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We present a computational theory of navigation in rodents based on interacting representations of place, head direction, and local view. An associated computer model is able to replicate a variety of behavioral and neurophysiological results from the rodent navigation literature. The theory and model generate predictions that are testable with current technologies.
Protein kinases exhibit various degrees of substrate specificity. The large number of different protein kinases in the eukaryotic proteomes makes it impractical to determine the specificity of each enzyme experimentally. To test if it were possible to discriminate potential substrates from non-substrates by simple computational techniques, we analysed the binding enthalpies of modelled enzyme-substrate complexes and attempted to correlate it with experimental enzyme kinetics measurements. The crystal structures of phosphorylase kinase and cAMP-dependent protein kinase were used to generate models of the enzyme with a series of known peptide substrates and non-substrates, and the approximate enthalpy of binding assessed following energy minimization. We show that the computed enthalpies do not correlate closely with kinetic measurements, but the method can distinguish good substrates from weak substrates and non-substrates.
Tissue zinc accumulation kinetics and compartmental analysis models were evaluated in thyroparathyroidectomized (TPTX) rats treated with parathyroid extract or calcitonin. The animals were injected with 65Zn and studied at multiple time intervals. SAAM-25 was utilized to generate models from the plasma specific activity decay curves. Calcitonin had a marked effect to decrease the fractional influx and efflux transfer coefficients in the model and inhibit tissue zinc accumulation in several, but not all, tissues. Parathyroid extract increased accumulation in all tissues studied, but had minimal effects on the model. It is concluded that parathyroid hormone acts nonspecifically and that calcitonin appears to have specific influences on zinc homeostasis.
Though the development of human-to-mouse xenotransplant models is in its infancy, astonishing progress has been made in a short period of time. Two experimental applications have been developed: short-term transfer of human lymphocytes to generate models for autoimmunity and infectious diseases, and long-term engraftment of tissues with self-renewal potential. Human PBL-SCID mice have been used by multiple laboratories to study normal and autoimmune antibody responses, and have been shown to be readily infectable with HIV-1. SCID mice grafted with fetal tissue have been developed for studies of HIV-1 infection and its therapy as well as for studies of human hematopoietic cell differentiation. Human tumors appear to grow better in SCID mice than in nude mice, and hu-PBL-SCID mice can develop EBV-related B cell lymphoproliferative disease that resembles the immunoblastic lymphomas appearing in immunosuppressed transplant recipients. There is some evidence of mouse NK cells responding to the human xenograft, and of human T and B cells responding to mouse xenoantigens in these models, but these responses are not generally strong enough to have a major impact on human immune function. The use of these surrogate human models is expected to have a major impact on the understanding and treatment of human disease.
Cytochromes P450 are important heme-containing enzymes that catalyze the oxidation of a vast array of endogenous and exogenous compounds, including drugs and carcinogens. One of the more successful approaches to study P450 function involves molecular modeling. Because none of the mammalian P450s have been crystallized, a number of homology models have been constructed based on the structures of known bacterial P450s. Molecular models, generated using molecular replacement or distance geometry methods, can be used to dock substrates and/or inhibitors in the active site to explain various aspects of enzyme function. The majority of modeling research has dealt with enzyme-substrate interactions in the active site. The analysis of these interactions has helped us to better understand the mechanism of P450 catalysis and provided the structural basis for the regio- and stereospecificity of substrate oxidation as well as susceptibility to inhibition or inactivation. The models have been utilized to identify and/or confirm key residues and to rationally interpret experimental data. The alteration in activity in a mutant P450 can be related to changes in enzyme-substrate/inhibitor interactions, such as the removal or appearance of van der Waals overlaps or changes in compound mobility. Homology models can also help to analyze P450-redox partner interactions and identify critical determinants of protein stability. We can expect further development of molecular modeling methods and their increasing contribution into research on P450 function as an integral part of a combined theoretical-experimental approach.
