Journal of Theoretical Biology

【Journal of Theoretical Biology】介绍
英文名称:Journal of Theoretical Biology
  • 简介:J THEOR BIOL 杂志属于生物行业,“数学与计算生物学”子行业的优秀级杂志(MedugO/99 


  • 数学与计算生物学类别期刊排行榜
  • 投稿难度MedSci智能评价:影响因子不是很高,与此细分类别影响因子普遍偏低有关,但不代表容易投中,文章仍然需要一定的水平"_soL1C?
  • 审稿速度:较快,2-4周_yb#OS%.Un
  • 级别/热度:暗红
  • MednGHz?€D19N?



  1. The community structure of human cellular signaling network

  2. Journal of Theoretical Biology,2007 Aug 21;247(4):608-615

    YuanBo DIAO  , Menglong Li*  , Fang Zheng 


    Living cell is highly responsive to specific chemicals in its environment, such as hormones and molecules in food or aromas. The reason is ascribed to the existence of widespread and diverse signal transduction pathways, between which crosstalks usually exist, thus constitute a complex signaling network. Evidently, knowledge of topology characteristic of this network could contribute a lot to the understanding of diverse cellular behaviors and life phenomena thus come into being. In this presentation, signal transduction data is extracted from KEGG to construct a cellular signaling network of Homo sapiens, which has 931 nodes and 6798 links in total. Computing the degree distribution, we find it is not a random network, but a scale-free network following a power-law of P(K) approximately K(-gamma), with gamma approximately equal to 2.2. Among three graph partition algorithms, the Guimera's simulated annealing method is chosen to study the details of topology structure and other properties of this cellular signaling network, as it shows the best performance. To reveal the underlying biological implications, further investigation is conducted on ad hoc community and sketch map of individual community is drawn accordingly. The involved experiment data can be found in the supplementary material.

  3. SecretP: Identifying bacterial secreted proteins by fusing new features into Chou’s pseudo-amino acid composition

  4. Journal of Theoretical Biology,(2010)1–6

    LeZheng Yu  , Yanzhi Guo  , Yizhou Li  , Menglong Li*  , Jiesi Luo  , WenJia Xiong  , WenLi Qin 


    Protein secretion plays an important role in bacterial lifestyles. Secreted proteins are crucial for bacterial pathogenesis by making bacteria interact with their environments, particularly delivering pathogenic and symbiotic bacteria into their eukaryotic hosts. Therefore, identification of bacterial secreted proteins becomes an important process for the study of various diseases and the corresponding drugs. In this paper, fusing several new features into Chou’s pseudo-amino acid composition (PseAAC), two support vector machine (SVM)-based ternary classifiers are developed to predict secreted proteins of Gram-negative and Gram-positive bacteria. For the two types of bacteria, the high accuracy of 94.03% and 94.36% are obtained in distinguishing classically secreted, non-classically secreted and non-secreted proteins by our method. In order to compare the practical ability of our method in identifying bacterial secreted proteins with those of six published methods, proteins in Escherichia coli and Bacillus subtilis are collected to construct the test sets of Gram-negative and Gram-positive bacteria, and the prediction results of our method are comparable to those of existing methods. When performed on two public independent data sets for predicting NCSPs, it also yields satisfactory results for Gram-negative bacterial proteins. The prediction server SecretP can be accessed at

  5. Using the augmented Chou‘s pseudo amino acid composition for predicting protein submitochondria locations based on auto covariance approach.

  6. Journal of Theoretical Biology,(2009)366–372

    Yuhong Zeng  , Yanzhi Guo  , Rongquan Xiao  , Li Yang  , LeZheng Yu  , Menglong Li


    The submitochondria location of a mitochondrial protein is very important for further understanding the structure and function of this protein. Hence, it is of great practical significance to develop an automated and reliable method for timely identifying the submitochondria locations of novel mitochondrial proteins. In this study, a sequence-based algorithm combining the augmented Chou's pseudo amino acid composition (Chou's PseAA) based on auto covariance (AC) is developed to predict protein submitochondria locations and membrane protein types in mitochondria inner membrane. The model fully considers the sequence-order effects between residues a certain distance apart in the sequence by AC combined with eight representative descriptors for both common proteins and membrane proteins. As a result of jackknife cross-validation tests, the method for submitochondria location prediction yields the accuracies of 91.8%, 96.4% and 66.1% for inner membrane, matrix, and outer membrane, respectively. The total accuracy is 89.7%. When predicting membrane protein types in mitochondria inner membrane, the method achieves the prediction performance with the accuracies of 98.4%, 64.3% and 86.7% for multi-pass inner membrane, single-pass inner membrane, and matrix side inner membrane, where the total accuracy is 93.6%. The overall performance of our method is better than the achievements of the previous studies. So our method can be an effective supplementary tool for future proteomics studies. The prediction software and all data sets used in this article are freely available at