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PubMed2005

On the binding determinants of the glutamate agonist with the glutamate receptor ligand binding domain.

Ionotropic glutamate receptors (GluRs) are ligand-gated membrane channel proteins found in the central neural system that mediate a fast excitatory response of neurons. In this paper, we report theoretical analysis of the ligand-protein interactions in the binding pocket of the S1S2 (ligand binding) domain of the GluR2 receptor in the closed conformation. By utilizing several theoretical methods ranging from continuum electrostatics to all-atom molecular dynamics simulations and quantum chemical calculations, we were able to characterize in detail glutamate agonist binding to the wild-type and E705D mutant proteins. A theoretical model of the protein-ligand interactions is validated via direct comparison of theoretical and Fourier transform infrared spectroscopy (FTIR) measured frequency shifts of the ligand's carboxylate group vibrations [Jayaraman et al. (2000) Biochemistry 39, 8693-8697; Cheng et al. (2002) Biochemistry 41, 1602-1608]. A detailed picture of the interactions in the binding site is inferred by analyzing contributions to vibrational frequencies produced by protein residues forming the ligand-binding pocket. The role of mobility and hydrogen-bonding network of water in the ligand-binding pocket and the contribution of protein residues exposed in the binding pocket to the binding and selectivity of the ligand are discussed. It is demonstrated that the molecular surface of the protein in the ligand-free state has mainly positive electrostatic potential attractive to the negatively charged ligand, and the potential produced by the protein in the ligand-binding pocket in the closed state is complementary to the distribution of the electrostatic potential produced by the ligand itself. Such charge complementarity ensures specificity to the unique charge distribution of the ligand.

Algorithms↗

PubMedDate not supplied

Numerical simulation of a natural gradient tracer experiment for the natural attenuation study: flow and physical transport.

Results are presented for numerical simulations of ground water flow and physical transport associated with a natural gradient tracer experiment conducted within a heterogeneous alluvial aquifer of the Natural Attenuation Study (NATS) site near Columbus, Mississippi. A principal goal of NATS is to evaluate biogeochemical models that predict the rate and extent of natural biodegradation under field conditions. This paper describes the initial phase in the model evaluation process, i.e., calibration of flow and physical transport models that simulate conservative bromide tracer plume evolution during NATS. An initial large-scale flow model (LSM) is developed encompassing the experimental site and surrounding region. This model is subsequently scaled down in telescopic fashion to an intermediate-scale ground water flow model (ISM) covering the tracer-monitoring network, followed by a small-scale transport model (SSM) focused on the small region of hydrocarbon plume migration observed during NATS. The LSM uses inferred depositional features of the site in conjunction with hydraulic conductivity (K) data from aquifer tests and borehole flowmeter tests to establish large-scale K and flow field trends in and around the experimental site. The subsequent ISM incorporates specified flux boundary conditions and large-scale K trends obtained from the calibrated LSM, while preserving small-scale K structure based on some 4000 flowmeter data for solute transport modeling. The configuration of the ISM-predicted potentiometric surface approximates that of the observed surface within a root mean squared error of 0.15 m. The SSM is based on the dual-domain mass-transfer approach. Despite the well-recognized difficulties in modeling solute transport in extremely heterogeneous media as found at the NATS site, the dual-domain model adequately reproduced the observed bromide concentration distributions. Differences in observed and predicted bromide concentration distributions are attributed to aquifer heterogeneity at the decimeter (dm) and smaller scales. The calibrated transport parameters for the SSM (i.e., 1:7 for the ratio of mobile-to-total porosity; 2.5 x 10(-3) day-1 for the mass-transfer coefficient; 1 m for longitudinal dispersivity; and 0.1 m for transverse dispersivity) are consistent with separate numerical simulations of two earlier tracer experiments at the site. The multiscale modeling approach adopted in this study permits the incorporation of both large-scale geologic features important for flow simulation and small-scale heterogeneities critical for transport simulation. In addition, the dual-domain transport model provides a foundation for multispecies reactive transport modeling studies of natural attenuation of hydrocarbons during NATS.

Chemical Phenomena↗

PubMed2002

Inferring domain-domain interactions from protein-protein interactions.

