CiteULike: Tag modeling

From Pathways Databases to Network Models of Switching Behavior

Baltazar Aguda — 2007-09-29T10:56:33-00:00

PLoS Computational Biology, Vol. 3, No. 9. (1 September 2007), e152.

The Functional Genomics Experiment model (FuGE): an extensible framework for standards in functional genomics

Andrew Jones — 2007-10-11T16:03:11-00:00

Nat Biotech, Vol. 25, No. 10. (October 2007), pp. 1127-1133.

Ensemble modeling for analysis of cell signaling dynamics

Lars Kuepfer — 2007-09-11T23:43:24-00:00

Nature Biotechnology, Vol. 25, No. 9. (10 September 2007), pp. 1001-1006.

Evolutionary models for formation of network motifs and modularity in the Saccharomyces transcription factor network.

JJ Ward — 2007-11-05T11:12:10-00:00

PLoS Comput Biol, Vol. 3, No. 10. (26 October 2007), pp. 1993-2002.

Many natural and artificial networks contain overrepresented subgraphs, which have been termed network motifs. In this article, we investigate the processes that led to the formation of the two most common network motifs in eukaryote transcription factor networks: the bi-fan motif and the feed-forward loop. Around 100 million y ago, the common ancestor of the Saccharomyces clade underwent a whole-genome duplication event. The simultaneous duplication of the genes created by this event enabled the origin of many network motifs to be established. The data suggest that there are two primary mechanisms that are involved in motif formation. The first mechanism, enabled by the substantial plasticity in promoter regions, is rewiring of connections as a result of positive environmental selection. The second is duplication of transcription factors, which is also shown to be involved in the formation of intermediate-scale network modularity. These two evolutionary processes are complementary, with the pre-existence of network motifs enabling duplicated transcription factors to bind different targets despite structural constraints on their DNA-binding specificities. This process may facilitate the creation of novel expression states and the increases in regulatory complexity associated with higher eukaryotes.

Mathematical modeling of pathogenicity of Cryptococcus neoformans

Jacqueline Garcia — 2008-06-13T11:17:05-00:00

Mol Syst Biol, Vol. 4 (15 April 2008)

On State-Space Reduction in Multi-Strain Pathogen Models, with an Application to Antigenic Drift in Influenza A

Sergey Kryazhimskiy — 2007-09-03T00:51:55-00:00

PLoS Computational Biology, Vol. 3, No. 8. (1 August 2007), e159.

Many pathogens exist in phenotypically distinct strains that interact with each other through competition for hosts. General models that describe such multi-strain systems are extremely difficult to analyze because their state spaces are enormously large. Reduced models have been proposed, but so far all of them necessarily allow for coinfections and require that immunity be mediated solely by reduced infectivity, a potentially problematic assumption. Here, we suggest a new state-space reduction approach that allows immunity to be mediated by either reduced infectivity or reduced susceptibility and that can naturally be used for models with or without coinfections. Our approach utilizes the general framework of status-based models. The cornerstone of our method is the introduction of immunity variables, which describe multi-strain systems more naturally than the traditional tracking of susceptible and infected hosts. Models expressed in this way can be approximated in a natural way by a truncation method that is akin to moment closure, allowing us to sharply reduce the size of the state space, and thus to consider models with many strains in a tractable manner. Applying our method to the phenomenon of antigenic drift in influenza A, we propose a potentially general mechanism that could constrain viral evolution to a one-dimensional manifold in a two-dimensional trait space. Our framework broadens the class of multi-strain systems that can be adequately described by reduced models. It permits computational, and even analytical, investigation and thus serves as a useful tool for understanding the evolution and ecology of multi-strain pathogens.

Network-Based Analysis of Affected Biological Processes in Type 2 Diabetes Models

Manway Liu — 2007-06-21T18:07:09-00:00

PLoS Genetics, Vol. 3, No. 6. (1 June 2007), e96.

