Fnaiech Mohamed Amine, Nounou Hazem, Nounou Mohamed, Datta Aniruddha
Electrical and Computer Engineering Program, Texas A&M University at Qatar, P.O. Box 23874, Doha, Qatar.
Adv Bioinformatics. 2012;2012:534810. doi: 10.1155/2012/534810. Epub 2012 Nov 6.
The problems of modeling and intervention of biological phenomena have captured the interest of many researchers in the past few decades. The aim of the therapeutic intervention strategies is to move an undesirable state of a diseased network towards a more desirable one. Such an objective can be achieved by the application of drugs to act on some genes/metabolites that experience the undesirable behavior. For the purpose of design and analysis of intervention strategies, mathematical models that can capture the complex dynamics of the biological systems are needed. S-systems, which offer a good compromise between accuracy and mathematical flexibility, are a promising framework for modeling the dynamical behavior of biological phenomena. Due to the complex nonlinear dynamics of the biological phenomena represented by S-systems, nonlinear intervention schemes are needed to cope with the complexity of the nonlinear S-system models. Here, we present an intervention technique based on feedback linearization for biological phenomena modeled by S-systems. This technique is based on perfect knowledge of the S-system model. The proposed intervention technique is applied to the glycolytic-glycogenolytic pathway, and simulation results presented demonstrate the effectiveness of the proposed technique.
在过去几十年里，生物现象的建模与干预问题吸引了众多研究人员的关注。治疗干预策略的目标是将患病网络的不良状态转变为更理想的状态。通过应用药物作用于表现出不良行为的某些基因/代谢物，可以实现这一目标。为了设计和分析干预策略，需要能够捕捉生物系统复杂动态的数学模型。S-系统在准确性和数学灵活性之间取得了良好的平衡，是对生物现象动态行为进行建模的一个有前途的框架。由于S-系统所代表的生物现象具有复杂的非线性动力学，因此需要非线性干预方案来应对非线性S-系统模型的复杂性。在此，我们提出一种基于反馈线性化的干预技术，用于对由S-系统建模的生物现象进行干预。该技术基于对S-系统模型的完全了解。所提出的干预技术应用于糖酵解-糖原分解途径，给出的仿真结果证明了该技术的有效性。
