Driving cellular biological systems to one of desired long-term behaviors, an important subject in systems biology, can be interpreted as the set stabilization problem. This article presents a realistic control approach to set stabilization of complex biological networks modeled by Boolean networks (BNs) wherein only open-loop control is applied. First, the considered BN is transformed into an auxiliary BN having redundant state variables. Based on the similarity between desired attractors and Boolean algebra, a number of reduced BNs are constructed from the auxiliary BN in favor of set stabilization. Next, the feedback vertex set (FVS) control law is applied to reduced BNs for obtaining open-loop control inputs that take partial state variables toward desired values. This procedure continues in a recursive way until ensuring that the controlled BN converges to an unspecified desired attractor. The proposed scheme has the superiority that both fixed-point and cyclic attractors can be included in the problem setting, and that it can tackle complex BNs with moderate computational burdens. Extensive numerical experiments on random BNs and real biological systems are provided to demonstrate the applicability of the proposed scheme.

This article presents attractor-transition control of complex biological networks represented by Boolean networks (BNs) wherein the BN is steered from a prescribed initial attractor toward a desired one. The proposed approach leverages the similarity between attractors and Boolean algebraic properties embedded in the underlying state transition equations. To enhance the clarity of expression regarding stabilization toward the desired attractor, a simple coordinate transformation is performed on the considered BN. Based on the characteristics of transformed state equations, self-stabilizing state variables requiring no control efforts are derived in the first. Next, by applying the feedback vertex set (FVS) control scheme, control inputs stabilizing the remaining state variables are determined. The proposed control scheme exhibits versatility by accommodating both fixed-point and cyclic attractors. We validate the effectiveness of the proposed strategy through extensive numerical experiments conducted on random BNs as well as complex biological systems. In adherence to the reproducible research initiative, detailed results of numerical experiments and all the implementation codes are provided on the authors' website: https://github.com/choonlog/AttractorTransition.

Output stabilizing control of biological systems is of utmost importance in systems biology since key phenotypes of biological networks are often encoded by a small subset of their phenotypic marker nodes. This study addresses the challenge of output stabilizing control for complex biological systems modeled by Boolean networks (BNs). The objective is to identify a set of constant control inputs capable of driving the BN toward a desirable long-term behavior with respect to specified output nodes. Leveraging the algebraic properties of Boolean logic, we develop a novel control algorithm that reformulates the output stabilizing control problem into a simple graph theoretic problem involving auxiliary BNs, the scale of which significantly decreases compared to the original BN. The proposed method ensures superiority over previous results in terms of both the number of control inputs and computational loads, since it searches for the solution within the reduced BNs while retaining essential structures needed for output stabilization. The efficacy of the proposed control scheme is demonstrated through extensive numerical experiments with complex random BNs and real biological networks. To support the reproducible research initiative, detailed results of numerical experiments are provided in the supplementary material, and all the implementation codes are made accessible at https://github.com/choonlog/OutputStabilization.

The tendency for cell fate to be robust to most perturbations, yet sensitive to certain perturbations raises intriguing questions about the existence of a key path within the underlying molecular network that critically determines distinct cell fates. Reprogramming and trans-differentiation clearly show examples of cell fate change by regulating only a few or even a single molecular switch. However, it is still unknown how to identify such a switch, called a master regulator, and how cell fate is determined by its regulation. Here, we present CAESAR, a computational framework that can systematically identify master regulators and unravel the resulting canalizing kernel, a key substructure of interconnected feedbacks that is critical for cell fate determination. We demonstrate that CAESAR can successfully predict reprogramming factors for de-differentiation into mouse embryonic stem cells and trans-differentiation of hematopoietic stem cells, while unveiling the underlying essential mechanism through the canalizing kernel. CAESAR provides a system-level understanding of how complex molecular networks determine cell fates.