Type 2 diabetes mellitus is a complex disorder associated with multiple genetic, epigenetic, developmental, and environmental factors. Animal models of type 2 diabetes differ based on diet, drug treatment, and gene knockouts, and yet all display the clinical hallmarks of hyperglycemia and insulin resistance in peripheral tissue. The recent advances in gene-expression microarray technologies present an unprecedented opportunity to study type 2 diabetes mellitus at a genome-wide scale and across different models. To date, a key challenge has been to identify the biological processes or signaling pathways that play significant roles in the disorder. Here, using a network-based analysis methodology, we identified two sets of genes, associated with insulin signaling and a network of nuclear receptors, which are recurrent in a statistically significant number of diabetes and insulin resistance models and transcriptionally altered across diverse tissue types. We additionally identified a network of protein–protein interactions between members from the two gene sets that may facilitate signaling between them. Taken together, the results illustrate the benefits of integrating high-throughput microarray studies, together with protein–protein interaction networks, in elucidating the underlying biological processes associated with a complex disorder.

Estimating dynamic models for gene regulation networks

Jiguo Cao — 2008-06-03T03:00:21-00:00

Bioinformatics (27 May 2008), btn246.

Motivation: Transcription regulation is a fundamental process in biology, and it is important to model the dynamic behavior of gene regulation networks. Many approaches have been proposed to specify the network structure. However, finding the network connectivity is not sufficient to understand the network dynamics. Instead, one needs to model the regulation reactions, usually with a set of ordinary differential equations (ODEs). Because some of the parameters involved in these ODEs are unknown, their values need to be inferred from the observed data. Results: In this article, we introduce the generalized profiling method to estimate ODE parameters in a gene regulation network from microarray gene expression data which can be rather noisy. Because numerically solving ODEs is computationally expensive, we apply the penalized smoothing technique, a fast and stable computational method to approximate ODE solutions. The ODE solutions with our parameter estimates fit the data well. A goodness-of-fit test of dynamic models is developed to identify gene regulation networks. Contact: jca76@sfu.ca, hongyu.zhao@yale.edu 10.1093/bioinformatics/btn246

Biology by numbers: mathematical modelling in developmental biology

Claire Tomlin — 2007-04-18T14:24:13-00:00

Nat Rev Genet, Vol. 8, No. 5. (May 2007), pp. 331-340.

Simulating replica exchange simulations of protein folding with a kinetic network model

Weihua Zheng — 2007-11-01T02:12:34-00:00

Proceedings of the National Academy of Sciences, Vol. 104, No. 39. (25 September 2007), pp. 15340-15345.

Replica exchange (RE) is a generalized ensemble simulation method for accelerating the exploration of free-energy landscapes, which define many challenging problems in computational biophysics, including protein folding and binding. Although temperature RE (T-RE) is a parallel simulation technique whose implementation is relatively straightforward, kinetics and the approach to equilibrium in the T-RE ensemble are very complicated; there is much to learn about how to best employ T-RE to protein folding and binding problems. We have constructed a kinetic network model for RE studies of protein folding and used this reduced model to carry out "simulations of simulations" to analyze how the underlying temperature dependence of the conformational kinetics and the basic parameters of RE (e.g., the number of replicas, the RE rate, and the temperature spacing) all interact to affect the number of folding transitions observed. When protein folding follows anti-Arrhenius kinetics, we observe a speed limit for the number of folding transitions observed at the low temperature of interest, which depends on the maximum of the harmonic mean of the folding and unfolding transition rates at high temperature. The results shown here for the network RE model suggest ways to improve atomic-level RE simulations such as the use of "training" simulations to explore some aspects of the temperature dependence for folding of the atomic-level models before performing RE studies. 10.1073/pnas.0704418104

A generative, probabilistic model of local protein structure

Wouter Boomsma — 2008-07-02T08:23:30-00:00

Proceedings of the National Academy of Sciences, Vol. 105, No. 26. (1 July 2008), pp. 8932-8937.

Despite significant progress in recent years, protein structure prediction maintains its status as one of the prime unsolved problems in computational biology. One of the key remaining challenges is an efficient probabilistic exploration of the structural space that correctly reflects the relative conformational stabilities. Here, we present a fully probabilistic, continuous model of local protein structure in atomic detail. The generative model makes efficient conformational sampling possible and provides a framework for the rigorous analysis of local sequence-structure correlations in the native state. Our method represents a significant theoretical and practical improvement over the widely used fragment assembly technique by avoiding the drawbacks associated with a discrete and nonprobabilistic approach. 10.1073/pnas.0801715105

The Ascent of the Abundant: How Mutational Networks Constrain Evolution

Matthew Cowperthwaite — 2008-07-19T10:33:34-00:00

PLoS Comput Biol, Vol. 4, No. 7. (18 July 2008), e1000110.