The interaction between proteins is one of the most important features of protein functions. Behind protein-protein interactions there are protein domains interacting physically with one another to perform the necessary functions. Therefore, understanding protein interactions at the domain level gives a global view of the protein interaction network, and possibly of protein functions. Two research groups used yeast two-hybrid assays to generate 5719 interactions between proteins of the yeast Saccharomyces cerevisiae. This allows us to study the large-scale conserved patterns of interactions between protein domains. Using evolutionarily conserved domains defined in a protein-domain database called PFAM (http://PFAM.wustl.edu), we apply a Maximum Likelihood Estimation method to infer interacting domains that are consistent with the observed protein-protein interactions. We estimate the probabilities of interactions between every pair of domains and measure the accuracies of our predictions at the protein level. Using the inferred domain-domain interactions, we predict interactions between proteins. Our predicted protein-protein interactions have a significant overlap with the protein-protein interactions (MIPS: http://mips.gfs.de) obtained by methods other than the two-hybrid assays. The mean correlation coefficient of the gene expression profiles for our predicted interaction pairs is significantly higher than that for random pairs. Our method has shown robustness in analyzing incomplete data sets and dealing with various experimental errors. We found several novel protein-protein interactions such as RPS0A interacting with APG17 and TAF40 interacting with SPT3, which are consistent with the functions of the proteins.

Computational Biology↗

PubMed2004

Efficient learning algorithms for three-layer regular feedforward fuzzy neural networks.

A key step of using gradient descend methods to develop learning algorithms of a regular feedforward fuzzy neural network (FNN) is to differentiate max--min functions, which contain max and min operations. The paper aims at several objectives. First, investigate further the differentiation of max--min functions. Second, employ general fuzzy numbers, which include triangular and trapezoidal fuzzy numbers as special cases to define a three-layer regular FNN. The general fuzzy numbers related can be approximately determined by their corresponding finite level sets. So, we can approximately represent the input-output (I/O) relationship of the regular FNN as functions of the endpoints of all finite level sets. Third, a fuzzy back-propagation algorithm is presented. And to speed up the convergence of the learning algorithm, a fuzzy conjugate gradient algorithm for fuzzy weights and biases is developed, furthermore, the convergence of the algorithm is analyzed, systematically. Finally, some real simulations demonstrate the efficiency of our learning algorithms. The regular FNN is applied to the approximate realization of fuzzy inference rules and fuzzy functions defined on given compact sets.

Algorithms↗

PubMed2023

Inferring Metabolic States from Single Cell Transcriptomic Data via Geometric Deep Learning.

The ability to measure gene expression at single-cell resolution has elevated our understanding of how biological features emerge from complex and interdependent networks at molecular, cellular, and tissue scales. As technologies have evolved that complement scRNAseq measurements with things like single-cell proteomic, epigenomic, and genomic information, it becomes increasingly apparent how much biology exists as a product of multimodal regulation. Biological processes such as transcription, translation, and post-translational or epigenetic modification impose both energetic and specific molecular demands on a cell and are therefore implicitly constrained by the metabolic state of the cell. While metabolomics is crucial for defining a holistic model of any biological process, the chemical heterogeneity of the metabolome makes it particularly difficult to measure, and technologies capable of doing this at single-cell resolution are far behind other multiomics modalities. To address these challenges, we present GEFMAP (Gene Expression-based Flux Mapping and Metabolic Pathway Prediction), a method based on geometric deep learning for predicting flux through reactions in a global metabolic network using transcriptomics data, which we ultimately apply to scRNAseq. GEFMAP leverages the natural graph structure of metabolic networks to learn both a biological objective for each cell and estimate a mass-balanced relative flux rate for each reaction in each cell using novel deep learning models.

Preprint↗

PubMed2005

A flexible coefficient smooth transition time series model.

In this paper, we consider a flexible smooth transition autoregressive (STAR) model with multiple regimes and multiple transition variables. This formulation can be interpreted as a time varying linear model where the coefficients are the outputs of a single hidden layer feedforward neural network. This proposal has the major advantage of nesting several nonlinear models, such as, the self-exciting threshold autoregressive (SETAR), the autoregressive neural network (AR-NN), and the logistic STAR models. Furthermore, if the neural network is interpreted as a nonparametric universal approximation to any Borel measurable function, our formulation is directly comparable to the functional coefficient autoregressive (FAR) and the single-index coefficient regression models. A model building procedure is developed based on statistical inference arguments. A Monte Carlo experiment showed that the procedure works in small samples, and its performance improves, as it should, in medium size samples. Several real examples are also addressed.

Algorithms↗

PubMed2005

A stochastic differential equation model for quantifying transcriptional regulatory network in Saccharomyces cerevisiae.

