MacLeod Miles, Nersessian Nancy J
Centre of Excellence in the Philosophy of Social Sciences, Department of Political and Economic Studies, University of Helsinki, P.O. Box 24, 00014, Finland.
Department of Psychology, Harvard University, 1160 William James Hall, 33 Kirkland St., Cambridge, MA 02138, USA.
Stud Hist Philos Biol Biomed Sci. 2015 Feb;49:1-11. doi: 10.1016/j.shpsc.2014.10.004. Epub 2014 Nov 25.
In this paper we draw upon rich ethnographic data of two systems biology labs to explore the roles of explanation and understanding in large-scale systems modeling. We illustrate practices that depart from the goal of dynamic mechanistic explanation for the sake of more limited modeling goals. These processes use abstract mathematical formulations of bio-molecular interactions and data fitting techniques which we call top-down abstraction to trade away accurate mechanistic accounts of large-scale systems for specific information about aspects of those systems. We characterize these practices as pragmatic responses to the constraints many modelers of large-scale systems face, which in turn generate more limited pragmatic non-mechanistic forms of understanding of systems. These forms aim at knowledge of how to predict system responses in order to manipulate and control some aspects of them. We propose that this analysis of understanding provides a way to interpret what many systems biologists are aiming for in practice when they talk about the objective of a "systems-level understanding."
在本文中，我们利用两个系统生物学实验室丰富的人种志数据，探讨解释和理解在大规模系统建模中的作用。我们举例说明了一些实践，这些实践背离了动态机制解释的目标，以实现更有限的建模目标。这些过程使用生物分子相互作用的抽象数学公式和数据拟合技术，我们称之为自上而下的抽象，以便用关于这些系统某些方面的特定信息，来换取对大规模系统的精确机制描述。我们将这些实践描述为对许多大规模系统建模者所面临的限制的务实回应，这些限制反过来又产生了对系统的更有限的务实性非机械形式的理解。这些形式旨在获得如何预测系统反应的知识，以便操纵和控制它们的某些方面。我们认为，这种对理解的分析提供了一种方式，来解读许多系统生物学家在实践中谈论 “系统层面的理解” 目标时所追求的东西。
