1 Introduction
One of the most challenging research problems for System Biology nowadays is the inference (or reverseengineering) of gene regulatory networks (GRNs) from expression profiles. This research issue became important after the debioinformaticsvelopment of highthroughput technologies for extraction of gene expressions (mRNA abundances or transcripts), such as DNA microarrays [1] or SAGE [2], and more recently RNASeq [3]. This problem regards revealing regulatory relationships between biological molecules in order to recover a complex network of interrelationships, which can describe not just diverse biological functions but also the dynamics of molecular activities. It is very important to understand how many biological processes happen and in most cases, how to prevent it from happening (diseases).
In the context of expression profiles, a big challenge that researchers need to face is the large number of variables or genes (thousands) for just a few experiments available (dozens). In order to infer relationships among those variables, it is needed a great effort in developing novel computational and statistical techniques that are able to alleviate the intrinsic error estimation committed in the presence of small number of samples with huge dimensionalities.
In general, it is not possible to recover the GRNs very accurately. The main reasons for this are the lack of information about the biological organism, the high complexity of the networks and the intrinsic noise of the expression measurements. In this context, there are several recent initiatives to overcome such limitations by incorporating other information on the inference/prediction method. One kind of initiative is the use of the functional gene information, e.g., from the Gene Ontology, Proteome, KEGG, among others, into the clustering process, resulting in more biologically meaningful clusters [4, 5, 6]. Another alternative is by using biological information for the discovery of transcriptional regulation relationships, i.e., to infer GRNs [7, 8, 9]. A variety of biological data integration techniques for GRNs inference are described in [10, 11, 12, 13].
Although the integration of biological information with mathematical models is critically important in discovering novel biological knowledge, it is restricted by the prior biological information of each gene or biological entity. One way to use prior information and make the methods less restrictive is the use of information about local or global prior knowledge of an organism instead of an information about a single gene, e.g., to use the network structure/topology as prior information. In this way, the integrated use of multiple data types together with local and global topological properties could be decisive for the effective prediction of GRNs and their functions in face of the known limitations [10, 14, 15, 16, 17].
The analysis of local and global biological network properties and its application on the inference process is very recent and promising [18, 19, 11, 20, 21]. For example, the application of network structure by the inference methods makes use of similarities of connected network modules [22], structural and graphtheoretic interpretation for the network components [23, 17], taking into account the network sparseness [24, 25, 26], gene network motifs [27] and the search for cliques in network graphs [28]. More important, biological networks and particularly GRNs are known not only to be sparse, but also organized, so as nodes belonging to different connectivity classes [29]. These examples show the importance of such problem and the need for new methodologies to overcome it.
The information about network topology can help the investigation of biological process by adopting the complex network theory and its properties [30, 31, 32, 33]. It is known that many kinds of relationships can be successfully described by complex networks. In particular, the complex networks theory describes various types of network topologies, each one with well defined properties, and it has been broadly applied to characterize biological processes and gene relationships involved. Some biological networks, for example, were shown to present the scalefree property, in which many nodes have a low degree and a few of them have a high degree (hubs), in which the degree distribution is approximated by a powerlaw distribution [34, 35, 36, 37, 38, 33, 39]. In general, topological patterns and its structural analysis is one of the most promising topics in the analysis of complex networks [40, 41], and particularly the application of structural properties of the network can be a very valuable prior information to be used by the GRNs inference methods.
The use of network topology models for the simulation and analysis of GRNs have been recently described in [42] and the same models are further explored in the present paper. In this work, a new method is proposed for the inference of GRNs from expression profiles by incorporating a scalefree topology and applying a conditional dependency criterion function. Thus, it is suggested a method that takes the scalefree topology into account as prior information in the inference process. This leads to a better inference of networks presenting scalefree property. The main purpose of this paper is to show the interest of taking into account information about the scalefree topology of the network in order to improve the inference process and make it more suitable for a class of problems, i.e., scalefree networks.
The inference process is conducted by observing the conditional dependence of a target gene given its potential predictors through temporal expression profiles, and by applying the mean conditional entropy as criterion function [43, 44, 45, 46]. This process has been recognized as an appropriate statistical tool to model direct interactions between genes [29].
The main contribution of this work is the proposal of a new feature selection method for GRNs inference from temporal expression profiles. Our inference method is based on a previous feature selection algorithm [47, 48], with the inclusion of scalefree topology as a prior information, in which the search space traversed is relatively small and provides encouraging results.
Next sections (Sections 2 and 3) introduce a brief background on the complex network theory and the network inference problem. In Section 4, the feature selection problem is discussed in more detail, including a short description of the SFS and SFFS techniques. Section 4.3 discusses the intrinsically multivariate prediction issue and how it can affect the greedy feature selection algorithms in such way that the achieved solution may be relatively far away from the optimal. Section 6 describes our proposed feature selection method (SFFSBA). Section 7 shows some experimental results. Finally, Section 8 concludes the work, discussing future perspectives.
2 Complex Networks Theory and Biological Networks
The genes of a network can be characterized by its degree, i.e., the number of connections with other genes of these network that it has. By considering directed networks, there are two kinds distinct relationships. The indegree is the number of directed connections received by a gene. The outdegree is the number of directed connections edges sent by a gene. The individual gene degrees can be used to estimate the degree distribution and as a result, characterize the whole network, i.e., a global network property.
The uniformlyrandom ErdösRényi (ER) [49] complex network model is based on randomly connected vertices. This model assume the hypothesis that complex systems are connected at random, leading to a Poisson degree distribution with peak at the average degree , indicating that the most of the genes have a degree close to . In other works, the ER model has a statistically homogeneous degree distribution [38].