Tunas are known to be able to travel long distances. The aim of this paper is to propose new ethological models which reproduce some tuna movements using the dynamics of their environment. We use sea surface temperature animations (from remote sensing data) to model the South West Indian Ocean, and French purse seiners data are used to estimate movements of fish. The objective of the models will be to find a northern movement from the Mozambique Channel to the Seychelles Islands at the appropriate time (May-July). The initial model uses our ecological knowledge of tunas, i.e. the search behavior for high concentrations of food commonly associated with thermal fronts. In some cases, this simple model creates some northern movements from the Mozambique Channel, but it cannot be used to reproduce large-scale movements between the Mozambique Channel and the Seychelles Islands. The next generation model is created where tuna behaviors are modeled by an artificial neural network, using a genetic algorithm to adjust the connection weights. The tuna school-network receives daily information from its local environment and chooses the best actions in order to be able to pass from the Mozambique Channel to the Seychelles Islands at the appropriate time. One neural network emerges and represents an adaptive behavior able to interpret daily sea surface temperatures to mimic large-scale tuna movements. This artificial behavior can be generalized to each possible departure position from the Mozambique Channel. This modelling represents a new tool to study large-scale movements of pelagic fish, and is a first step towards real-time management of fisheries.
We present a novel approach for ligand-based virtual screening by combining query molecules into a multiple feature tree model called MTree. All molecules are described by the established feature tree descriptor, which is derived from a topological molecular graph. A new pairwise alignment algorithm leads to a consistent topological molecular alignment based on chemically reasonable matching of corresponding functional groups. These multiple feature tree models find application in ligand-based virtual screening to identify new lead structures for chemical optimization. Retrospective virtual screening with MTree models generated for angiotensin-converting enzyme and the alpha1a receptor on a large candidate database yielded enrichment factors up to 71 for the first 1% of the screened database. MTree models outperformed database searches using single feature trees in terms of hit rates and quality and additionally identified alternative molecular scaffolds not included in any of the query molecules. Furthermore, relevant molecular features, which are known to be important for affinity to the target, are identified by this new methodology.
The purpose of this study was to determine if facial dimensions for a group of subjects were predictive of the fit factors measured while one brand of half-mask respirator was worn. Fit factors and 12 facial dimensions measured on 30 female and 38 male subjects were analyzed by correlation coefficients; weighted, multiple linear regression; and discriminant analysis. Data were analyzed for all subjects, gender subgroups, a race subgroup (whites only), and race/gender subgroups. Significant correlation coefficients with the log-transformed fit factors were found for four dimensions; four dimensions had significant coefficients in four or more multiple linear regression models. Only two dimensions had significant coefficients in four or more discriminant analysis models. Menton-subnasale (lower face) length was the only dimension included in all three of these groups. Gender-specific regression models had very high coefficient of determination values (R2 > 0.85). Discriminant analysis of the data for all subjects and race/gender subgroups found very good predictive scores for statistical software-generated models and menton-subnasale length alone; these scores were significantly better than those for the model with the respirator test panel dimensions (face length and lip width). These analyses found that facial dimensions were good predictors of respirator fit for those subjects wearing one brand of half-mask respirator. Lower face length was consistently indicated as being correlated or associated with fit. These results would indicate that dimensions other than those currently used may be more appropriate to define a half-mask respirator test panel.
1. In the companion paper we reported on the activity of neurons in the inferior temporal (IT) cortex during a sequential pattern matching task. In this task a sample stimulus was followed by a test stimulus that was either a match or a nonmatch. Many of the neurons encoded information about the patterns of both current and previous stimuli in the temporal modulation of their responses. 2. A simple information processing model of visual memory can be formed with just four steps: 1) encode the current stimulus; 2) recall the code of a remembered stimulus; 3) compare the two codes; 4) and decide whether they are similar or different. The analysis presented in the first paper suggested that some IT neurons were performing the comparison step of visual memory. 3. We propose that IT neurons participate in the comparison of temporal waveforms related to vision and memory by multiplying them together. This product could form the basis of a crosscorrelation-based comparison. 4. We tested our hypothesis by fitting a simple multiplicative model to data from IT neurons. The model generated waveforms in separate memory and visual channels. The waveforms arising from the two channels were then multiplied on a point by point basis to yield the output waveform. The model was fitted to the actual neuronal data by a gradient descent method to find the best fit waveforms that also had the lowest total energy. 5. The multiplicative model fit the neuronal responses quite well. The multiplicative model made consistently better predictions of the actual response waveforms than did an additive model. Furthermore, the fit was better when the actual relationship between the responses and the sample and test stimuli were preserved than when that relationship was randomized. 6. We infer from the superior fit of the multiplicative model that IT neurons are multiplying temporally modulated waveforms arising from separate visual and memory systems in the comparison step of visual memory.