MOTIVATION: The explosion of microarray studies has promised to shed light on the temporal expression patterns of thousands of genes simultaneously. However, available methods are far from adequate in efficiently extracting useful information to aid in a greater understanding of transcriptional regulatory network. Biological systems have been modeled as dynamic systems for a long history, such as genetic networks and cell regulatory network. This study evaluated if the stochastic differential equation (SDE), which is prominent for modeling dynamic diffusion process originating from the irregular Brownian motion, can be applied in modeling the transcriptional regulatory network in Saccharomyces cerevisiae. RESULTS: To model the time-continuous gene-expression datasets, a model of SDE is applied to depict irregular patterns. Our goal is to fit a generalized linear model by combining putative regulators to estimate the transcriptional pattern of a target gene. Goodness-of-fit is evaluated by log-likelihood and Akaike Information Criterion. Moreover, estimations of the contribution of regulators and inference of transcriptional pattern are implemented by statistical approaches. Our SDE model is basic but the test results agree well with the observed dynamic expression patterns. It implies that advanced SDE model might be perfectly suited to portray transcriptional regulatory networks. AVAILABILITY: The R code is available on request. CONTACT: cykao@csie.ntu.edu.tw SUPPLEMENTARY INFORMATION: http://www.csie.ntu.edu.tw/~b89x035/yeast/

Gene Expression Regulation↗

PubMed2005

Mental state inference using visual control parameters.

Although we can often infer the mental states of others by observing their actions, there are currently no computational models of this remarkable ability. Here we develop a computational model of mental state inference that builds upon a generic visuomanual feedback controller, and implements mental simulation and mental state inference functions using circuitry that subserves sensorimotor control. Our goal is (1) to show that control mechanisms developed for manual manipulation are readily endowed with visual and predictive processing capabilities and thus allows a natural extension to the understanding of movements performed by others; and (2) to give an explanation on how cortical regions, in particular the parietal and premotor cortices, may be involved in such dual mechanism. To analyze the model, we simulate tasks in which an observer watches an actor performing either a reaching or a grasping movement. The observer's goal is to estimate the 'mental state' of the actor: the goal of the reaching movement or the intention of the agent performing the grasping movement. We show that the motor modules of the observer can be used in a 'simulation mode' to infer the mental state of the actor. The simulations with different grasping and non-straight line reaching strategies show that the mental state inference model is applicable to complex movements. Moreover, we simulate deceptive reaching, where an actor imposes false beliefs about his own mental state on an observer. The simulations show that computational elements developed for sensorimotor control are effective in inferring the mental states of others. The parallels between the model and cortical organization of movement suggest that primates might have developed a similar resource utilization strategy for action understanding, and thus lead to testable predictions about the brain mechanisms of mental state inference.

Cerebral Cortex↗

PubMed2001

Connectionist inference models.

The performance of symbolic inference tasks has long been a challenge to connectionists. In this paper, we present an extended survey of this area. Existing connectionist inference systems are reviewed, with particular reference to how they perform variable binding and rule-based reasoning and whether they involve distributed or localist representations. The benefits and disadvantages of different representations and systems are outlined, and conclusions drawn regarding the capabilities of connectionist inference systems when compared with symbolic inference systems or when used for cognitive modelling.

Animals↗

PubMed2003

A symbolic-connectionist theory of relational inference and generalization.

The authors present a theory of how relational inference and generalization can be accomplished within a cognitive architecture that is psychologically and neurally realistic. Their proposal is a form of symbolic connectionism: a connectionist system based on distributed representations of concept meanings, using temporal synchrony to bind fillers and roles into relational structures. The authors present a specific instantiation of their theory in the form of a computer simulation model, Learning and Inference with Schemas and Analogies (LISA). By using a kind of self-supervised learning, LISA can make specific inferences and form new relational generalizations and can hence acquire new schemas by induction from examples. The authors demonstrate the sufficiency of the model by using it to simulate a body of empirical phenomena concerning analogical inference and relational generalization.

Generalization, Psychological↗

PubMed2002

MIAX: a new paradigm for modeling biomacromolecular interactions and complex formation in condensed phases.

