Adsorption and reaction of CO on (Pd–)Al2O3 and (Pd–)ZrO2: vibrational spectroscopy of carbonate formation

γ-Alumina is widely used as an oxide support in catalysis, and palladium nanoparticles supported by alumina represent one of the most frequently used dispersed metals. The surface sites of the catalysts are often probed via FTIR spectroscopy upon CO adsorption, which may result in the formation of surface carbonate species. We have examined this process in detail utilizing FTIR to monitor carbonate formation on γ-alumina and zirconia upon exposure to isotopically labelled and unlabelled CO and CO2. The same was carried out for well-defined Pd nanoparticles supported on Al2O3 or ZrO2. A water gas shift reaction of CO with surface hydroxyls was detected, which requires surface defect sites and adjacent OH groups. Furthermore, we have studied the effect of Cl synthesis residues, leading to strongly reduced carbonate formation and changes in the OH region (isolated OH groups were partly replaced or were even absent). To corroborate this finding, samples were deliberately poisoned with Cl to an extent comparable to that of synthesis residues, as confirmed by Auger electron spectroscopy. For catalysts prepared from Cl-containing precursors a new CO band at 2164 cm−1 was observed in the carbonyl region, which was ascribed to Pd interacting with Cl. Finally, the FTIR measurements were complemented by quantification of the amount of carbonates formed via chemisorption, which provides a tool to determine the concentration of reactive defect sites on the alumina surface

Indeterminacy of Reverse Engineering of Gene Regulatory Networks: The Curse of Gene Elasticity

Gene Regulatory Networks (GRNs) have become a major focus of interest in recent years. A number of reverse engineering approaches have been developed to help uncover the regulatory networks giving rise to the observed gene expression profiles. However, this is an overspecified problem due to the fact that more than one genotype (network wiring) can give rise to the same phenotype. We refer to this phenomenon as “gene elasticity.” In this work, we study the effect of this particular problem on the pure, data-driven inference of gene regulatory networks.We simulated a four-gene network in order to produce “data” (protein levels) that we use in lieu of real experimental data. We then optimized the network connections between the four genes with a view to obtain the original network that gave rise to the data. We did this for two different cases: one in which only the network connections were optimized and the other in which both the network connections as well as the kinetic parameters (given as reaction probabilities in our case) were estimated. We observed that multiple genotypes gave rise to very similar protein levels. Statistical experimentation indicates that it is impossible to differentiate between the different networks on the basis of both equilibrium as well as dynamic data.We show explicitly that reverse engineering of GRNs from pure expression data is an indeterminate problem. Our results suggest the unsuitability of an inferential, purely data-driven approach for the reverse engineering transcriptional networks in the case of gene regulatory networks displaying a certain level of complexity

Boolean Dynamics with Random Couplings

This paper reviews a class of generic dissipative dynamical systems called N-K models. In these models, the dynamics of N elements, defined as Boolean variables, develop step by step, clocked by a discrete time variable. Each of the N Boolean elements at a given time is given a value which depends upon K elements in the previous time step. We review the work of many authors on the behavior of the models, looking particularly at the structure and lengths of their cycles, the sizes of their basins of attraction, and the flow of information through the systems. In the limit of infinite N, there is a phase transition between a chaotic and an ordered phase, with a critical phase in between. We argue that the behavior of this system depends significantly on the topology of the network connections. If the elements are placed upon a lattice with dimension d, the system shows correlations related to the standard percolation or directed percolation phase transition on such a lattice. On the other hand, a very different behavior is seen in the Kauffman net in which all spins are equally likely to be coupled to a given spin. In this situation, coupling loops are mostly suppressed, and the behavior of the system is much more like that of a mean field theory. We also describe possible applications of the models to, for example, genetic networks, cell differentiation, evolution, democracy in social systems and neural networks.Comment: 69 pages, 16 figures, Submitted to Springer Applied Mathematical Sciences Serie

