Genetic networks contain important information about biological processes, including regulatory relationships and gene-gene interactions. Numerous methods, using high-dimensional gene expression data have been developed to capture these interactions. These gene expression data, generated using high-throughput technologies, are prone to noise. However, most existing network inference methods are unable to cope with noisy data, making genetic network reconstruction challenging. In this paper, we propose a novel ensemble regression model combining quantile regression and cross-validated Ridge regression, RidgeCV, to infer interactions from noisy gene expression data. The application of quantile regression to GRN inference is novel, and its design makes it appropriate for noisy data. RidgeCV also addresses other important issues, such as data overfitting and multicollinearity. First, each regression method is independently applied to gene expression data and the output of these methods, in the form of ranked gene lists, is aggregated using a novel gene score-based method by considering the gene rank and model importance. The model importance score is evaluated based on an adjusted coefficient of determination. This method implicitly includes majority voting by averaging each gene score value across all models. The proposed model was tested on the DREAM4 datasets and publicly available small-scale real-world network datasets. Experiments with noisy datasets showed that the proposed ensemble model is more accurate and efficient than other state-of-the-art methods.

An accurate reconstruction of Gene Regulatory Networks (GRNs) from time series gene expression data is crucial for discovering complex biological interactions. Among many different approaches for inferring GRNs, there are several methods which produce high false positive interactions, and are unstable, requiring fine tuning for many of their parameters. In this paper, we consider the GRN inference problem as a regression problem, and propose a simple ensemble regression-based feature selection model which is a combination of cross-validated Lasso and cross-validated Ridge algorithms for reconstructing GRNs. Due to the novelty of the proposed ensemble model, it is able to eliminate overfitting, multi co-linearity issues, and irrelevant genes within one computational approach. While observing the type of gene-gene regulatory interactions the regression model also identifies the direction of these interactions. A new coefficient of determination (R2)-based approach identifies the best model to fit the data among LassoCV and RidgeCV, and evaluates the model importance in term of gene-wise maximum in-degree which decides the maximum number of regulatory genes including self-regulations that can be selected from a given method. Then, an evaluated gene score-based majority voting technique aggregates the selected gene lists from each method. In our experiments, the performance of the proposed ensemble approach was evaluated using gene expression datasets from three small-scale real gene networks. Our proposed model outperformed other state-of-the-art methods, producing high true positives, reducing false positives, and obtaining high Structural Accuracy, while maintaining model stability and efficiency.

Reverse engineering of Gene Regulatory Networks (GRNs) from experimentally obtained high-throughput data is an active and promising area of research. Among several modelling techniques, the S-System model, a set of tightly coupled differential equations, mimics the complexities and dynamics of biochemical systems, and thus provides realistic GRN representation. While it offers mathematical flexibility and biological relevance, the high number of learning parameters can lead to a computational burden. In our earlier work, we addressed this issue by judicious use of prior knowledge. However, another major cause of computational load is the need for numerical integration of the differential equations for the estimation of S-system model parameters. In this paper, we propose a method to obtain initial model parameter values from the steady state of the system, thereby computing simpler and less complex algebraic equations compared to the regular differential equations of S-systems. These network parameters are input as prior knowledge for the optimization of the dynamic S-System using differential equations. The proposed framework includes a novel fitness evaluation for steady-state S-System models, a novel evolutionary parameter learning framework, and a technique to incorporate the candidate solutions in dynamic S-System modelling. Our proposed methodology reached optimal model parameter values quickly, requiring only one-third of the fitness function evaluations, compared to our previously reported DRNI (Dynamically regulated network initialization) method for S-System modelling.

