Medical treatment of pediatric urolithiasis

In recent years the incidence of pediatric stone disease has increased several fold, mostly due to hypercalciuria and hypocitraturia. The goal of medical treatment is to protect the patient from formation of new stones and expansion of existing ones. The non-pharmacological means to address stone disease include high fluid intake and, frequently, modification of nutritional habits. The pharmacological treatment is based on the chemical composition of the stone and the biochemical abnormalities causing its formation; hence, chemical analysis of the stone, urine and blood is of paramount importance and should be done when the first stone is detected. This review discusses the current options of medical treatment of pediatric urolithiasis

Unraveling the Design Principle for Motif Organization in Signaling Networks

Cellular signaling networks display complex architecture. Defining the design principle of this architecture is crucial for our understanding of various biological processes. Using a mathematical model for three-node feed-forward loops, we identify that the organization of motifs in specific manner within the network serves as an important regulator of signal processing. Further, incorporating a systemic stochastic perturbation to the model we could propose a possible design principle, for higher-order organization of motifs into larger networks in order to achieve specific biological output. The design principle was then verified in a large, complex human cancer signaling network. Further analysis permitted us to classify signaling nodes of the network into robust and vulnerable nodes as a result of higher order motif organization. We show that distribution of these nodes within the network at strategic locations then provides for the range of features displayed by the signaling network

Automated Ensemble Modeling with modelMaGe: Analyzing Feedback Mechanisms in the Sho1 Branch of the HOG Pathway

In systems biology uncertainty about biological processes translates into alternative mathematical model candidates. Here, the goal is to generate, fit and discriminate several candidate models that represent different hypotheses for feedback mechanisms responsible for downregulating the response of the Sho1 branch of the yeast high osmolarity glycerol (HOG) signaling pathway after initial stimulation. Implementing and testing these candidate models by hand is a tedious and error-prone task. Therefore, we automatically generated a set of candidate models of the Sho1 branch with the tool modelMaGe. These candidate models are automatically documented, can readily be simulated and fitted automatically to data. A ranking of the models with respect to parsimonious data representation is provided, enabling discrimination between candidate models and the biological hypotheses underlying them. We conclude that a previously published model fitted spurious effects in the data. Moreover, the discrimination analysis suggests that the reported data does not support the conclusion that a desensitization mechanism leads to the rapid attenuation of Hog1 signaling in the Sho1 branch of the HOG pathway. The data rather supports a model where an integrator feedback shuts down the pathway. This conclusion is also supported by dedicated experiments that can exclusively be predicted by those models including an integrator feedback

Quantifying uncertainty, variability and likelihood for ordinary differential equation models

<p>Abstract</p> <p>Background</p> <p>In many applications, ordinary differential equation (ODE) models are subject to uncertainty or variability in initial conditions and parameters. Both, uncertainty and variability can be quantified in terms of a probability density function on the state and parameter space.</p> <p>Results</p> <p>The partial differential equation that describes the evolution of this probability density function has a form that is particularly amenable to application of the well-known method of characteristics. The value of the density at some point in time is directly accessible by the solution of the original ODE extended by a single extra dimension (for the value of the density). This leads to simple methods for studying uncertainty, variability and likelihood, with significant advantages over more traditional Monte Carlo and related approaches especially when studying regions with low probability.</p> <p>Conclusions</p> <p>While such approaches based on the method of characteristics are common practice in other disciplines, their advantages for the study of biological systems have so far remained unrecognized. Several examples illustrate performance and accuracy of the approach and its limitations.</p

Diagnostic examination of the child with urolithiasis or nephrocalcinosis

Urolithiasis and nephrocalcinosis are more frequent in children then currently anticipated, but still remain under- or misdiagnosed in a significant proportion of patients, since symptoms and signs may be subtle or misleading. All children with colicky abdominal pain or macroscopic hematuria should be examined thoroughly for urolithiasis. Also, other, more general, abdominal manifestations can be the first symptoms of renal stones. The patients and their family histories, as well as physical examination, are important initial steps for diagnostic evaluation. Thereafter, diagnostic imaging should be aimed at the location of calculi but also at identification of urinary tract anomalies or acute obstruction due to stone disease. This can often be accomplished by ultrasound examination alone, but sometimes radiological methods such as plain abdominal films or more sensitive non-enhanced computed tomography are necessary. Since metabolic causes are frequent in children, diagnostic evaluation should be meticulous so that metabolic disorders that cause recurrent urolithiasis or even renal failure, such as the primary hyperoxalurias and others, can be ruled out. The stone is not the disease itself; it is only one serious sign! Therefore, thorough and early diagnostic examination is mandatory for every infant and child with the first stone event, or with nephrocalcinosis