Evolution by natural selection is fundamentally shaped by the fitness landscapes in which it occurs. Yet fitness landscapes are vast and complex, and thus we know relatively little about the long-range constraints they impose on evolutionary dynamics. Here, we exhaustively survey the structural landscapes of RNA molecules of lengths 12 to 18 nucleotides, and develop a network model to describe the relationship between sequence and structure. We find that phenotype abundance—the number of genotypes producing a particular phenotype—varies in a predictable manner and critically influences evolutionary dynamics. A study of naturally occurring functional RNA molecules using a new structural statistic suggests that these molecules are biased toward abundant phenotypes. This supports an “ascent of the abundant” hypothesis, in which evolution yields abundant phenotypes even when they are not the most fit.

New models of collaboration in genome-wide association studies: the Genetic Association Information Network

2007-09-05T00:52:38-00:00

Nat Genet, Vol. 39, No. 9. (2007), pp. 1045-1051.

An Evolutionary-Network Model Reveals Stratified Interactions in the V3 Loop of the HIV-1 Envelope

Art Poon — 2007-12-10T04:39:24-00:00

PLoS Computational Biology, Vol. 3, No. 11. (1 November 2007), e231.

The third variable loop (V3) of the human immunodeficiency virus type 1 (HIV-1) envelope is a principal determinant of antibody neutralization and progression to AIDS. Although it is undoubtedly an important target for vaccine research, extensive genetic variation in V3 remains an obstacle to the development of an effective vaccine. Comparative methods that exploit the abundance of sequence data can detect interactions between residues of rapidly evolving proteins such as the HIV-1 envelope, revealing biological constraints on their variability. However, previous studies have relied implicitly on two biologically unrealistic assumptions: (1) that founder effects in the evolutionary history of the sequences can be ignored, and; (2) that statistical associations between residues occur exclusively in pairs. We show that comparative methods that neglect the evolutionary history of extant sequences are susceptible to a high rate of false positives (20%–40%). Therefore, we propose a new method to detect interactions that relaxes both of these assumptions. First, we reconstruct the evolutionary history of extant sequences by maximum likelihood, shifting focus from extant sequence variation to the underlying substitution events. Second, we analyze the joint distribution of substitution events among positions in the sequence as a Bayesian graphical model, in which each branch in the phylogeny is a unit of observation. We perform extensive validation of our models using both simulations and a control case of known interactions in HIV-1 protease, and apply this method to detect interactions within V3 from a sample of 1,154 HIV-1 envelope sequences. Our method greatly reduces the number of false positives due to founder effects, while capturing several higher-order interactions among V3 residues. By mapping these interactions to a structural model of the V3 loop, we find that the loop is stratified into distinct evolutionary clusters. We extend our model to detect interactions between the V3 and C4 domains of the HIV-1 envelope, and account for the uncertainty in mapping substitutions to the tree with a parametric bootstrap.

MORPH: Probabilistic Alignment Combined with Hidden Markov Models of cis-Regulatory Modules

Saurabh Sinha — 2007-11-10T02:00:31-00:00

PLoS Computational Biology, Vol. 3, No. 11. (1 November 2007), e216.

The discovery and analysis of cis-regulatory modules (CRMs) in metazoan genomes is crucial for understanding the transcriptional control of development and many other biological processes. Cross-species sequence comparison holds much promise for improving computational prediction of CRMs, for elucidating their binding site composition, and for understanding how they evolve. Current methods for analyzing orthologous CRMs from multiple species rely upon sequence alignments produced by off-the-shelf alignment algorithms, which do not exploit the presence of binding sites in the sequences. We present here a unified probabilistic framework, called MORPH, that integrates the alignment task with binding site predictions, allowing more robust CRM analysis in two species. The framework sums over all possible alignments of two sequences, thus accounting for alignment ambiguities in a natural way. We perform extensive tests on orthologous CRMs from two moderately diverged species Drosophila melanogaster and D. mojavensis, to demonstrate the advantages of the new approach. We show that it can overcome certain computational artifacts of traditional alignment tools and provide a different, likely more accurate, picture of cis-regulatory evolution than that obtained from existing methods. The burgeoning field of cis-regulatory evolution, which is amply supported by the availability of many related genomes, is currently thwarted by the lack of accurate alignments of regulatory regions. Our work will fill in this void and enable more reliable analysis of CRM evolution.

