BACKGROUND
gene regulation is a key factor in gaining a full understanding of molecular biology.
by studying gene regulation, we uncover the mechanisms underlying gene expression, and we learn more about such biological processes as embryonic development and disease pathogenesis.
transcription factors  compose one crucial class of regulator  <cit> .
tfs exercise co-operation in their regulation by forming cis-regulatory modules , which consist of multiple tf-binding sites.
it has been established that most genes are controlled by crms, and genes targeted by the same crm have increased similarity of expression patterns and functions  <cit> .
as such, crms are the most important combinatorial regulators in gene regulation.
in the recent years, another class of regulator, micrornas , has also been discovered.
mirnas are a novel class of non-coding small rnas.
they bind to the 3'-untranslated region  of target transcripts and repress the translation of mrnas or directly degrade them to regulate gene expression at the posttranscriptional level  <cit> .
experimental analysis has established that mirnas have considerable effect on embryonic development, cell growth, cell death and other biological processes  <cit> .
the combinatorial regulation of tfs and mirnas has been widely identified, and it plays a major role in a variety of biological processes  <cit> .
because some tfs regulate the formation of mirnas and some mirnas affect the translation of tfs  <cit> , tfs and mirnas make up a complex regulatory network.
previous studies have investigated the structure of this network and have found that a network motif termed feed-forward loop  is a significant factor in stabilizing the gene regulation mechanism  <cit> .
in the context of ffl, tf and mirna exert effect on each other during their co-regulation.
specifically, ffls have been found to play important roles in many biological processes, such as tumor proliferation and embryo development  <cit> .
experimental analysis has detected that e2f <dig>  a well known tf that controls the cell cycle, interacts with mir-106/93/ <dig>  mir-17- <dig> and mir-106a- <dig>  while, at the same time, these mirnas silence key members of e2f <dig> target gene.
consequently, this ffl balances the proliferation process and plays a crucial role in proliferation  <cit> .
considering the importance of crms in gene regulation, we expand previous findings by developing a new combinatorial regulation paradigm which is formed by crms and mirnas.
we then examined the expression of genes co-regulated by crms and mirnas.
meanwhile, we constructed and investigated the regulatory network composed of crms and mirnas to discover the mechanism underlying their co-regulation and interaction.
furthermore, since mirnas have been found to play a key role in embryonic development  <cit> , we selected some mirnas for detailed analysis of their effect on embryonic development.
RESULTS
we identified crms for mouse developmental genes using a computational method named mopat  <cit> , and we assembled mirnas and their target genes from targetscanmouse  <cit>  .
the effect of the combinatorial regulation of crms and micrornas on gene expressions
we constructed combinatorial regulatory modules for crms and mirnas.
to characterize their co-regulated genes, we examined the gene co-expression patterns  <cit> .
mouse brain development microarray expression datasets were used for this analysis.
previous studies have found that genes targeted by the same crm have significantly similar expression patterns <cit> .
such similarity is characterized by "coherence", which is defined as the mean of the pearson coefficients between all gene pairs in the gene set.
here we characterized the crm and mirna combinatorial regulatory gene set in a similar manner.
for every crm and every mirna, we constructed a new gene set consisting of genes co-regulated by the crm and the mirna and termed it as a crm-mirna module.
note that gene sets with less than two genes were eliminated.
for a crm-mirna module, we calculated the pearson correlations between all gene pairs.
the distribution of the correlations of all crm-mirna modules is displayed in figure  <dig>  compared with the correlations between genes targeted by the same crm or mirna, we found that crm-mirna co-regulated genes have increased similarity of expression patterns .
meanwhile, for each crm-mirna module, we took the mean of the correlations between all gene pairs as its coherence.
similarly, we obtained the coherences of all crms and mirnas.
to compare their coherences, we selected the corresponding crm-mirna modules for each crm and found the mean of their coherence to be significantly higher than the crm's original coherence.
the mean coherences and original coherences of all crms are displayed in figure2a.
the comparison result of mirna is similar .
in other words, in both cases, the mean coherences of crm-mirna modules are higher than the original coherence of either the corresponding crm or mirna.