Piecewise-Linear Models of Genetic Regulatory Networks: Theory and Example

International audienceThe experimental study of genetic regulatory networks has made tremendous progress in recent years resulting in a huge amount of data on the molecular interactions in model organisms. It is therefore not possible anymore to intuitively understand how the genes and interactions together influence the behavior of the system. In order to answer such questions, a rigorous modeling and analysis approach is necessary. In this chapter, we present a family of such models and analysis methods enabling us to better understand the dynam-ics of genetic regulatory networks. We apply such methods to the network that underlies the nutritional stress response of the bacterium E. coli. The functioning and development of living organisms is controlled by large and complex networks of genes, proteins, small molecules, and their interactions, so-called genetic regulatory networks. The study of these networks has recently taken a qualitative leap through the use of modern genomic techniques that allow for the simultaneous measurement of the expression levels of all genes of an organism. This has resulted in an ever growing description of the interactions in the studied genetic regulatory networks. However, it is necessary to go beyond the simple description of the interactions in order to understand the behavior of these networks and their relation with the actual functioning of the organism. Since the networks under study are usually very large, an intuitive approach for their understanding is out of ques-tion. In order to support this work, mathematical and computer tools are necessary: the unambiguous description of the phenomena that mathematical models provide allows for a detailed analysis of the behaviors at play, though they might not exactly represent the exact behavior of the networks. In this chapter, we will be mostly interested in the modeling of the genetic reg-ulatory networks by means of differential equations. This classical approach allows precise numerical predictions of deterministic dynamic properties of genetic regu-latory networks to be made. However, for most networks of biological interest the application of differential equations is far from straightforward. First, the biochemi-cal reaction mechanisms underlying the interactions are usually not or incompletel

Bistability versus Bimodal Distributions in Gene Regulatory Processes from Population Balance

In recent times, stochastic treatments of gene regulatory processes have appeared in the literature in which a cell exposed to a signaling molecule in its environment triggers the synthesis of a specific protein through a network of intracellular reactions. The stochastic nature of this process leads to a distribution of protein levels in a population of cells as determined by a Fokker-Planck equation. Often instability occurs as a consequence of two (stable) steady state protein levels, one at the low end representing the “off” state, and the other at the high end representing the “on” state for a given concentration of the signaling molecule within a suitable range. A consequence of such bistability has been the appearance of bimodal distributions indicating two different populations, one in the “off” state and the other in the “on” state. The bimodal distribution can come about from stochastic analysis of a single cell. However, the concerted action of the population altering the extracellular concentration in the environment of individual cells and hence their behavior can only be accomplished by an appropriate population balance model which accounts for the reciprocal effects of interaction between the population and its environment. In this study, we show how to formulate a population balance model in which stochastic gene expression in individual cells is incorporated. Interestingly, the simulation of the model shows that bistability is neither sufficient nor necessary for bimodal distributions in a population. The original notion of linking bistability with bimodal distribution from single cell stochastic model is therefore only a special consequence of a population balance model

Threshold-dominated regulation hides genetic variation in gene expression networks

<p>Abstract</p> <p>Background</p> <p>In dynamical models with feedback and sigmoidal response functions, some or all variables have thresholds around which they regulate themselves or other variables. A mathematical analysis has shown that when the dose-response functions approach binary or on/off responses, any variable with an equilibrium value close to one of its thresholds is very robust to parameter perturbations of a homeostatic state. We denote this threshold robustness. To check the empirical relevance of this phenomenon with response function steepnesses ranging from a near on/off response down to Michaelis-Menten conditions, we have performed a simulation study to investigate the degree of threshold robustness in models for a three-gene system with one downstream gene, using several logical input gates, but excluding models with positive feedback to avoid multistationarity. Varying parameter values representing functional genetic variation, we have analysed the coefficient of variation (<it>CV</it>) of the gene product concentrations in the stable state for the regulating genes in absolute terms and compared to the <it>CV </it>for the unregulating downstream gene. The sigmoidal or binary dose-response functions in these models can be considered as phenomenological models of the aggregated effects on protein or mRNA expression rates of all cellular reactions involved in gene expression.</p> <p>Results</p> <p>For all the models, threshold robustness increases with increasing response steepness. The <it>CV</it>s of the regulating genes are significantly smaller than for the unregulating gene, in particular for steep responses. The effect becomes less prominent as steepnesses approach Michaelis-Menten conditions. If the parameter perturbation shifts the equilibrium value too far away from threshold, the gene product is no longer an effective regulator and robustness is lost. Threshold robustness arises when a variable is an active regulator around its threshold, and this function is maintained by the feedback loop that the regulator necessarily takes part in and also is regulated by. In the present study all feedback loops are negative, and our results suggest that threshold robustness is maintained by negative feedback which necessarily exists in the homeostatic state.</p> <p>Conclusion</p> <p>Threshold robustness of a variable can be seen as its ability to maintain an active regulation around its threshold in a homeostatic state despite external perturbations. The feedback loop that the system necessarily possesses in this state, ensures that the robust variable is itself regulated and kept close to its threshold. Our results suggest that threshold regulation is a generic phenomenon in feedback-regulated networks with sigmoidal response functions, at least when there is no positive feedback. Threshold robustness in gene regulatory networks illustrates that hidden genetic variation can be explained by systemic properties of the genotype-phenotype map.</p