Boolean networks have been widely used in systems biology to study the dynamical characteristics of biological networks such as steady-states or cycles, yet there has been little attention to the dynamic properties of network structures. Here, we systematically reveal the core network structures using a recursive self-composite of the logic update rules. We find that all Boolean update rules exhibit repeated cyclic logic structures, where each converged logic leads to the same states, defined as kernel states. Consequently, the period of state cycles is upper bounded by the number of logics in the converged logic cycle. In order to uncover the underlying dynamical characteristics by exploiting the repeating structures, we propose leaping and filling algorithms. The algorithms provide a way to avoid large string explosions during the self-composition procedures. Finally, we present three examples-a simple network with a long feedback structure, a T-cell receptor network and a cancer network-to demonstrate the usefulness of the proposed algorithm.

This article investigates robust stabilizing control of biological systems modeled by Boolean networks (BNs). A population of BNs is considered where a majority of BNs have the same BN dynamics, but some BNs are inflicted by mutations damaging particular nodes, leading to perturbed dynamics that prohibit global stabilization to the desired attractor. The proposed control strategy consists of two steps. First, the nominal BN is transformed and curtailed into a sub-BN via a simple coordinate transformation and network reduction associated with the desired attractor. The feedback vertex set (FVS) control is then applied to the reduced BN to determine the control inputs for the nominal BN. Next, the control inputs derived in the first step and mutated nodes are applied to the nominal BN so as to identify residual dynamics of perturbed BNs, and additional control inputs are selected according to the canalization effect of each node. The overall control inputs are applied to the BN population, so that the nominal BN converges to the desired attractor and perturbed BNs to their own attractors that are the closest possible to the desired attractor. The performance of the proposed robust control scheme is validated through numerical experiments on random BNs and a complex biological network.

MOTIVATION: Cellular behavior is determined by complex non-linear interactions between numerous intracellular molecules that are often represented by Boolean network models. To achieve a desired cellular behavior with minimal intervention, we need to identify optimal control targets that can drive heterogeneous cellular states to the desired phenotypic cellular state with minimal node intervention. Previous attempts to realize such global stabilization were based solely on either network structure information or simple linear dynamics. Other attempts based on non-linear dynamics are not scalable. RESULTS: Here, we investigate the underlying relationship between structurally identified control targets and optimal global stabilizing control targets based on non-linear dynamics. We discovered that optimal global stabilizing control targets can be identified by analyzing the dynamics between structurally identified control targets. Utilizing these findings, we developed a scalable global stabilizing control framework using both structural and dynamic information. Our framework narrows down the search space based on strongly connected components and feedback vertex sets then identifies global stabilizing control targets based on the canalization of Boolean network dynamics. We find that the proposed global stabilizing control is superior with respect to the number of control target nodes, scalability, and computational complexity. AVAILABILITY AND IMPLEMENTATION: We provide a GitHub repository that contains the DCGS framework written in Python as well as biological random Boolean network datasets (https://github.com/sugyun/DCGS). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Timing error is now getting increased attention due to the high rate of error-occurrence on semiconductors. Even slight external disturbance can threaten the timing margin between successive clocks since the latest semiconductor operates with high frequency and small supply voltage. To deal with a timing error, many techniques have been introduced. Nevertheless, existing methods that mitigate a timing error mostly have time-delaying mechanisms and too complex operation, resulting in a timing problem on clock-based systems and hardware overhead. In this article, we propose a novel timing-error-tolerant method that can correct a timing error instantly through a simple mechanism. By modifying a clock in a flip-flop, the proposed system can recover a timing error without the loss of time in the clock-based system. Furthermore, due to the compact mechanism, the proposed system has low hardware overhead in comparison with existing timing-error-tolerant systems that can recover the error instantly. To verify our method, the proposed circuit was extensively simulated by addressing PVT variations. Moreover, it was implemented in several benchmark designs, including a microprocessor.

