Global and threshold-free transcriptional regulatory networks reconstruction through integrating ChIP-Chip and expression data

Inferring transcriptional regulatory networks from high-throughput biological data is a major challenge to bioinformatics today. To address this challenge, we developed TReNGO (Transcriptional Regulatory Networks reconstruction based on Global Optimization), a global and threshold-free algorithm with simulated annealing for inferring regulatory networks by the integration of ChIP-chip and expression data. Superior to existing methods, TReNGO was expected to find the optimal structure of transcriptional regulatory networks without any arbitrary thresholds or predetermined number of transcriptional modules (TMs). TReNGO was applied to both synthetic data and real yeast data in the rapamycin response. In these applications, we demonstrated an improved functional coherence of TMs and TF (transcription factor)- target predictions by TReNGO when compared to GRAM, COGRIM or to analyzing ChIP-chip data alone. We also demonstrated the ability of TReNGO to discover unexpected biological processes that TFs may be involved in and to also identify interesting novel combinations of TFs.

A hybrid ART-GRNN online learning neural network with a ε-insensitive loss function

In this brief, a new neural network model called generalized adaptive resonance theory (GART) is introduced. GART is a hybrid model that comprises a modified Gaussian adaptive resonance theory (MGA) and the generalized regression neural network (GRNN). It is an enhanced version of the GRNN, which preserves the online learning properties of adaptive resonance theory (ART). A series of empirical studies to assess the effectiveness of GART in classification, regression, and time series prediction tasks is conducted. The results demonstrate that GART is able to produce good performances as compared with those of other methods, including the online sequential extreme learning machine (OSELM) and sequential learning radial basis function (RBF) neural network models.

Transcriptional regulation of the yghJ-pppA-yghG-gspCDEFGHIJKLM cluster, encoding the type II secretion pathway in Enterotoxigenic Escherichia coli

The gene cluster gspCDEFGHIJKLM codes for various structural components of the type II secretion pathway which is responsible for the secretion of heat-labile enterotoxin by enterotoxigenic Escherichia coli (ETEC). In this work, we used a variety of molecular approaches to elucidate the transcriptional organization of the ETEC type II secretion system and to unravel the mechanisms by which the expression of these genes is controlled. We showed that the gspCDEFGHIJKLM cluster and three other upstream genes, yghJ, pppA, and yghG, are cotranscribed and that a promoter located in the upstream region of yghJ plays a major role in the expression of this 14-gene transcriptional unit. Transcription of the yghJ promoter was repressed 168-fold upon a temperature downshift from 37°C to 22°C. This temperature-induced repression was mediated by the global regulatory proteins H-NS and StpA. Deletion mutagenesis showed that the promoter region encompassing positions −321 to +301 relative to the start site of transcription of yghJ was required for full repression. The yghJ promoter region is predicted to be highly curved and bound H-NS or StpA directly. The binding of H-NS or StpA blocked transcription initiation by inhibiting promoter open complex formation. Unraveling the mechanisms of regulation of type II secretion by ETEC enhances our understanding of the pathogenesis of ETEC and other pathogenic varieties of E. coli.

Computational aspects of the numerical solution of SDEs

In the last 30 to 40 years, many researchers have combined to build the knowledge base of theory and solution techniques that can be applied to the case of differential equations which include the effects of noise. This class of ``noisy'' differential equations is now known as stochastic differential equations (SDEs). Markov diffusion processes are included within the field of SDEs through the drift and diffusion components of the Itô form of an SDE. When these drift and diffusion components are moderately smooth functions, then the processes' transition probability densities satisfy the Fokker-Planck-Kolmogorov (FPK) equation -- an ordinary partial differential equation (PDE). Thus there is a mathematical inter-relationship that allows solutions of SDEs to be determined from the solution of a noise free differential equation which has been extensively studied since the 1920s. The main numerical solution technique employed to solve the FPK equation is the classical Finite Element Method (FEM). The FEM is of particular importance to engineers when used to solve FPK systems that describe noisy oscillators. The FEM is a powerful tool but is limited in that it is cumbersome when applied to multidimensional systems and can lead to large and complex matrix systems with their inherent solution and storage problems. I show in this thesis that the stochastic Taylor series (TS) based time discretisation approach to the solution of SDEs is an efficient and accurate technique that provides transition and steady state solutions to the associated FPK equation. The TS approach to the solution of SDEs has certain advantages over the classical techniques. These advantages include their ability to effectively tackle stiff systems, their simplicity of derivation and their ease of implementation and re-use. Unlike the FEM approach, which is difficult to apply in even only two dimensions, the simplicity of the TS approach is independant of the dimension of the system under investigation. Their main disadvantage, that of requiring a large number of simulations and the associated CPU requirements, is countered by their underlying structure which makes them perfectly suited for use on the now prevalent parallel or distributed processing systems. In summary, l will compare the TS solution of SDEs to the solution of the associated FPK equations using the classical FEM technique. One, two and three dimensional FPK systems that describe noisy oscillators have been chosen for the analysis. As higher dimensional FPK systems are rarely mentioned in the literature, the TS approach will be extended to essentially infinite dimensional systems through the solution of stochastic PDEs. In making these comparisons, the advantages of modern computing tools such as computer algebra systems and simulation software, when used as an adjunct to the solution of SDEs or their associated FPK equations, are demonstrated.

