In Silico Modeling: the Human Factor

Marta Bertolaso, Miles MacLeod
2016 Humana.Mente: Journal of Philosophical Studies  
Technology is playing an increasingly larger role in biological research and investigation. We are building ever more sophisticated experimental systems which can sample data at rates well beyond anything possible using traditional methods. To analyse this data computational systems do the bulk of the work. Mathematical and computational modelling methods are being applied to large scale systems. These methods can perform analysis and derive information from such systems even with only
more » ... e information and partial pictures of networks, which is beyond the powers of what experimental work, or indeed ordinary unaided human cognition, could itself achieve. While human and economic investments in technology development and adoption are growing fast (supported by pressure from many stakeholders) some observers have begun to ask what will all this mean for biology as a science. Undoubtedly, the future of biology is as a technoscience, in which technical and engineering expertise are as important as biological knowledge and experimental skill. As such many of the practices and cultures that have characterized 20 th century biology may be supplanted by more automated and algorithmic machine-driven processes. But what can we really expect from technology? How effective will it be and what impact will it have on biological knowledge? How will the role of scientists as human beings be transformed by this epochal transformation? How autonomous will the role of technology be with respect to human contributions in driving research? In sum, how does this human-technology partnership work? Are there any risks or negative drifts that we can foresee and try to counter? In this Special Issue we try to lay some foundations for answering these questions by focusing on in silico models. In silico stands for 'computational'. Historically, the term in silico has played the rhetorical function of giving computational models and simulations the same scientific dignity as in vitro and in vivo experiments. The same reinforcing function is exploited today, although there is no doubt that in silico models are rapidly advancing with new experimental and analytical tools that generate information-rich, highthroughput biological data. For example, they can be used to study complex diseases such as cancer which involve multiple types of biological interactions across diverse physical, temporal, and biological scales. Edelman et al. (2010) express a widespread opinion when they affirm that "Even though cancer has been among the most-studied human diseases using systems approaches, significant challenges remain before the enormous potential of in silico cancer biology can be fully realized". Statistical inferences and models provide an important means for data fitting and discovery; mechanistic knowledge can be reached and used in unprecedented ways; agent-based models enable modeling of cancer cell populations and tumor progression. The promise of in silico models is to produce information that can then be used in diagnosis, prognosis and therapy in the clinical setting. We do not yet necessarily have a good understanding of the affordances and limits of our technological approaches. One can evaluate technological trends by studying how well they live up to their own high expectations and promises. At this point there are still worthy doubts and scepticism to be had. Some promised revolutions have not panned out as hoped (see for instance Genome Wide Association Studies), or at least technology proponents are taking longer than anticipated to find the most appropriate ways to use technology to disentangle biological complexity and variability. Our use of technology for investigation needs constant shaping. Philosophers tend to turn to the conceptions of biological systems that underlie technological practices and ask deeper methodological and ontological questions about how well technologically supported practices are really adapted to pick up reliable and useful biological signals. They attempt to understand how better notions can frame and inform proper technological use. In this vein we can study the philosophical rationale for systems-level approaches to understanding biological systems, the suitability of biological materials to synthetic manipulation, and the conceptual challenges and practical advantages of reformulating biological knowledge in computationally
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