"The differences between both approaches are not technical but rather philosophical, given that both are committed to mathematical modeling. The former uses bottom-up reductionistic approaches, the latter uses more "organicist" approaches that take into consideration bottom-up and top-down causality [31-33]. The tools of mathematical modeling and computer simulation, guided by a sound epistemological foundation, have the potential of providing the means to address the widening intellectual vacuum both theoretically and pragmatically. "
[Show abstract][Hide abstract] ABSTRACT: At the beginning of the 21st century cancer research has reached an impasse similar to that experienced in developmental biology in the first decades of the 20th century when conflicting results and interpretations co-existed for a long time until these differences were resolved and contradictions were eliminated. In cancer research, instead of this healthy "weeding-out" process, there have been attempts to reach a premature synthesis, while no hypothesis is being rejected. Systems Biology could help cancer research to overcome this stalemate by resolving contradictions and identifying spurious data. First, in silico experiments should allow cancer researchers to be bold and a priori reject sets of data and hypotheses in order to gain a deeper understanding of how each dataset and each hypothesis contributes to the overall picture. In turn, this process should generate novel hypotheses and rules, which could be explored using these in silico approaches. These activities are significantly less costly and much faster than "wet-experiments". Consequently, Systems Biology could be advantageously used both as a heuristic tool to guide "wet-experiments" and to refine hypotheses and test predictions.
Cancer Cell International 03/2012; 12(1):12. DOI:10.1186/1475-2867-12-12 · 1.99 Impact Factor
"To study individual biological components alone is not sufficient to discover the rules underlying complex biological systems. Therefore, a systems level study of genetic networks and identification of differentially expressed genetic networks holds the key to unraveling the relationship between genotype and phenotype (Xiong, et al., 2004; Khalil and Hill, 2005; Lu et al., 2005). The ideal statistics for testing differentially expressed genetic networks should have high power while keeping false positive rates at a specified level. "
[Show abstract][Hide abstract] ABSTRACT: One of the recently developed statistics for identifying differentially expressed genetic networks is Hotelling T2 statistic, which is a quadratic form of difference in linear functions of means of gene expressions between two types of tissue samples, and so their power is limited.
To improve the power of test statistics, a general statistical framework for construction of non-linear tests is presented, and two specific non-linear test statistics that use non-linear transformations of means are developed. Asymptotical distributions of the non-linear test statistics under the null and alternative hypothesis are derived. It has been proved that under some conditions the power of the non-linear test statistics is higher than that of the T2 statistic. Besides theory, to evaluate in practice the performance of the non-linear test statistics, they are applied to two real datasets. The preliminary results demonstrate that the P-values of the non-linear statistics for testing differential expressions of the genetic networks are much smaller than those of the T2 statistic. And furthermore simulations show the Type I errors of the non-linear statistics agree with the threshold used and the statistics fit the chi2 distribution.
Supplementary data are available on Bioinformatics online.
[Show abstract][Hide abstract] ABSTRACT: One of the important goals of cancer research is to understand the nature of gene expression regulation and biological pathways
and to apply this knowledge to find the mechanism by which small drug molecules interfere with the biological system through
interactions with gene products and pathways. We have utilized the gene expression and small molecule screening data available
at the National Cancer Institute (NCI) for 60 immortalized cell lines representing a range of major cancers. This extensive
data set potentially contains the complete information necessary to understand and target cancer cells. In our experience
it is most fruitful to adopt systems biology and pharmacogenomic approaches to deconvolute the necessary chemistry and biology
in order to conduct a rational anti-cancer drug design effort. In this undertaking, existing biological pathway and gene expression
information is merged with drug chemosensitivity data to both elucidate a drug’s mechanism of action and to find cancer-specific
targets. This framework offers a rational design strategy to mine novel anti-cancer candidates that are both potent and show
specificity to targets in cancer pathways.
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