Voice activity detection (VAD) involves discriminating speech segments from background noise and is a critical step in numerous speech‐related applications. However, distinguishing speech from noise based on the properties of noise is fallible, because it is difficult to predict and characterise the noise occurring in real life. In this study, the authors instead focus on the intrinsic characteristics of speech. The harmonic peaks of vowel sounds have higher energies than the other spectral components of speech and are the speech features most likely to survive in most cases of severe noise. Therefore, the energy differences between harmonic peaks and other spectral features show promise for enabling robust VAD. To exploit this feature, the harmonic peaks must be accurately located. For this purpose, this study proposes an efficient harmonic peak location detection (HPD) method. Based on extensive experiments conducted in the presence of various noise types and signal‐to‐noise ratios, we found that VAD with the proposed HPD approach outperforms existing VAD methods and does so with reasonable computational cost and higher robustness.

Increasing attention is being paid to soft errors due to their high incidence. Although various approaches have been developed to mitigate such soft errors in digital storage components such as latches and flip-flops, most of them can detect only a single soft error and correct it only in limited cases, with a high overhead of hardware resources. In this paper, we propose a new self-repairing digital system based on the state attractor-converging mechanism, inspired by the recovery process of a living cell. To implement the state attractor-converging mechanism, the proposed system introduces additional states other than a working state, such that any abnormal transition to an additional state caused by a soft error can be recovered immediately. Moreover, the proposed system employs a reconfiguration method that enables a system with an erroneous state to be recovered regardless of any working state of the system while having a similar hardware overhead compared with the existing systems. From simulation analysis and experimental verification, we show that the proposed system can detect and correct not only a single soft error without any restriction but also two simultaneous soft errors under a few conditions. Furthermore, the proposed system can recover a soft error occurring in the control hardware used for error detection and correction, which is not possible with any other existing approaches.

Recent systems biological studies of cardiac systems have greatly advanced our understanding of cardiac physiology with a particular focus on the excitation-contraction coupling. With these advancements, there is a growing interest in systems analysis of the cardiac signaling network because its dynamical property is closely associated with cardiac diseases. In this article, we review recent attempts at computational modeling of the cardiac signaling network and provide a system-level perspective on the analysis of the large-scale cardiac signaling network. We discuss why the systems biological approach is useful and what novel insights it can provide for the development of personalized therapeutic strategies for cardiac diseases in the post-genomic era.

It has been known that the robust behavior of a cellular signaling network is strongly related to the structural characteristics of the network, such as connectivity, the number of feedback loops, and the number of feed-forward loops. Previous studies proved such relationships through dynamical simulations of various random network models. Most of them, however, focused on robustness against a single node mutation. Considering that complex diseases such as cancer are mostly caused by simultaneous dysfunction of multiple genes, it is needed to investigate the robustness of a network against multiple node mutations. In this paper, we investigated the robustness of a network against multiple node mutations through extensive simulations on the basis of Boolean network models. We found that the robustness against multiple mutations is, in most cases, weaker than the robustness against a single node mutation on average. Moreover, we found that the robustness against multiple mutations is strongly positively correlated with the robustness against single mutation. The difference between the multiple- and single-mutation robustness became larger as the number of mutated nodes increased or the number of nodes that are robust to single-mutation decreased. We further found that a node of relatively large connectivity or being involved with many feedback loops tends to be non-robust against multiple mutations. This finding is supported by the observation that poly-genic disease genes have high connectivity and are involved with a large number of feedback loops than mono-genic disease genes in a human signaling network. Together, our study shows that previous studies for a single node mutation can be extended to understand the network dynamics for multiple node mutations.

