In silico modeling of the effects of alpha-synuclein oligomerization on dopaminergic neuronal homeostasis.

BACKGROUND: Alpha-synuclein (ASYN) is central in Parkinson's disease (PD) pathogenesis. Converging pieces of evidence suggest that the levels of ASYN expression play a critical role in both familial and sporadic Parkinson's disease. ASYN fibrils are the main component of inclusions called Lewy Bodies (LBs) which are found mainly in the surviving neurons of the substantia nigra. Despite the accumulated knowledge regarding the involvement of ASYN in molecular mechanisms underlying the development of PD, there is much information missing which prevents understanding the causes of the disease and how to stop its progression. RESULTS: Using a Systems Biology approach, we develop a biomolecular reactions model that describes the intracellular ASYN dynamics in relation to overexpression, post-translational modification, oligomerization and degradation of the protein. Especially for the proteolysis of ASYN, the model takes into account the biological knowledge regarding the contribution of Chaperone Mediated Autophagy (CMA), macro-autophagic and proteasome pathways in the protein's degradation. Importantly, inhibitory phenomena, caused by ASYN, concerning CMA (more specifically the lysosomal-associated membrane protein 2a, abbreviated as Lamp2a receptor, which is the rate limiting step of CMA) and the proteasome are carefully modeled. The model is validated by simulation studies of known experimental overexpression data from SH-SY5Y cells and the unknown model parameters are estimated either computationally or by experimental fitting. The calibrated model is then tested under three hypothetical intervention scenarios and in all cases predicts increased cell viability that agrees with experimental evidence. The biomodel has been annotated and is made available in SBML format. CONCLUSIONS: The mathematical model presented here successfully simulates the dynamic phenomena of ASYN overexpression and oligomerization and predicts the biological system's behavior in a number of scenarios not used for model calibration. It allows, for the first time, to qualitatively estimate the protein levels that are capable of deregulating proteolytic homeostasis. In addition, it can help form new hypotheses for intervention that could be tested experimentally.

How neurons migrate: a dynamic in-silico model of neuronal migration in the developing cortex.

BACKGROUND: Neuronal migration, the process by which neurons migrate from their place of origin to their final position in the brain, is a central process for normal brain development and function. Advances in experimental techniques have revealed much about many of the molecular components involved in this process. Notwithstanding these advances, how the molecular machinery works together to govern the migration process has yet to be fully understood. Here we present a computational model of neuronal migration, in which four key molecular entities, Lis1, DCX, Reelin and GABA, form a molecular program that mediates the migration process. RESULTS: The model simulated the dynamic migration process, consistent with in-vivo observations of morphological, cellular and population-level phenomena. Specifically, the model reproduced migration phases, cellular dynamics and population distributions that concur with experimental observations in normal neuronal development. We tested the model under reduced activity of Lis1 and DCX and found an aberrant development similar to observations in Lis1 and DCX silencing expression experiments. Analysis of the model gave rise to unforeseen insights that could guide future experimental study. Specifically: (1) the model revealed the possibility that under conditions of Lis1 reduced expression, neurons experience an oscillatory neuron-glial association prior to the multipolar stage; and (2) we hypothesized that observed morphology variations in rats and mice may be explained by a single difference in the way that Lis1 and DCX stimulate bipolar motility. From this we make the following predictions: (1) under reduced Lis1 and enhanced DCX expression, we predict a reduced bipolar migration in rats, and (2) under enhanced DCX expression in mice we predict a normal or a higher bipolar migration. CONCLUSIONS: We present here a system-wide computational model of neuronal migration that integrates theory and data within a precise, testable framework. Our model accounts for a range of observable behaviors and affords a computational framework to study aspects of neuronal migration as a complex process that is driven by a relatively simple molecular program. Analysis of the model generated new hypotheses and yet unobserved phenomena that may guide future experimental studies. This paper thus reports a first step toward a comprehensive in-silico model of neuronal migration.

