Science子刊：如何筛选致病基因

　　

生物通报道，美国新泽西州技术研究所电子与计算机工程系信号研究中心，东北大学电子与计算机工程系的科学家研究发明了一种新的生物计算机检测技术，可对活体细胞的复杂信号网络进行检测，找出出现故障的网络部分，相关的成果公布在Science子刊Science Singnaling上。

 

科学家们相信，机体的多种疾病是由于多种基因功能突变所导致，因此疾病的发生往往有一个复杂的网络结构，并不是由某个单一的因素所引起。研究者对多种疾病开展研究，发现细胞内的分子网络机能异常，是导致疾病发生的罪魁祸首，这些异常的分子机制通常是由于基因表达异常或是蛋白异常所致。异常的分子产物会导致生物化学的错误反应，导致身体机能失常发生疾病。

 

因此，研究者开发一种新的生物信号系统检测技术，通过计算机检测细胞内复杂的网络系统，定位找出有异常的分子或是蛋白，以达到找出致病基因的目的。

 

为了检验该检测方法，研究者将该技术应用在3个研究背景已经清晰的信号网络系统中。它们分别是细胞凋亡蛋白酶3（caspase3）的网络系统；调节p53活性的网络系统；中枢神经系统调节转录因子CREB的网络系统。

 

研究结果表明，这套检测系统可准确找出影响CREB系统的关键调节子。

 

研究者认为，该技术将有助科学家找出疾病的关键治疗靶位，或是进行药物靶位的筛选工作。

 

原文摘要：Fault Diagnosis Engineering of Digital Circuits Can Identify Vulnerable Molecules in Complex Cellular Pathways

 

【Abstract】

The application of complex system engineering approaches to cell signaling networks should lead to novel understandings and, subsequently, new treatments for complex disorders. In the area of circuit fault diagnosis engineering, there are various methods to identify the defective or vulnerable components of complex digital electronic circuits. In biological systems, however, knowledge is limited regarding the vulnerability of interconnected signaling pathways to the dysfunction of each specific molecule. By developing proper biologically driven digital vulnerability assessment methods, the vulnerability of complex signaling networks to the possible dysfunction of each molecule can be determined. To show the utility of this approach, we analyzed three well-characterized signaling networks—a cellular network that regulates the activity of caspase3, a network that regulates the activity of p53, and a central nervous system network that regulates the activity of the transcription factor CREB (adenosine 3',5'-monophosphate response element–binding protein). We found important differences among the vulnerability values of different molecules. Most of the identified highly vulnerable molecules are functionally related and known key regulators of these networks. Experimental data confirmed the ability of digital vulnerability assessment to correctly predict key regulators in the CREB network. Because this approach may provide insight into key molecules that contribute to human diseases, it may aid in the identification of critical targets for drug development.

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