Fundamental issues in systems biology.

types: Journal Article; Research Support, Non-U.S. Gov'tIn the context of scientists' reflections on genomics, we examine some fundamental issues in the emerging postgenomic discipline of systems biology. Systems biology is best understood as consisting of two streams. One, which we shall call 'pragmatic systems biology', emphasises large-scale molecular interactions; the other, which we shall refer to as 'systems-theoretic biology', emphasises system principles. Both are committed to mathematical modelling, and both lack a clear account of what biological systems are. We discuss the underlying issues in identifying systems and how causality operates at different levels of organisation. We suggest that resolving such basic problems is a key task for successful systems biology, and that philosophers could contribute to its realisation. We conclude with an argument for more sociologically informed collaboration between scientists and philosophers.Funding received from the Economic and Social Research Council (ESRC), UK, and Overseas Conference Funding from the British Academy

Analysis of feedback loops and robustness in network evolution based on Boolean models

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusion</p> <p>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.</p

Soft Matrices Suppress Cooperative Behaviors among Receptor-Ligand Bonds in Cell Adhesion

The fact that biological tissues are stable over prolonged periods of time while individual receptor-ligand bonds only have limited lifetime underscores the critical importance of cooperative behaviors of multiple molecular bonds, in particular the competition between the rate of rupture of closed bonds (death rate) and the rate of rebinding of open bonds (birth rate) in a bond cluster. We have recently shown that soft matrices can greatly increase the death rate in a bond cluster by inducing severe stress concentration near the adhesion edges. In the present paper, we report a more striking effect that, irrespective of stress concentration, soft matrices also suppress the birth rate in a bond cluster by increasing the local separation distance between open bonds. This is shown by theoretical analysis as well as Monte Carlo simulations based on a stochastic-elasticity model in which stochastic descriptions of molecular bonds and elastic descriptions of interfacial force/separation are unified in a single modeling framework. Our findings not only are important for understanding the role of elastic matrices in cell adhesion, but also have general implications on adhesion between soft materials

Propagation of kinetic uncertainties through a canonical topology of the TLR4 signaling network in different regions of biochemical reaction space

<p>Abstract</p> <p>Background</p> <p>Signal transduction networks represent the information processing systems that dictate which dynamical regimes of biochemical activity can be accessible to a cell under certain circumstances. One of the major concerns in molecular systems biology is centered on the elucidation of the robustness properties and information processing capabilities of signal transduction networks. Achieving this goal requires the establishment of causal relations between the design principle of biochemical reaction systems and their emergent dynamical behaviors.</p> <p>Methods</p> <p>In this study, efforts were focused in the construction of a relatively well informed, deterministic, non-linear dynamic model, accounting for reaction mechanisms grounded on standard mass action and Hill saturation kinetics, of the canonical reaction topology underlying Toll-like receptor 4 (TLR4)-mediated signaling events. This signaling mechanism has been shown to be deployed in macrophages during a relatively short time window in response to lypopolysaccharyde (LPS) stimulation, which leads to a rapidly mounted innate immune response. An extensive computational exploration of the biochemical reaction space inhabited by this signal transduction network was performed via local and global perturbation strategies. Importantly, a broad spectrum of biologically plausible dynamical regimes accessible to the network in widely scattered regions of parameter space was reconstructed computationally. Additionally, experimentally reported transcriptional readouts of target pro-inflammatory genes, which are actively modulated by the network in response to LPS stimulation, were also simulated. This was done with the main goal of carrying out an unbiased statistical assessment of the intrinsic robustness properties of this canonical reaction topology.</p> <p>Results</p> <p>Our simulation results provide convincing numerical evidence supporting the idea that a canonical reaction mechanism of the TLR4 signaling network is capable of performing information processing in a robust manner, a functional property that is independent of the signaling task required to be executed. Nevertheless, it was found that the robust performance of the network is not solely determined by its design principle (topology), but this may be heavily dependent on the network's current position in biochemical reaction space. Ultimately, our results enabled us the identification of key rate limiting steps which most effectively control the performance of the system under diverse dynamical regimes.</p> <p>Conclusions</p> <p>Overall, our <it>in silico </it>study suggests that biologically relevant and non-intuitive aspects on the general behavior of a complex biomolecular network can be elucidated only when taking into account a wide spectrum of dynamical regimes attainable by the system. Most importantly, this strategy provides the means for a suitable assessment of the inherent variational constraints imposed by the structure of the system when systematically probing its parameter space.</p