The state of indoor air quality in Pakistan—a review

Background and purpose: In Pakistan, almost 70% of the population lives in rural areas. Ninety-four percent of households in rural areas and 58% in urban areas depend on biomass fuels (wood, dung, and agricultural waste). These solid fuels have poor combustion efficiency. Due to incomplete combustion of the biomass fuels, the resulting smoke contains a range of health-deteriorating substances that, at varying concentrations, can pose a serious threat to human health. Indoor air pollution accounts for 28,000 deaths a year and 40 million cases of acute respiratory illness. It places a significant economic burden on Pakistan with an annual cost of 1% of GDP. Despite the mounting evidence of an association between indoor air pollution and ill health, policy makers have paid little attention to it. This review analyzes the existing information on levels of indoor air pollution in Pakistan and suggests suitable intervention methods. Methods: This review is focused on studies of indoor air pollution, due to biomass fuels, in Pakistan published in both scientific journals and by the Government and international organizations. In addition, the importance of environmental tobacco smoke as an indoor pollutant is highlighted. Results: Unlike many other developing countries, there are no long-term studies on the levels of indoor air pollution. The limited studies that have been undertaken indicate that indoor air pollution should be a public health concern. High levels of particulate matter and carbon monoxide have been reported, and generally, women and children are subject to the maximum exposure. There have been a few interventions, with improved stoves, in some areas since 1990. However, the effectiveness of these interventions has not been fully evaluated. Conclusion: Indoor air pollution has a significant impact on the health of the population in Pakistan. The use of biomass fuel as an energy source is the biggest contributor to poor indoor air quality followed by smoking. In order to arrest the increasing levels of indoor pollution, there is a dire need to recognize it as a major health hazard and formulate a national policy to combat it. An integrated effort, with involvement of all stakeholders, could yield promising results. A countrywide public awareness campaign, on the association of indoor air pollution with ill health, followed by practical intervention would be an appropriate approach. Due to the current socioeconomic conditions in the country, development and adoption of improved cooking stoves for the population at large would be the most suitable choice. However, the potential of biogas as a fuel should be explored further, and modern fuels (natural gas and LPG) need to be accessible and economical. Smoking in closed public spaces should be banned, and knowledge of the effect of smoking on indoor air quality needs to be quantified. © 2010 Springer-Verlag

A framework for integrated environmental health impact assessment of systemic risks

Traditional methods of risk assessment have provided good service in support of policy, mainly in relation to standard setting and regulation of hazardous chemicals or practices. In recent years, however, it has become apparent that many of the risks facing society are systemic in nature – complex risks, set within wider social, economic and environmental contexts. Reflecting this, policy-making too has become more wide-ranging in scope, more collaborative and more precautionary in approach. In order to inform such policies, more integrated methods of assessment are needed. Based on work undertaken in two large EU-funded projects (INTARESE and HEIMTSA), this paper reviews the range of approaches to assessment now in used, proposes a framework for integrated environmental health impact assessment (both as a basis for bringing together and choosing between different methods of assessment, and extending these to more complex problems), and discusses some of the challenges involved in conducting integrated assessments to support policy