On deducing causality in metabolic networks.
On deducing causality in metabolic networks.
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DOI：
10.1186/1471-2105-9-s4-s8
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发表时间：
2008-04-25
影响因子：
3
通讯作者：
Chiarugi D
中科院分区：
文献类型：
--
作者：
Bodei C;Bracciali A;Chiarugi D
代谢网络呈现复杂的互连结构，其理解通常是一项重要的任务。已经制定了一些正式的方法来支持对这种网络的调查。在这种情况下的相关问题之一是理解的因果关系之间的依赖关系的分子参与代谢过程。我们应用技术，从形式化的方法和计算逻辑开发一个抽象的定性模型的代谢网络，以确定可能的因果关系。考虑到表达性和易用性，我们的目标是提供：i）一个最小的符号来表示生物化学相互作用中的因果关系，以及ii）一个自动化工具，允许人类专家轻松地改变计算机实验的条件。我们利用化学反应的逻辑含义的阅读：从反应规则和初始条件的代谢网络的描述开始，反应链，因果地相互依赖，可以自动推导出来。根据假设研究策略，初始状态的组成部分和支配反应的条款都可以很容易地改变，并开始新的实验试验。我们的方法旨在利用计算逻辑作为一个正式的建模框架，其中几个可用的，这是自然接近人类推理。它直接导致可执行的实现，并可能支持，在透视，各种推理模式。事实上，我们的抽象支持的计算对应，基于Prolog实现，它允许一个表示语言密切对应于所采用的化学抽象符号。所提出的方法已经验证了大肠杆菌K12的代谢网络模型的基因敲除和必要性的结果，这表明与可用的湿实验室实验数据的相关一致性。从所提出的工作开始，我们的目标是提供一个有效的分析工具包，由一个高效的成熟的计算对应物支持，目的是通过有效地修剪非有希望的方向来有效地驱动体外实验。
Metabolic networks present a complex interconnected structure, whose understanding is in general a non-trivial task. Several formal approaches have been developed to support the investigation of such networks. One of the relevant problems in this context is the comprehension of causality dependencies amongst the molecules involved in the metabolic process. We apply techniques from formal methods and computational logic to develop an abstract qualitative model of metabolic networks in order to determine possible causal dependencies. Keeping in mind both expressiveness and ease of use, we aimed at providing: i) a minimal notation to represent causality in biochemical interactions, and ii) an automated tool allowing human experts to easily vary conditions of in silico experiments. We exploit a reading of chemical reactions in terms of logical implications: starting from a description of a metabolic network in terms of reaction rules and initial conditions, chains of reactions, causally depending one from the another, can be automatically deduced. Both the components of the initial state and the clauses ruling reactions can be easily varied and a new trial of the experiment started, according to a what-if investigation strategy. Our approach aims at exploiting computational logic as a formal modeling framework, amongst the several available, that is naturally close to human reasoning. It directly leads to executable implementations and may support, in perspective, various reasoning schemata. Indeed, our abstractions are supported by a computational counterpart, based on a Prolog implementation, which allows for a representation language closely correspondent to the adopted chemical abstract notation. The proposed approach has been validated by results regarding gene knock-out and essentiality for a model of the metabolic network of Escherichia coli K12, which show a relevant coherence with available wet-lab experimental data. Starting from the presented work, our goal is to provide an effective analysis toolkit, supported by an efficient full-fledged computational counterpart, with the aim of fruitfully driving in vitro experiments by effectively pruning non promising directions.