Nadav S. Bar

Landscape of transcription in human cells

Eukaryotic cells make many types of primary and processed RNAs that are found either in specific subcellular compartments or throughout the cells. A complete catalogue of these RNAs is not yet available and their characteristic subcellular localizations are also poorly understood. Because RNA represents the direct output of the genetic information encoded by genomes and a significant proportion of a cell’s regulatory capabilities are focused on its synthesis, processing, transport, modification and translation, the generation of such a catalogue is crucial for understanding genome function. Here we report evidence that three-quarters of the human genome is capable of being transcribed, as well as observations about the range and levels of expression, localization, processing fates, regulatory regions and modifications of almost all currently annotated and thousands of previously unannotated RNAs. These observations, taken together, prompt a redefinition of the concept of a gene.

More efficient predictive control

An approach for constrained predictive control of linear systems (or uncertain systems described by polytopic uncertainty models) is presented. The approach consists of (in general non-convex, but often convex) offline optimization, and very efficient online optimization. Two examples, one being a laboratory experiment, compare the approach to existing approaches, revealing both advantages and disadvantages.

Adaptation of the Physiological, Endocrine, and Digestive System Functions to Prolonged Food Deprivation in Fish

Most of the theory about starvation physiology in fish is based on observations of birds and mammals. Such observations have given rise to the idea that starving animals undergo three distinct physiological and/or morphological phases. These phases are typically defined by the type of physiological fuel (e.g. carbohydrates, lipids, or proteins) that is being utilized. Transitions from one phase to another may be indirectly identified by changes in hormone levels, enzyme activities, blood metabolites, or body mass. Similar to birds and mammals, we notice three distinctive transitions in the sequential compositional changes during long-term starvation of fish. The first phase is a short transient one where both protein tissues and fat reserves are mobilized, and where the concentration of several hormones (such as ghrelin and growth hormone levels) deviates significantly from the normal steady-state levels. The second phase appears to be a (usually long) steady state, with mobilization of fat as the main source of energy. During this phase the change in concentration of endocrine factors is minimal and protein breakdown is nearly constant. When the primary lipid source which was utilized as energy during the second phase reaches a critical value, a transition to the third stage occurs, in which proteins are being mobilized as the primary energy source. It appears that various fish species use different lipid sources (e.g. liver, viscera, muscle) and exhibit transition to Phase III at different critical values. Hormonal levels also change significantly at this final stage and they may facilitate the transition to alternative energy source (e.g. muscle protein). We also notice changes in composition and structure of the gut system of fish during these stages. A loss of vacuolization of the mucosa cells and the transformation to finely granular cytoplasm is already noticed after 2 days and is prominent after 7 days of starvation (Phases I and II, respectively). Changes in the mucosa folds can be observed after a short fasting period, at Phase II. Generally, the cells kept their regular cylindrical form and the number of goblet cells increased during the second stage of starvation. To conclude, data on starvation in fish suggest three distinctive phases during prolonged starvation periods, with transitions that are triggered by hormonal changes and are strongly effected by temperature.

Metabolomic studies of human gastric cancer: Review

Metabolomics is a field of study in systems biology that involves the identification and quantification of metabolites present in a biological system. Analyzing metabolic differences between unperturbed and perturbed networks, such as cancerous and non-cancerous samples, can provide insight into underlying disease pathology, disease prognosis and diagnosis. Despite the large number of review articles concerning metabolomics and its application in cancer research, biomarker and drug discovery, these reviews do not focus on a specific type of cancer. Metabolomics may provide biomarkers useful for identification of early stage gastric cancer, potentially addressing an important clinical need. Here, we present a short review on metabolomics as a tool for biomarker discovery in human gastric cancer, with a primary focus on its use as a predictor of anticancer drug chemosensitivity, diagnosis, prognosis, and metastasis.

Graphical methods for analysing feedback in biological networks-A survey

Observed phenotypes usually arise from complex networks of interacting cell components. Qualitative information about the structure of these networks is often available, while quantitative information may be partial or absent. It is natural then to ask what, if anything, we can learn about the behaviour of the system solely from its qualitative structure. In this article we review some techniques which can be applied to answer this question, focussing in particular on approaches involving graphical representations of model structure. By applying these techniques to various cellular network examples, we discuss their strengths and limitations, and point to future research directions.

Long-term prediction of fish growth under varying ambient temperature using a multiscale dynamic model.

BackgroundFeed composition has a large impact on the growth of animals, particularly marine fish. We have developed a quantitative dynamic model that can predict the growth and body composition of marine fish for a given feed composition over a timespan of several months. The model takes into consideration the effects of environmental factors, particularly temperature, on growth, and it incorporates detailed kinetics describing the main metabolic processes (protein, lipid, and central metabolism) known to play major roles in growth and body composition.ResultsFor validation, we compared our models predictions with the results of several experimental studies. We showed that the model gives reliable predictions of growth, nutrient utilization (including amino acid retention), and body composition over a timespan of several months, longer than most of the previously developed predictive models.ConclusionWe demonstrate that, despite the difficulties involved, multiscale models in biology can yield reasonable and useful results. The model predictions are reliable over several timescales and in the presence of strong temperature fluctuations, which are crucial factors for modeling marine organism growth. The model provides important improvements over existing models.

