Fitting density functions and diffusion tensors to three-dimensional drug transport within brain tissue

In the research on drug transport within brain tissue, typical data sets consist of a collection of volume integrals from various locations in the brain, with each datum measuring the total amount of drug in a sample of tissue. These samples contain medically useful information about the extent and orientation of the distribution of the drug, but extracting this information requires some mathematical analysis. The method of analysis presented here performs a nonlinear regression on the data to fit a multivariate density function (for example, a Gaussian density) to model such transport processes as diffusion and dispersion. The principal components of that density then characterize the size and shape of the distribution of drug.     

Computational methods for predicting structure of membrane proteins using amino acid sequences

The structure of membrane proteins specifies their functional properties, which are important for medicine and pharmacology and, therefore, is of significant interest. The repetition of transmembrane regions that consist of hydrophobic amino acids is a characteristic and organic feature of polytopic membrane proteins. The ordered repetition (periodicity) can be detected by the Fourier method applied to a digital image of the symbolic amino acid sequence of a protein. In the present work, this investigation was carried out for 24 transmembrane proteins (successfully for 14 of them). If the repetition of transmembrane regions is aperiodic, it can be revealed by another method, that is, the method of the reiterated (four to five times) averaging of the protein hydrophobicity function in a window within the limits of 9–11 amino acids that moves along the sequence. This novel method was applied to the 24 transmembrane proteins (successfully for 19 of them) and demonstrated higher suitability than the Fourier method for predicting the secondary structure of these proteins and the corresponding functional properties.     

Computational methods for the design of effective therapies against drug resistant HIV strains

The development of drug resistance is a major obstacle to successful treatment of HIV infection. The extraordinary replication dynamics of HIV facilitates its escape from selective pressure exerted by the human immune system and by combination drug therapy. We have developed several computational methods whose combined use can support the design of optimal antiretroviral therapies based on viral genomic data.     

Computational fluid dynamics modeling of steady-state momentum and mass transport in a bioreactor for cartilage tissue engineering

Computational fluid dynamics (CFD) models to quantify momentum and mass transport under conditions of tissue growth will aid bioreactor design for development of tissue-engineered cartilage constructs. Fluent CFD models are used to calculate flow fields, shear stresses, and oxygen profiles around nonporous constructs simulating cartilage development in our concentric cylinder bioreactor. The shear stress distribution ranges from 1.5 to 12 dyn/cm(2) across the construct surfaces exposed to fluid flow and varies little with the relative number or placement of constructs in the bioreactor. Approximately 80% of the construct surface exposed to flow experiences shear stresses between 1.5 and 4 dyn/cm(2), validating the assumption that the concentric cylinder bioreactor provides a relatively homogeneous hydrodynamic environment for construct growth. Species mass transport modeling for oxygen demonstrates that fluid-phase oxygen transport to constructs is uniform. Some O(2) depletion near the down stream edge of constructs is noted with minimum pO(2) values near the constructs of 35 mmHg (23% O(2) saturation). These values are above oxygen concentrations in cartilage in vivo, suggesting that bioreactor oxygen concentrations likely do not affect chondrocyte growth. Scale-up studies demonstrate the utility and flexibility of CFD models to design and characterize bioreactors for growth of tissue-engineered cartilage.     

Computational methods for exon detection

Computer methods for the complete and accurate detection of genes in vertebrate genomic sequences are still a long way to perfection. The intermediate task of identifying the coding moiety of genes (coding exons) is now reasonably well achieved using a combination of methods. After reviewing the intrinsic difficulties in interpreting vertebrate genomic sequences, this article presents the state-of-the-art, with an emphasis on similarity search methods and the resources available through Internet.     

Computational methods for transcriptional regulation

How is the information from a thousand gene-expression arrays, the location of more than two hundred regulatory factors, and nine sequenced genomes to be integrated into a global view of the regulatory network in budding yeast? Computational methods that fit incomplete noisy data provide the outlines of regulatory pathways, but the errors are not quantified. In the fly, embryonic patterning has proved amenable to computational prediction, but only when the DNA-binding preferences of the relevant factors are taken into account. In both these model organisms, simply restricting attention to regulatory sequences that align with related species (i.e. "conserved") discards much information regarding what is functional.     

Computational fluid dynamics modeling of momentum transport in rotating wall perfused bioreactor for cartilage tissue engineering

In this study, computational fluid dynamics (CFD) analysis of a rotating-wall perfused-vessel (RWPV) bioreactor is performed to characterize the complex hydrodynamic environment for the simulation of cartilage development in RWPV bioreactor in the presence of tissue-engineered cartilage constructs, i.e., cell-chitosan scaffolds. Shear stress exerted on chitosan scaffolds in bioreactor was calculated for different rotational velocities in the range of 33-38 rpm. According to the calculations, the lateral and lower surfaces were exposed to 0.07926-0.11069 dyne/cm(2) and 0.05974-0.08345 dyne/cm(2), respectively, while upper surfaces of constructs were exposed to 0.09196-0.12847 dyne/cm(2). Results validate adequate hydrodynamic environment for scaffolds in RWPV bioreactor for cartilage tissue development which concludes the suitability of operational conditions of RWPV bioreactor.     