The inference of Gene Regulatory Networks (GRNs) from time series gene expression data is an effective approach for unveiling important underlying gene-gene relationships and dynamics. While various computational models exist for accurate inference of GRNs, many are computationally inefficient, and do not focus on simultaneous inference of both network topology and dynamics. In this paper, we introduce a simple, Boolean network model-based solution for efficient inference of GRNs. First, the microarray expression data are discretized using the average gene expression value as a threshold. This step permits an experimental approach of defining the maximum indegree of a network. Next, regulatory genes, including the self-regulations for each target gene, are inferred using estimated multivariate mutual information-based Min-Redundancy Max-Relevance Criterion, and further accurate inference is performed by a swapping operation. Subsequently, we introduce a new method, combining Boolean network regulation modelling and Pearson correlation coefficient to identify the interaction types (inhibition or activation) of the regulatory genes. This method is utilized for the efficient determination of the optimal regulatory rule, consisting AND, OR, and NOT operators, by defining the accurate application of the NOT operation in conjunction and disjunction Boolean functions. The proposed approach is evaluated using two real gene expression datasets for an Escherichia coli gene regulatory network and a fission yeast cell cycle network. Although the Structural Accuracy is approximately the same as existing methods (MIBNI, REVEAL, Best-Fit, BIBN, and CST), the proposed method outperforms all these methods with respect to efficiency and Dynamic Accuracy.

Reverse engineering of gene regulatory networks through temporal gene expression data is an active area of research. Among the plethora of modelling techniques under investigation is the decoupled S-system model, which attempts to capture the non-linearity of biological systems in detail. For the model, number of parameters to be estimated are significantly high even when the network is of small or medium scale. Thus, the inference process poses a significant computational burden. In this paper, we propose: (1) a novel population initialization technique, Dynamically Regulated Prediction Initialization (DRPI), which utilises prior knowledge of biological gene expression data to create a feedback loop to produce dynamically regulated high-quality individuals for initial population; (2) an adaptive fitness function; and (3) a method for the maintenance of population diversity. The aim of this work is to reduce the computational complexity of the inference algorithm, to speed up the entire process of reverse engineering. The performance of the proposed algorithm was evaluated against a benchmark dataset and compared with other methods from earlier work. The experimental results show that we succeeded in achieving higher accuracy results in lesser fitness evaluations, considerably reducing the computational burden of the inference process.

The use of microorganisms for the production of industrially important compounds and enzymes is becoming increasingly important. Eukaryotes have been less widely used than prokaryotes in biotechnology, because of the complexity of their genomic structure and biology. The Yeast2.0 project is an international effort to engineer the yeast Saccharomyces cerevisiae to make it easy to manipulate, and to generate random variants using a system called SCRaMbLE. SCRaMbLE relies on artificial evolution in vitro to identify useful variants, an approach which is time consuming and expensive. We developed an in silico simulator for the SCRaMbLE system, using an evolutionary computing approach, which can be used to investigate and optimize the fitness landscape of the system. We applied the system to the investigation of the fitness landscape of one of the S. cerevisiae chromosomes, and found that our results fitted well with those previously published. Our simulator can be applied to the analysis of the fitness landscapes of any organism for which SCRaMbLE has been implemented.

Metabolic engineering is increasingly being used for the production of industrial products such as pharmaceuticals and enzymes. These chemicals have traditionally been chemically synthesized, but the application of synthetic biology techniques to microbes facilitates faster, cheaper production. Modelling and the integration of existing data can help inform the design of synthetic pathways. We applied an evolutionary algorithm to a flux balance model of metabolism in the industrially important bacterium Bacillus subtilis. Our target metabolites are sedoheptulose-7-phosphate and riboflavin, components of the pentose phosphate pathway. The algorithm combines the results of the flux balance analysis with phylogenetic information derived from data warehouses, to predict several potential interventions to the metabolic network, mostly involving knockouts of genes related to the pathway.

