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The creative industry of integrative systems biology

机译:整合系统生物学的创意产业

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Integrative systems biology (ISB) is among the most innovative fields of contemporary science, bringing together scientists from a range of diverse backgrounds and disciplines to tackle biological complexity through computational and mathematical modeling. The result is a plethora of problem-solving techniques, theoretical perspectives, lab-structures and organizations, and identity labels that have made it difficult for commentators to pin down precisely what systems biology is, philosophically or sociologically. In this paper, through the ethnographic investigation of two ISB laboratories, we explore the particular structural features of ISB thinking and organization and its relations to other disciplines that necessitate cognitive innovation at all levels from lab PI’s to individual researchers. We find that systems biologists face numerous constraints that make the production of models far from straight-forward, while at the same time they inhabit largely unstructured task environments in comparison to other fields. We refer to these environments as adaptive problem spaces. These environments they handle by relying substantially on the flexibility and affordances of model-based reasoning to integrate these various constraints and find novel adaptive solutions. Ultimately what is driving this innovation is a determination to construct new cognitive niches in the form of functional model building frameworks that integrate systems biology within the biological sciences. The result is an industry of diverse and different innovative practices and solutions to the problem of modeling complex, large-scale biological systems.
机译:集成系统生物学(ISB)是当代科学领域中最具创新性的领域,它汇集了来自不同背景和学科的科学家,以通过计算和数学建模解决生物学的复杂性。结果是大量的解决问题的技术,理论观点,实验室结构和组织以及身份标签,这使得评论员很难精确地确定哲学或社会生物学是什么系统。在本文中,通过对两个ISB实验室的人种学调查,我们探索了ISB思维和组织的特殊结构特征,以及与其他学科之间的关系,这些学科需要从实验室PI到各个研究人员的各个层面的认知创新。我们发现系统生物学家面临众多限制,这使得模型的产生远非直截了当,而与其他领域相比,他们却居住在很大程度上是非结构化的任务环境。我们将这些环境称为自适应问题空间。他们通过基本依赖基于模型的推理的灵活性和能力来处理这些环境,以集成这些各种约束并找到新颖的自适应解决方案。最终,推动这一创新的因素是决心以功能模型构建框架的形式构建新的认知壁ni,该模型将系统生物学整合到生物科学中。结果形成了一个行业,该行业针对建模复杂的大规模生物系统的问题进行了多种多样的创新实践和解决方案。

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