Tumors are developed and worsen with the accumulated mutations on DNA sequences during tumorigenesis. Identifying the temporal order of gene mutations in cancer initiation and development is a challenging topic. It not only provides a new insight into the study of tumorigenesis at the level of genome sequences but also is an effective tool for early diagnosis of tumors and preventive medicine. In this paper, we develop a novel method to accurately estimate the sequential order of gene mutations during tumorigenesis from genome sequencing data based on Markov chain model as TOMC (Temporal Order based on Markov Chain), and also provide a new criterion to further infer the order of samples or patients, which can characterize the severity or stage of the disease. We applied our method to the analysis of tumors based on several high-throughput datasets. Specifically, first, we revealed that tumor suppressor genes (TSG) tend to be mutated ahead of oncogenes, which are considered as important events for key functional loss and gain during tumorigenesis. Second, the comparisons of various methods demonstrated that our approach has clear advantages over the existing methods due to the consideration on the effect of mutation dependence among genes, such as co-mutation. Third and most important, our method is able to deduce the ordinal sequence of patients or samples to quantitatively characterize their severity of tumors. Therefore, our work provides a new way to quantitatively understand the development and progression of tumorigenesis based on high throughput sequencing data.

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

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

MOTIVATION: Computational multiscale models help cancer biologists to study the spatiotemporal dynamics of complex biological systems and to reveal the underlying mechanism of emergent properties. RESULTS: To facilitate the construction of such models, we have developed a next generation modelling platform for cancer systems biology, termed 'ELECANS' (electronic cancer system). It is equipped with a graphical user interface-based development environment for multiscale modelling along with a software development kit such that hierarchically complex biological systems can be conveniently modelled and simulated by using the graphical user interface/software development kit combination. Associated software accessories can also help users to perform post-processing of the simulation data for visualization and further analysis. In summary, ELECANS is a new modelling platform for cancer systems biology and provides a convenient and flexible modelling and simulation environment that is particularly useful for those without an intensive programming background. AVAILABILITY AND IMPLEMENTATION: ELECANS, its associated software accessories, demo examples, documentation and issues database are freely available at http://sbie.kaist.ac.kr/sub_0204.php. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Self-repairing digital systems have recently emerged as the most promising alternative for fault-tolerant systems. However, such systems are still impractical in many cases, particularly due to the complex rerouting process that follows cell replacement. They lose efficiency when the circuit size increases, due to the extra hardware in addition to the functional circuit and the unutilization of normal operating hardware for fault recovery. In this paper, we propose a system inspired by endocrine cellular communication, which simplifies the rerouting process in two ways: 1) by lowering the hardware overhead along with the increasing size of the circuit and 2) by reducing the hardware unutilized for fault recovery while maintaining good fault-coverage. The proposed system is composed of a structural layer and a gene-control layer. The structural layer consists of novel modules and their interconnections. In each module of our system, the encoded data, called the genome, contains information about the function and the connection. Therefore, a faulty module can be replaced and the whole system's functions and connections are maintained by simply assigning the same encoded data to a spare (stem) module. In existing systems, a huge amount of hardware, such as a dynamic routing system, is required for such an operation. The gene-control layer determines the neighboring spare module in the structural layer to replace the faulty module without collision. We verified the proposed mechanism by implementing the system with a field-programmable gate array with the application of a digital clock whose status can be monitored with light-emitting-diodes. In comparison with existing methods, the proposed architecture and mechanism are efficient enough for application with real fault-tolerant systems dealing with harsh and remote environments, such as outer space or deep sea.

MOTIVATION: Feedback circuits are crucial network motifs, ubiquitously found in many intra- and inter-cellular regulatory networks, and also act as basic building blocks for inducing synchronized bursting behaviors in neural network dynamics. Therefore, the system-level identification of feedback circuits using time-series measurements is critical to understand the underlying regulatory mechanism of synchronized bursting behaviors. RESULTS: Multi-Step Granger Causality Method (MSGCM) was developed to identify feedback loops embedded in biological networks using time-series experimental measurements. Based on multivariate time-series analysis, MSGCM used a modified Wald test to infer the existence of multi-step Granger causality between a pair of network nodes. A significant bi-directional multi-step Granger causality between two nodes indicated the existence of a feedback loop. This new identification method resolved the drawback of the previous non-causal impulse response component method which was only applicable to networks containing no co-regulatory forward path. MSGCM also significantly improved the ratio of correct identification of feedback loops. In this study, the MSGCM was testified using synthetic pulsed neural network models and also in vitro cultured rat neural networks using multi-electrode array. As a result, we found a large number of feedback loops in the in vitro cultured neural networks with apparent synchronized oscillation, indicating a close relationship between synchronized oscillatory bursting behavior and underlying feedback loops. The MSGCM is an efficient method to investigate feedback loops embedded in in vitro cultured neural networks. The identified feedback loop motifs are considered as an important design principle responsible for the synchronized bursting behavior in neural networks.