A First-Principles Model of Early Evolution: Emergence of Gene Families, Species, and Preferred Protein Folds

Konstantin Zeldovich — 2007-07-13T18:27:36-00:00

PLoS Computational Biology, Vol. 3, No. 7. (1 July 2007), e139.

In this work we develop a microscopic physical model of early evolution where phenotype—organism life expectancy—is directly related to genotype—the stability of its proteins in their native conformations—which can be determined exactly in the model. Simulating the model on a computer, we consistently observe the “Big Bang” scenario whereby exponential population growth ensues as soon as favorable sequence–structure combinations (precursors of stable proteins) are discovered. Upon that, random diversity of the structural space abruptly collapses into a small set of preferred proteins. We observe that protein folds remain stable and abundant in the population at timescales much greater than mutation or organism lifetime, and the distribution of the lifetimes of dominant folds in a population approximately follows a power law. The separation of evolutionary timescales between discovery of new folds and generation of new sequences gives rise to emergence of protein families and superfamilies whose sizes are power-law distributed, closely matching the same distributions for real proteins. On the population level we observe emergence of species—subpopulations that carry similar genomes. Further, we present a simple theory that relates stability of evolving proteins to the sizes of emerging genomes. Together, these results provide a microscopic first-principles picture of how first-gene families developed in the course of early evolution.

A modular network model of aging

Huiling Xue — 2007-12-04T18:22:43-00:00

Mol Syst Biol, Vol. 3 (4 December 2007)

Structured analysis approaches for large Markov chains

Peter Buchholz — 2007-04-23T22:22:49-00:00

Applied Numerical Mathematics: Transactions of IMACS, Vol. 31, No. 4. (1999), pp. 375-404.

The tutorial introduces structured analysis approaches for continuous time Markov chains (CTMCs) which are a means to extend the size of analyzable state spaces significantly compared with conventional techniques. It is shown how generator matrices of large CTMCs can be represented in a very compact form, how this representation can be exploited in numerical solution techniques and how numerical analysis profits from this exploitation. Additionally, recent results covering implementation...

Multiple structural alignment by secondary structures: algorithm and applications.

O Dror — 2005-02-23T16:17:59-00:00

Protein Sci, Vol. 12, No. 11. (November 2003), pp. 2492-2507.

We present MASS (Multiple Alignment by Secondary Structures), a novel highly efficient method for structural alignment of multiple protein molecules and detection of common structural motifs. MASS is based on a two-level alignment, using both secondary structure and atomic representation. Utilizing secondary structure information aids in filtering out noisy solutions and achieves efficiency and robustness. Currently, only a few methods are available for addressing the multiple structural alignment task. In addition to using secondary structure information, the advantage of MASS as compared to these methods is that it is a combination of several important characteristics: (1) While most existing methods are based on series of pairwise comparisons, and thus might miss optimal global solutions, MASS is truly multiple, considering all the molecules simultaneously; (2) MASS is sequence order-independent and thus capable of detecting nontopological structural motifs; (3) MASS is able to detect not only structural motifs, shared by all input molecules, but also motifs shared only by subsets of the molecules. Here, we show the application of MASS to various protein ensembles. We demonstrate its ability to handle a large number (order of tens) of molecules, to detect nontopological motifs and to find biologically meaningful alignments within nonpredefined subsets of the input. In particular, we show how by using conserved structural motifs, one can guide protein-protein docking, which is a notoriously difficult problem. MASS is freely available at http://bioinfo3d.cs.tau.ac.il/MASS/.

A quantitative description of membrane current and its application to conduction and excitation in nerve.

AL HODGKIN — 2006-09-20T10:56:18-00:00

J Physiol, Vol. 117, No. 4. (August 1952), pp. 500-544.

Bayesian comparison of spatially regularised general linear models

Will Penny — 2007-03-28T15:50:08-00:00

Human Brain Mapping, Vol. 28, No. 4. (2007), pp. 275-293.

In previous work (Penny et al., [2005]: Neuroimage 24:350-362) we have developed a spatially regularised General Linear Model for the analysis of functional magnetic resonance imaging data that allows for the characterisation of regionally specific effects using Posterior Probability Maps (PPMs). In this paper we show how it also provides an approximation to the model evidence. This is important as it is the basis of Bayesian model comparison and provides a unified framework for Bayesian Analysis of Variance, Cluster of Interest analyses and the principled selection of signal and noise models. We also provide extensions that implement spatial and anatomical regularisation of noise process parameters. Hum Brain Mapp 2007. © 2006 Wiley-Liss, Inc.