Quantifying efficacy of chemotherapy of brain tumors with homogeneous and heterogeneous drug delivery

Gliomas are diffuse and invasive brain tumors with the nefarious ability to evade even seemingly draconian treatment measures. Here we introduce a simple mathematical model for drug delivery of chemotherapeutic agents to treat such a tumor. The model predicts that heterogeneity in drug delivery related to variability in vascular density throughout the brain results in an apparent tumor reduction based on imaging studies despite continual spread beyond the resolution of the imaging modality. We discuss a clinical example for which the model-predicted scenario is relevant. The analysis and results suggest an explanation for the clinical problem of the long-standing confounding observation of shrinkage of the lesion in certain areas of the brain with continued growth in other areas.     

Computational methods for diffusion-influenced biochemical reactions

MOTIVATION: We compare stochastic computational methods accounting for space and discrete nature of reactants in biochemical systems. Implementations based on Brownian dynamics (BD) and the reaction-diffusion master equation are applied to a simplified gene expression model and to a signal transduction pathway in Escherichia coli. RESULTS: In the regime where the number of molecules is small and reactions are diffusion-limited predicted fluctuations in the product number vary between the methods, while the average is the same. Computational approaches at the level of the reaction-diffusion master equation compute the same fluctuations as the reference result obtained from the particle-based method if the size of the sub-volumes is comparable to the diameter of reactants. Using numerical simulations of reversible binding of a pair of molecules we argue that the disagreement in predicted fluctuations is due to different modeling of inter-arrival times between reaction events. Simulations for a more complex biological study show that the different approaches lead to different results due to modeling issues. Finally, we present the physical assumptions behind the mesoscopic models for the reaction-diffusion systems. AVAILABILITY: Input files for the simulations and the source code of GMP can be found under the following address: http://www.cwi.nl/projects/sic/bioinformatics2007/     

Computational methods for protein function analysis

Two recent advances have had the greatest impact on protein function analysis so far: the complete sequences of genomes and mRNA expression level profiles. The former has spurred the development of novel techniques to study protein function: phylogenetic profiles and gene clusters. The latter has introduced a method, not based on sequence homology, that enables one to group together functionally related genes.     

Computational methods for Gene Orthology inference

Accurate inference of orthologous genes is a pre-requisite for most comparative genomics studies, and is also important for functional annotation of new genomes. Identification of orthologous gene sets typically involves phylogenetic tree analysis, heuristic algorithms based on sequence conservation, synteny analysis, or some combination of these approaches. The most direct tree-based methods typically rely on the comparison of an individual gene tree with a species tree. Once the two trees are accurately constructed, orthologs are straightforwardly identified by the definition of orthology as those homologs that are related by speciation, rather than gene duplication, at their most recent point of origin. Although ideal for the purpose of orthology identification in principle, phylogenetic trees are computationally expensive to construct for large numbers of genes and genomes, and they often contain errors, especially at large evolutionary distances. Moreover, in many organisms, in particular prokaryotes and viruses, evolution does not appear to have followed a simple 'tree-like' mode, which makes conventional tree reconciliation inapplicable. Other, heuristic methods identify probable orthologs as the closest homologous pairs or groups of genes in a set of organisms. These approaches are faster and easier to automate than tree-based methods, with efficient implementations provided by graph-theoretical algorithms enabling comparisons of thousands of genomes. Comparisons of these two approaches show that, despite conceptual differences, they produce similar sets of orthologs, especially at short evolutionary distances. Synteny also can aid in identification of orthologs. Often, tree-based, sequence similarity- and synteny-based approaches can be combined into flexible hybrid methods.     

Computational methods and challenges for large-scale circuit mapping

The connectivity architecture of neuronal circuits is essential to understand how brains work, yet our knowledge about the neuronal wiring diagrams remains limited and partial. Technical breakthroughs in labeling and imaging methods starting more than a century ago have advanced knowledge in the field. However, the volume of data associated with imaging a whole brain or a significant fraction thereof, with electron or light microscopy, has only recently become amenable to digital storage and analysis. A mouse brain imaged at light-microscopic resolution is about a terabyte of data, and 1mm(3) of the brain at EM resolution is about half a petabyte. This has given rise to a new field of research, computational analysis of large-scale neuroanatomical data sets, with goals that include reconstructions of the morphology of individual neurons as well as entire circuits. The problems encountered include large data management, segmentation and 3D reconstruction, computational geometry and workflow management allowing for hybrid approaches combining manual and algorithmic processing. Here we review this growing field of neuronal data analysis with emphasis on reconstructing neurons from EM data cubes.     

Computational methods and applications for quantitative systems pharmacology

Background: Quantitative systems pharmacology (QSP) is an emerging discipline that integrates diverse data to quantitatively explore the interactions between drugs and multi-scale systems including small compounds, nucleic acids, proteins, pathways, cells, organs and disease processes. Results: Various computational methods such as ADME/T evaluation, molecular modeling, logical modeling, network modeling, pathway analysis, multi-scale systems pharmacology platforms and virtual patient for QSP have been developed. We reviewed the major progresses and broad applications in medical guidance, drug discovery and exploration of pharmacodynamic material basis and mechanism of traditional Chinese medicine. Conclusion: QSP has significant achievements in recent years and is a promising approach for quantitative evaluation of drug efficacy and systematic exploration of mechanisms of action of drugs.