A Sensory-Motor Control Model of Animal Flight Explains Why Bats Fly Differently in Light Versus Dark

Animal flight requires fine motor control. However, it is unknown how flying animals rapidly transform noisy sensory information into adequate motor commands. Here we developed a sensorimotor control model that explains vertebrate flight guidance with high fidelity. This simple model accurately reconstructed complex trajectories of bats flying in the dark. The model implies that in order to apply appropriate motor commands, bats have to estimate not only the angle-to-target, as was previously assumed, but also the angular velocity (“proportional-derivative” controller). Next, we conducted experiments in which bats flew in light conditions. When using vision, bats altered their movements, reducing the flight curvature. This change was explained by the model via reduction in sensory noise under vision versus pure echolocation. These results imply a surprising link between sensory noise and movement dynamics. We propose that this sensory-motor link is fundamental to motion control in rapidly moving animals under different sensory conditions, on land, sea, or air.

Analysis of protein synthesis dynamic model in eukaryotic cells: Input control

This paper presents the analysis of initiation control model of protein synthesis via eukaryotic initiation factor (eIF)-2 unit, introduced by [N.S. Bar, D.R. Morris, Dynamic model of the process of protein synthesis in eukaryoric cells, Bulletin of Mathematical Biology 69 (2007) 361-393, doi:10.1007/s11538-006-9128-2.] and propose methods to control it. Linearization of the model is presented as a measure to simplify the analysis and control application. The properties of the linear model were investigated and compared to the non-linear model using simulations. It was shown that the linear model is (marginally) stable and the states converge to a finite value. Linear optimal control theory can then be applied to the model under the value range where the linearized model is accurate. The effect of the input signals GCN2.tRNA and eIF-2 on the non-linear system was investigated. A few characteristics known from in vitro experiments of the initiation process were proven from a mathematical aspect and some conclusions about the function of the initiation complexes such as eIF2B and the ternary complex were derived. Consistent with published experiments, it was shown that overexpression of eIF-2 increases the concentration of 48S initiation complex and promote initiation rate. A state feedback control was applied in order to manipulate the initiation rate and it was proven that the 48S initiation complex can be driven to a desired value by calculating an input control law using measurement techniques available today. If this strategy can be implemented de facto, then a genuine control on protein synthesis process can be obtained.

Iterative sub-network component analysis enables reconstruction of large scale genetic networks.

BackgroundNetwork component analysis (NCA) became a popular tool to understand complex regulatory networks. The method uses high-throughput gene expression data and a priori topology to reconstruct transcription factor activity profiles. Current NCA algorithms are constrained by several conditions posed on the network topology, to guarantee unique reconstruction (termed compliancy). However, the restrictions these conditions pose are not necessarily true from biological perspective and they force network size reduction, pruning potentially important components.ResultsTo address this, we developed a novel, Iterative Sub-Network Component Analysis (ISNCA) for reconstructing networks at any size. By dividing the initial network into smaller, compliant subnetworks, the algorithm first predicts the reconstruction of each subntework using standard NCA algorithms. It then subtracts from the reconstruction the contribution of the shared components from the other subnetwork. We tested the ISNCA on real, large datasets using various NCA algorithms. The size of the networks we tested and the accuracy of the reconstruction increased significantly. Importantly, FOXA1, ATF2, ATF3 and many other known key regulators in breast cancer could not be incorporated by any NCA algorithm because of the necessary conditions. However, their temporal activities could be reconstructed by our algorithm, and therefore their involvement in breast cancer could be analyzed.ConclusionsOur framework enables reconstruction of large gene expression data networks, without reducing their size or pruning potentially important components, and at the same time rendering the results more biological plausible. Our ISNCA method is not only suitable for prediction of key regulators in cancer studies, but it can be applied to any high-throughput gene expression data.

Dynamics of Regulatory Networks in Gastrin-Treated Adenocarcinoma Cells

Understanding gene transcription regulatory networks is critical to deciphering the molecular mechanisms of different cellular states. Most studies focus on static transcriptional networks. In the current study, we used the gastrin-regulated system as a model to understand the dynamics of transcriptional networks composed of transcription factors (TFs) and target genes (TGs). The hormone gastrin activates and stimulates signaling pathways leading to various cellular states through transcriptional programs. Dysregulation of gastrin can result in cancerous tumors, for example. However, the regulatory networks involving gastrin are highly complex, and the roles of most of the components of these networks are unknown. We used time series microarray data of AR42J adenocarcinoma cells treated with gastrin combined with static TF-TG relationships integrated from different sources, and we reconstructed the dynamic activities of TFs using network component analysis (NCA). Based on the peak expression of TGs and activity of TFs, we created active sub-networks at four time ranges after gastrin treatment, namely immediate-early (IE), mid-early (ME), mid-late (ML) and very late (VL). Network analysis revealed that the active sub-networks were topologically different at the early and late time ranges. Gene ontology analysis unveiled that each active sub-network was highly enriched in a particular biological process. Interestingly, network motif patterns were also distinct between the sub-networks. This analysis can be applied to other time series microarray datasets, focusing on smaller sub-networks that are activated in a cascade, allowing better overview of the mechanisms involved at each time range.