Additive functions in boolean models of gene regulatory network modules.

Gene-on-gene regulations are key components of every living organism. Dynamical abstract models of genetic regulatory networks help explain the genome's evolvability and robustness. These properties can be attributed to the structural topology of the graph formed by genes, as vertices, and regulatory interactions, as edges. Moreover, the actual gene interaction of each gene is believed to play a key role in the stability of the structure. With advances in biology, some effort was deployed to develop update functions in Boolean models that include recent knowledge. We combine real-life gene interaction networks with novel update functions in a Boolean model. We use two sub-networks of biological organisms, the yeast cell-cycle and the mouse embryonic stem cell, as topological support for our system. On these structures, we substitute the original random update functions by a novel threshold-based dynamic function in which the promoting and repressing effect of each interaction is considered. We use a third real-life regulatory network, along with its inferred Boolean update functions to validate the proposed update function. Results of this validation hint to increased biological plausibility of the threshold-based function. To investigate the dynamical behavior of this new model, we visualized the phase transition between order and chaos into the critical regime using Derrida plots. We complement the qualitative nature of Derrida plots with an alternative measure, the criticality distance, that also allows to discriminate between regimes in a quantitative way. Simulation on both real-life genetic regulatory networks show that there exists a set of parameters that allows the systems to operate in the critical region. This new model includes experimentally derived biological information and recent discoveries, which makes it potentially useful to guide experimental research. The update function confers additional realism to the model, while reducing the complexity and solution space, thus making it easier to investigate.

Modelling world investment markets using threshold conditional correlation models

In this paper we propose a parsimonious regime-switching approach to model the correlations between assets, the threshold conditional correlation (TCC) model. This method allows the dynamics of the correlations to change from one state (or regime) to another as a function of observable transition variables. Our model is similar in spirit to Silvennoinen and Teräsvirta (2009) and Pelletier (2006) but with the appealing feature that it does not suffer from the course of dimensionality. In particular, estimation of the parameters of the TCC involves a simple grid search procedure. In addition, it is easy to guarantee a positive definite correlation matrix because the TCC estimator is given by the sample correlation matrix, which is positive definite by construction. The methodology is illustrated by evaluating the behaviour of international equities, govenrment bonds and major exchange rates, first separately and then jointly. We also test and allow for different parts in the correlation matrix to be governed by different transition variables. For this, we estimate a multi-threshold TCC specification. Further, we evaluate the economic performance of the TCC model against a constant conditional correlation (CCC) estimator using a Diebold-Mariano type test. We conclude that threshold correlation modelling gives rise to a significant reduction in portfolio´s variance.

Neuronal in vitro models for the estimation of acute systemic toxicity.

The objective of the EU funded integrated project "ACuteTox" is to develop a strategy in which general cytotoxicity, together with organ-specific endpoints and biokinetic features, are taken into consideration in the in vitro prediction of oral acute systemic toxicity. With regard to the nervous system, the effects of 23 reference chemicals were tested with approximately 50 endpoints, using a neuronal cell line, primary neuronal cell cultures, brain slices and aggregated brain cell cultures. Comparison of the in vitro neurotoxicity data with general cytotoxicity data generated in a non-neuronal cell line and with in vivo data such as acute human lethal blood concentration, revealed that GABA(A) receptor function, acetylcholine esterase activity, cell membrane potential, glucose uptake, total RNA expression and altered gene expression of NF-H, GFAP, MBP, HSP32 and caspase-3 were the best endpoints to use for further testing with 36 additional chemicals. The results of the second analysis showed that no single neuronal endpoint could give a perfect improvement in the in vitro-in vivo correlation, indicating that several specific endpoints need to be analysed and combined with biokinetic data to obtain the best correlation with in vivo acute toxicity.

Two-sided learning in new keynesian models: dynamics, (lack of) convergence and the value of information

This paper investigates the role of learning by private agents and the central bank (two-sided learning) in a New Keynesian framework in which both sides of the economy have asymmetric and imperfect knowledge about the true data generating process. We assume that all agents employ the data that they observe (which may be distinct for different sets of agents) to form beliefs about unknown aspects of the true model of the economy, use their beliefs to decide on actions, and revise these beliefs through a statistical learning algorithm as new information becomes available. We study the short-run dynamics of our model and derive its policy recommendations, particularly with respect to central bank communications. We demonstrate that two-sided learning can generate substantial increases in volatility and persistence, and alter the behavior of the variables in the model in a signifficant way. Our simulations do not converge to a symmetric rational expectations equilibrium and we highlight one source that invalidates the convergence results of Marcet and Sargent (1989). Finally, we identify a novel aspect of central bank communication in models of learning: communication can be harmful if the central bank's model is substantially mis-specified

Suppression of tumor angiogenesis through the inhibition of integrin function and signaling in endothelial cells: which side to target?

