Quantitative Molecular Medicine, Faculty of Medicine and Health Sciences, The Manchester Interdisciplinary Biocentre, University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK.
Expert Rev Mol Diagn. 2010 Mar;10(2):131-45. doi: 10.1586/erm.10.4.
Neuroblastoma (NB) is a common pediatric malignancy characterized by clinical and biological heterogeneity. A host of prognostic markers are available, contributing to accurate risk stratification and appropriate treatment allocation. Unfortunately, outcome is still poor for many patients, indicating the need for a new approach with enhanced utilization of the available biological data. Systems biology is a holistic approach in which all components of a biological system carry equal importance. Systems biology uses mathematical modeling and simulation to investigate dynamic interactions between system components, as a means of explaining overall system behavior. Systems biology can benefit the biomedical sciences by providing a more complete understanding of human disease, enhancing the development of targeted therapeutics. Systems biology is largely contiguous with current approaches in NB, which already employ an integrative and pseudo-holistic approach to disease management. Systems modeling of NB offers an optimal method for continuing progression in this field, and conferring additional benefit to current risk stratification and management. Likewise, NB provides an opportunity for systems biology to prove its utility in the context of human disease, since the biology of NB is comprehensively characterized and, therefore, suited to modeling. The purpose of this review is to outline the benefits, challenges and fundamental workings of systems modeling in human disease, using a specific example of bottom-up modeling in NB. The intention is to demonstrate practical requirements to begin bridging the gap between biological research and applied mathematical approaches for the mutual gain of both fields, and with additional benefits for clinical management.
神经母细胞瘤（NB）是一种常见的儿科恶性肿瘤，具有临床和生物学异质性。有许多预后标志物可用，有助于进行准确的风险分层和适当的治疗分配。不幸的是，许多患者的预后仍然很差，这表明需要采用一种新的方法，更充分地利用现有生物学数据。系统生物学是一种整体方法，其中生物系统的所有组成部分都同等重要。系统生物学使用数学建模和模拟来研究系统组件之间的动态相互作用，以此来解释整个系统的行为。系统生物学可以通过提供对人类疾病的更全面的理解，增强靶向治疗的开发，从而使生物医学科学受益。系统生物学与 NB 当前的方法基本一致，后者已经采用综合和拟整体的方法来管理疾病。对 NB 的系统建模提供了在该领域继续取得进展的最佳方法，并为当前的风险分层和管理提供了额外的好处。同样，NB 为系统生物学在人类疾病的背景下证明其效用提供了机会，因为 NB 的生物学特征得到了全面的描述，因此适合建模。本文的目的是概述系统建模在人类疾病中的益处、挑战和基本工作原理，使用 NB 中自下而上建模的具体示例。目的是展示开始弥合生物学研究和应用数学方法之间的差距的实际要求，为两个领域的共同利益，以及为临床管理带来额外的好处。
