Biomedical subjects

Haluk Resat

Publications and source records attributed to Haluk Resat.

7 recordsLinked to original sources

PubMed2006

Modeling the effects of HER/ErbB1-3 coexpression on receptor dimerization and biological response.

The human epidermal growth factor receptor (HER/ErbB) system comprises the epidermal growth factor receptor (EGFR/HER1) and three other homologs, namely HERs 2-4. This receptor system plays a critical role in cell proliferation and differentiation and receptor overexpression has been associated with poor prognosis in cancers of the epithelium. Here, we examine the effect of coexpressing varying levels of HERs 1-3 on the receptor dimerization patterns using a detailed kinetic model for HER/ErbB dimerization and trafficking. Our results indicate that coexpression of EGFR with HER2 or HER3 biases signaling to the cell surface and retards signal downregulation. In addition, simultaneous coexpression of HERs 1-3 leads to an abundance of HER2-HER3 heterodimers, which are known to be potent inducers of cell growth and transformation. Our new approach to use parameter dependence analysis in experimental design reveals that measurements of HER3 phosphorylation and HER2 internalization ratio may prove to be especially useful for the estimation of critical model parameters. Further, we examine the effect of receptor dimerization patterns on biological response using a simple phenomenological model. Results indicate that coexpression of EGFR with HER2 and HER3 at low to moderate levels may enable cells to match the response of a high HER2 expresser.

Biological Transport↗

PubMed2005

Modeling signal transduction networks: a comparison of two stochastic kinetic simulation algorithms.

Computational efficiency of stochastic kinetic algorithms depend on factors such as the overall species population, the total number of reactions, and the average number of nodal interactions or connectivity in a network. These size measures of the network model can have a significant impact on computational efficiency. In this study, two scalable biological networks are used to compare the size scaling efficiencies of two popular and conceptually distinct stochastic kinetic simulation algorithms--the random substrate method of Firth and Bray (FB), and the Gillespie algorithm as implemented using the Gibson-Bruck method (GGB). The arithmetic computational efficiencies of these two algorithms, respectively, scale with the square of the total species population and the logarithm of the total number of active reactions. The two scalable models considered are the size scalable model (SSM), a four compartment reaction model for a signal transduction network involving receptors with single phosphorylation binding sites, and the variable connectivity model (VCM), a single compartment model where receptors possess multiple phosphorylation binding sites. The SSM has fixed species connectivity while the connectivity between species in VCM increases with the number of phosphorylation sites. For SSM, we find that, as the total species population is increased over four orders of magnitude, the GGB algorithm performs significantly better than FB for all three SSM compartment models considered. In contrast, for VCM, we find that as the overall species population decreases while the number of phosphorylation sites increases (implying an increase in network linkage) there exists a crossover point where the computational demands of the GGB method exceed that of the FB.

Computer Simulation↗

PubMed2005

Combining microarray and genomic data to predict DNA binding motifs.

The ability to detect regulatory elements within genome sequences is important in understanding how gene expression is controlled in biological systems. In this work, microarray data analysis is combined with genome sequence analysis to predict DNA sequences in the photosynthetic bacterium Rhodobacter sphaeroides that bind the regulators PrrA, PpsR and FnrL. These predictions were made by using hierarchical clustering to detect genes that share similar expression patterns. The DNA sequences upstream of these genes were then searched for possible transcription factor recognition motifs that may be involved in their co-regulation. The approach used promises to be widely applicable for the prediction of cis-acting DNA binding elements. Using this method the authors were independently able to detect and extend the previously described consensus sequences that have been suggested to bind FnrL and PpsR. In addition, sequences that may be recognized by the global regulator PrrA were predicted. The results support the earlier suggestions that the DNA binding sequence of PrrA may have a variable-sized gap between its conserved block elements. Using the predicted DNA binding sequences, a whole-genome-scale analysis was performed to determine the relative importance of the interplay between the three regulators PpsR, FnrL and PrrA. Results of this analysis showed that, compared to the regulation by PpsR and FnrL, a much larger number of genes are candidates to be regulated by PrrA. The study demonstrates by example that integration of multiple data types can be a powerful approach for inferring transcriptional regulatory patterns in microbial systems, and it allowed the detection of photosynthesis-related regulatory patterns in R. sphaeroides.

Bacterial Proteins↗

PubMed2004

Probabilistic representation of gene regulatory networks.

