>Summary: Often competing hypotheses for biochemical networks exist in the form of different mathematical models with unknown parameters. Considering available experimental data, it is then desired to reject model hypotheses that are inconsistent with the data, or to estimate the unknown parameters. However, these tasks are complicated because experimental data are typically sparse, uncertain, and are frequently only available in form of qualitative if–then observations. ADMIT (>Analysis, >Design and >Model >Invalidation >Toolbox) is a MatLabTM-based tool for guaranteed model invalidation, state and parameter estimation. The toolbox allows the integration of quantitative measurement data, a priori knowledge of parameters and states, and qualitative information on the dynamic or steady-state behavior. A constraint satisfaction problem is automatically generated and algorithms are implemented for solving the desired estimation, invalidation or analysis tasks. The implemented methods built on convex relaxation and optimization and therefore provide guaranteed estimation results and certificates for invalidity.>Availability: ADMIT, tutorials and illustrative examples are available free of charge for non-commercial use at >Contact:
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机译：>摘要：生化网络的竞争假设通常以参数未知的不同数学模型的形式存在。考虑可用的实验数据，然后希望拒绝与数据不一致的模型假设，或者估计未知参数。但是，这些任务很复杂，因为实验数据通常是稀疏的，不确定的，并且通常只能以定性的“ if-then”观察形式获得。 ADMIT（> A 分析，> D 签名和> M odel > I 验证> T oolbox）是基于MatLab TM 的工具，用于保证模型无效，状态和参数估计。该工具箱允许集成定量测量数据，参数和状态的先验知识以及有关动态或稳态行为的定性信息。自动生成约束满足问题，并实施算法来解决所需的估计，失效或分析任务。已实现的方法基于凸松弛和优化，因此可提供有保证的估计结果和无效性证明。>可用性： ADMIT，教程和示例性示例可免费用于非商业用途，>联系方式：
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