On the other hand, the scalefree network structure proposed by Barabási and Albert [50] (BA), is based on a heterogeneous distribution on its vertex degree, in which few genes have a large number of connections and the most of genes have few connections. The absence of a typical degree led to this complex network model to be described as “scalefree” [38]
. More specifically, the scalefree structural property is characterized by a powerlaw in its connections (degree) distribution. In other words, the probability
of a gene to interact with other genes decays as a power law(1) 
in which is a numerical constant.
In general, numerous networks, such as the Internet, human collaboration networks and metabolic networks, follow a scalefree structure [38]. In particular, most known biological networks present a scalefree structure [33], implying that their distribution on its gene relationships (degree ) is irregular, a large number of connections (edges) is concentrated on a small number of genes, while large number of genes have few connections. In this case, scalefree networks have a high probability of exhibiting hubs [50].
In particular, by considering the transcriptional regulations of the Saccharomyces cerevisiae [35], it was found a close relation between proteins and genes which presents a degree distribution with an exponential decay very similar to a power law. In the work developed by Farkas et al. [36], it was found an overlap between the connectivity distribution of scalefree and Saccharomyces cerevisiae transcriptome networks. In such work it was suggested a potential regulatory relationship among its genes, in which a small number of transcription factors are responsible for a complex set of expression patterns under diverse conditions.
Regarding the constant decay , it was reported that scalefree networks describe the Escherichia coli metabolic networks and its metabolic reactions follows a powerlaw, with [34]. It is also known that the probability of a given yeast protein to interact with other yeast proteins follows a powerlaw, with [51, 52]. In general, the degree exponent is usually in the range [31, 38]. In summary, the scalefree complex network model has been effectively used to simulate and describe the behavior of biological networks [33, 34, 35, 36, 37, 38, 39].
3 GRN Inference
The combination of expression analysis, perturbations, treatments and gene mutations may indicate information about molecular or specific functions of the genes. Gene regulatory networks (GRN) inference from expression data, a process also known as reverse engineering, is a difficult computational task due to the huge data volume (number of genes or expression profiles) and to the small number of available samples, including the large complexity of biological networks, thus representing an important challenge in bioinformatics and computational biology researches [53].
The GRNs inference from temporal expression data tries to identify the variation of the expression levels along time, becoming possible to indicate information such as metabolic pathways, cell cycle and mapping of modifications caused by stimuli. It can be used as a model for functional representation of gene interactions.
It is important to highlight that the network inference has the objective of discovering interaction networks between genes that are potentially interesting from the biological point of view. The relationships among genes are suggested according to some estimator and can be examined or validated by wetlab experiments. As such experiments have a high cost in terms of financial, human and time resources, the main idea is to offer to the specialists a reduced number of hypotheses that satisfactorily identify a certain phenomenon of interest.
There are several approaches for modeling and identification of GRNs. Examples include Boolean Networks [54] and its stochastic version (Probabilistic Boolean Networks) [55], Differential Equations [56]
[57], to name but a few. This work focuses on the Probabilistic Boolean Networks (PBN) model, since it captures global properties of GRNs while dealing well with settings presenting limited number of samples.Regarding the feature selection approaches to infer GRNs, there are mainly three types of criterion functions. The correlation based criterion functions are those that measure 1to1 relationships, often employed to identify coregulation between genes, functional modules and clusters [58]
. It does not take into account multivariate relationships, i.e., the expression of a given target being regulated by a set of two or more genes with multivariate interaction. Bayesian error estimation based criterion functions evaluate the estimated errors present in the joint probability distribution of a target gene given its candidate predictor genes
[59, 60, 61], which is capable to detect multivariate (Nto1) relationships. Finally, information theory based criterion functions are used to detect 1to1 and Nto1 relationships [62, 63, 64, 45, 65], relying on the uniformity of the conditional probability distributions of the target given the candidate predictors as a whole (larger uniformity leads to higher entropy, which in turn leads to smaller mutual information).
4 Feature Selection
Pattern recognition methods allow the classification of objects, i.e., a class or label is assigned to each object based on its features. In many applications, and specifically in GRN inference, the feature space dimension of such objects tends to be very high while the number of samples is very limited. In this context, the study and development of techniques for dimensionality reduction become mandatory.
Feature selection is a possible approach to perform dimensionality reduction. A feature selection method looks for subsets of features that lead to a good representation, classification or prediction of the objects classes. It is composed by two main parts: a search algorithm and a criterion function which assigns a quality value to the feature subsets.
The only way to guarantee optimality of the solution is to investigate the entire space of possible subsets (exhaustive search), although depending on some criterion function constraints adopted (e.g. monotonical or Ushaped), it is possible to reach the optimal solution by searching for a constrained subset space by applying “branchandbound” methods [70, 71]
. The exhaustive search is computationally unfeasible in general, and especially for inference of GRNs which involves data with thousands of features (genes). There are many heuristics proposed for feature selection. Two classical feature selection heuristics are briefly introduced and discussed below.
4.1 Sequential Forward Selection (SFS)
The Sequential Forward Selection (SFS) is a genuinely greedy feature selection algorithm in the sense that it includes the best feature according to the criterion function in each step. It starts with an empty set (bottomup approach) and adds the best individual feature to the partial solution. In the next step, it looks for the best feature that, jointly with the feature already included in the partial solution, forms the best pair. This process continues until a stop condition is satisfied, which normally is based on a fixed dimension value (cardinality of the subset to be returned) or based on the variation of the criterion function from the previous to the next step. A variant of this algorithm is the Sequential Backward Selection (SBS) which starts with the full feature set and eliminates the least relevant feature according to the criterion function (topdown approach), repeating this process successively until a stop condition is satisfied [47].