The automated design of synthetic biological circuits is an active area of research. A particularly promising area of research is the engineering of populations of communicating bacteria, in order to produce behaviour more complex than is possible with the engineering of individual bacteria. We present a computational approach to the engineering of communicating bacterial populations, using a multi-level approach. Circuits are designed using an evolutionary algorithm, at a high level of abstraction, with an agent-based model. Evolved agents can then be mapped onto previously-defined, lower-level components such as Standard Virtual Parts. This approach is applied to the evolution of a two-dimensional pattern, the French Flag.

Populations of bacteria acting in collaboration can produce complex behaviors which are not achievable by individual cells. There has, consequently, been considerable interest in the engineering of bacterial populations. Here we describe an approach for the engineering of aspects of bacterial quorum communication, using Standard Virtual Parts, a synthetic biology programming language, dubbed SVPWrite, and an evolutionary algorithm. We apply this system to engineering the output characteristics of the subtilin receiver system of Bacillus subtilis. Simple modifications, such as altering the strength of the output response to a subtilin input, are easily achieved. More complex adaptations, such as modifying the shape of the receiver response curve, necessitate alterations to the topology of the regulatory network. More generally, the use of Standard Virtual Parts and a programming language allow circuit design, simulation and evaluation to easily be automated, permitting exploration of a far larger proportion of design space than would be possible using standard manual design approaches.

Modelling and computational simulation are crucial for the large-scale engineering of biological circuits since they allow the system under design to be simulated prior to implementation in vivo . To support automated, model-driven design it is desirable that in silico models are modular, composable and use standard formats. The synthetic biology design process typically involves the composition of genetic circuits from individual parts. At the most basic level, these parts are representations of genetic features such as promoters, ribosome binding sites (RBSs), and coding sequences (CDSs). However, it is also desirable to model the biological molecules and behaviour that arise when these parts are combined in vivo . Modular models of parts can be composed and their associated systems simulated, facilitating the process of model-centred design. The availability of databases of modular models is essential to support software tools used in the model-driven design process. In this article, we present an approach to support the development of composable, modular models for synthetic biology, termed Standard Virtual Parts. We then describe a programmatically accessible and publicly available database of these models to allow their use by computational design tools.

MOTIVATION: Biological experiments give insight into networks of processes inside a cell, but are subject to error and uncertainty. However, due to the overlap between the large number of experiments reported in public databases it is possible to assess the chances of individual observations being correct. In order to do so, existing methods rely on high-quality 'gold standard' reference networks, but such reference networks are not always available. RESULTS: We present a novel algorithm for computing the probability of network interactions that operates without gold standard reference data. We show that our algorithm outperforms existing gold standard-based methods. Finally, we apply the new algorithm to a large collection of genetic interaction and protein-protein interaction experiments. AVAILABILITY: The integrated dataset and a reference implementation of the algorithm as a plug-in for the Ondex data integration framework are available for download at http://bio-nexus.ncl.ac.uk/projects/nogold/

MOTIVATION: The need for the automated computational design of genetic circuits is becoming increasingly apparent with the advent of ever more complex and ambitious synthetic biology projects. Currently, most circuits are designed through the assembly of models of individual parts such as promoters, ribosome binding sites and coding sequences. These low level models are combined to produce a dynamic model of a larger device that exhibits a desired behaviour. The larger model then acts as a blueprint for physical implementation at the DNA level. However, the conversion of models of complex genetic circuits into DNA sequences is a non-trivial undertaking due to the complexity of mapping the model parts to their physical manifestation. Automating this process is further hampered by the lack of computationally tractable information in most models. RESULTS: We describe a method for automatically generating DNA sequences from dynamic models implemented in CellML and Systems Biology Markup Language (SBML). We also identify the metadata needed to annotate models to facilitate automated conversion, and propose and demonstrate a method for the markup of these models using RDF. Our algorithm has been implemented in a software tool called MoSeC. AVAILABILITY: The software is available from the authors' web site http://research.ncl.ac.uk/synthetic_biology/downloads.html.