IPA: Integrated predictive gene signature from gene expression based breast cancer patient samples

Background: Novel predictive markers are needed to accurately diagnose the breast cancer patients so they do not need to undergo any unnecessary aggressive therapies. Various gene expression studies based predictive gene signatureshave generated in the recent past to predict the binary estrogen-receptor subclass or to predict the therapy response subclass. However, the existing algorithms comes with many limitations, including low predictive performances over multiple cohorts of patients and non-significant or limited biological roles associated with thepredictive gene signatures. Therefore, the aim of this study is to develop novel predictive markers with improved performances.Methods: We propose a novel prediction algorithm called IPA to construct a predictive gene signature for performing multiple prediction tasks of predicting estrogen-receptor based binary subclass and predicting chemotherapy response (neoadjuvantly) based binary subclass. The constructed gene signature with considering multiple classification techniques was used to evaluate the algorithm performance on multiple cohorts of breast cancer patients.Results: The evaluation on multiple validation cohorts demonstrated that proposed algorithm achieved stable and high performance to perform prediction tasks, with consideration given to any classification techniques. We show that the predictive gene signature of our proposed algorithm reflects the mechanisms underlying the estrogen-receptors or response to therapy with significant greater biological interpretations, compared with the other existing algorithm.

NKX2-5 mutations causative for congenital heart disease retain functionality and are directed to hundreds of targets

We take a functional genomics approach to congenital heart disease mechanism. We used DamID to establish a robust set of target genes for NKX2-5 wild type and disease associated NKX2-5 mutations to model loss-of-function in gene regulatory networks. NKX2-5 mutants, including those with a crippled homeodomain, bound hundreds of targets including NKX2-5 wild type targets and a unique set of "off-targets", and retained partial functionality. NKXΔHD, which lacks the homeodomain completely, could heterodimerize with NKX2-5 wild type and its cofactors, including E26 transformationspecific (ETS) family members, through a tyrosine-rich homophilic interaction domain (YRD). Off-targets of NKX2-5 mutants, but not those of an NKX2-5 YRD mutant, showed overrepresentation of ETS binding sites and were occupied by ETS proteins, as determined by DamID. Analysis of kernel transcription factor and ETS targets show that ETS proteins are highly embedded within the cardiac gene regulatory network. Our study reveals binding and activities of NKX2-5 mutations on WT target and off-targets, guided by interactions with their normal cardiac and general cofactors, and suggest a novel type of gainof- function in congenital heart disease.

Liquidity creation, regulatory capital, and bank profitability

We examine the interrelationships among liquidity creation, regulatory capital, and bank profitability of US banks. We find that regulatory capital and liquidity creation affect each other positively after controlling for bank profitability. However, this relationship is largely driven by small banks and primarily during non-crisis periods. It is also sensitive to the level of banks' regulatory capital and how it is measured. Furthermore, we find that banks which create more liquidity and exhibit higher illiquidity risk have lower profitability. Finally, the relationship between regulatory capital and bank performance is not linear and depends on the level of capitalization. Regulatory capital is negatively related to bank profitability for higher capitalized banks but positively related to profitability for lower capitalized banks. Therefore, a change in regulatory capital has differential impacts on bank performance. Our findings have various implications for policymakers and bank regulators.

Similarity solution of axisymmetric flow in porous media

Applications of the axisymmetric Boussinesq equation to groundwater hydrology and reservoir engineering have long been recognised. An archetypal example is invasion by drilling fluid into a permeable bed where there is initially no such fluid present, a circumstance of some importance in the oil industry. It is well known that the governing Boussinesq model can be reduced to a nonlinear ordinary differential equation using a similarity variable, a transformation that is valid for a certain time-dependent flux at the origin. Here, a new analytical approximation is obtained for this case. The new solution,, which has a simple form, is demonstrated to be highly accurate.