The greedy algorithms such as SFS and SBS present a drawback known as nesting effect. This effect occurs because the discarded features by using the topdown approach are never inserted again to the partial solution, and the inserted features in the bottomup approach are never discarded from the partial solution. Section 4.3 presents the reason why this phenomenon occurs.
4.2 Sequential Floating Forward Selection (SFFS)
The Sequential Floating Forward Selection (SFFS) algorithm tries to alleviate the nesting effect by allowing the inclusion and exclusion of features in the partial solution in a floating way, i.e., without requiring the definition of the number of insertions or exclusions [47]. It starts with an empty set (cardinality ). The SFS algorithm is applied until . For , the SBS algorithm is applied in order to exclude bad features. The SFFS applies alternately the SFS and SBS until a stop condition is reached. The best solution of each cardinality is stored in a list. The best solution among them is selected as the returned solution of the algorithm and, in case of ties, the solution with the smallest cardinality is returned. A schematic flowchart of the SFFS algorithm is presented in Figure 1.
SFFS is renowned for presenting an excellent costbenefit in terms of the computational complexity and the quality of the returned solution. There are some variants of this algorithm (adaptive and generalized floating search methods) that try to improve the SFFS solutions at the expense of an increase on the computational cost. However, they can not avoid the nesting effect completely [72].
4.3 Intrinsically Multivariate Prediction
This section briefly discusses one of the main reasons why the feature selection heuristics do not guarantee the optimum solution. A target feature is intrinsically multivariate predicted (IMP) by its predictor feature set if all predictors combined greatly predicts the target behavior, while every properly contained subset of the mentioned predictor set has an almost null prediction power regarding the target. Formally, a set of features is intrinsically multivariate predictive for the target feature with respect to and , for and , if
(2) 
where is a criterion function that varies from 0 to 1 (0 meaning absence of prediction and 1 meaning full prediction) [73]. The parameters and usually assume small (less than 0.2) and large (greater than 0.8) values, respectively, to reflect the IMP property. Additionally, an intrinsically multivariate predictiveness degree (IMP score) through the maximum value of can be defined as:
(3) 
Considering Boolean features, the deterministic exclusiveor binary logic (XOR) is an example of an IMP set when two predictors may assume any value from with uniform probability distribution. In this case, it is impossible to predict the target based on the observation of just one of its predictors, since the target can assume the value 0 or 1 regardless of the values of each individual predictor. Of course when the predictors are combined, the prediction is perfect for every instance from (the prediction is given by the XOR logic).
The nesting effect occurs in most feature selection algorithms and can be explained by the IMP phenomenon. Considering an IMP set, its individual features (or subsets of features) are not good to predict the target, so they hardly will be included in the partial solution of a given suboptimal feature selection. However, an optimum solution can be formed by such features together (large IMP score), which implies that the considered feature selection probably does not reach this solution. Besides, two good individual predictors may not lead to a very good pair, since they have a relatively high correlation with the target feature, meaning that they have high correlation with each other. Section 6 shows that the method proposed here can eventually return IMP sets as solutions when its IMP score is moderately large, i.e., each individual feature has little, but not null, contribution in predicting the target feature, and also when the cardinality of such IMP sets is not very high (in this case, any good predictor set with large cardinality would not be returned due to the high estimation error performed when evaluating large dimensionality sets).
5 Probabilistic Genetic Networks
Probabilistic Genetic Networks (PGN) is a model proposed to represent GRNs. PGNs are based on PBNs, in which the selection of the transition function is not deterministic and the states of the genes and networks are represented by discrete values. PGNs describe a finite dynamical system, discrete in time, composed by a finite number of states, in which each transcript is represented by a variable. The composition of all variables form a vector considered the system state. Each vector component has an associated transition function which calculates its next value from the previous state of other genes (predictors). These functions are components of a transition functions vector, which defines the transition from a network state to the next state and represents the gene regulation mechanism
[45].In this model, the gene expression networks are represented as a stochastic process ruled by a Markov chain. In other words, assuming this principle means that a conditional probability of a future event, given the previous events, depends only on the immediately previous event. A Markov chain is characterized by a transition matrix
of conditional probabilities among states, and its elements are denoted by , and a vector of initial states . A PGN is a Markov chain in which some axioms are assumed [45]:
the transition matrix is homogeneous, i.e., is not a function of . The state transition probability is constant;

, i.e., all state pairs can be reached (ergodic Markov chain);

the transition matrix is conditionally independent, i.e., for every state pair , ;

is quasideterministic, i.e., for every state , exists a state such that .
Theses axioms are motivated by biological phenomena or simplifications due to the lack of samples for model estimation. The first axiom is a constraint to simplify the estimation problem. The second axiom states that all states are reachable, i.e., the presence of perturbation or noise can eventually lead the system to any state. The third axiom determines that a gene expression at a given time instant is independent of the expression of other genes at the same instant . The last axiom says that the system has a structural dynamics which is prone to small noises [45].
6 SFFS with Structural Properties (SFFSBA)
The proposed method is based on the Probabilistic Genetic Networks (PGN), which is described in [45] and in Section 5. The proposed method considers the four axioms established by the PGN model and proposes two new constraints:

For every target gene , by adding a new predictor in the result set with cardinality , there should be an information gain in the prediction of the target gene, whenever the chosen predictor is part of the true result set. If the information gain by adding a new predictor in the result set is poor, the predictor could not be part of the true result set or there is no data enough to make the prediction. In both cases, the inclusion of the predictor in the result set should be avoided.