Self-repairing digital systems have received increasing attention as modern systems are getting more complex and fast. Currently available self-repairing architectures have, however, some limitations such as storage overhead required to prepare all possible rewiring strategies and temporal incorrectness caused by elongated repairing time. In this paper, we propose a novel self-repairing architecture for fast fault recovery with an efficient use of limited resources, which can be easily applied to real complex digital systems. The proposed architecture consists of three layers: a working layer, a control layer, and an interface layer. The working layer employs a hybrid scheme of using both redundant and empty cells with a newly devised self-test. This relieves the overhead of redundant cells required to be prepared in advance by considering every possible fault situation. In the control layer, an ordered assignment control is proposed. The order of working-priority of each processor that controls a normal cell in the working layer is predetermined. A faulty processor is detected by a majority decision among neighboring control processors and corrected by rearranging the order of working-priority. The interface layer connects an external PC for reprogramming. Through this fault recovery mechanism, the system can keep normal functioning under noisy environments. We implemented the proposed self-repairing architecture using an field-programmable gate array board with an application of a dot-matrix LED display and verified its robust operation. The proposed architecture can be widely used as a new platform for self-repairing systems.

BACKGROUND: Network motifs within biological networks show non-random abundances in systems at different scales. Large directed protein networks at the cellular level are now well defined in several diverse species. We aimed to compare the nature of significantly observed two- and three-node network motifs across three different kingdoms (Arabidopsis thaliana for multicellular plants, Saccharomyces cerevisiae for unicellular fungi and Homo sapiens for animals). RESULTS: 'Two-node feedback' is the most significant motif in all three species. By considering the sign of each two-node feedback interaction, we examined the enrichment of the three types of two-node feedbacks [positive-positive (PP), negative-negative (NN) and positive-negative (PN)]. We found that PN is enriched in the network of A.thaliana, NN in the network of S.cerevisiae and PP and NN in the network of H.sapiens. Each feedback type has characteristic features of robustness, multistability and homeostasis. CONCLUSIONS: We suggest that amplification of particular network motifs emerges from contrasting dynamical and topological properties of the motifs, reflects the evolutionary design principles selected by the characteristic behavior of each species and provides a signature pointing to their behavior and function.

The Ninth Asia Pacific Bioinformatics Conference (APBC2011) was held in Incheon, South Korea, the first time in this dynamic country.The conference spanning the dates of the 11 th to the 14 th of January brought together more than 300 researchers, professional, industry leaders and students from all over the globe.The participants came from institutions in the following 19 countries and regions (in alphabetical order): Australia,

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

MOTIVATION: Synchronized bursting behavior is a remarkable phenomenon in neural dynamics. So, identification of the underlying functional structure is crucial to understand its regulatory mechanism at a system level. On the other hand, we noted that feedback loops (FBLs) are commonly used basic building blocks in engineering circuit design, especially for synchronization, and they have also been considered as important regulatory network motifs in systems biology. From these motivations, we have investigated the relationship between synchronized bursting behavior and feedback motifs in neural networks. RESULTS: Through extensive simulations of synthetic spike oscillation models, we found that a particular structure of FBLs, coupled direct and indirect positive feedback loops (PFLs), can induce robust synchronized bursting behaviors. To further investigate this, we have developed a novel FBL identification method based on sampled time-series data and applied it to synchronized spiking records measured from cultured neural networks of rat by using multi-electrode array. As a result, we have identified coupled direct and indirect PFLs. CONCLUSION: We therefore conclude that coupled direct and indirect PFLs might be an important design principle that causes the synchronized bursting behavior in neuronal networks although an extrapolation of this result to in vivo brain dynamics still remains an unanswered question.