OBJECTIVE: To determine the epidemiologic plausibility of a sylvatic transmission cycle for Neospora caninum between wild canids and beef cattle. DESIGN: Spatial analysis study. ANIMALS: 1,009 weaned beef steers from 94 beef herds in Texas. PROCEDURE: Calves were grouped on the basis of seroprevalence for N caninum and ecologic region in Texas. The Morans I test was used to evaluate spatial interdependence for adjusted seroprevalence by ecologic region. Cattle density (Number of cattle/259 km2 [Number of cattle/100 mile2] of each ecologic region) and abundance indices for gray foxes and coyotes (Number of animals/161 spotlight-transect [census] km [Number of animals/100 census miles] of each ecologic region) were used as covariates in spatial regression models, with adjusted seroprevalence as the outcome variable. A geographic information system (GIS) that used similar covariate information for each county was used to validate spatial regression models. Results-Spatial interdependence was not detected for ecologic regions. Three spatial regression models were tested. Each model contained a variable for cattle density for the ecologic regions. Results for the 3 models revealed that seroprevalence was associated with cattle density and abundances of gray foxes, coyotes, or both. Abundances of gray foxes and coyotes were collinear. Results of a GIS-generated model validated these spatial models. CONCLUSIONS AND CLINICAL RELEVANCE: In Texas, beef cattle are at increased risk of exposure to N caninum as a result of the abundance of wild canids and the density of beef cattle. It is plausible that a sylvatic transmission cycle for neosporosis exists.
A model is presented which is a simple representation of cochlear micromechanics in the lizard and mammal. The model is a linear, time-invariant mechanical system, consisting of two coupled mechanical filters. It is capable of a sharply tuned first and second filter, consistent with the sharply tuned basilar membrane and auditory nerve fibers in the mammal. When parameters are adjusted for the alligator lizard (Gerrhonotus multicarinatus), the model generates a sharply tuned second filter without a sharply tuned first filter. In the alligator lizard, the auditory nerve fibers are sharply tuned but the basilar membrane is not. The model supports the hypothesis that cochlear transduction is not fundamentally different in the mammal and the alligator lizard.
Individuals with HIV disease face ongoing and phasic challenges that may generate psychological stress or clinically significant distress. Such distress may in part be understood as a response to challenges to fundamental personal conceptions of self and world. The authors describe models generated from social cognition research and the stress response literature and then apply them to common psychological themes that are salient for persons with HIV disease. Implications for psychotherapeutic assessment and intervention are discussed, and a case report is presented to illustrate the use of this approach with HIV and AIDS patients.
Lysosomal enzymes contain a common protein determinant that is recognized by UDP-GlcNAc:lysosomal enzyme N-acetylglucosamine-1-phosphotransferase, the initial enzyme in the biosynthesis of mannose-6-P residues. Previously, we generated a lysosomal enzyme recognition domain by substituting two regions (lysine 203 and amino acids 265-292) of the lysosomal hydrolase cathepsin D into a related secretory protein glycopepsinogen. When expressed in Xenopus oocytes, the oligosaccharides of the chimeric protein were efficiently phosphorylated (Baranski, T. J., Faust, P. L., and Kornfeld, S. (1990) Cell 63, 281-291). In the current study, incremental substitutions of cathepsin D residues into glycopepsinogen and alanine-scanning mutagenesis were utilized to define the recognition domain more precisely. A computer-generated model of the cathepsin D/pepsinogen chimeric molecule served as a guide for mutagenesis and for the interpretation of results. These studies indicate that the recognition domain is a surface patch that contains multiple interacting sites. There is a strict positional requirement for the lysine residue at position 203.