The network topology follows a powerlaw in its connections distribution, i.e., a scalefree network structure such as described in Section 2.
By assuming the PGN model and these new constraints, the main contribution of our method is to include structural information as a prior knowledge to perform a search on a reduced space, thus achieving better results.
The idea is based on the assumption that a gene with no predictors tends to have a random behavior, while a gene with predictors tends to have a more ordered behavior. In this way, it is possible to expect that a source gene in a GRN (i.e., with no predictors) presents a behavior with small variations in the criterion function on trying to identify a possible predictor to it. In other words, small variations are expected on the criterion function values by adding new predictors on the result set of a source, as shown by Figure 2 (Source).
On the other hand, when a gene has predictors, it is expected a distinct behavior, mainly on trying a possible predictor that is part of the result set (true positive). In this way, it is expected some significant variation on criterion function values when performing a search for possible predictors, as showed by Figure 2 (Normal and IMP).
The curves shown in Figure 2 were obtained using the AGN model described in [42], for which it was applied the mean conditional entropy as a criterion function [46] in order to exemplify the adopted assumption for the three discussed cases, i.e., normal, source and IMP.
Given the network topology constraint and the IMP property, the proposed algorithm performs a search for best and worst individual predictors, based on the SFFSMR algorithm [48], by considering all genes of the network. SFFSMR is an algorithm that applies the classical SFFS for several initial features, considering good and bad individual features. In the second step, the search is performed again for all genes, but the algorithm chooses the target genes that present a prediction gain (i.e., the criterion function value moves closer to the optimum) by adding a new predictor to its result set. This increased result set is preserved in the next iteration. The target genes with small information gain, i.e., poor predictions when increasing its predictor set, are not considered for the next iteration, as well as the predictor set that reaches the optimal value of the criterion function or gets too close to it.
In this context, the scalefree topology and the above presented assumption are considered by the proposed algorithm in order to integrate the prior knowledge (scalefree topology), applied to reverseengineering of GRNs from temporal expression profiles. By considering that the scalefree network model is characterized by a power law in its connections distribution (Section 2), the same power law is considered to prune the search space on each iteration.
Algorithm 1 starts by considering the targets and all available samples (temporal expression profile, called exps) in order to select the individual predictors, i.e., . In the following, Algorithm 2 (SFFSBA) is applied in order to discover the best features of each target gene, which are ranked according to the adopted criterion function. One important difference of the proposed feature selection method is that the algorithm will explore the search space in steps, i.e., the predictors are chosen iteratively according to the cardinality parameter . Another difference is that for the SFFSBA algorithm will return all predictor sets and the best criterion function value in order to explore all the individual predictors in the next iteration and to better recover the predictors of the IMP targets. From , the algorithm begins to return only the best set, assuming that some of the true predictors would be in the selected predictors set.
At the end of each iteration of the Algorithm 1, the target genes are sorted by the prediction gain, the number of considered targets for the next iteration is updated following a power law, given by and the cardinality of the result set is updated ().
In this way, when , the next iterations will consider just the target genes with higher prediction gain when increasing its predictor cardinality . It is important to notice that target genes that reach the optimal value of criterion function or get too close are not considered for the increasing on its predictor cardinality. The search is performed while the number of target genes (stop condition).
In summary, the SFFSBA differs from SFFS (Section 4.2) because of its iterativity, the exploration of all combinations of predictors set with cardinality and the inclusion of a search space pruning method based on the assumption that the expression data (input) were generated from a scalefree network.
The parameter represents the temporal expression profile, in which the genes are generally arranged in the rows and experiments in the columns. The parameter is a constant value that determines the exponential decay, i.e., the number of targets that will be considered in the next iteration (predictor set expansion). A criterion function value variation () is also included. The value prevents that minor variations of the criterion function () cause the increase of the predictors subset. The present paper adopted and . The adopted is related with the mean value found in the literature, which is usually in the range [31, 38].
The application of the algorithm to predict a single gene or a set of genes of interest instead of the entire network is straightforward by selecting the targets parameter.
7 Experimental Results
This section presents the experimental results obtained by considering a synthetic networks approach, which is described in [42]. The artificial gene networks (AGNs) were generated by considering the uniformlyrandom ErdösRényi (ER) topology, the scalefree BarabásiAlbert (BA) and the smallworld WattsStrogatz (WS).
For all experiments, the network models (ER, BA and WS) were applied with vertices (genes). The average degree per gene varied from 1 to 5, and the number of observed time instants (signal size) varied from 5, 10, 15, 20 to 100 in steps of 20. For each gene of the network, its value was given by a randomly selected function from 3 possible Boolean functions , which represents different behaviors or functions assumed by each gene . In order to assign a robust structural dynamics with small noise to the networks, the probabilities of each function be selected are given by . With these probabilities, the PGN axioms ii (all possible states are reachable) and iv (quasideterministic setting) are satisfied (Section 5).
The network identification method described in [46] implements feature selection methods for network inference. By applying the SFS and SFFS as search strategies and the mean conditional entropy as criterion function. This method was applied in order to identify the networks from simulated temporal expressions. The same method, criterion function and other parameters (default) were kept fixed during the comparative analysis with SFFSBA.
