Albert Réka, Dasgupta Bhaskar, Sontag Eduardo
Department of Physics, Penn State University, University Park, PA, USA.
Methods Mol Biol. 2010;673:239-51. doi: 10.1007/978-1-60761-842-3_16.
Here, we present a novel computational method, and related software, to synthesize signal transduction networks from single and double causal evidences. This is a significant and topical problem because there are currently no high-throughput experimental methods for constructing signal transduction networks, and because the understanding of many signaling processes is limited to the knowledge of the signal(s) and of key mediators' positive or negative effects on the whole process. Our software NET-SYNTHESIS is freely downloadable from http://www.cs.uic.edu/∼dasgupta/network-synthesis/ .Our methodology serves as an important first step in formalizing the logical substrate of a signal transduction network, allowing biologists to simultaneously synthesize their knowledge and formalize their hypotheses regarding a signal transduction network. Therefore, we expect that our work will appeal to a broad audience of biologists. The novelty of our algorithmic methodology based on nontrivial combinatorial optimization techniques makes it appealing to computational biologists as well.
在此，我们提出了一种新颖的计算方法及相关软件，用于从单因果证据和双因果证据合成信号转导网络。这是一个重要且热门的问题，因为目前尚无用于构建信号转导网络的高通量实验方法，而且对许多信号传导过程的理解仅限于对信号以及关键介质对整个过程的正向或负向影响的了解。我们的软件NET - SYNTHESIS可从http://www.cs.uic.edu/∼dasgupta/network - synthesis/免费下载。我们的方法是在形式化信号转导网络的逻辑基础方面迈出的重要第一步，使生物学家能够同时整合他们的知识，并将他们关于信号转导网络的假设形式化。因此，我们预计我们的工作将吸引广大生物学家群体。我们基于非平凡组合优化技术的算法方法的新颖性也使其对计算生物学家具有吸引力。