A new paradigm is proposed for modeling biomacromolecular interactions and complex formation in solution (protein-protein interactions so far in this report) that constitutes the scaffold of the automatic system MIAX (acronym for Macromolecular Interaction Assessment X). It combines in a rational way a series of computational methodologies, the goal being the prediction of the most native-like protein complex that may be formed when two isolated (unbound) protein monomers interact in a liquid environment. The overall strategy consists of first inferring putative precomplex structures by identification of binding sites or epitopes on the proteins surfaces and a simultaneous rigid-body docking process using geometric instances alone. Precomplex configurations are defined here as all those decoys the interfaces of which comply substantially with the inferred binding sites and whose free energy values are lower. Retaining all those precomplex configurations with low energies leads to a reasonable number of decoys for which a flexible treatment is amenable. A novel algorithm is introduced here for automatically inferring binding sites in proteins given their 3-D structure. The procedure combines an unsupervised learning algorithm based on the self-organizing map or Kohonen network with a 2-D Fourier spectral analysis. To model interaction, the potential function proposed here plays a central role in the system and is constituted by empirical terms expressing well-characterized factors influencing biomacromolecular interaction processes, essentially electrostatic, van der Waals, and hydrophobic. Each of these procedures is validated by comparing results with observed instances. Finally, the more demanding process of flexible docking is performed in MIAX embedding the potential function in a simulated annealing optimization procedure. Whereas search of the entire configuration hyperspace is a major factor precluding hitherto systems from efficiently modeling macromolecular interaction modes and complex structures, the paradigm presented here may constitute a step forward in the field because it is shown that a rational treatment of the information available from the 3-D structure of the interacting monomers combined with conveniently selected computational techniques can assist to elude search of regions of low probability in configuration space and indeed lead to a highly efficient system oriented to solve this intriguing and fundamental biologic problem.

Algorithms↗

PubMed2005

Subcellular localization and trafficking of kainate receptors.

Glutamate receptors of the kainate type have been identified recently as key players in the modulation of neuronal-network activity. The role of kainate receptors depends on their precise subcellular localization in presynaptic, postsynaptic and extrasynaptic domains. Subcellular localization of kainate receptors has been inferred mainly from electrophysiological studies with the help of selective pharmacological tools and kainate receptor mutant mice. These studies, combined with recent ultrastructural data, highlight the diversity of subcellular localizations of kainate receptors. It is important to understand the molecular mechanisms that underlie the polarized trafficking of kainate receptors in distinct neuronal domains. In this article, we review recent data that shed light on the trafficking and membrane delivery of kainate receptor isoforms, and on the identification of proteins that interact with kainate receptors and might regulate this trafficking.

Animals↗

PubMed2000

Neurological bases of behavioral development in infancy.

This article presents selected psychological competences that emerge in children during the first 2 years, together with correlated structural, biochemical and physiological changes in the brain. Major behavioral events of the 1st year are the disappearance of the neonatal reflexes, the improvement of recognition and working memory and the appearance of the universal fears of strangers and of separation from the caretaker. These behaviors are correlated in time with changes in the brain that allow the increased ability of the cortex to inhibit brainstem reflexes, with processes in the prefrontal cortex and hippocampus that facilitate the formation, storage and retrieval of memories, and with strengthened connections between the cortex and limbic system. Behavioral events of the 2nd year include the explosion of language and the emergence of self-awareness, both of which depend on the capacity for inference. The emergence of these capabilities is correlated with the intensified connectivity of the two hemispheres, the maturation of the prefrontal cortex and cortical-subcortical network.

Behavior↗

PubMed2002

Analysis of binding of mannosides in relation to Langerin (CD207) in Langerhans cells of normal and transformed epithelia.

Tandem-repeat C-type lectins (pattern-recognition receptors) with specificity for mannosides are intimately involved in antigen recognition, uptake, routing and presentation in macrophages and dendritic cells. In Langerhans cells, Langerin (CD207), a type-II transmembrane protein with a single C-type carbohydrate recognition domain attached to a heptad repeat in the neck region, which is likely to establish oligomers with an alpha-coiled-coil stalk, has been implicated in endocytosis and the formation of Birbeck granules. The structure of Langerin harbours essential motifs for Ca2+-binding and sugar accommodation. Lectin activity has previously been inferred by diminished antibody binding to cells in the presence of the glycan ligand mannan. In view of the complexity of the C-type lectin/lectin-like network, it is unclear what role Langerin plays for Langerhans cells in binding mannosides. In order to reveal in frozen tissue sections to what extent mannose-binding activity co-localizes with Langerin, we have used a synthetic marker, i.e. a neoglycoprotein carrying mannose maxiclusters, as a histochemical ligand, and computer-assisted fluorescence monitoring in a double-labelling procedure. Mannoside-binding capacity was detected in normal epithelial cells. Double labelling ensured the unambiguous assessment of the binding of the neoglycoprotein in Langerhans cells. Light-microscopically, its localization profile resembled the pattern of immunohistochemical detection of Langerin. This result has implications for suggesting rigorous controls in histochemical analysis of this cell type, because binding of kit reagents, i.e. mannose-rich glycoproteins horseradish peroxidase or avidin, to Langerin (or a spatially closely associated lectin) could yield false-positive signals. To show that recognition of carbohydrate ligands in dendritic cells is not restricted to mannose clusters, we have also documented binding of carrier-immobilized histo-blood group A trisaccharide, a ligand of galectin-3, which was not affected by the presence of a blocking antibody to Langerin. Remarkably, access to the carbohydrate recognition domain of Langerin appeared to be impaired in proliferatively active environments (malignancies, hair follicles), indicating presence of an endogenous ligand with high affinity to saturate the C-type lectin under these conditions.