In order to measure the similarity between the synthetic () and the inferred () networks, we adopt the (Positive Predictive Value, also known as accuracy or precision) and (or recall) [74, 42], which are widely used to compare the results of the GRNs inference methods. Since the and
are not independent of each other, we take into account the geometrical mean between them as a similarity measure, given by:
.The experimental results were obtained from 50 simulations by using different signal sizes (i.e., number of time points) and values. The first experiment was performed in order to compare the three methods (SFS, SFFS and SFFSBA) with respect to the temporal expressions size, which is a critical issue in bioinformatics problems. Figure 3 presents these results, in which the similarity measure was calculated by taking into account the average results for all variations of .
(a) ER  
(b) WS  
(c) BA 
(a) ER  
(b) WS  
(c) BA 
It is possible to notice that all methods have an increase on its performance by increasing the number of observations in the three network topologies (ER, WS and BA). However, the improvement of the SFFSBA occurs earlier by using just 20 time points, consistently outperforming the other two methods from this point forward. Meanwhile, the SFFS slightly outperforms the SFS only with the signal size greater than 80 time points, i.e., the difference of the similarity rates is smaller than the difference achieved by SFFSBA by considering all network topologies.
In addition, the SFFSBA similarity curve (Figure 3) shows a more significant improvement with the expansion on signal size by considering the BA network topology, as expected. However, considering ER and WS network topologies, the improvement of the SFFSBA not only outperforms the other two methods but also it is consistent, even in the presence of some perturbations in the temporal signal, which is implied by the stochasticity in the application of transition functions. Figure 4 (a) and (b) shows the boxplots of the similarity values for each number of time points. It is possible to notice a very small variation in the boxplots, indicating stable results for all time points. These results are an important indicative of the stability of the proposed methodology.
(a) ER  
(b) WS  
(c) BA 
The second experiment was performed in order to compare the robustness of the methods by increasing the complexity of the networks in terms of its average degree . Figure 5 presents the average results for all variations of signal size (number of time points). It is possible to notice the similarity downgrade with the increase of average degree for the three algorithms. However, there was an improvement in results from to for ER and BA topologies. This behavior can be explained by the fact that less complex networks have several genes with no predictor, but the inference methods tend to find false positives, thus reducing its similarity ratio.
In this context, the SFFSBA algorithm also outperforms the SFS and SFFS, presenting a soft decrease of similarity with the increase of average degree for ER topology. In the presence of a network structure as is the case of BA and WS topologies, the decrease of similarity was less smooth, but even in these cases the SFFSBA presents better results.
With regard to the IMP genes with cardinality greater or equal than 3, it is important to notice that the SFFSBA tends to consider them if, at each step, an individual predictor added to the subset has a prediction gain larger than the predictors of the genes considered as sources (absence of predictors). The tendency is that a moderately IMP set is detected if the number of samples contained in the gene expression matrix is sufficient to estimate its joint probability distributions. However, there are two situations in which it is possible that SFFSBA considers the target gene from an IMP set as source gene. The first situation is the case in which the dimension of the IMP set is excessive for the number of samples available, making the error estimation of the joint probability distributions a crucial factor (the number of parameter to be estimated grows exponentially as a function of the cardinality). The second case refers to the strongly IMP sets where all its properly contained subsets offer a very poor information gain with regard to the target. This problem is inherent to the feature selection methods that explore only a subspace of all possible solutions, as SFFSBA does. The only way to guarantee that IMP features are returned is through a exhaustive search for the whole solution space.
8 Conclusion
This work presents an iterative floating search strategy for the inference of gene regulatory networks by including the scalefree assumption as a prior information in the inference process. Given the known limitations, our focus is the inclusion of prior knowledge on search methods. In particular, by presenting a more suitable and efficient algorithm for the inference of GRNs from temporal expression profiles, which presents a small number of samples and huge dimensionalities (genes).
The proposed algorithm is based on the assumption that several biological networks can be approximated by a scalefree topology. The presented method exploits this property by pruning the search space and using a power law as a weight for reducing the search space. In this context, the search space traversed by the SFFSBA method combines a breadthfirst search when the number of combinations is small () with a depthfirst search when the number of combinations becomes explosive ().
The experimental results show that the SFFSBA provides better inference accuracy than SFS and SFFS, when considering small signal sizes with 2030 time points and also with large ones, with 100 time points. In addition, the SFFSBA was able to achieve of similarity on network recovery after only observations from a state space of size , presenting very good results.
The SFFSBA has also proved to be robust and stable, as SFS and SFFS, when submitted to the increasing complexity of the networks in terms of its average degree . The robustness ans stability are important properties for the inference methods, even in the presence of some perturbations in the temporal signal, implied by the stochasticity in the application of transition functions. Besides, the SFFSBA showed better results than the SFS and SFFS.
A possible extension of the present work would be the inclusion of the smallworld (WS) [75] topology information in order to guide the search process for the correct topology inference of these networks. Also, we plan to apply this technique to infer GRNs from real data.
Acknowledgments
This work was supported by FAPESP, CNPq and CAPES.
References
 [1] D. Shalon, S. J. Smith, and P. O. Brown, “A DNA microarray system for analyzing complex DNA samples using twocolor fluorescent probe hybridization.” Genome Research, vol. 6, no. 7, pp. 639–645, 1996.
 [2] V. E. Velculescu, L. Zhang, B. Vogelstein, and K. W. Kinzler, “Serial Analysis of Gene Expression,” Science, vol. 270, no. 5235, pp. 484–487, 1995.
 [3] Z. Wang, M. Gerstein, and M. Snyder, “RNASeq: a revolutionary tool for transcriptomics,” Nat Rev Genet, vol. 10, no. 1, pp. 57–63, 2009.
 [4] G. Macintyre, J. Bailey, D. Gustafsson, I. Haviv, and A. Kowalczyk, “Using gene ontology annotations in exploratory microarray clustering to understand cancer etiology,” Pattern Recognition Letters, vol. In Press, Corrected Proof, pp. –, 2010.