MOTIVATION: The rise of high-throughput technologies in the post-genomic era has led to the production of large amounts of biological data. Many of these datasets are freely available on the Internet. Making optimal use of these data is a significant challenge for bioinformaticians. Various strategies for integrating data have been proposed to address this challenge. One of the most promising approaches is the development of semantically rich integrated datasets. Although well suited to computational manipulation, such integrated datasets are typically too large and complex for easy visualization and interactive exploration. RESULTS: We have created an integrated dataset for Saccharomyces cerevisiae using the semantic data integration tool Ondex, and have developed a view-based visualization technique that allows for concise graphical representations of the integrated data. The technique was implemented in a plug-in for Cytoscape, called OndexView. We used OndexView to investigate telomere maintenance in S. cerevisiae. AVAILABILITY: The Ondex yeast dataset and the OndexView plug-in for Cytoscape are accessible at http://bsu.ncl.ac.uk/ondexview.

MOTIVATION: Fabrication of synthetic biological systems is greatly enhanced by incorporating engineering design principles and techniques such as computer-aided design. To this end, the ongoing standardization of biological parts presents an opportunity to develop libraries of standard virtual parts in the form of mathematical models that can be combined to inform system design. RESULTS: We present an online Repository, populated with a collection of standardized models that can readily be recombined to model different biological systems using the inherent modularity support of the CellML 1.1 model exchange format. The applicability of this approach is demonstrated by modeling gold-medal winning iGEM machines. AVAILABILITY AND IMPLEMENTATION: The Repository is available online as part of http://models.cellml.org. We hope to stimulate the worldwide community to reuse and extend the models therein, and contribute to the Repository of Standard Virtual Parts thus founded. Systems Model architecture information for the Systems Model described here, along with an additional example and a tutorial, is also available as Supplementary information. The example Systems Model from this manuscript can be found at http://models.cellml.org/workspace/bugbuster. The Template models used in the example can be found at http://models.cellml.org/workspace/SVP_Templates200906.

Many biological systems can be modeled as networks. Hence, network analysis is of increasing importance to systems biology. We describe an evolutionary algorithm for selecting clusters of nodes within a large network based upon network topology together with a measure of the relevance of nodes to a set of independently identified genes of interest. We apply the algorithm to a previously published integrated functional network of yeast genes, using a set of query genes derived from a whole genome screen of yeast strains with a mutation in a telomere uncapping gene. We find that the algorithm identifies biologically plausible clusters of genes which are related to the cell cycle, and which contain interactions not previously identified as potentially important. We conclude that the algorithm is valuable for the querying of complex networks, and the generation of biological hypotheses.

The concept of a network motif-a small set of interacting genes which produce a predictable behaviour at the network level-has attracted considerable attention amongst network analysts. It is of particular interest to synthetic biology, a new discipline which aims to apply engineering principles to biological systems. The modular nature of network motifs would make them ideal candidates for the basic components of an engineered organism. In this paper we investigate the relationship between the presence of network motifs and oscillatory dynamics in a yeast transcriptional network and a set of computational networks, evolved to exhibit oscillatory behaviour. Our results do not support the hypothesis that network motifs are critical to network dynamics, possibly because they are tightly connected to many other components of the complex cell-wide transcriptional network.

The integration of diverse data sets into probabilistic functional networks is an active and important area of research in systems biology. In this paper we fracture a previously published integrated network into its component networks, and investigate the overlap between the information provided by each data set to the final network. Using three-node network motifs as a surrogate for information about genetic circuits, we find that the same motifs are over-represented in all of the networks, but different genes contribute to the motifs in different data sets. We conclude that the data integration approach is valuable because it clearly does combine different insights into a biological system. However, the fact that the information contained in different data sets is so diverse raises issues of how best to perform data integration so as to accurately estimate error rates for different data sets, whilst including as much data as possible in the integrated network