to evaluate the significance of the coherences, we applied randomization tests to build the background distributions .
note that for each new module, we built two kinds of randomization sets, one randomly assigned crm-mirna co-regulated genes from the crm target gene set , and the other randomly assigned mirna target genes and then selects the co-regulated genes  .
these two backgrounds focus on the comparison of crm-mirna modules with their corresponding crms and mirnas, respectively.
we applied the same examinations on the background coherences, and the mean coherences were shown in figure  <dig>  the results demonstrate that the coherences of real crm-mirna modules are higher than the random sets.
meanwhile, we calculated the p values by repeating the tests  <dig> times , and  <dig>  crm-mirna modules were found to have significant p values .
the results showed that crm and mirna co-regulated genes have significantly similar expression patterns.
thus, it follows that crm-mirna modules, as a combinatorial construct, can provide more insight into gene variation, inspiring us to further investigate the regulatory network formed by such modules.
analysis of regulatory network
based on the above findings, we further investigated the inherent mechanism underlying crm-mirna modules.
similar to previous studies  <cit> , we constructed a gene regulatory network consisting of crms, mirnas, and their target genes.
here a crm was predicted to target a mirna if it had binding sites upstream of the pri-mirna.
meanwhile, a mirna was considered to regulate a crm if it targeted at least one tf in the crm .
we further intensively investigated the structure of the network.
ten combinatorial regulation patterns have been discovered .
we measured their significance by constructing  <dig> random networks and calculating a z-score for each pattern   <cit> .
the first five rows are the patterns with high z-scores.
there are five significant patterns in the regulatory network .
they show that mirnas and crms tend to interact during their regulatory process.
crms control the regulation of mirnas , but mirnas also affect the regulation of crms .
meanwhile, crms and mirnas interact in their co-regulation of target genes .
the last network motifs are typical ffl, which have been found in a wide variety of biological networks.
these network patterns show that genes are generally affected by multiple direct or indirect regulators, and the presence of ffl acts to minimize excessive fluctuations in gene expression, thus contributing to the stability of the whole biological system and robustness against noise  <cit> .
in addition, previous studies have reported that some mirnas locate in a gene's intron  <cit> .
therefore, we further examined host genes of mirnas, and for a majority of mirnas, we found that crms regulated both mirna and host gene .
this strongly suggests that a crm may simultaneously regulate a gene and activate a mirna in its intron.
since this mirna regulates its host gene and forms an ffl, the whole biological process is stabilized.
such network performances support for our previous findings of gene co-expression and display the inherent mechanisms underlying crm-mirna combinatorial regulation.
examples related to embryonic development
mir-154
we selected some mirnas for a detailed analysis.
previous studies demonstrated that mir- <dig> enriches in embryonic tissues and is related to embryonic development  <cit> .
therefore, we focused on mir- <dig> for further investigation.
the co-regulated gene sets formed by mir- <dig> and crms have higher coherences .
this finding demonstrates that genes targeted by crms and mir- <dig> have similar expression patterns, further confirming that mir- <dig> plays an important role in embryonic development.
we then selected the crm consisting of m <dig>  m <dig>  m <dig> for more intensive examination.
based on clustering data , we found that genes targeted by mir- <dig> were apparently classified in the same subset .
tcf3
there are four core tfs in embryonic cells: sox <dig>  tcf <dig>  oct <dig> and nanog  <cit> .
we further explored their combinatorial regulation with mirnas.
since the results were similar, we use tcf <dig> as our example.
we examined gene expressions in mouse embryo cells before and after tcf <dig> depletion  <cit> .
the log ratio of gene expression after tcf <dig> depletion and before treatment was used to measure the expression changing level, and we modeled this level as a linear combination of regulatory effects of tcf <dig> and mirnas <cit> .
a feature-selection procedure was performed , and we finally selected  <dig> mirnas having significant co-regulatory effect with tcf <dig>  nine of them were verified to be targeted by the crms containing tcf <dig> by our previous prediction as well as chip-seq analysis  <cit> .