Antigens, CD↗

PubMed1999

Disrupted temporal lobe connections in semantic dementia.

Semantic dementia refers to the variant of frontotemporal dementia in which there is progressive semantic deterioration and anomia in the face of relative preservation of other language and cognitive functions. Structural imaging and SPECT studies of such patients have suggested that the site of damage, and by inference the region critical to semantic processing, is the anterolateral temporal lobe, especially on the left. Recent functional imaging studies of normal participants have revealed a network of areas involved in semantic tasks. The present study used PET to examine the consequences of focal damage to the anterolateral temporal cortex for the operation of this semantic network. We measured PET activation associated with a semantic decision task relative to a visual decision task in four patients with semantic dementia compared with six age-matched normal controls. Normals activated a network of regions consistent with previous studies. The patients activated some areas consistently with the normals, including some regions of significant atrophy, but showed substantially reduced activity particularly in the left posterior inferior temporal gyrus (iTG) (Brodmann area 37/19). Voxel-based morphometry, used to identify the regions of structural deficit, revealed significant anterolateral temporal atrophy (especially on the left), but no significant structural damage to the posterior inferior temporal lobe. Other evidence suggests that the left posterior iTG is critically involved in lexical-phonological retrieval: the lack of activation here is consistent with the observation that these patients are all anomic. We conclude that changes in activity in regions distant from the patients' structural damage support the argument that their prominent anomia is due to disrupted temporal lobe connections.

Anomia↗

PubMed1986

Evolutionary change of restriction cleavage sites and phylogenetic inference.

Mathematical formulas are developed for the evolutionary change of restriction cleavage sites in a DNA sequence, allowing unequal rates between transitional and transversional types of nucleotide substitution. Formulas are also developed for the probability of having a particular pattern of site changes among evolutionary lineages, such as parallel gains or losses of sites, and for inferring the presence or absence of a restriction site in an ancestral sequence from data on the present-day sequences. The unordered compatibility method is proposed for inferring the phylogenetic relationships among relatively closely related organisms, treating restriction sites as cladistic characters. Formulas are derived for the probability (P+) of obtaining the correct network for a given number (N) of informative sites for the cases of four and five species. These formulas are applied to evaluate the performance of the method and to estimate the N value required for P+ to be 95% or larger. The method performs well when the branches between ancestral nodes and the branches leading to the two most recent species are more or less equal in length, but performs poorly when the latter two branches are considerably longer than the former.

Base Sequence↗

PubMed2001

Probabilistic prediction of unknown metabolic and signal-transduction networks.

Regulatory networks provide control over complex cell behavior in all kingdoms of life. Here we describe a statistical model, based on representing proteins as collections of domains or motifs, which predicts unknown molecular interactions within these biological networks. Using known protein-protein interactions of Saccharomyces cerevisiae as training data, we were able to predict the links within this network with only 7% false-negative and 10% false-positive error rates. We also use Markov chain Monte Carlo simulation for the prediction of networks with maximum probability under our model. This model can be applied across species, where interaction data from one (or several) species can be used to infer interactions in another. In addition, the model is extensible and can be analogously applied to other molecular data (e.g., DNA sequences).

Amino Acid Motifs↗

PubMed2004

Does a tree-like phylogeny only exist at the tips in the prokaryotes?

The extent to which prokaryotic evolution has been influenced by horizontal gene transfer (HGT) and therefore might be more of a network than a tree is unclear. Here we use supertree methods to ask whether a definitive prokaryotic phylogenetic tree exists and whether it can be confidently inferred using orthologous genes. We analysed an 11-taxon dataset spanning the deepest divisions of prokaryotic relationships, a 10-taxon dataset spanning the relatively recent gamma-proteobacteria and a 61-taxon dataset spanning both, using species for which complete genomes are available. Congruence among gene trees spanning deep relationships is not better than random. By contrast, a strong, almost perfect phylogenetic signal exists in gamma-proteobacterial genes. Deep-level prokaryotic relationships are difficult to infer because of signal erosion, systematic bias, hidden paralogy and/or HGT. Our results do not preclude levels of HGT that would be inconsistent with the notion of a prokaryotic phylogeny. This approach will help decide the extent to which we can say that there is a prokaryotic phylogeny and where in the phylogeny a cohesive genomic signal exists.

Bacteria↗