 [5] X. Cui, T. Wang, H.S. Chen, V. Busov, and H. Wei, “Tffinder: A software package for identifying transcription factors involved in biological processes using microarray data and existing knowledge base,” BMC Bioinformatics, vol. 11, no. 1, p. 425, 2010.
 [6] J. De Haan, E. Piek, R. van Schaik, J. de Vlieg, S. Bauerschmidt, L. Buydens, and R. Wehrens, “Integrating gene expression and go classification for pca by preclustering,” BMC Bioinformatics, vol. 11, no. 1, p. 158, 2010.
 [7] A. V. Werhli and D. Husmeier, “Gene regulatory network reconstruction by bayesian integration of prior knowledge and/or different experimental conditions,” Journal of Bioinformatics and Computational Biology, vol. 6, no. 3, pp. 543–572, 2008.
 [8] J. Ernst, Q. K. Beg, K. A. Kay, G. Balázsi, Z. N. Oltvai, and Z. BarJoseph, “A semisupervised method for predicting transcription factorgene interactions in Escherichia coli,” PLoS Comput Biol, vol. 4, no. 3, p. e1000044, 03 2008.
 [9] J. Seok, A. Kaushal, R. Davis, and W. Xiao, “Knowledgebased analysis of microarrays for the discovery of transcriptional regulation relationships,” BMC Bioinformatics, vol. 11, no. Suppl 1, p. S8, 2010.
 [10] O. G. Troyanskaya, “Putting microarrays in a context: Integrated analysis of diverse biological data,” Brief Bioinform, vol. 6, no. 1, pp. 34–43, 2005.
 [11] G. Karlebach and R. Shamir, “Modelling and analysis of gene regulatory networks,” Nat Rev Mol Cell Biol, vol. 9, no. 10, pp. 770–780, 2008.
 [12] J. Baumbach, A. Tauch, and S. Rahmann, “Towards the integrated analysis, visualization and reconstruction of microbial gene regulatory networks,” Brief Bioinform, vol. 10, no. 1, pp. 75–83, 2009.
 [13] M. Hecker, S. Lambeck, S. Toepfer, E. van Someren, and R. Guthke, “Gene regulatory network inference: Data integration in dynamic models  A review,” Biosystems, vol. 96, no. 1, pp. 86–103, 2009.
 [14] M. Vidal, “Interactome modeling,” FEBS Letters, vol. 579, no. 8, pp. 1834–1838, 2005, systems Biology.
 [15] T. Aittokallio and B. Schwikowski, “Graphbased methods for analysing networks in cell biology,” Brief Bioinform, vol. 7, no. 3, pp. 243–255, 2006.
 [16] S. Ray, S. Bandyopadhyay, and S. Pal, “Combining multisource information through functionalannotationbased weighting: Gene function prediction in yeast,” Biomedical Engineering, IEEE Transactions on, vol. 56, no. 2, pp. 229–236, 2 2009.
 [17] O. Kuchaiev, T. Milenkovic, V. Memisevic, W. Hayes, and N. Przulj, “Topological network alignment uncovers biological function and phylogeny,” Journal of The Royal Society Interface, vol. 7, no. 50, pp. 1341–1354, March 2010.
 [18] S. Klamt, J. SaezRodriguez, and E. Gilles, “Structural and functional analysis of cellular networks with cellnetanalyzer,” BMC Systems Biology, vol. 1, no. 1, p. 2, 2007.
 [19] V. Lacroix, L. Cottret, P. Thébault, and M.F. Sagot, “An introduction to metabolic networks and their structural analysis,” IEEE/ACM Trans. Comput. Biol. Bioinformatics, vol. 5, no. 4, pp. 594–617, 2008.
 [20] P. Lenas, M. Moos, and F. P. Luyten, “Developmental engineering: A new paradigm for the design and manufacturing of cellbased products. part ii. from genes to networks: Tissue engineering from the viewpoint of systems biology and network science,” Tissue Engineering Part B: Reviews, vol. 15, no. 4, pp. 395–422, 2009.
 [21] T. Przytycka and Y.A. Kim, “Network integration meets network dynamics,” BMC Biology, vol. 8, no. 1, p. 48, 2010.
 [22] I. Ulitsky and R. Shamir, “Identification of functional modules using network topology and highthroughput data,” BMC Systems Biology, vol. 1, no. 1, p. 8, 2007.
 [23] S. Narasimhan, R. Rengaswamy, and R. Vadigepalli, “Structural properties of gene regulatory networks: Definitions and connections,” IEEE/ACM Trans. Comput. Biol. Bioinformatics, vol. 6, no. 1, pp. 158–170, Jan.March 2009.
 [24] M. Andrecut and S. Kauffman, “On the sparse reconstruction of gene networks,” Journal of Computational Biology, vol. 15, no. 1, pp. 21–30, 2008.
 [25] M. Andrecut, S. Huang, and S. Kauffman, “Heuristic approach to sparse approximation of gene regulatory networks,” Journal of Computational Biology, vol. 15, no. 9, pp. 1173–1186, 2008.
 [26] S. Christley, Q. Nie, and X. Xie, “Incorporating existing network information into gene network inference,” PLoS ONE, vol. 4, no. 8, p. e6799, 08 2009.
 [27] S. Ott, A. Hansen, S.Y. Kim, and S. Miyano, “Superiority of network motifs over optimal networks and an application to the revelation of gene network evolution,” Bioinformatics, vol. 21, no. 2, pp. 227–238, 2005.
