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The Impact of Formal Reasoning in Computational Biology
  • fridolin.gross

Corresponding Author:fridolin.gross@ifom.eu

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Computational methods have become increasingly prevalent in molecular biology over the last decades. This development has been met with mixed expectations. While many people are optimistic and highlight the potential of these methods in shaping a new and more adequate paradigm for the life sciences, others worry that computers will gain too much influence and erase the human element from research. Yet, everyone appears to agree that we are witnessing a deep transformation of the epistemic and methodological conditions of the life sciences.
In this contribution I would like to investigate the influence of computational methods in molecular biology by focusing on the very meaning of the concept of computation. At the most general level,  computation can be understood as the transformation of a given sequence of symbols according to a set of formal rules. On this view, computational methods are not restricted to processes carried out on a (digital) computer, but may include other practices that involve the use of formal languages. The basic idea, though, is that computational methods are in some sense ‘mechanical’, i.e. procedures that can in principle be carried out by a machine. This broad characterization will allow me to pin down the differences between computational methods and the “informal” cognitive methods of human scientists.
As research in cognitive psychology has revealed, human reasoning can deviate substantially from the model of formal computation, for example when it relies on implicit background assumptions or external information. It would be wrong to exclusively consider this a defect, given that precisely those features are responsible for the superiority of human cognition in certain contexts. Computational methods do not necessarily represent an optimized version of informal reasoning. Instead, they should be understood as cognitive tools that can support and extend, but also fundamentally transform human cognition.
With this in mind one may ask whether science might sometimes actually do better without formal methods. Molecular biology, for example, has been very successful in the second half of the twentieth century, relying predominantly on modes of informal reasoning. Did this success come because of or in spite of the absence of formal methods? Answering this question, however, requires a better understanding of informal reasoning, and it is important to draw a distinction between formal and standardized informal elements (e.g. deductive patterns of reasoning).
Clearly, formal/computational and informal/non-computational approaches do not represent mutually exclusive approaches to science. For example, a mathematical model can be considered a purely formal tool only during a limited stage of its use. The construction of the model and the interpretation of its results are usually not reducible to a set of formal rules. Conversely, even the traditional, informal ways of doing molecular biology rely on formal methods to a certain extent, for instance in the statistical treatment of measurement data. Formal and informal approaches are often combined in practice and may support each other. An account of the impact of computational methods in biology must therefore also investigate the interactions between the different ‘cognitive styles’.
To illustrate my perspective, I will discuss a number of contemporary case studies from different areas in computational biology: computational modeling, image analysis, and bioinformatics. Even though the influence of computational methods reveals itself in different ways, I show that an analysis of the interactions of formal and informal elements can serve as a key to a deeper understanding of the transformations taking place in each case.
In summary, I suggest that an analysis of computational methods as tools of formal reasoning  allows for a meaningful assessment of the differences between human and machine-aided cognition and of how they interact in scientific practice.