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

MOTIVATION: Inherent non-linearities in biomolecular interactions make the identification of network interactions difficult. One of the principal problems is that all methods based on the use of linear time-invariant models will have fundamental limitations in their capability to infer certain non-linear network interactions. Another difficulty is the multiplicity of possible solutions, since, for a given dataset, there may be many different possible networks which generate the same time-series expression profiles. RESULTS: A novel algorithm for the inference of biomolecular interaction networks from temporal expression data is presented. Linear time-varying models, which can represent a much wider class of time-series data than linear time-invariant models, are employed in the algorithm. From time-series expression profiles, the model parameters are identified by solving a non-linear optimization problem. In order to systematically reduce the set of possible solutions for the optimization problem, a filtering process is performed using a phase-portrait analysis with random numerical perturbations. The proposed approach has the advantages of not requiring the system to be in a stable steady state, of using time-series profiles which have been generated by a single experiment, and of allowing non-linear network interactions to be identified. The ability of the proposed algorithm to correctly infer network interactions is illustrated by its application to three examples: a non-linear model for cAMP oscillations in Dictyostelium discoideum, the cell-cycle data for Saccharomyces cerevisiae and a large-scale non-linear model of a group of synchronized Dictyostelium cells. AVAILABILITY: The software used in this article is available from http://sbie.kaist.ac.kr/software

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

MOTIVATION: It has been widely reported that biological networks are robust against perturbations such as mutations. On the contrary, it has also been known that biological networks are often fragile against unexpected mutations. There is a growing interest in these intriguing observations and the underlying design principle that causes such robust but fragile characteristics of biological networks. For relatively small networks, a feedback loop has been considered as an important motif for realizing the robustness. It is still, however, not clear how a number of coupled feedback loops actually affect the robustness of large complex biological networks. In particular, the relationship between fragility and feedback loops has not yet been investigated till now. RESULTS: Through extensive computational experiments, we found that networks with a larger number of positive feedback loops and a smaller number of negative feedback loops are likely to be more robust against perturbations. Moreover, we found that the nodes of a robust network subject to perturbations are mostly involved with a smaller number of feedback loops compared with the other nodes not usually subject to perturbations. This topological characteristic eventually makes the robust network fragile against unexpected mutations at the nodes not previously exposed to perturbations.

MOTIVATION: It is widely accepted that cell signaling networks have been evolved to be robust against perturbations. To investigate the topological characteristics resulting in such robustness, we have examined large-scale signaling networks and found that a number of feedback loops are present mostly in coupled structures. In particular, the coupling was made in a coherent way implying that same types of feedback loops are interlinked together. RESULTS: We have investigated the role of such coherently coupled feedback loops through extensive Boolean network simulations and found that a high proportion of coherent couplings can enhance the robustness of a network against its state perturbations. Moreover, we found that the robustness achieved by coherently coupled feedback loops can be kept evolutionarily stable. All these results imply that the coherent coupling of feedback loops might be a design principle of cell signaling networks devised to achieve the robustness.

