Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139.
Biotechnol Prog. 2010 Jul-Aug;26(4):919-37. doi: 10.1002/btpr.387.
Cytokines and growth factors are critical regulators that connect intracellular and extracellular environments through binding to specific cell-surface receptors. They regulate a wide variety of immunological, growth, and inflammatory response processes. The overall signal initiated by a population of cytokine molecules over long time periods is controlled by the subtle interplay of binding, signaling, and trafficking kinetics. Building on the work of others, we abstract a simple kinetic model that captures relevant features from cytokine systems as well as related growth factor systems. We explore a large range of potential biochemical behaviors, through systematic examination of the model's parameter space. Different rates for the same reaction topology lead to a dramatic range of biochemical network properties and outcomes. Evolution might productively explore varied and different portions of parameter space to create beneficial behaviors, and effective human therapeutic intervention might be achieved through altering network kinetic properties. Quantitative analysis of the results reveals the basis for tensions among a number of different network characteristics. For example, strong binding of cytokine to receptor can increase short-term receptor activation and signal initiation but decrease long-term signaling due to internalization and degradation. Further analysis reveals the role of specific biochemical processes in modulating such tensions. For instance, the kinetics of cytokine binding and receptor activation modulate whether ligand-receptor dissociation can generally occur before signal initiation or receptor internalization. Beyond analysis, the same models and model behaviors provide an important basis for the design of more potent cytokine therapeutics by providing insight into how binding kinetics affect ligand potency.
细胞因子和生长因子是重要的调节剂,通过与特定的细胞表面受体结合,将细胞内和细胞外环境连接起来。它们调节着广泛的免疫、生长和炎症反应过程。在很长一段时间内,细胞因子分子群体引发的整体信号是由结合、信号传递和运输动力学的微妙相互作用控制的。在他人工作的基础上,我们抽象出一个简单的动力学模型,该模型从细胞因子系统以及相关的生长因子系统中捕捉到了相关的特征。我们通过系统地检查模型的参数空间,探索了大量潜在的生化行为。同一反应拓扑的不同速率导致了生化网络性质和结果的显著差异。进化可能会有效地探索参数空间的不同和不同部分,以创造有益的行为,而通过改变网络动力学特性,可能会实现有效的人类治疗干预。对结果的定量分析揭示了许多不同网络特征之间存在紧张关系的基础。例如,细胞因子与受体的强结合可以增加短期受体激活和信号起始,但由于内化和降解,会降低长期信号。进一步的分析揭示了特定生化过程在调节这种紧张关系中的作用。例如,细胞因子结合和受体激活的动力学调节了信号起始或受体内化之前,配体-受体解离是否通常会发生。除了分析之外,相同的模型和模型行为为设计更有效的细胞因子治疗药物提供了重要的基础,因为它们深入了解了结合动力学如何影响配体效力。