Automatic noise quantification for confocal fluorescence microscopy images.

Due to photo-toxicity, fluorescence microscopy imaging is a trade-off between signal to noise ratio, total observation time and spatio-temporal resolution. We propose a new and simple method to quantify the signal-dependent and the non-signal-dependent components of the noise from a single fluorescence microscopy image. No reference image is required and the computation time allows on line quantification of the noise. The estimation is realized in two steps. We first estimate the signal-dependent noise by fitting the intensity of an estimated noise free image, computed by median filtering, to the estimated global noise variance. The second step estimates the signal-independent noise as the background variance, by computing the variance of the most homogeneous sub blocks of the image. After having shown the validity of our approach, we then use this method to quantify the noise in one experiment and show its correlation with the qualitative expected image quality.

Sequential activation of metabolic pathways: a dynamic optimization approach.

The regulation of cellular metabolism facilitates robust cellular operation in the face of changing external conditions. The cellular response to this varying environment may include the activation or inactivation of appropriate metabolic pathways. Experimental and numerical observations of sequential timing in pathway activation have been reported in the literature. It has been argued that such patterns can be rationalized by means of an underlying optimal metabolic design. In this paper we pose a dynamic optimization problem that accounts for time-resource minimization in pathway activation under constrained total enzyme abundance. The optimized variables are time-dependent enzyme concentrations that drive the pathway to a steady state characterized by a prescribed metabolic flux. The problem formulation addresses unbranched pathways with irreversible kinetics. Neither specific reaction kinetics nor fixed pathway length are assumed.In the optimal solution, each enzyme follows a switching profile between zero and maximum concentration, following a temporal sequence that matches the pathway topology. This result provides an analytic justification of the sequential activation previously described in the literature. In contrast with the existent numerical approaches, the activation sequence is proven to be optimal for a generic class of monomolecular kinetics. This class includes, but is not limited to, Mass Action, Michaelis-Menten, Hill, and some Power-law models. This suggests that sequential enzyme expression may be a common feature of metabolic regulation, as it is a robust property of optimal pathway activation.

ALISSA: an automated live-cell imaging system for signal transduction analyses.

Probe photobleaching and a specimen's sensitivity to phototoxicity severely limit the number of possible excitation cycles in time-lapse fluorescent microscopy experiments. Consequently, when a study of cellular processes requires measurements over hours or days, temporal resolution is limited, and spontaneous or rapid events may be missed, thus limiting conclusions about transduction events. We have developed ALISSA, a design framework and reference implementation for an automated live-cell imaging system for signal transduction analysis. It allows an adaptation of image modalities and laser resources tailored to the biological process, and thereby extends temporal resolution from minutes to seconds. The system employs online image analysis to detect cellular events that are then used to exercise microscope control. It consists of a reusable image analysis software for cell segmentation, tracking, and time series extraction, and a measurement-specific process control software that can be easily adapted to various biological settings. We have applied the ALISSA framework to the analysis of apoptosis as a demonstration case for slow onset and rapid execution signaling. The demonstration provides a clear proof-of-concept for ALISSA, and offers guidelines for its application in a broad spectrum of signal transduction studies.

Near-infrared spectroscopic measurements of blood analytes using multi-layer perceptron neural networks.

Near-infrared (NIR) spectroscopy is being applied to the solution of problems in many areas of biomedical and pharmaceutical research. In this paper we investigate the use of NIR spectroscopy as an analytical tool to quantify concentrations of urea, creatinine, glucose and oxyhemoglobin (HbO2). Measurements have been made in vitro with a portable spectrometer developed in our labs that consists of a two beam interferometer operating in the range of 800-2300 nm. For the data analysis a pattern recognition philosophy was used with a preprocessing stage and a multi-layer perceptron (MLP) neural network for the measurement stage. Results show that the interferogram signatures of the above compounds are sufficiently strong in that spectral range. Measurements of three different concentrations were possible with mean squared error (MSE) of the order of 10(-6).