BACKGROUND: We consider the problem of identifying the dynamic interactions in biochemical networks from noisy experimental data. Typically, approaches for solving this problem make use of an estimation algorithm such as the well-known linear Least-Squares (LS) estimation technique. We demonstrate that when time-series measurements are corrupted by white noise and/or drift noise, more accurate and reliable identification of network interactions can be achieved by employing an estimation algorithm known as Constrained Total Least Squares (CTLS). The Total Least Squares (TLS) technique is a generalised least squares method to solve an overdetermined set of equations whose coefficients are noisy. The CTLS is a natural extension of TLS to the case where the noise components of the coefficients are correlated, as is usually the case with time-series measurements of concentrations and expression profiles in gene networks. RESULTS: The superior performance of the CTLS method in identifying network interactions is demonstrated on three examples: a genetic network containing four genes, a network describing p53 activity and mdm2 messenger RNA interactions, and a recently proposed kinetic model for interleukin (IL)-6 and (IL)-12b messenger RNA expression as a function of ATF3 and NF-kappaB promoter binding. For the first example, the CTLS significantly reduces the errors in the estimation of the Jacobian for the gene network. For the second, the CTLS reduces the errors from the measurements that are corrupted by white noise and the effect of neglected kinetics. For the third, it allows the correct identification, from noisy data, of the negative regulation of (IL)-6 and (IL)-12b by ATF3. CONCLUSION: The significant improvements in performance demonstrated by the CTLS method under the wide range of conditions tested here, including different levels and types of measurement noise and different numbers of data points, suggests that its application will enable more accurate and reliable identification and modelling of biochemical networks.

BACKGROUND: Many biological networks such as protein-protein interaction networks, signaling networks, and metabolic networks have topological characteristics of a scale-free degree distribution. Preferential attachment has been considered as the most plausible evolutionary growth model to explain this topological property. Although various studies have been undertaken to investigate the structural characteristics of a network obtained using this growth model, its dynamical characteristics have received relatively less attention. RESULTS: In this paper, we focus on the robustness of a network that is acquired during its evolutionary process. Through simulations using Boolean network models, we found that preferential attachment increases the number of coupled feedback loops in the course of network evolution. Whereas, if networks evolve to have more coupled feedback loops rather than following preferential attachment, the resulting networks are more robust than those obtained through preferential attachment, although both of them have similar degree distributions. CONCLUSION: The presented analysis demonstrates that coupled feedback loops may play an important role in network evolution to acquire robustness. The result also provides a hint as to why various biological networks have evolved to contain a number of coupled feedback loops.

BACKGROUND: A number of studies on biological networks have been carried out to unravel the topological characteristics that can explain the functional importance of network nodes. For instance, connectivity, clustering coefficient, and shortest path length were previously proposed for this purpose. However, there is still a pressing need to investigate another topological measure that can better describe the functional importance of network nodes. In this respect, we considered a feedback loop which is ubiquitously found in various biological networks. RESULTS: We discovered that the number of feedback loops (NuFBL) is a crucial measure for evaluating the importance of a network node and verified this through a signal transduction network in the hippocampal CA1 neuron of mice as well as through generalized biological network models represented by Boolean networks. In particular, we observed that the proteins with a larger NuFBL are more likely to be essential and to evolve slowly in the hippocampal CA1 neuronal signal transduction network. Then, from extensive simulations based on the Boolean network models, we proved that a network node with the larger NuFBL is likely to be more important as the mutations of the initial state or the update rule of such a node made the network converge to a different attractor. These results led us to infer that such a strong positive correlation between the NuFBL and the importance of a network node might be an intrinsic principle of biological networks in view of network dynamics. CONCLUSION: The presented analysis on topological characteristics of biological networks showed that the number of feedback loops is positively correlated with the functional importance of network nodes. This result also suggests the existence of unknown feedback loops around functionally important nodes in biological networks.

MOTIVATION: Most previous approaches to model biochemical networks have focused either on the characterization of a network structure with a number of components or on the estimation of kinetic parameters of a network with a relatively small number of components. For system-level understanding, however, we should examine both the interactions among the components and the dynamic behaviors of the components. A key obstacle to this simultaneous identification of the structure and parameters is the lack of data compared with the relatively large number of parameters to be estimated. Hence, there are many plausible networks for the given data, but most of them are not likely to exist in the real system. RESULTS: We propose a new representation named S-trees for both the structural and dynamical modeling of a biochemical network within a unified scheme. We further present S-tree based genetic programming to identify the structure of a biochemical network and to estimate the corresponding parameter values at the same time. While other evolutionary algorithms require additional techniques for sparse structure identification, our approach can automatically assemble the sparse primitives of a biochemical network in an efficient way. We evaluate our algorithm on the dynamic profiles of an artificial genetic network. In 20 trials for four settings, we obtain the true structure and their relative squared errors are <5% regardless of releasing constraints about structural sparseness. In addition, we confirm that the proposed algorithm is robust within +/-10% noise ratio. Furthermore, the proposed approach ensures a reasonable estimate of a real yeast fermentation pathway. The comparatively less important connections with non-zero parameters can be detected even though their orders are below 10(-2). To demonstrate the usefulness of the proposed algorithm for real experimental biological data, we provide an additional example on the transcriptional network of SOS response to DNA damage in Escherichia coli. We confirm that the proposed algorithm can successfully identify the true structure except only one relation.