Optimal H∞ insulin injection control for blood glucose regulation in diabetic patients

The theory of H/sup /spl infin// optimal control has the feature of minimizing the worst-case gain of an unknown disturbance input. When appropriately modified, the theory can be used to design a "switching" controller that can be applied to insulin injection for blood glucose (BG) regulation. The "switching" controller is defined by a collection of basic insulin rates and a rule that switches the insulin rates from one value to another. The rule employed an estimation of BG from noisy measurements, and the subsequent optimization of a performance index that involves the solution of a "jump" Riccati differential equation and a discrete-time dynamic programming equation. With an appropriate patient model, simulation studies have shown that the controller could correct BG deviation using clinically acceptable insulin delivery rates.

Semi-hyperbolic mappings in Banach spaces

The definition of semi-hyperbolic dynamical systems generated by Lipschitz continuous and not necessarily invertible mappings in Banach spaces is presented in this thesis. Like hyperbolic mappings, they involve a splitting into stable and unstable spaces, but a slight leakage from the strict invariance of the spaces is possible and the unstable subspaces are assumed to be finite dimensional. Bi-shadowing is a combination of the concepts of shadowing and inverse shadowing and is usually used to compare pseudo-trajectories calculated by a computer with the true trajectories. In this thesis, the concept of bi-shadowing in a Banach space is defined and proved for semi-hyperbolic dynamical systems generated by Lipschitz mappings. As an application to the concept of bishadowing, linear delay differential equations are shown to be bi-shadowing with respect to pseudo-trajectories generated by nonlinear small perturbations of the linear delay equation. This shows robustness of solutions of the linear delay equation with respect to small nonlinear perturbations. Complicated dynamical behaviour is often a consequence of the expansivity of a dynamical system. Semi-hyperbolic dynamical systems generated by Lipschitz mappings on a Banach space are shown to be exponentially expansive, and explicit rates of expansion are determined. The result is applied to a nonsmooth noninvertible system generated by delay differential equation. It is shown that semi-hyperbolic mappings are locally φ-contracting, where -0 is the Hausdorff measure of noncompactness, and that a linear operator is semi-hyperbolic if and only if it is φ-contracting and has no spectral values on the unit circle. The definition of φ-bi-shadowing is given and it is shown that semi-hyperbolic mappings in Banach spaces are φ-bi-shadowing with respect to locally condensing continuous comparison mappings. The result is applied to linear delay differential equations of neutral type with nonsmooth perturbations. Finally, it is shown that a small delay perturbation of an ordinary differential equation with a homoclinic trajectory is ‘chaotic’.

Fuzzy neural networks for control of dynamic systems

This thesis provides a unified and comprehensive treatment of the fuzzy neural networks as the intelligent controllers. This work has been motivated by a need to develop the solid control methodologies capable of coping with the complexity, the nonlinearity, the interactions, and the time variance of the processes under control. In addition, the dynamic behavior of such processes is strongly influenced by the disturbances and the noise, and such processes are characterized by a large degree of uncertainty. Therefore, it is important to integrate an intelligent component to increase the control system ability to extract the functional relationships from the process and to change such relationships to improve the control precision, that is, to display the learning and the reasoning abilities. The objective of this thesis was to develop a self-organizing learning controller for above processes by using a combination of the fuzzy logic and the neural networks. An on-line, direct fuzzy neural controller using the process input-output measurement data and the reference model with both structural and parameter tuning has been developed to fulfill the above objective. A number of practical issues were considered. This includes the dynamic construction of the controller in order to alleviate the bias/variance dilemma, the universal approximation property, and the requirements of the locality and the linearity in the parameters. Several important issues in the intelligent control were also considered such as the overall control scheme, the requirement of the persistency of excitation and the bounded learning rates of the controller for the overall closed loop stability. Other important issues considered in this thesis include the dependence of the generalization ability and the optimization methods on the data distribution, and the requirements for the on-line learning and the feedback structure of the controller. Fuzzy inference specific issues such as the influence of the choice of the defuzzification method, T-norm operator and the membership function on the overall performance of the controller were also discussed. In addition, the e-completeness requirement and the use of the fuzzy similarity measure were also investigated. Main emphasis of the thesis has been on the applications to the real-world problems such as the industrial process control. The applicability of the proposed method has been demonstrated through the empirical studies on several real-world control problems of industrial complexity. This includes the temperature and the number-average molecular weight control in the continuous stirred tank polymerization reactor, and the torsional vibration, the eccentricity, the hardness and the thickness control in the cold rolling mills. Compared to the traditional linear controllers and the dynamically constructed neural network, the proposed fuzzy neural controller shows the highest promise as an effective approach to such nonlinear multi-variable control problems with the strong influence of the disturbances and the noise on the dynamic process behavior. In addition, the applicability of the proposed method beyond the strictly control area has also been investigated, in particular to the data mining and the knowledge elicitation. When compared to the decision tree method and the pruned neural network method for the data mining, the proposed fuzzy neural network is able to achieve a comparable accuracy with a more compact set of rules. In addition, the performance of the proposed fuzzy neural network is much better for the classes with the low occurrences in the data set compared to the decision tree method. Thus, the proposed fuzzy neural network may be very useful in situations where the important information is contained in a small fraction of the available data.