Tumor angiogenesis is an essential step in tumor progression and metastasis formation. Suppression of tumor angiogenesis results in the inhibition of tumor growth. Recent evidence indicates that vascular integrins, in particular alpha V beta 3, are important regulators of angiogenesis, including tumor angiogenesis. Integrin alpha V beta 3 antagonists, such as blocking antibodies or peptides, suppress tumor angiogenesis and tumor progression in many preclinical tumor models. The potential therapeutic efficacy of extracellular integrin antagonists in human cancer is currently being tested in clinical trials. Selective disruption of the tumor vasculature by high doses of tumor necrosis factor (TNF) and interferon gamma (IFN-gamma), and the antiangiogenic activity of nonsteroidal anti-inflammatory drugs are associated with the suppression of integrin alpha V beta 3 function and signaling in endothelial cells. Furthermore, expression of isolated integrin cytoplasmic domains disrupts integrin-dependent adhesion, resulting in endothelial cell detachment and apoptosis. These results confirm the critical role of vascular integrins in promoting endothelial cell survival and angiogenesis and suggest that intracellular targeting of integrin function and signaling may be an alternative strategy to extracellular integrin antagonists for the therapeutic inhibition of tumor angiogenesis.

Bayesian nash equilibrium in ''linear'' cournot models with private information about costs

Calculating explicit closed form solutions of Cournot models where firms have private information about their costs is, in general, very cumbersome. Most authors consider therefore linear demands and constant marginal costs. However, within this framework, the nonnegativity constraint on prices (and quantities) has been ignored or not properly dealt with and the correct calculation of all Bayesian Nash equilibria is more complicated than expected. Moreover, multiple symmetric and interior Bayesianf equilibria may exist for an open set of parameters. The reason for this is that linear demand is not really linear, since there is a kink at zero price: the general ''linear'' inverse demand function is P (Q) = max{a - bQ, 0} rather than P (Q) = a - bQ.

Structure-function analyses point to a polynucleotide-accommodating groove essential for APOBEC3A restriction activities.

Members of the human APOBEC3 family of editing enzymes can inhibit various mobile genetic elements. APOBEC3A (A3A) can block the retrotransposon LINE-1 and the parvovirus adeno-associated virus type 2 (AAV-2) but does not inhibit retroviruses. In contrast, APOBEC3G (A3G) can block retroviruses but has only limited effects on AAV-2 or LINE-1. What dictates this differential target specificity remains largely undefined. Here, we modeled the structure of A3A based on its homology with the C-terminal domain of A3G and further compared the sequence of human A3A to those of 11 nonhuman primate orthologues. We then used these data to perform a mutational analysis of A3A, examining its ability to restrict LINE-1, AAV-2, and foreign plasmid DNA and to edit a single-stranded DNA substrate. The results revealed an essential functional role for the predicted single-stranded DNA-docking groove located around the A3A catalytic site. Within this region, amino acid differences between A3A and A3G are predicted to affect the shape of the polynucleotide-binding groove. Correspondingly, transferring some of these A3A residues to A3G endows the latter protein with the ability to block LINE-1 and AAV-2. These results suggest that the target specificity of APOBEC3 family members is partly defined by structural features influencing their interaction with polynucleotide substrates.

Predictions from information-theoretical models of nonequilibrium radiation

The radiation distribution function used by Domínguez and Jou [Phys. Rev. E 51, 158 (1995)] has been recently modified by Domínguez-Cascante and Faraudo [Phys. Rev. E 54, 6933 (1996)]. However, in these studies neither distribution was written in terms of directly measurable quantities. Here a solution to this problem is presented, and we also propose an experiment that may make it possible to determine the distribution function of nonequilibrium radiation experimentally. The results derived do not depend on a specific distribution function for the matter content of the system

Genetic loci influencing kidney function and chronic kidney disease.

