An exploratory investigation of brain collateral circulation plasticity after cerebral ischemia in two experimental C57BL/6 mouse models

Brain collateral circulation is an essential compensatory mechanism in response to acute brain ischemia. To study the temporal evolution of brain macro and microcollateral recruitment and their reciprocal interactions in response to different ischemic conditions, we applied a combination of complementary techniques (T2-weighted magnetic resonance imaging [MRI], time of flight [TOF] angiography [MRA], cerebral blood flow [CBF] imaging and histology) in two different mouse models. Hypoperfusion was either induced by permanent bilateral common carotid artery stenosis (BCCAS) or 60-min transient unilateral middle cerebral artery occlusion (MCAO). In both models, collateralization is a very dynamic phenomenon with a global effect affecting both hemispheres. Patency of ipsilateral posterior communicating artery (PcomA) represents the main variable survival mechanism and the main determinant of stroke lesion volume and recovery in MCAO, whereas the promptness of external carotid artery retrograde flow recruitment together with PcomA patency, critically influence survival, brain ischemic lesion volume and retinopathy in BCCAS mice. Finally, different ischemic gradients shape microcollateral density and size

Normative and self-perceived orthodontic treatment need of a Peruvian university population

BACKGROUND: Previous studies on orthodontic treatment need in young adults have shown that up to 50% had malocclusions that needed orthodontic treatment. The aims of this study were to assess the normative and self-perceived need for orthodontic treatment using the Index of Orthodontic Treatment Need (IOTN) and to determine if the treatment need levels were influenced by sex, age and socio-economic status (SES) in a sample of Peruvian young adults. METHODS: 281 first-year students (157 male and 124 female students) with a mean age of 18.1 +/- 1.6 years were randomly selected and evaluated through the Dental Health Component (DHC) and Aesthetic Component (AC) of the IOTN. Structured interview and clinical examination were used to assess the students. Descriptive statistics and Chi-square tests were used for data analysis with statistical significance set at P < 0.05. RESULTS: An intra-examiner reliability of 0.89 was obtained (weighted Kappa). The percentage of students according to SES was 51.2%, 40.6% and 8.2% corresponding to low, medium and high SES respectively. The percentage of students with DHC grades 4–5 was 29.9% whereas the percentage of students with AC grades 8–10 was 1.8%. There were no significant differences in the distribution of normative and self-perceived orthodontic treatment need based on sex, age and SES comparisons. CONCLUSION: Normative orthodontic treatment need was not matched by a similar level of self-perceived treatment need in these young adults. Sex, age and SES were non-significant factors associated with levels of treatment need

Global Analysis of Dynamical Decision-Making Models through Local Computation around the Hidden Saddle

Bistable dynamical switches are frequently encountered in mathematical modeling of biological systems because binary decisions are at the core of many cellular processes. Bistable switches present two stable steady-states, each of them corresponding to a distinct decision. In response to a transient signal, the system can flip back and forth between these two stable steady-states, switching between both decisions. Understanding which parameters and states affect this switch between stable states may shed light on the mechanisms underlying the decision-making process. Yet, answering such a question involves analyzing the global dynamical (i.e., transient) behavior of a nonlinear, possibly high dimensional model. In this paper, we show how a local analysis at a particular equilibrium point of bistable systems is highly relevant to understand the global properties of the switching system. The local analysis is performed at the saddle point, an often disregarded equilibrium point of bistable models but which is shown to be a key ruler of the decision-making process. Results are illustrated on three previously published models of biological switches: two models of apoptosis, the programmed cell death and one model of long-term potentiation, a phenomenon underlying synaptic plasticity

Inferring robust gene networks from expression data by a sensitivity-based incremental evolution method

<p>Abstract</p> <p>Background</p> <p>Reconstructing gene regulatory networks (GRNs) from expression data is one of the most important challenges in systems biology research. Many computational models and methods have been proposed to automate the process of network reconstruction. Inferring robust networks with desired behaviours remains challenging, however. This problem is related to network dynamics but has yet to be investigated using network modeling.</p> <p>Results</p> <p>We propose an incremental evolution approach for inferring GRNs that takes network robustness into consideration and can deal with a large number of network parameters. Our approach includes a sensitivity analysis procedure to iteratively select the most influential network parameters, and it uses a swarm intelligence procedure to perform parameter optimization. We have conducted a series of experiments to evaluate the external behaviors and internal robustness of the networks inferred by the proposed approach. The results and analyses have verified the effectiveness of our approach.</p> <p>Conclusions</p> <p>Sensitivity analysis is crucial to identifying the most sensitive parameters that govern the network dynamics. It can further be used to derive constraints for network parameters in the network reconstruction process. The experimental results show that the proposed approach can successfully infer robust GRNs with desired system behaviors.</p

Calcium Signals Driven by Single Channel Noise

Usually, the occurrence of random cell behavior is appointed to small copy numbers of molecules involved in the stochastic process. Recently, we demonstrated for a variety of cell types that intracellular Ca2+ oscillations are sequences of random spikes despite the involvement of many molecules in spike generation. This randomness arises from the stochastic state transitions of individual Ca2+ release channels and does not average out due to the existence of steep concentration gradients. The system is hierarchical due to the structural levels channel - channel cluster - cell and a corresponding strength of coupling. Concentration gradients introduce microdomains which couple channels of a cluster strongly. But they couple clusters only weakly; too weak to establish deterministic behavior on cell level. Here, we present a multi-scale modelling concept for stochastic hierarchical systems. It simulates active molecules individually as Markov chains and their coupling by deterministic diffusion. Thus, we are able to follow the consequences of random single molecule state changes up to the signal on cell level. To demonstrate the potential of the method, we simulate a variety of experiments. Comparisons of simulated and experimental data of spontaneous oscillations in astrocytes emphasize the role of spatial concentration gradients in Ca2+ signalling. Analysis of extensive simulations indicates that frequency encoding described by the relation between average and standard deviation of interspike intervals is surprisingly robust. This robustness is a property of the random spiking mechanism and not a result of control

