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姓 名:
Christine Nardini
性    别:
专家类别:
研究员
学 历:
博士
所属部门:
计算生物学伙伴研究所
学科类别:
临床工程学
电 话:
传 真:
86 21 54920451
电子邮件:
christine@picb.ac.cn
通讯地址:
上海市岳阳路320号 200031

简历:
University qualification •2003-2005 - PhD in Electronics and Informatics at University of Bologna. Supervisor: Prof. Luca Benini. •2003-2004 – Visiting researcher at the Computer Science Department at Stanford University (CA, USA), with Prof Giovanni de Micheli. For the development of data mining algorithms to be applied to microarrays for gene expression analyses. •1999 National Habilitation to Engineers Professional Activity. •1992-1998 –Master Degree in Electrical Engineering – Biomedical specialization at the University of Bologna (90/100). Tutor: Prof. Mauro Ursino •1992 – BS Degree at Istituto N. Copernico in Bologna (60/60). Employment record since 02/2008 Research group leader of the Clinical Genomic Network group, in a joint appointment of the Max-Planck-Society and the Chinese Academy of Sciences, at the PICB, Shanghai. 2007. Collaborator at the Telethon Institute for Genetics and Medicine (Naples, Italy) with the Systems Biology Lab 2006- 2007. Research Assistant at the Department of Electronic Informatics and Systems (DEIS) at the University of Bologna (Italy). 2006-2008. Professor for the module of systems biology-biosensors at the International Master of Bioinformatics, University of Bologna, Italy. 1999 - 2002. Field Engineer in the Cardiac Rhythm Management Division at Medtronic S.p.A. for assistance during implant and follow-up of patients with implantable devices for brady/tachy arrhythmias. Education activity on brady/tachy arrhythmias and cardiac stimulation for nurses and medical doctors in various hospitals in the regions of Emilia Romagna and Marche. Teaching experience Systems biology module in the course Biosensors at the International Master of Bioinformatics, University of Bologna, Italy Thank to the innovative approaches to genetic (genomics), medicine is facing the tangible possibility to shift its conventional approach toward personalized medicine. This is an evidence based science, able to improve dramatically the efficiency of prevention and treatments by reducing side effects and empowering therapeutic approaches. This science is rooted in the concept of genomic profile, that is the ensemble not only of specific genes variants (genotype), but also of specific patterns of (disrupted) genes' activation, typical of each unique patient-and-disease complex. Knowing the genomic profile of an individual in a given condition might allow to define a better targeted therapeutic approach. However, the identification of disrupted patterns in a genomic profile is extremely complex, and different approaches as well as different samples under study can highlight different and sometimes non-overlapping sets of genes responsible for the complex changes that are undergoing in a cell during a disease. Thus, the translation of these concepts into medical practice represents still and important challenge. Our research focuses on the identification of approaches that try to resolve such apparent incongruities, notably using gene network reverse engineering approaches and systemic approaches, in strong connection with the use of experimental data. The objective is two-fold: * identify approaches that can use diverse gene signatures as a source of information enrichment to build more complex, multidimensional (networks vs lists) gene signatures * use these approaches to get insight into the functioning of innovative/alternative medical approaches

研究方向:
 系统生物学、临床基因网络
 
研究领域:

