Handling variability and incompleteness of biological data by flexible nets: a case study for Wilson disease.
Handling variability and incompleteness of biological data by flexible nets: a case study for Wilson disease.
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DOI：
10.1038/s41540-017-0044-x
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发表时间：
2018
影响因子：
4
通讯作者：
Oliver SG
中科院分区：
文献类型：
--
作者：
Júlvez J;Dikicioglu D;Oliver SG
结合预测准确性和解释力的数学模型是系统和合成生物学进步的核心，但生物数据的异质性和不完整性阻碍了我们构建这种模型的能力。此外，许多生物系统所显示的鲁棒性意味着它们具有在一系列生理条件下运行的灵活性，这对于许多建模形式来说是难以处理的。灵活网络（FNs）解决了这些挑战，代表了基于模型的生物系统分析的范式转变。FNs可以：(i)处理浓度、化学计量、网络拓扑和过渡速率等方面的不确定性、范围和缺失信息，而无需诉诸统计方法；（ii）在整合各种细胞机制的统一模型中容纳不同类型的数据；(3)用于系统优化和模型预测控制。我们提出了FNs，并通过模拟一个完善的系统来说明它们的能力，微生物种群的葡萄糖消耗动力学。我们进一步证明了FNs在应对遗传或代谢扰动时采取控制行动的能力。在对该系统进行基准测试后，我们构建了威尔逊病的第一个定量模型——威尔逊病是一种罕见的遗传性疾病，会损害肝脏对铜的利用。我们使用这个模型来研究使用维生素E补充疗法改善症状的可行性。我们的研究结果表明，由铜积累引起的肝细胞炎症不会因内源性抗氧化剂供应的限制而加重，这意味着抗氧化剂治疗患者不太可能有效。为了研究复杂的动力系统，需要适当的数学模型来捕捉系统的特征。生物系统的研究尤其需要灵活的建模方法，因为它们在不同条件下表现出可变的可量化反应。此外，关于特定生物系统的数据往往是不确定的或不可用的。在这里，来自剑桥大学的一组科学家介绍了柔性网络（FNs），这是一种用于建模、分析和控制生物系统的新方法。在介绍FN方法后，他们展示了酵母葡萄糖消耗和利用的众所周知的系统如何建模，分析和控制。然后，FNs用于建立和分析Wilson病（铜利用的遗传性缺陷）的第一个定量和预测模型。他们证明FN模拟允许对不同治疗方案的相对疗效进行评估。
Mathematical models that combine predictive accuracy with explanatory power are central to the progress of systems and synthetic biology, but the heterogeneity and incompleteness of biological data impede our ability to construct such models. Furthermore, the robustness displayed by many biological systems means that they have the flexibility to operate under a range of physiological conditions and this is difficult for many modeling formalisms to handle. Flexible nets (FNs) address these challenges and represent a paradigm shift in model-based analysis of biological systems. FNs can: (i) handle uncertainties, ranges and missing information in concentrations, stoichiometry, network topology, and transition rates without having to resort to statistical approaches; (ii) accommodate different types of data in a unified model that integrates various cellular mechanisms; and (iii) be employed for system optimization and model predictive control. We present FNs and illustrate their capabilities by modeling a well-established system, the dynamics of glucose consumption by a microbial population. We further demonstrate the ability of FNs to take control actions in response to genetic or metabolic perturbations. Having bench-marked the system, we then construct the first quantitative model for Wilson disease—a rare genetic disorder that impairs copper utilization in the liver. We used this model to investigate the feasibility of using vitamin E supplementation therapy for symptomatic improvement. Our results indicate that hepatocytic inflammation caused by copper accumulation was not aggravated by limitations on endogenous antioxidant supplies, which means that treating patients with antioxidants is unlikely to be effective. In order to study complex dynamical systems, appropriate mathematical models that capture the system features are necessary. Biological systems, in particular, require flexible modeling approaches for their study since they exhibit variable quantifiable responses under different conditions. Moreover, data about a given biological system are often uncertain or unavailable. Here, a group of scientists from the University of Cambridge introduce Flexible Nets (FNs), a novel approach for the modeling, analysis, and control of biological systems. After presenting the FN approach, they show how a well-known system of glucose consumption and utilization by yeast can be modeled, analyzed and controlled. Then, FNs are used to build and analyze the first quantitative and predictive model of Wilson disease (a heritable defect in copper utilization). They demonstrate that FN simulations permit an evaluation of the relative efficacy of different therapeutic options.
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影响因子：
9.9
作者：
Orth, Jeffrey D.;Conrad, Tom M.;Na, Jessica;Lerman, Joshua A.;Nam, Hojung;Feist, Adam M.;Palsson, Bernhard O.
通讯作者：
Palsson, Bernhard O.
DOI：
10.1016/s1474-4422(14)70190-5
发表时间：
2015-01
期刊：
The Lancet. Neurology
影响因子：
--
作者：
Bandmann O;Weiss KH;Kaler SG
通讯作者：
Kaler SG
DOI：
10.1197/jamia.m1637
发表时间：
2005-03-01
影响因子：
6.4
作者：
Peleg, M;Rubin, D;Altman, RB
通讯作者：
Altman, RB
影响因子：
29.4
作者：
Huster D;Kühne A;Bhattacharjee A;Raines L;Jantsch V;Noe J;Schirrmeister W;Sommerer I;Sabri O;Berr F;Mössner J;Stieger B;Caca K;Lutsenko S
通讯作者：
Lutsenko S
影响因子：
4.3
作者：
Needham CJ;Bradford JR;Bulpitt AJ;Westhead DR
通讯作者：
Westhead DR