they are mir- <dig>  mir- <dig>  mir- <dig>  mir- <dig>  mir- <dig>  mir- <dig>  mir- <dig>  mir- <dig> and mir- <dig>  all these mirnas form ffls with the crms containing tcf <dig> .
these findings provided us with further evidence that mirna play an important role in embryonic development.
discussion and
CONCLUSIONS
we have developed a new type of combinatorial regulatory module, which is formed by crms and mirnas.
based on our investigation of gene expressions using this paradigm, we found that genes co-regulated by this crm-mirna module have more similar expression patterns than the genes regulated by either the corresponding crm or mirna.
it verified that mirna could buffering gene expression noise  <cit> .
further investigation led to the discovery of a gene regulatory network consisting of several network motifs, including ffl, which has been found to be essential in a variety of gene networks.
the ffls of crms and mirnas play an important role in many biological processes, including embryonic development  <cit> .
as a result of our assessments, we have gained further insight into the structure of crm-mirna combinatorial regulation, as well as the gene regulatory network in which these elements interact.
moreover, since mirnas were previously demonstrated to affect embryonic development  <cit> , we selected some mirnas related to embryonic development for further analysis.
the performance of mir- <dig> and other mirnas gave us more insight into the effect of crm-mirna co-regulation on embryonic development.
meanwhile, we applied a linear model to characterize in detail the effect of tf and mirna.
it provides more evidence to show that crms and the mirnas have co-regulation.
generally, our study sheds light on the importance of crm-mirna combinatorial regulation, which we demonstrated to be more powerful than either crm or mirna alone in gene regulation.
furthermore, we found that crms and mirnas are likely to form ffl inside their network structure, helping us to gain further understanding of gene regulation.

 gene regulation is a key factor in gaining a full understanding of molecular biology. by studying gene regulation, we uncover the mechanisms underlying gene expression, and we learn more about such biological processes as embryonic development and disease pathogenesis. transcription factors compose one crucial class of regulator. tfs exercise co-operation in their regulation by forming cis-regulatory modules, which consist of multiple tf-binding sites. it has been established that most genes are controlled by crms, and genes targeted by the same crm have increased similarity of expression patterns and functions. as such, crms are the most important combinatorial regulators in gene regulation. in the recent years, another class of regulator, micrornas, has also been discovered. mirnas are a novel class of non-coding small rnas. they bind to the 3'-untranslated region of target transcripts and repress the translation of mrnas or directly degrade them to regulate gene expression at the posttranscriptional level. experimental analysis has established that mirnas have considerable effect on embryonic development, cell growth, cell death and other biological processes. the combinatorial regulation of tfs and mirnas has been widely identified, and it plays a major role in a variety of biological processes. because some tfs regulate the formation of mirnas and some mirnas affect the translation of tfs, tfs and mirnas make up a complex regulatory network. previous studies have investigated the structure of this network and have found that a network motif termed feed-forward loop is a significant factor in stabilizing the gene regulation mechanism. in the context of ffl, tf and mirna exert effect on each other during their co-regulation. specifically, ffls have been found to play important roles in many biological processes, such as tumor proliferation and embryo development. experimental analysis has detected that e2f a well known tf that controls the cell cycle, interacts with mir-106/93/ mir-17- and mir-106a- while, at the same time, these mirnas silence key members of e2f target gene. consequently, this ffl balances the proliferation process and plays a crucial role in proliferation. considering the importance of crms in gene regulation, we expand previous findings by developing a new combinatorial regulation paradigm which is formed by crms and mirnas. we then examined the expression of genes co-regulated by crms and mirnas. meanwhile, we constructed and investigated the regulatory network composed of crms and mirnas to discover the mechanism underlying their co-regulation and interaction. furthermore, since mirnas have been found to play a key role in embryonic development, we selected some mirnas for detailed analysis of their effect on embryonic development.  