Temporal gene expression profiles have been widely considered to uncover the mechanism of cancer development and progression. Gene expression patterns, however, have been analyzed for limited stages with small samples, without proper data pre-processing, in many cases. With those approaches, it is difficult to unveil the mechanism of cancer development over time. In this study, we analyzed gene expression profiles of two independent colorectal cancer sample datasets, each of which contained 556 and 566 samples, respectively. To find specific gene expression changes according to cancer stage, we applied the linear mixed-effect regression model (LMER) that controls other clinical variables. Based on this methodology, we found two types of gene expression patterns: continuously increasing and decreasing genes as cancer develops. We found that continuously increasing genes are related to the nervous and developmental system, whereas the others are related to the cell cycle and metabolic processes. We further analyzed connected sub-networks related to the two types of genes. From these results, we suggest that the gene expression profile analysis can be used to understand underlying the mechanisms of cancer development such as cancer growth and metastasis. Furthermore, our approach can provide a good guideline for advancing our understanding of cancer developmental processes.

There are fundamental limitations in inferring the functional interaction structure of a gene (regulatory) network only from sequence information such as binding motifs. To overcome such limitations, various approaches have been developed to infer the functional interaction structure from expression profiles. However, most of them have not been so successful due to the experimental limitations and computational complexity. Hence, there is a pressing need to develop a simple but effective methodology that can systematically identify the functional interaction structure of a gene network from time-series expression profiles. In particular, we need to take into account the different time delay effects in gene regulation since they are ubiquitously present. We have considered a new experiment that measures the overall expression changes after a perturbation on a specific gene. Based on this experiment, we have proposed a new inference method that can take account of the time delay induced while the perturbation affects its primary target genes. Specifically, we have developed an algebraic equation from which we can identify the subnetwork structure around the perturbed gene. We have also analyzed the influence of time delay on the inferred network structure. The proposed method is particularly useful for identification of a gene network with small variations in the time delay of gene regulation.

Biological systems are known to be both robust and evolvable to internal and external perturbations, but what causes these apparently contradictory properties? We used Boolean network modeling and attractor landscape analysis to investigate the evolvability and robustness of the human signaling network. Our results show that the human signaling network can be divided into an evolvable core where perturbations change the attractor landscape in state space, and a robust neighbor where perturbations have no effect on the attractor landscape. Using chemical inhibition and overexpression of nodes, we validated that perturbations affect the evolvable core more strongly than the robust neighbor. We also found that the evolvable core has a distinct network structure, which is enriched in feedback loops, and features a higher degree of scale-freeness and longer path lengths connecting the nodes. In addition, the genes with high evolvability scores are associated with evolvability-related properties such as rapid evolvability, low species broadness, and immunity whereas the genes with high robustness scores are associated with robustness-related properties such as slow evolvability, high species broadness, and oncogenes. Intriguingly, US Food and Drug Administration-approved drug targets have high evolvability scores whereas experimental drug targets have high robustness scores.

MOTIVATION: Viewing a cellular system as a collection of interacting parts can lead to new insights into the complex cellular behavior. In this study, we have investigated aryl hydrocarbon receptor (AhR) signal transduction pathway from such a system-level perspective. AhR detects various xenobiotics, such as drugs or endocrine disruptors (e.g. dioxin), and mediates transcriptional regulation of target genes such as those in the cytochrome P450 (CYP450) family. On binding with 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), however, AhR becomes abnormally activated and conveys toxic effects on cells. Despite many related studies on the TCDD-mediated toxicity, quantitative system-level understanding of how TCDD-mediated toxicity generates various toxic responses is still lacking. RESULTS: Here, we present a manually curated TCDD-mediated AhR signaling pathway including crosstalks with the hypoxia pathway that copes with oxygen deficiency and the p53 pathway that induces a DNA damage response. Based on the integrated pathway, we have constructed a mathematical model and validated it through quantitative experiments. Using the mathematical model, we have investigated: (i) TCDD dose-dependent effects on AhR target genes; (ii) the crosstalk effect between AhR and hypoxia signals; and (iii) p53 inhibition effect of TCDD-liganded AhR. Our results show that cellular intake of TCDD induces AhR signaling pathway to be abnormally up-regulated and thereby interrupts other signaling pathways. Interruption of hypoxia and p53 pathways, in turn, can incur various hazardous effects on cells. Taken together, our study provides a system-level understanding of how AhR signal mediates various TCDD-induced toxicities under the presence of hypoxia and/or DNA damage in cells.

MOTIVATION: Spatio-temporal regulation of gene expression is an indispensable characteristic in the development processes of all animals. 'Master switches', a central set of regulatory genes whose states (on/off or activated/deactivated) determine specific developmental fate or cell-fate specification, play a pivotal role for whole developmental processes. In this study on genome-wide integrative network analysis the underlying design principles of developmental gene regulatory networks are examined. RESULTS: We have found an intriguing design principle of developmental networks: hub nodes, genes with high connectivity, equipped with positive feedback loops are prone to function as master switches. This raises the important question of why the positive feedback loops are frequently found in these contexts. The master switches with positive feedback make the developmental signals more decisive and robust such that the overall developmental processes become more stable. This finding provides a new evolutionary insight: developmental networks might have been gradually evolved such that the master switches generate digital-like bistable signals by adopting neighboring positive feedback loops. We therefore propose that the combined presence of positive feedback loops and hub genes in regulatory networks can be used to predict plausible master switches. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Gene regulatory networks (GRNs) govern cellular differentiation processes and enable construction of multicellular organisms from single cells. Although such networks are complex, there must be evolutionary design principles that shape the network to its present form, gaining complexity from simple modules. RESULTS: To isolate particular design principles, we have computationally evolved random regulatory networks with a preference to result either in hysteresis (switching threshold depending on current state), or in multistationarity (having multiple steady states), two commonly observed dynamical features of GRNs related to differentiation processes. We have analyzed the resulting evolved networks and compared their structures and characteristics with real GRNs reported from experiments. CONCLUSION: We found that the artificially evolved networks have particular topologies and it was notable that these topologies share important features and similarities with the real GRNs, particularly in contrasting properties of positive and negative feedback loops. We conclude that the structures of real GRNs are consistent with selection to favor one or other of the dynamical features of multistationarity or hysteresis. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.