Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India.
PLoS One. 2010 Sep 3;5(9):e12475. doi: 10.1371/journal.pone.0012475.
Gene Regulatory Networks (GRNs) have become a major focus of interest in recent years. Elucidating the architecture and dynamics of large scale gene regulatory networks is an important goal in systems biology. The knowledge of the gene regulatory networks further gives insights about gene regulatory pathways. This information leads to many potential applications in medicine and molecular biology, examples of which are identification of metabolic pathways, complex genetic diseases, drug discovery and toxicology analysis. High-throughput technologies allow studying various aspects of gene regulatory networks on a genome-wide scale and we will discuss recent advances as well as limitations and future challenges for gene network modeling. Novel approaches are needed to both infer the causal genes and generate hypothesis on the underlying regulatory mechanisms.
In the present article, we introduce a new method for identifying a set of optimal gene regulatory pathways by using structural equations as a tool for modeling gene regulatory networks. The method, first of all, generates data on reaction flows in a pathway. A set of constraints is formulated incorporating weighting coefficients. Finally the gene regulatory pathways are obtained through optimization of an objective function with respect to these weighting coefficients. The effectiveness of the present method is successfully tested on ten gene regulatory networks existing in the literature. A comparative study with the existing extreme pathway analysis also forms a part of this investigation. The results compare favorably with earlier experimental results. The validated pathways point to a combination of previously documented and novel findings.
We show that our method can correctly identify the causal genes and effectively output experimentally verified pathways. The present method has been successful in deriving the optimal regulatory pathways for all the regulatory networks considered. The biological significance and applicability of the optimal pathways has also been discussed. Finally the usefulness of the present method on genetic engineering is depicted with an example.
近年来，基因调控网络（GRNs）已成为研究的重点。阐明大规模基因调控网络的结构和动态是系统生物学的重要目标。对基因调控网络的了解进一步提供了关于基因调控途径的见解。这些信息在医学和分子生物学中有许多潜在的应用，例如鉴定代谢途径、复杂的遗传疾病、药物发现和毒理学分析。高通量技术允许在全基因组范围内研究基因调控网络的各个方面，我们将讨论基因网络建模的最新进展以及局限性和未来挑战。需要新的方法来推断因果基因，并对潜在的调控机制提出假设。
在本文中，我们引入了一种新的方法，通过使用结构方程作为建模基因调控网络的工具来识别一组最优的基因调控途径。该方法首先生成途径中反应流的数据。制定了一组约束条件，其中包含权重系数。最后，通过对这些权重系数进行优化，得到基因调控途径。该方法的有效性在十个人类基因调控网络的文献中得到了成功的验证。与现有的极端途径分析的比较研究也是本研究的一部分。结果与早期的实验结果相比具有优势。验证后的途径指向了先前记录的和新发现的组合。
我们表明，我们的方法可以正确识别因果基因，并有效地输出经过实验验证的途径。本方法已成功推导出所考虑的所有调控网络的最优调控途径。还讨论了最优途径的生物学意义和适用性。最后，通过一个例子说明了本方法在基因工程中的有用性。