As genome-sequencing projects rapidly increase the database of protein sequences, the gap between known sequences and known structures continues to grow exponentially, increasing the demand to accelerate structure determination methods. Residual dipolar couplings (RDCs) are an attractive source of experimental restraints for NMR structure determination, particularly rapid, high-throughput methods, because they yield both local and long-range orientational information and can be easily measured and assigned once the backbone resonances of a protein have been assigned. While very extensive RDC data sets have been used to determine the structure of ubiquitin, it is unclear to what extent such methods will generalize to larger proteins with less complete data sets. Here we incorporate experimental RDC restraints into Rosetta, an ab initio structure prediction method, and demonstrate that the combined algorithm provides a general method for de novo determination of a variety of protein folds from RDC data. Backbone structures for multiple proteins up to approximately 125 residues in length and spanning a range of topological complexities are rapidly and reproducibly generated using data sets that are insufficient in isolation to uniquely determine the protein fold de novo, although ambiguities and errors are observed for proteins with symmetry about an axis of the alignment tensor. The models generated are not high-resolution structures completely defined by experimental data but are sufficiently accurate to accelerate traditional high-resolution NMR structure determination and provide structure-based functional insights.
A method for building statistical point distribution models is proposed. The novelty in this paper is the adaption of Markov random field regularization of the correspondence field over the set of shapes. The new approach leads to a generative model that produces highly homogeneous polygonized shapes and improves the capability of reconstruction of the training data. Furthermore, the method leads to an overall reduction in the total variance of the point distribution model. Thus, it finds correspondence between semi-landmarks that are highly correlated in the shape tangent space. The method is demonstrated on a set of human ear canals extracted from 3D-laser scans.
The objective of this study is to compare the predictive accuracy of a neural network (NN) model versus the standard Cox proportional hazard model. Data about the 3811 patients included in this study were collected within the 'El Alamo' Project, the largest dataset on breast cancer (BC) in Spain. The best prognostic model generated by the NN contains as covariates age, tumour size, lymph node status, tumour grade and type of treatment. These same variables were considered as having prognostic significance within the Cox model analysis. Nevertheless, the predictions made by the NN were statistically significant more accurate than those from the Cox model (p < 0.0001). Seven different time intervals were also analyzed to find that the NN predictions were much more accurate than those from the Cox model in particular in the early intervals between 1-10 and 11-20 months, and in the later one considered from 61 months to maximum follow-up time (MFT). Interestingly, these intervals contain regions of high relapse risk that have been observed in different studies and that are also present in the analyzed dataset.
The amino acid sequence of a two (4Fe-4S) ferredoxin from the methanogenic bacterium Methanococcus thermolithotrophicus (FdMt) has been determined. This thermostable protein comprises 60 amino acid residues (Mr 6541) and two (4Fe-4S) clusters chelated to the protein through the eight cysteines. FdMt contains a relatively high number of lysines [5], threonines [4] and valines [10]. The three-dimensional molecular model generated from the Peptococcus aerogenes X-ray structure keeps the characteristic overall ferredoxin folding thanks to complementary substitutions of residues of the hydrophobic core. The major structural features of the model are the different environments of both clusters, and the patch of three lysines at one end of the molecule. The possible role of several structural factors in the thermostability of the protein is discussed.
This paper describes a system written to carry out regression analyses under certain generalized linear models that are widely used in biomedical research. These include continuous response models such as the Weibull, log logistic, log normal and Cox proportional hazards models used in survival analysis, and also discrete Poisson, binomial and multinomial response regression models. The system fits models, generates residuals and other diagnostic output, and also has an all-subsets regression feature. This paper describes the ISMOD system and presents examples of its application; Part I describes the models implemented and gives statistical background.