感谢人类基因组计划和后基因组时代所提供的海量数据信息,它使得研究者可以对多种复杂的生物问题进行系统生物学研究。高通量的分子生物学研究手段和强有力的计算生物学分析方法为研究某些人类疾病的发病机制和治疗方法的分子机理打开了方便之门。
我们的研究方向主要有:(1)鉴别与疾病发生、发展和治愈过程相关的基因;(2)鉴别并行性治疗或诊断相关的基因;(3)鉴别与某些特定功能相关的基因,并且研究这些基因之间的相互作用网络。
目前,我们的研究课题包括以下两个方面:
1.自身免疫性疾病基因组学
自身免疫性疾病是一种快速发展的复杂性疾病,目前其发病机制还不清楚,并且当前的各种西药治疗手段都有让患者难以接受的副作用,这大大降低了患者自身的免疫系统抵抗疾病的能力。 因此,阐明自身免疫性疾病发生发展的分子机制,了解疾病发展过程中发生改变的各种复杂的细胞信号传导通路,对于提高此类疾病的治疗水平是至关重要的。通过高通量的分子生物学实验方法产生可以应用于计算基因组学分析的数据,能够有效的从基因组水平系统地诠释与此类疾病相关的信号传导通路。这种研究方法不仅可以同时比较不同的治疗手段以找到更为有效的新的治疗方法,还可以减少药物治疗过程中所产生的各种副作用,从而加速患者的康复过程,为新药研发开辟了道路。
2.人类肠道微生物系统生物学
为从整体上理解人类疾病的发生和发展,从根本上阐明保持和破坏人类健康状态的各种关键性因素就显得尤为重要。近年来,人们已将注意力转移到生存在人类肠道中的、与人类生活密切相关的微生物菌群系统。利用系统生物学的研究方法将有助于阐明这些肠道菌群的作用机制,并且为调控、增强和修复人类健康系统的功能提供帮助。已有研究表明:这些代谢调控机制和生命系统信息鲜为人知的肠道菌群正以一种非常有效经济的方式影响着人类的健康。因此,通过高效的基因网络逆向工程学方法将使我们可以洞悉这类菌群的代谢和遗传互作机制,开发出能够用于疾病治疗的新方法。


职称:
研究员

职务:

社会任职:

获奖及荣誉:
代表论著:
 [1] C. Guiducci, C. Nardini, High Parallelism, Portability and Broad Accessibility: Technologies for Genomics, ACM Journal on Emerging Technologies in Computing Systems, 2008. 

[2] C. Nardini, M. D. Kuo and L. Benini, Statistical Significance in omic data analyses, Proceedings of International Conference on bio-inspired systems and signal processing Biosignals2008, Portugal. 

[3] D. Masotti, C. Nardini, S. Rossi, E. Bonora, G. Romeo, S. Volinia and L. Benini, TOM: enhancement and extension of a tool suite for in silico approaches to multigenic complex disorders, Bioinformatics, 2007, doi: 10.1093/bioinformatics/btm588. 

[4] C. Nardini, L. Benini and G. de Micheli, Circuits and Systems for High-Throughput Biology, IEEE Circuits and Systems Magazine, 2006, 6(3), pp.10-20. 

[5] C. Nardini, D. Masotti, S. Yoon, E. Macii, M.D. Kuo, G. de Micheli and L. Benini, Mining gene sets for measuring similarities, Proceedings of IEEE Symposium on Computers and Communications (ISCC), 2006. 

[6] M. Diehn, C. Nardini, D.S. Wang, A. Hsiao, S. Cha and M.D. Kuo Neuroimaging Characteristics Reflect Tumor Gene Expression Signatures and Predict Survival in Glioblastoma Multiforme, International Journal of Radiation Oncology Biology Physics, Volume 66, Issue 3, Supplement 1, 2006, Page S18 

[7] S. Rossi-D. Masotti, C. Nardini, E. Bonora, G. Romeo, E. Macii, L. Benini and S. Volinia, TOM: a web-based integrated approach for efficient identification of candidate disease genes, Nucleic Acids Res., 34: W285 - W292; doi:10.1093/nar/gkl340. 

[8] S. Yoon, C. Nardini, L. Benini and G. de Micheli, Discovering coherent biclusters from gene Expression Data Using Zero-Suppressed Binary Decision Diagrams, The IEEE/ACM Transaction on Computational Biology and Bioinformatics, Vol. 3, N. 2, 2005. 

[9] S. Yoon, C. Nardini, L. Benini and G. de Micheli. Enhanced pClustering and its applications to gene expression data, In Proceedings of IEEE Symposium on BIBE (Bioinformatics and Bioengineering), pp. 275-282, 2004. 

承担科研项目情况:
 
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