Modulation of the CD95-Induced Apoptosis: The Role of CD95 N-Glycosylation

Protein modifications of death receptor pathways play a central role in the regulation of apoptosis. It has been demonstrated that O-glycosylation of TRAIL-receptor (R) is essential for sensitivity and resistance towards TRAIL-mediated apoptosis. In this study we ask whether and how glycosylation of CD95 (Fas/APO-1), another death receptor, influences DISC formation and procaspase-8 activation at the CD95 DISC and thereby the onset of apoptosis. We concentrated on N-glycostructure since O-glycosylation of CD95 was not found. We applied different approaches to analyze the role of CD95 N-glycosylation on the signal transduction: in silico modeling of CD95 DISC, generation of CD95 glycosylation mutants (at N136 and N118), modulation of N-glycosylation by deoxymannojirimycin (DMM) and sialidase from Vibrio cholerae (VCN). We demonstrate that N-deglycosylation of CD95 does not block DISC formation and results only in the reduction of the procaspase-8 activation at the DISC. These findings are important for the better understanding of CD95 apoptosis regulation and reveal differences between apoptotic signaling pathways of the TRAIL and CD95 systems

Parameter estimate of signal transduction pathways

BACKGROUND: The "inverse" problem is related to the determination of unknown causes on the bases of the observation of their effects. This is the opposite of the corresponding "direct" problem, which relates to the prediction of the effects generated by a complete description of some agencies. The solution of an inverse problem entails the construction of a mathematical model and takes the moves from a number of experimental data. In this respect, inverse problems are often ill-conditioned as the amount of experimental conditions available are often insufficient to unambiguously solve the mathematical model. Several approaches to solving inverse problems are possible, both computational and experimental, some of which are mentioned in this article. In this work, we will describe in details the attempt to solve an inverse problem which arose in the study of an intracellular signaling pathway. RESULTS: Using the Genetic Algorithm to find the sub-optimal solution to the optimization problem, we have estimated a set of unknown parameters describing a kinetic model of a signaling pathway in the neuronal cell. The model is composed of mass action ordinary differential equations, where the kinetic parameters describe protein-protein interactions, protein synthesis and degradation. The algorithm has been implemented on a parallel platform. Several potential solutions of the problem have been computed, each solution being a set of model parameters. A sub-set of parameters has been selected on the basis on their small coefficient of variation across the ensemble of solutions. CONCLUSION: Despite the lack of sufficiently reliable and homogeneous experimental data, the genetic algorithm approach has allowed to estimate the approximate value of a number of model parameters in a kinetic model of a signaling pathway: these parameters have been assessed to be relevant for the reproduction of the available experimental data

Modeling the TNFα-Induced Apoptosis Pathway in Hepatocytes

The proinflammatory cytokine TNFα fails to provoke cell death in isolated hepatocytes but has been implicated in hepatocyte apoptosis during liver diseases associated with chronic inflammation. Recently, we showed that TNFα is able to sensitize primary murine hepatocytes cultured on collagen to Fas ligand-induced apoptosis and presented a mathematical model of the sensitizing effect. Here, we analyze how TNFα induces apoptosis in combination with the transcriptional inhibitor actinomycin D (ActD). Accumulation of reactive oxygen species (ROS) in response to TNFR activation turns out to be critical for sustained activation of JNK which then triggers mitochondrial pathway-dependent apoptosis. In addition, the amount of JNK is strongly upregulated in a ROS-dependent way. In contrast to TNFα plus cycloheximide no cFLIP degradation is observed suggesting a different apoptosis pathway in which the Itch-mediated cFLIP degradation and predominantly caspase-8 activation is not involved. Time-resolved data of the respective pro- and antiapoptotic factors are obtained and subjected to mathematical modeling. On the basis of these data we developed a mathematical model which reproduces the complex interplay regulating the phosphorylation status of JNK and generation of ROS. This model was fully integrated with our model of TNFα/Fas ligand sensitizing as well as with a published NF-κB-model. The resulting comprehensive model delivers insight in the dynamical interplay between the TNFα and FasL pathways, NF-κB and ROS and gives an example for successful model integration

The modular systems biology approach to investigate the control of apoptosis in Alzheimer's disease neurodegeneration

Apoptosis is a programmed cell death that plays a critical role during the development of the nervous system and in many chronic neurodegenerative diseases, including Alzheimer's disease (AD). This pathology, characterized by a progressive degeneration of cholinergic function resulting in a remarkable cognitive decline, is the most common form of dementia with high social and economic impact. Current therapies of AD are only symptomatic, therefore the need to elucidate the mechanisms underlying the onset and progression of the disease is surely needed in order to develop effective pharmacological therapies. Because of its pivotal role in neuronal cell death, apoptosis has been considered one of the most appealing therapeutic targets, however, due to the complexity of the molecular mechanisms involving the various triggering events and the many signaling cascades leading to cell death, a comprehensive understanding of this process is still lacking. Modular systems biology is a very effective strategy in organizing information about complex biological processes and deriving modular and mathematical models that greatly simplify the identification of key steps of a given process. This review aims at describing the main steps underlying the strategy of modular systems biology and briefly summarizes how this approach has been successfully applied for cell cycle studies. Moreover, after giving an overview of the many molecular mechanisms underlying apoptosis in AD, we present both a modular and a molecular model of neuronal apoptosis that suggest new insights on neuroprotection for this disease