increasing number of components), the less explanatory it will be. Since machine learning increases its performances when more components are added, then it generates models which are not intelligible, and hence not explanatory

Mechanistic Models and the Explanatory Limits of Machine Learning

We argue that mechanistic models elaborated by machine learning cannot be explanatory by discussing the relation between mechanistic models, explanation and the notion of intelligibility of models. We show that the ability of biologists to understand the model that they work with (i.e. intelligibility) severely constrains their capacity of turning the model into an explanatory model. The more a mechanistic model is complex (i.e. it includes an increasing number of components), the less explanatory it will be. Since machine learning increases its performances when more components are added, then it generates models which are not intelligible, and hence not explanatory