SBMLKinetics: a tool for annotation-independent classification of reaction kinetics for SBML models.
SBMLKinetics: a tool for annotation-independent classification of reaction kinetics for SBML models.
复制标题
DOI：
10.1186/s12859-023-05380-3
复制
发表时间：
2023-06-13
影响因子：
3
通讯作者：
Xu, Jin
中科院分区：
文献类型：
--
作者：
Xu, Jin
反应网络在系统生物学中被广泛用作揭示生物系统原理的机制模型。反应受描述反应速率的动力学定律支配。选择合适的动力学定律对许多建模者来说是困难的。有一些工具试图根据注释找到正确的动力学定律。在这里，我开发了独立于注释的技术，通过专注于寻找通常用于类似反应的动力学定律来帮助建模者。推荐动力学定律和对反应网络的其他分析可以看作是一个分类问题。现有的确定相似反应的方法在很大程度上依赖于良好的注释，而在生物模型等模型存储库中，这一条件通常是不满足的。我开发了一种独立于注释的方法，通过反应分类来寻找相似的反应。我提出了一个二维动力学分类方案（2DK），沿着动力学类型（K型）和反应类型（R型）的维度分析反应。我确定了大约十种相互排斥的K类型，包括零阶，质量作用，Michaelis-Menten， Hill动力学等。R型按不同反应物的数量和不同反应生成物的数量排列。我构建了一个工具SBMLKinetics，它输入一系列SBML模型，然后计算反应分类作为每个2DK类的概率。在生物模型上对2DK的有效性进行了评价，该方案对95%以上的反应进行了分类。2DK有很多应用。它提供了一种数据驱动的不依赖于注释的方法，通过使用该类模型的共同类型结合反应的R类型来推荐动力学定律。另外，2DK也可以用来提醒用户，K型和R型的动力学定律是不寻常的。最后，2DK提供了一种分析模型组以比较其动力学规律的方法。我将2DK应用于biommodels，比较了信号网络的动力学和代谢网络的动力学，发现K型分布存在显著差异。
Reaction networks are widely used as mechanistic models in systems biology to reveal principles of biological systems. Reactions are governed by kinetic laws that describe reaction rates. Selecting the appropriate kinetic laws is difficult for many modelers. There exist tools that attempt to find the correct kinetic laws based on annotations. Here, I developed annotation-independent technologies that assist modelers by focusing on finding kinetic laws commonly used for similar reactions. Recommending kinetic laws and other analyses of reaction networks can be viewed as a classification problem. Existing approaches to determining similar reactions rely heavily on having good annotations, a condition that is often unsatisfied in model repositories such as BioModels. I developed an annotation-independent approach to find similar reactions via reaction classifications. I proposed a two-dimensional kinetics classification scheme (2DK) that analyzed reactions along the dimensions of kinetics type (K type) and reaction type (R type). I identified approximately ten mutually exclusive K types, including zeroth order, mass action, Michaelis–Menten, Hill kinetics, and others. R types were organized by the number of distinct reactants and the number of distinct products in reactions. I constructed a tool, SBMLKinetics, that inputted a collection of SBML models and then calculated reaction classifications as the probability of each 2DK class. The effectiveness of 2DK was evaluated on BioModels, and the scheme classified over 95% of the reactions. 2DK had many applications. It provided a data-driven annotation-independent approach to recommending kinetic laws by using type common for the kind of models in combination with the R type of the reactions. Alternatively, 2DK could also be used to alert users that a kinetic law was unusual for the K type and R type. Last, 2DK provided a way to analyze groups of models to compare their kinetic laws. I applied 2DK to BioModels to compare the kinetics of signaling networks with the kinetics of metabolic networks and found significant differences in K type distributions.
登录
查看更多内容
影响因子：
46.9
作者：
Le Novère, N;Finney, A;Wanner, BL
通讯作者：
Wanner, BL
影响因子：
5.8
作者：
Bornstein, Benjamin J.;Keating, Sarah M.;Hucka, Michael
通讯作者：
Hucka, Michael
影响因子：
9.9
作者：
Keating SM;Waltemath D;König M;Zhang F;Dräger A;Chaouiya C;Bergmann FT;Finney A;Gillespie CS;Helikar T;Hoops S;Malik-Sheriff RS;Moodie SL;Moraru II;Myers CJ;Naldi A;Olivier BG;Sahle S;Schaff JC;Smith LP;Swat MJ;Thieffry D;Watanabe L;Wilkinson DJ;Blinov ML;Begley K;Faeder JR;Gómez HF;Hamm TM;Inagaki Y;Liebermeister W;Lister AL;Lucio D;Mjolsness E;Proctor CJ;Raman K;Rodriguez N;Shaffer CA;Shapiro BE;Stelling J;Swainston N;Tanimura N;Wagner J;Meier-Schellersheim M;Sauro HM;Palsson B;Bolouri H;Kitano H;Funahashi A;Hermjakob H;Doyle JC;Hucka M;SBML Level 3 Community members
通讯作者：
SBML Level 3 Community members
影响因子：
1.9
作者：
Hucka, Michael;Bergmann, Frank T.;Wilkinson, Darren J.
通讯作者：
Wilkinson, Darren J.
影响因子：
5.8
作者：
Liebermeister, Wolfram;Uhlendorf, Jannis;Klipp, Edda
通讯作者：
Klipp, Edda