Using genome-wide association, we identify common variants at 2p12-p13, 6q26, 17q23 and 19q13 associated with serum creatinine, a marker of kidney function (P = 10(-10) to 10(-15)). Of these, rs10206899 (near NAT8, 2p12-p13) and rs4805834 (near SLC7A9, 19q13) were also associated with chronic kidney disease (P = 5.0 x 10(-5) and P = 3.6 x 10(-4), respectively). Our findings provide insight into metabolic, solute and drug-transport pathways underlying susceptibility to chronic kidney disease.

Severe hyperlipidemia causes impaired renin-angiotensin system function in apolipoprotein E deficient mice.

Dyslipidemia is a known risk factor for cardiovascular diseases and may associate with renal injury. Using mouse models with various degrees of hypercholesterolemia and hypertryliceridemia, we investigated the effects of lipids on the renin-angiotensin system (RAS). ApoE-/- mice were fed either a high fat diet (HF-ApoE-/-; mice developed hypertriglyceridemia and severe hypercholesterolemia) or regular chow (R-ApoE(-/-); mice developed less severe hypercholesterolemia only). Renal histopathology in the HF-ApoE-/- revealed massive lipid accumulation especially at the glomerular vascular pole. In these mice plasma renin concentration was significantly reduced (489+/-111 ng/(ml h) versus 1023+/-90 ng/(ml h) in R-ApoE-/- mice) and blood pressure was consequently significantly lower than in R-ApoE-/- (104+/-2 mmHg versus 115+/-2 mmHg, respectively). A model of renin-dependent renovascular hypertension (two-kidney, one clip) was generated and HF-ApoE-/- mice proved unable to increase renin secretion, and blood pressure, in response to diminished renal perfusion as compared to regular chow fed mice (665+/-90 ng/(ml h) versus 2393+/-372 ng/(ml h), respectively and 106+/-3 mmHg versus 140+/-2 mmHg, respectively). Hypertriglyceridemia and severe hypercholesterolemia are associated with renal lipid deposition and impaired renin secretion in ApoE-/- mice exposed to high fat diet. These observations further characterize the phenotype of this widely used mouse model and provide a rationale for the use of these mice to study lipid induced organ damage.

Pharmacological stimulation of edar signaling in the adult enhances sebaceous gland size and function.

Impaired ectodysplasin A (EDA) receptor (EDAR) signaling affects ectodermally derived structures including teeth, hair follicles, and cutaneous glands. The X-linked hypohidrotic ectodermal dysplasia (XLHED), resulting from EDA deficiency, can be rescued with lifelong benefits in animal models by stimulation of ectodermal appendage development with EDAR agonists. Treatments initiated later in the developmental period restore progressively fewer of the affected structures. It is unknown whether EDAR stimulation in adults with XLHED might have beneficial effects. In adult Eda mutant mice treated for several weeks with agonist anti-EDAR antibodies, we find that sebaceous gland size and function can be restored to wild-type levels. This effect is maintained upon chronic treatment but reverses slowly upon cessation of treatment. Sebaceous glands in all skin regions respond to treatment, although to varying degrees, and this is accompanied in both Eda mutant and wild-type mice by sebum secretion to levels higher than those observed in untreated controls. Edar is expressed at the periphery of the glands, suggesting a direct homeostatic effect of Edar stimulation on the sebaceous gland. Sebaceous gland size and sebum production may serve as biomarkers for EDAR stimulation, and EDAR agonists may improve skin dryness and eczema frequently observed in XLHED.

Targeted Gene Deletion and In Vivo Analysis of Putative Virulence Gene Function in the Pathogenic Dermatophyte Arthroderma benhamiae.

Dermatophytes cause the majority of superficial mycoses in humans and animals. However, little is known about the pathogenicity of this specialized group of filamentous fungi, for which molecular research has been limited thus far. During experimental infection of guinea pigs by the human pathogenic dermatophyte Arthroderma benhamiae, we recently detected the activation of the fungal gene encoding malate synthase AcuE, a key enzyme of the glyoxylate cycle. By the establishment of the first genetic system for A. benhamiae, specific ΔacuE mutants were constructed in a wild-type strain and, in addition, in a derivative in which we inactivated the nonhomologous end-joining pathway by deletion of the A. benhamiae KU70 gene. The absence of AbenKU70 resulted in an increased frequency of the targeted insertion of linear DNA by homologous recombination, without notably altering the monitored in vitro growth abilities of the fungus or its virulence in a guinea pig infection model. Phenotypic analyses of ΔacuE mutants and complemented strains depicted that malate synthase is required for the growth of A. benhamiae on lipids, major constituents of the skin. However, mutant analysis did not reveal a pathogenic role of the A. benhamiae enzyme in guinea pig dermatophytosis or during epidermal invasion of the fungus in an in vitro model of reconstituted human epidermis. The presented efficient system for targeted genetic manipulation in A. benhamiae, paired with the analyzed infection models, will advance the functional characterization of putative virulence determinants in medically important dermatophytes.