we identified crms for mouse developmental genes using a computational method named mopat, and we assembled mirnas and their target genes from targetscanmouse. the effect of the combinatorial regulation of crms and micrornas on gene expressions we constructed combinatorial regulatory modules for crms and mirnas. to characterize their co-regulated genes, we examined the gene co-expression patterns. mouse brain development microarray expression datasets were used for this analysis. previous studies have found that genes targeted by the same crm have significantly similar expression patterns. such similarity is characterized by "coherence", which is defined as the mean of the pearson coefficients between all gene pairs in the gene set. here we characterized the crm and mirna combinatorial regulatory gene set in a similar manner. for every crm and every mirna, we constructed a new gene set consisting of genes co-regulated by the crm and the mirna and termed it as a crm-mirna module. note that gene sets with less than two genes were eliminated. for a crm-mirna module, we calculated the pearson correlations between all gene pairs. the distribution of the correlations of all crm-mirna modules is displayed in figure compared with the correlations between genes targeted by the same crm or mirna, we found that crm-mirna co-regulated genes have increased similarity of expression patterns. meanwhile, for each crm-mirna module, we took the mean of the correlations between all gene pairs as its coherence. similarly, we obtained the coherences of all crms and mirnas. to compare their coherences, we selected the corresponding crm-mirna modules for each crm and found the mean of their coherence to be significantly higher than the crm's original coherence. the mean coherences and original coherences of all crms are displayed in figure2a. the comparison result of mirna is similar. in other words, in both cases, the mean coherences of crm-mirna modules are higher than the original coherence of either the corresponding crm or mirna. to evaluate the significance of the coherences, we applied randomization tests to build the background distributions. note that for each new module, we built two kinds of randomization sets, one randomly assigned crm-mirna co-regulated genes from the crm target gene set, and the other randomly assigned mirna target genes and then selects the co-regulated genes. these two backgrounds focus on the comparison of crm-mirna modules with their corresponding crms and mirnas, respectively. we applied the same examinations on the background coherences, and the mean coherences were shown in figure the results demonstrate that the coherences of real crm-mirna modules are higher than the random sets. meanwhile, we calculated the p values by repeating the tests times, and crm-mirna modules were found to have significant p values. the results showed that crm and mirna co-regulated genes have significantly similar expression patterns. thus, it follows that crm-mirna modules, as a combinatorial construct, can provide more insight into gene variation, inspiring us to further investigate the regulatory network formed by such modules. analysis of regulatory network based on the above findings, we further investigated the inherent mechanism underlying crm-mirna modules. similar to previous studies, we constructed a gene regulatory network consisting of crms, mirnas, and their target genes. here a crm was predicted to target a mirna if it had binding sites upstream of the pri-mirna. meanwhile, a mirna was considered to regulate a crm if it targeted at least one tf in the crm. we further intensively investigated the structure of the network. ten combinatorial regulation patterns have been discovered. we measured their significance by constructing random networks and calculating a z-score for each pattern. the first five rows are the patterns with high z-scores. there are five significant patterns in the regulatory network. they show that mirnas and crms tend to interact during their regulatory process. crms control the regulation of mirnas, but mirnas also affect the regulation of crms. meanwhile, crms and mirnas interact in their co-regulation of target genes. the last network motifs are typical ffl, which have been found in a wide variety of biological networks. these network patterns show that genes are generally affected by multiple direct or indirect regulators, and the presence of ffl acts to minimize excessive fluctuations in gene expression, thus contributing to the stability of the whole biological system and robustness against noise. in addition, previous studies have reported that some mirnas locate in a gene's intron. therefore, we further examined host genes of mirnas, and for a majority of mirnas, we found that crms regulated both mirna and host gene. this strongly suggests that a crm may simultaneously regulate a gene and activate a mirna in its intron. since this mirna regulates its host gene and forms an ffl, the whole biological process is stabilized. such network performances support for our previous findings of gene co-expression and display the inherent mechanisms underlying crm-mirna combinatorial regulation. examples related to embryonic development mir-154 we selected some mirnas for a detailed analysis. previous studies demonstrated that mir- enriches in embryonic tissues and is related to embryonic development. therefore, we focused on mir- for further investigation. the co-regulated gene sets formed by mir- and crms have higher coherences. this finding demonstrates that genes targeted by crms and mir- have similar expression patterns, further confirming that mir- plays an important role in embryonic development. we then selected the crm consisting of m m m for more intensive examination. based on