Analysis on relationship between extreme pathways and correlated reaction sets

Background: Constraint-based modeling of reconstructed genome-scale metabolic networks has been successfully applied on several microorganisms. In constraint-based modeling, in order to characterize all allowable phenotypes, network-based pathways, such as extreme pathways and elementary flux modes, are defined. However, as the scale of metabolic network rises, the number of extreme pathways and elementary flux modes increases exponentially. Uniform random sampling solves this problem to some extent to study the contents of the available phenotypes. After uniform random sampling, correlated reaction sets can be identified by the dependencies between reactions derived from sample phenotypes. In this paper, we study the relationship between extreme pathways and correlated reaction sets.

Results: Correlated reaction sets are identified for E. coli core, red blood cell and Saccharomyces cerevisiae metabolic networks respectively. All extreme pathways are enumerated for the former two metabolic networks. As for Saccharomyces cerevisiae metabolic network, because of the large scale, we get a set of extreme pathways by sampling the whole extreme pathway space. In most cases, an extreme pathway covers a correlated reaction set in an 'all or none' manner, which means either all reactions in a correlated reaction set or none is used by some extreme pathway. In rare cases, besides the 'all or none' manner, a correlated reaction set may be fully covered by combination of a few extreme pathways with related function, which may bring redundancy and flexibility to improve the survivability of a cell. In a word, extreme pathways show strong complementary relationship on usage of reactions in the same correlated reaction set.

Conclusion: Both extreme pathways and correlated reaction sets are derived from the topology information of metabolic networks. The strong relationship between correlated reaction sets and extreme pathways suggests a possible mechanism: as a controllable unit, an extreme pathway is regulated by its corresponding correlated reaction sets, and a correlated reaction set is further regulated by the organism's regulatory network.

On a class of contest success functions

In this article, I analyze a class of contest success functions (CSFs) that satisfy Luce's Choice Axiom. I show that the functional forms of these CSFs can be fully identified if they are characterized by a partial differential equation (PDE), which has several intuitive economic interpretations. This POE approach provides foundations for popular CSFs and their generalizations.

Strain hardening behavior of high performance FBDP, TRIP and TWIP steels

Based on n-value differential equation and microstructural observation, strain hardening behaviors of FBDP, TRIP, and TWIP steels during uniaxial tension were investigated. TRIP steel exhibits both superior strength and ductility than FBDP steel, and TWIP steel displays much higher total and uniform elongations in comparison to FBDP and TRIP steels. The instantaneous n values of FBDP and TRIP steels increase at small strains, reach a maximum value, smoothly decrease at higher strains, and then rapidly drop up to the specimen rupture. The strain hardening of TRIP steel persists at higher strains where that of FBDP steel begins to diminish. TWIP steel exhibits gradually increased instantaneous n values over the whole uniform plastic deformation, implying that TWIP steel shows a much larger strain hardening capability than FBDP and TRIP steels.

A mathematical model for the spread of Strepotococcus pneumoniae with transmission dependent on serotype

We examine a mathematical model for the transmission of Streptococcus Pneumoniae amongst young children when the carriage transmission coefficient depends on the serotype. Carriage means pneumococcal colonization. There are two sequence types (STs) spreading in a population each of which can be expressed as one of two serotypes. We derive the differential equation model for the carriage spread and perform an equilibrium and global stability analysis on it. A key parameter is the effective reproduction number R e. For R e ≤ 1,  there is only the carriage-free equilibrium (CFE) and the carriage will die out whatever be the starting values. For R e > 1, unless the effective reproduction numbers of the two STs are equal, in addition to the CFE there are two carriage equilibria, one for each ST. If the ST with the largest effective reproduction number is initially present, then in the long-term the carriage will tend to the corresponding equilibrium.