The general assumption in biology is that most genes encode proteins. However, it is now evident that much of the genome of humans and other complex organisms is transcribed into non-protein-coding RNAs. Some of these RNAs are processed into small regulatory RNAs, such as microRNAs, that control many aspects of animal and plant development. It has been suggested that regulatory RNAs represent an additional control layer that was critical to the emergence of complex organisms. We examine this possibility using a model of cell differentiation based on attractors in boolean networks. Our simulation studies show that an additional layer of RNA control modeled as fast temporal links can significantly increase the number of attractors in boolean models of genetic regulatory networks (analogous to the number of cell types in a complex organism). However, it also has the power to simplify the state space structure. We explore the conditions under which these different outcomes occur.

We investigate the effects of constitutive gene activation upon the dynamics of random Boolean networks using a suite of models. We find that constitutive activity leads to simpler state spaces, with fewer and larger basins of attraction. The major difference in patterns of basin number and distribution is seen between networks with no activation and those with a single constitutively active node. Increasing the proportion of constitutively active node intensifies, but does not qualitatively change, the observed patterns. The proportion of genes constitutively active interacts in a nonlinear way with other network parameters, such as average connectivity and proportion of inhibitory links. We conclude that constitutive gene activation has a fundamental effect on network behavior that has been overlooked in previous random Boolean network studies. It acts to constrain the start states of the networks, and the states which can be reached during development and differentiation, and we hypothesize that constitutive activation and repression of genes may help to guide the process whereby a single genetic regulatory network produces a range of different cell types.

Many different clustering algorithms have been applied to biological networks, with varying degrees of success. The output of a clustering algorithm may be hard to interpret in biological terms because such networks are often large and highly interconnected, with structural and functional modules overlapping to varying degrees. In this paper we describe an evolutionary network clustering algorithm specifically designed for the analysis of large, complex biological networks. It identifies variably sized, overlapping clusters of nodes. The identification of points of overlap between clusters facilitates the analysis of the biological nature of crosstalk between functional units in the network. We apply two variants of the algorithm (one using probabilistic weights on edges and one ignoring them) to a recently published network of functional gene interactions in the yeast Saccharomyces cerevisiae and assess the biological validity of the resulting clusters in terms of ontological similarity

We describe a network module detection approach which combines a rapid and robust clustering algorithm with an objective measure of the coherence of the modules identified. The approach is applied to the network of genetic regulatory interactions surrounding the tumor suppressor gene p53. This algorithm identifies ten clusters in the p53 network, which are visually coherent and biologically plausible.

Recent research in molecular evolution has raised awareness of the importance of selective neutrality. Several different models of neutrality have been proposed based on Kauffman's well-known NK landscape model. Two of these models, NKp and NKq, are investigated and found to display significantly different structural properties. The fitness distributions of these neutral landscapes reveal that their levels of correlation with non-neutral landscapes are significantly different, as are the distributions of neutral mutations. In this paper we describe a series of simulations of a hill climbing search algorithm on NK, NKp and NKq landscapes with varying levels of epistatic interaction. These simulations demonstrate differences in the way that epistatic interaction affects the 'searchability' of neutral landscapes. We conclude that the method used to implement neutrality has an impact on both the structure of the resulting landscapes and on the performance of evolutionary search algorithms on these landscapes. These model-dependent effects must be taken into consideration when modelling biological phenomena.

This paper describes a stochastic implementation of Austin et al.'s (1999) model of the spread of antibiotic resistance in a population of fixed size under varying conditions of antibiotic use. The population is divided into sub-groups: individuals colonized by commensal bacteria and an uncolonized group. The colonized group is further divided according to whether the commensal bacteria are sensitive or resistant to antibiotics. This study uses Monte Carlo techniques to model the dynamics of the evolution of the antibiotic resistant population, a study that cannot be done in the original model. The Monte Carlo approach allows the investigation of the transient dynamics of the spread of resistance, the effects of finite (especially small) populations and the interaction of model parameters.