Gene expression time-series are discrete, noisy, short and usually unevenly sampled. Most of the existing methods used to compare expression profiles, operate directly on the time points. While modelling, the profiles can lead to more generalised, smooth characterisation of gene expressions. In this paper, a radial basis function neural network is employed to model gene expression time-series. The orthogonal least square method, used for selection of centres, is further combined with a width optimisation scheme. The experiments on a number of expression datasets have shown the advantages of the approach in terms of generalisation and approximation. The results on known datasets have indeed coincided with biological interpretations.

Abstract Motivation: When analyzing microarray data, non-biological variation introduces uncertainty in the analysis and interpretation. In this paper we focus on the validation of significant differences in gene expression levels, or normalized channel intensity levels with respect to different experimental conditions and with replicated measurements. A myriad of methods have been proposed to study differences in gene expression levels and to assign significance values as a measure of confidence. In this paper we compare several methods, including SAM, regularized t-test, mixture modeling, Wilk's lambda score and variance stabilization. From this comparison we developed a weighted resampling approach and applied it to gene deletions in Mycobacterium bovis. Results: We discuss the assumptions, model structure, computational complexity and applicability to microarray data. The results of our study justified the theoretical basis of the weighted resampling approach, which clearly outperforms the others. Availability: Algorithms were implemented using the statistical programming language R and available on the author's web-page. Supplementary information: For additional material see http://www.sbi.uni-rostock.de/

As the interest in flexible manufacturing systems and computer integrated manufacturing systems increases, the distribution of centralized control systems using industrial control networks is becoming more interesting. We investigate the rate-based traffic control of industrial control networks to improve the performance regarding the throughput fairness and error rates. Especially, we consider the protocol of LonWorks(TM) which consists of all OSI 7-layers and supports various communication media with low cost. Basically, the proposed rate-based traffic control system is closed loop by utilizing the feedback channel errors, which shows improved performance compared with other industrial control networks commonly operating in an open loop. To do this, an additional network node, called the monitoring node, is introduced to check the channel status without increasing the channel load. The proposed control loop is in effect whenever the feedback channel error becomes greater than an admittable value. We demonstrate the improved performance of the controlled network system in view of throughput and fairness measures by implementing the lab-scale network system and through the experimentation upon it.

There has been much interest in studying the stability properties of discrete event dynamic systems (DEDS), and several definitions for stability with some kinds of stability analysis methods have been proposed, recently. However, they are too much theory-oriented and do not provide any sufficient understanding based on real applications. Therefore, in this paper, we explore the meaning of stability concepts in DEDS through the case study of a manufacturing system, the plasma etching system in semiconductor manufacturing processes. We apply the concepts of stability and stabilizability upon a general characterization of the stability properties of automata-theoretic DEDS models. The stability notion is expounded by considering the control-law synthesis of the plasma etching system in view of stabilizability.

A discrete event dynamic system (DEDS) approach is utilized to improve the reliability of a system from the fault-tolerance viewpoint. We propose a systematic way to classify faults and failures quantitatively and to find tolerable fault event sequences embedded in DEDSs. After this, the synthesis of a fault-tolerant supervisory control system is investigated. A case study of a photolithographic process in a semiconductor manufacturing system is provided to illustrate these techniques.