Concise reviews: in vitro-produced pancreas organogenesis models in three dimensions: self-organization from few stem cells or progenitors.

Three-dimensional models of organ biogenesis have recently flourished. They promote a balance between stem/progenitor cell expansion and differentiation without the constraints of flat tissue culture vessels, allowing for autonomous self-organization of cells. Such models allow the formation of miniature organs in a dish and are emerging for the pancreas, starting from embryonic progenitors and adult cells. This review focuses on the currently available systems and how these allow new types of questions to be addressed. We discuss the expected advancements including their potential to study human pancreas development and function as well as to develop diabetes models and therapeutic cells.

Towards robust network based complex systems : from evolutionary cellular automata to biological models

Abstract Sitting between your past and your future doesn't mean you are in the present. Dakota Skye Complex systems science is an interdisciplinary field grouping under the same umbrella dynamical phenomena from social, natural or mathematical sciences. The emergence of a higher order organization or behavior, transcending that expected of the linear addition of the parts, is a key factor shared by all these systems. Most complex systems can be modeled as networks that represent the interactions amongst the system's components. In addition to the actual nature of the part's interactions, the intrinsic topological structure of underlying network is believed to play a crucial role in the remarkable emergent behaviors exhibited by the systems. Moreover, the topology is also a key a factor to explain the extraordinary flexibility and resilience to perturbations when applied to transmission and diffusion phenomena. In this work, we study the effect of different network structures on the performance and on the fault tolerance of systems in two different contexts. In the first part, we study cellular automata, which are a simple paradigm for distributed computation. Cellular automata are made of basic Boolean computational units, the cells; relying on simple rules and information from- the surrounding cells to perform a global task. The limited visibility of the cells can be modeled as a network, where interactions amongst cells are governed by an underlying structure, usually a regular one. In order to increase the performance of cellular automata, we chose to change its topology. We applied computational principles inspired by Darwinian evolution, called evolutionary algorithms, to alter the system's topological structure starting from either a regular or a random one. The outcome is remarkable, as the resulting topologies find themselves sharing properties of both regular and random network, and display similitudes Watts-Strogtz's small-world network found in social systems. Moreover, the performance and tolerance to probabilistic faults of our small-world like cellular automata surpasses that of regular ones. In the second part, we use the context of biological genetic regulatory networks and, in particular, Kauffman's random Boolean networks model. In some ways, this model is close to cellular automata, although is not expected to perform any task. Instead, it simulates the time-evolution of genetic regulation within living organisms under strict conditions. The original model, though very attractive by it's simplicity, suffered from important shortcomings unveiled by the recent advances in genetics and biology. We propose to use these new discoveries to improve the original model. Firstly, we have used artificial topologies believed to be closer to that of gene regulatory networks. We have also studied actual biological organisms, and used parts of their genetic regulatory networks in our models. Secondly, we have addressed the improbable full synchronicity of the event taking place on. Boolean networks and proposed a more biologically plausible cascading scheme. Finally, we tackled the actual Boolean functions of the model, i.e. the specifics of how genes activate according to the activity of upstream genes, and presented a new update function that takes into account the actual promoting and repressing effects of one gene on another. Our improved models demonstrate the expected, biologically sound, behavior of previous GRN model, yet with superior resistance to perturbations. We believe they are one step closer to the biological reality.

Detection of Optimal Models in Parameter Space with Support Vector Machines

The paper proposes an approach aimed at detecting optimal model parameter combinations to achieve the most representative description of uncertainty in the model performance. A classification problem is posed to find the regions of good fitting models according to the values of a cost function. Support Vector Machine (SVM) classification in the parameter space is applied to decide if a forward model simulation is to be computed for a particular generated model. SVM is particularly designed to tackle classification problems in high-dimensional space in a non-parametric and non-linear way. SVM decision boundaries determine the regions that are subject to the largest uncertainty in the cost function classification, and, therefore, provide guidelines for further iterative exploration of the model space. The proposed approach is illustrated by a synthetic example of fluid flow through porous media, which features highly variable response due to the parameter values' combination.