MOTIVATION: Recent experiments have established unambiguously that biological systems can have significant cell-to-cell variations in gene expression levels even in isogenic populations. Computational approaches to studying gene expression in cellular systems should capture such biological variations for a more realistic representation. RESULTS: In this paper, we present a new fully probabilistic approach to the modeling of gene regulatory networks that allows for fluctuations in the gene expression levels. The new algorithm uses a very simple representation for the genes, and accounts for the repression or induction of the genes and for the biological variations among isogenic populations simultaneously. Because of its simplicity, introduced algorithm is a very promising approach to model large-scale gene regulatory networks. We have tested the new algorithm on the synthetic gene network library bioengineered recently. The good agreement between the computed and the experimental results for this library of networks, and additional tests, demonstrate that the new algorithm is robust and very successful in explaining the experimental data. AVAILABILITY: The simulation software is available upon request. SUPPLEMENTARY INFORMATION: Supplementary material will be made available on the OUP server.

Algorithms↗

PubMed2003

An integrated model of epidermal growth factor receptor trafficking and signal transduction.

Endocytic trafficking of many types of receptors can have profound effects on subsequent signaling events. Quantitative models of these processes, however, have usually considered trafficking and signaling independently. Here, we present an integrated model of both the trafficking and signaling pathway of the epidermal growth factor receptor (EGFR) using a probability weighted-dynamic Monte Carlo simulation. Our model consists of hundreds of distinct endocytic compartments and approximately 13,000 reactions/events that occur over a broad spatio-temporal range. By using a realistic multicompartment model, we can investigate the distribution of the receptors among cellular compartments as well as their potential signal transduction characteristics. Our new model also allows the incorporation of physiochemical aspects of ligand-receptor interactions, such as pH-dependent binding in different endosomal compartments. To determine the utility of this approach, we simulated the differential activation of the EGFR by two of its ligands, epidermal growth factor (EGF) and transforming growth factor-alpha (TGF-alpha). Our simulations predict that when EGFR is activated with TGF-alpha, receptor activation is biased toward the cell surface whereas EGF produces a signaling bias toward the endosomal compartment. Experiments confirm these predictions from our model and simulations. Our model accurately predicts the kinetics and extent of receptor downregulation induced by either EGF or TGF-alpha. Our results suggest that receptor trafficking controls the compartmental bias of signal transduction, rather than simply modulating signal magnitude. Our model provides a new approach to evaluating the complex effect of receptor trafficking on signal transduction. Importantly, the stochastic and compartmental nature of the simulation allows these models to be directly tested by high-throughput approaches, such as quantitative image analysis.

Cell Line↗

PubMed2002

Comparative molecular dynamics simulations of amphotericin B-cholesterol/ergosterol membrane channels.

Amphotericin B (AmB) is a very effective anti-fungal polyene macrolide antibiotic whose usage is limited by its toxicity. Lack of a complete understanding of AmB's molecular mechanism has impeded attempts to design less toxic AmB derivatives. The antibiotic is known to interact with sterols present in the cell membrane to form ion channels that disrupt membrane function. The slightly higher affinity of AmB toward ergosterol (dominant sterol in fungal cells) than cholesterol (mammalian sterol) is regarded as the most essential factor on which antifungal chemotherapy is based. To study these differences at the molecular level, two realistic model membrane channels containing molecules of AmB, sterol (cholesterol or ergosterol), phospholipid, and water were studied by molecular dynamics (MD) simulations. Comparative analysis of the simulation data revealed that the sterol type has noticeable effect on the properties of AmB membrane channels. In addition to having a larger size, the AmB channel in the ergosterol-containing membrane has a more pronounced pattern of intermolecular hydrogen bonds. The interaction between the antibiotic and ergosterol is more specific than between the antibiotic and cholesterol. These observed differences suggest that the channel in the ergosterol-containing membrane is more stable and, due to its larger size, would have a higher ion conductance. These observations are in agreement with experiments.

Amphotericin B↗

PubMed2002

Ion passage pathways and thermodynamics of the amphotericin B membrane channel.

Amphotericin B is a polyene macrolide antibiotic used to treat systemic fungal infections. Amphotericin B's chemotherapeutic action requires the formation of transmembrane channels, which are known to transmit monovalent ions. We have investigated the ion passage pathways through the pore of a realistic model structure of the channel and computed the associated thermodynamic properties. Our calculations combined the free energy computations using the Poisson equation with a continuum solvent model and the molecular simulations in which solvent molecules were present explicitly. It was found that there are no substantial structural barriers to a single sodium or chloride ion passage. Thermodynamic free energy calculations showed that the path along which the ions prefer to move is off center from the channel's central axis. In accordance with experiments, Monte Carlo molecular simulations established that sodium ions can pass through the pore. When it encounters a chloride anion in the channel, the sodium cation prefers to form a solvent-bridged pair configuration with the anion.

Amphotericin B↗