 [28] H. Yu, A. Paccanaro, V. Trifonov, and M. Gerstein, “Predicting interactions in protein networks by completing defective cliques,” Bioinformatics, vol. 22, no. 7, pp. 823–829, 2006.
 [29] C. Charbonnier, J. Chiquet, and C. Ambroise, “Weightedlasso for structured network inference from time course data,” Statistical Applications in Genetics and Molecular Biology, vol. 9, no. 1, p. 15, 2010.
 [30] S. H. Strogatz, “Exploring complex networks,” Nature, vol. 410, no. 6825, pp. 268–276, 2001.
 [31] R. Albert and A.L. Barabási, “Statistical mechanics of complex networks,” Rev. Mod. Phys., vol. 74, no. 1, pp. 47–97, 2002.
 [32] M. E. J. Newman, “The structure and function of complex networks,” SIAM Review, vol. 45, no. 2, pp. 167–256, 2003.
 [33] L. d. F. Costa, F. A. Rodrigues, and A. S. Cristino, “Complex networks: the key to systems biology,” Genetics and Molecular Biology, vol. 31, no. 3, pp. 591–601, 2008.
 [34] H. Jeong, B. Tombor, R. Albert, Z. N. Oltvai, and A.L. Barabási, “The largescale organization of metabolic networks,” Nature, vol. 407, pp. 651–654, 2000.
 [35] N. Guelzim, S. Bottani, P. Bourgine, and F. Képès, “Topological and causal structure of the yeast transcriptional regulatory network,” Nature genetics, vol. 31, pp. 60–63, 2002.
 [36] I. J. Farkas, H. Jeong, T. Vicsek, A.L. Barabási, , and Z. N. Oltvai, “The topology of the transcription regulatory network in the yeast, saccharomyces cerevisiae,” Physica A: Statistical Mechanics and its Applications, vol. 318, no. 34, pp. 601–612, 2003.
 [37] A.L. Barabási and Z. Oltvai, “Network biology: Understanding the cell’s functional organization,” Nature Reviews Genetics, vol. 5, pp. 101–113, February 2004.
 [38] R. Albert, “Scalefree networks in cell biology,” J Cell Sci, vol. 118, no. 21, pp. 4947–4957, 2005.
 [39] A.L. Barabási, “ScaleFree Networks: A Decade and Beyond,” Science, vol. 325, no. 5939, pp. 412–413, 2009.
 [40] U. Alon, “Network motifs: theory and experimental approaches,” Nat Rev Genet, vol. 8, no. 6, pp. 450–461, 2007.
 [41] B. Goemann, E. Wingender, and A. Potapov, “An approach to evaluate the topological significance of motifs and other patterns in regulatory networks,” BMC Systems Biology, vol. 3, no. 1, p. 53, 2009.
 [42] F. M. Lopes, R. M. CesarJr, and L. d. F. Costa, “Gene expression complex networks: synthesis, identification and analysis,” Journal of Computational Biology, vol. In Press, Accepted Manuscript, 2011.
 [43] S. Liang, S. Fuhrman, and R. Somogyi, “Reveal: a general reverse engineering algorithm for inference of genetic network architectures,” in Proceedings of the Pacific Symposium on Biocomputing, 1998, pp. 18–29. [Online]. Available: http://psb.stanford.edu/psbonline/proceedings/psb98/abstracts/p18.html
 [44] D. C. MartinsJr, R. M. CesarJr, and J. Barrera, “Woperator window design by minimization of mean conditional entropy,” Pattern Analysis & Applications, vol. 9, pp. 139–153, 2006.
 [45] J. Barrera, R. M. CesarJr, D. C. MartinsJr, R. Z. N. Vencio, E. F. Merino, M. M. Yamamoto, F. G. Leonardi, C. A. B. Pereira, and H. A. Portillo, “Constructing probabilistic genetic networks of Plasmodium falciparum, from dynamical expression signals of the intraerythrocytic development cycle,” in Methods of Microarray Data Analysis V, P. McConnell, S. M. Lin, and P. Hurban, Eds. Springer US, 2007, pp. 11–26.
 [46] F. M. Lopes, D. C. MartinsJr, and R. M. CesarJr, “Feature selection environment for genomic applications,” BMC Bioinformatics, vol. 9, no. 1, p. 451, October 2008.
 [47] P. Pudil, J. Novovičová, and J. Kittler, “Floating search methods in featureselection,” Pattern Recognition Letters, vol. 15, no. 11, pp. 1119–1125, November 1994.
 [48] F. M. Lopes, D. C. MartinsJr, J. Barrera, and R. M. CesarJr, “SFFSMR: a floating search strategy for GRNs inference,” in Pattern Recognition in Bioinformatics, Proceedings, ser. Lecture Notes in Computer Science, vol. 6282. Springer Berlin / Heidelberg, 2010, Proceedings Paper, pp. 407–418, 5th IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB 2010), Nijmegen, The Netherlands, SEP 2224, 2010.
 [49] P. Erdös and A. Rényi, “On random graphs,” Publ. Math. Debrecen, vol. 6, pp. 290–297, 1959.
 [50] A.L. Barabási and R. Albert, “Emergence of scaling in random networks,” Science, vol. 286, no. 5439, pp. 509–512, 1999.
 [51] H. Jeong, S. P. Mason, A.L. Barabási, and Z. N. Oltvai, “Lethality and centrality in protein networks,” Nature, vol. 411, no. 6833, pp. 41–42, 2001.