clustering data, we found that genes targeted by mir- were apparently classified in the same subset. tcf3 there are four core tfs in embryonic cells: sox tcf oct and nanog. we further explored their combinatorial regulation with mirnas. since the results were similar, we use tcf as our example. we examined gene expressions in mouse embryo cells before and after tcf depletion. the log ratio of gene expression after tcf depletion and before treatment was used to measure the expression changing level, and we modeled this level as a linear combination of regulatory effects of tcf and mirnas. a feature-selection procedure was performed, and we finally selected mirnas having significant co-regulatory effect with tcf nine of them were verified to be targeted by the crms containing tcf by our previous prediction as well as chip-seq analysis. they are mir- mir- mir- mir- mir- mir- mir- mir- and mir- all these mirnas form ffls with the crms containing tcf. these findings provided us with further evidence that mirna play an important role in embryonic development. discussion and  we have developed a new type of combinatorial regulatory module, which is formed by crms and mirnas. based on our investigation of gene expressions using this paradigm, we found that genes co-regulated by this crm-mirna module have more similar expression patterns than the genes regulated by either the corresponding crm or mirna. it verified that mirna could buffering gene expression noise. further investigation led to the discovery of a gene regulatory network consisting of several network motifs, including ffl, which has been found to be essential in a variety of gene networks. the ffls of crms and mirnas play an important role in many biological processes, including embryonic development. as a result of our assessments, we have gained further insight into the structure of crm-mirna combinatorial regulation, as well as the gene regulatory network in which these elements interact. moreover, since mirnas were previously demonstrated to affect embryonic development, we selected some mirnas related to embryonic development for further analysis. the performance of mir- and other mirnas gave us more insight into the effect of crm-mirna co-regulation on embryonic development. meanwhile, we applied a linear model to characterize in detail the effect of tf and mirna. it provides more evidence to show that crms and the mirnas have co-regulation. generally, our study sheds light on the importance of crm-mirna combinatorial regulation, which we demonstrated to be more powerful than either crm or mirna alone in gene regulation. furthermore, we found that crms and mirnas are likely to form ffl inside their network structure, helping us to gain further understanding of gene regulation. 
in recent years, a novel regulator known as microrna has been found to play an important role in gene regulation. investigating its structure, we found that the feed forward loop is a significant network motif, which plays an important role in gene regulation. based on the co-regulation of crms and mirnas, we constructed a novel combinatorial regulatory network which was found to play an important role in gene regulation, particularly during embryonic development. gene regulation is a key factor in gaining a full understanding of molecular biology. cis-regulatory modules, consisting of multiple transcription factor binding sites, have been confirmed as the main regulators in gene expression. furthermore, we constructed a regulatory network composed of crms, mirnas, and their target genes. in addition, we further analyzed the effect of mirnas in embryonic cells, and we found that mir- as well as some other mirnas, have significant co-regulation effect with crms in embryonic development. by analyzing their effect on gene expression profiles, we found that genes targeted by both crms and mirnas express in a significantly similar way.
 gene regulation is a key factor in gaining a full understanding of molecular biology. cis-regulatory modules, consisting of multiple transcription factor binding sites, have been confirmed as the main regulators in gene expression. in recent years, a novel regulator known as microrna has been found to play an important role in gene regulation. meanwhile, transcription factor and microrna co-regulation has been widely identified. thus, the relationships between crms and micrornas have generated interest among biologists.  we constructed new combinatorial regulatory modules based on crms and mirnas. by analyzing their effect on gene expression profiles, we found that genes targeted by both crms and mirnas express in a significantly similar way. furthermore, we constructed a regulatory network composed of crms, mirnas, and their target genes. investigating its structure, we found that the feed forward loop is a significant network motif, which plays an important role in gene regulation. in addition, we further analyzed the effect of mirnas in embryonic cells, and we found that mir- as well as some other mirnas, have significant co-regulation effect with crms in embryonic development.  based on the co-regulation of crms and mirnas, we constructed a novel combinatorial regulatory network which was found to play an important role in gene regulation, particularly during embryonic development. 