 [52] S. Boccaletti, V. Latora, Y. Moreno, M. Chavez, and D. U. Hwang, “Complex networks: Structure and dynamics,” Physics Reports, vol. 424, no. 45, pp. 175–308, 2006.
 [53] I. Hovatta, K. Kimppa, A. Lehmussola, T. Pasanen, J. Saarela, I. Saarikko, J. Saharinen, P. Tiikkainen, T. Toivanen, M. Tolvanen et al., DNA microarray data analysis, 2nd ed. CSC, Scientific Computing Ltd, 2005.
 [54] S. A. Kauffman, “Metabolic stability and epigenesis in randomly constructed genetic nets,” Journal of Theoretical Biology, vol. 22, no. 3, pp. 437–467, March 1969.
 [55] I. Shmulevich, E. R. Dougherty, S. Kim, and W. Zhang, “Probabilistic boolean networks: a rulebased uncertainty model for gene regulatory networks,” Bioinformatics, vol. 18, no. 2, pp. 261–274, 2002.
 [56] T. Chen, H. L. He, and G. M. Church, “Modeling gene expression with differential equations,” in Pacific Symposium on Biocomputing, 1999, pp. 29–40.
 [57] N. Friedman, M. Linial, I. Nachman, and D. Pe’er, “Using bayesian networks to analyze expression data,” Journal of Computational Biology, vol. 7, no. 34, pp. 601–620, 2000.
 [58] J. M. Stuart, E. Segal, D. Koller, and S. K. Kim, “A genecoexpression network for global discovery of conserved genetic modules,” Science, vol. 302, no. 5643, pp. 249–255, 2003.
 [59] R. F. Hashimoto, S. Kim, I. Shmulevich, W. Zhang, M. L. Bittner, and E. R. Dougherty, “Growing genetic regulatory networks from seed genes,” Bioinformatics, vol. 20, no. 8, pp. 1241–1247, May 2004.
 [60] E. R. Dougherty, M. Brun, J. M. Trent, and M. L. Bittner, “Conditioningbased modeling of contextual genomic regulation,” IEEE/ACM TCBB, vol. 6, pp. 310–320, 2009.
 [61] N. Ghaffari, I. Ivanov, X. Qian, and E. R. Dougherty, “A CoDbased reduction algorithm for designing stationary control policies on Boolean networks,” Bioinformatics, vol. 26, no. 12, pp. 1556–1563, Apr 2010.
 [62] A. Margolin, K. N. Basso, C. Wiggins, G. Stolovitzky, R. Favera, and A. Califano, “ARACNE: An algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context,” BMC Bioinformatics, vol. 7, no. Suppl 1, p. S7, 2006.
 [63] J. Faith, B. Hayete, J. Thaden, I. Mogno, J. Wierzbowski, G. Cottarel, S. Kasif, J. Collins, and T. Gardner, “Largescale mapping and validation of escherichia coli transcriptional regulation from a compendium of expression profiles,” PLoS Biology, vol. 5, no. 1, pp. 259–265, 2007.
 [64] A. Rao, A. Hero III, D. States, and J. Engel, “Using directed information to build biologically relevant influence networks,” in Proc LSS Comput Syst Bioinform, August 2007, pp. 145–156. [Online]. Available: http://www.lifesciencessociety.org/CSB2007/toc/PDF/145.2007.pdf
 [65] W. Zhao, E. Serpedin, and E. R. Dougherty, “Inferring connectivity of genetic regulatory networks using informationtheoretic criteria,” IEEE/ACM TCBB, vol. 5, no. 2, pp. 262–274, April 2008.
 [66] P. D’haeseleer, S. Liang, and R. Somogyi, “Genetic network inference: from coexpression clustering to reverse engineering,” Bioinformatics, vol. 16, no. 8, pp. 707–726, 2000.
 [67] H. de Jong, “Modeling and simulation of genetic regulatory systems: A literature review,” Journal of Computational Biology, vol. 9, no. 1, pp. 67–103, 2002.
 [68] M. P. Styczynski and G. Stephanopoulos, “Overview of computational methods for the inference of gene regulatory networks,” Computers & Chemical Engineering, vol. 29, no. 3, pp. 519–534, 2005.
 [69] T. Schllit and A. Brazma, “Current approaches to gene regulatory network modelling,” BMC Bioinformatics, vol. 8, no. Suppl 6, p. S9, September 2007.
 [70] P. Somol, P. Pudil, and J. Kittler, “Fast branch & bound algorithms for optimal feature selection,” IEEE TPAMI, vol. 26, no. 7, pp. 900–912, July 2004.
 [71] M. Ris, D. C. MartinsJr, and J. Barrera, “Ucurve: A branchandbound optimization algorithm for ushaped cost functions on boolean lattices applied to the feature selection problem,” Pattern Recognition, vol. 43, no. 3, pp. 557–568, March 2010.
 [72] P. Somol, P. Pudil, J. Novovičová, and P. Paclík, “Adaptive floating search methods in feature selection,” Pattern Recognition Letters, vol. 20, pp. 1157–1163, 1999.
 [73] D. C. MartinsJr, U. BragaNeto, R. F. Hashimoto, E. R. Dougherty, and M. L. Bittner, “Intrinsically multivariate predictive genes,” IEEE Journal of Selected Topics in Signal Processing, vol. 2, no. 3, pp. 424–439, June 2008.
 [74] E. R. Dougherty, “Validation of inference procedures for gene regulatory networks,” Current Genomics, vol. 8, no. 6, pp. 351–359, 2007.
 [75] D. J. Watts and S. H. Strogatz, “Collective dynamics of smallworld networks,” Nature, vol. 393, pp. 440–442, 1998.
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