An Ontological Model of an Information System

Yair Wand — 2005-10-28T10:47:06-00:00

IEEE Trans. Softw. Eng., Vol. 16, No. 11. (November 1990), pp. 1282-1292.

A proposed framework for the analysis and evaluation of business models

Jean-Paul Van Belle — 2005-07-27T07:43:59-00:00

(2004), pp. 210-215.

This paper proposed and validates a new framework for the evaluation and comparison of enterprise models. After a broad literature survey, a large number of evaluation criteria were established and consolidated into a comprehensive framework. The main structuring principle of the framework is the dimension distinguishing syntactic, semantic and pragmatic criteria. The resulting framework is validated against some conceptual principles.

Evaluating modeling techniques based on models of learning

Andrew Gemino — 2005-07-27T07:38:51-00:00

Communications of the ACM, Vol. 46, No. 10. (October 2003), pp. 79-84.

To compare modeling techniques, combine grammar-based and cognitive-based approaches and test domain understanding.

Performance modeling of optical-burst switching with fiber delay lines

Xiaomin Lu — 2008-08-08T14:33:13-00:00

Communications, IEEE Transactions on, Vol. 52, No. 12. (2004), pp. 2175-2183.

We develop analytical models to evaluate the performance of optical-burst switch (OBS) architectures employing fiber delay lines (FDLs) as optical buffers to reduce burst-loss probability. The performance of such architectures cannot be captured accurately using traditional queueing models, since FDLs behave fundamentally differently from conventional electronic buffers. We formulate a Markovian model to evaluate the system performance when the burst-arrival process is Poisson and the burst lengths are exponentially distributed under an idealized model of FDL behavior. The model accurately captures both the balking and deterministic delay properties of FDLs, but the complexity of the model makes it infeasible for solving problems of practical interest. By considering approximations of the model in the regimes of short and long FDLs, we develop relatively simple closed-form expressions that can be used for dimensioning OBS architectures. We also extend the approximate model to include the impact of FDL delay granularity. We present numerical results that validate our modeling approach and demonstrate that significant performance gains in optical-burst switching are achievable when FDLs are employed as optical buffers.

Example-based model synthesis

Paul Merrell — 2007-05-28T06:10:39-00:00

(2007), pp. 105-112.

Modeling and performance analysis of BitTorrent-like peer-to-peer networks

Dongyu Qiu — 2004-12-20T08:30:31-00:00

SIGCOMM Comput. Commun. Rev., Vol. 34, No. 4. (October 2004), pp. 367-378.

On formal requirements modeling languages: RML revisited

Sol Greenspan — 2006-12-30T13:15:18-00:00

(1994), pp. 135-147.

Plucked-String Models: From the Karplus-Strong Algorithm to Digital Waveguides and beyond

Matti Karjalainen — 2007-11-29T17:53:11-00:00

Computer Music Journal, Vol. 22, No. 3. (1998), pp. 17-32.

Towards high-quality sound synthesis of the guitar and string instruments

M Karjalainen — 2007-11-29T17:50:38-00:00

(1993)

The sound quality of real-time synthesis based on physical models has so far been inferior to sampling techniques. In this paper we introduce new principles to make model-based sound synthesis of the guitar and other plucked string instruments more attractive from the viewpoint of sound quality. A major improvement is achieved by estimating the model parameters and the excitation signal from the sound of an acoustic instrument. It is shown that the impulse response of the body is included...

10 Criteria for Evaluating Physical Modeling Schemes for Music Creation

Nicolas Castagne — 2007-11-29T17:29:43-00:00

(2003)

The success recently encountered by physically-based modeling (or model-based approaches) for music should not mask the deep challenges that remain in this area. This article first proposes an overview of the various goals that researchers and musicians, respectively operating from scientific and end-user perspectives, may pursue. Among these goals, those recently proposed or particularly critical for the coming years of research are highlighted. The article then introduces ten criteria that...

Physical Modeling of Membranes for Percussion Instruments

Federico Fontana — 2008-01-16T11:49:44-00:00

pp. 529-542.

Recent research on Physical Modeling has led to 2-D discrete-time structures based on the Digital Waveguides. These structures are well suited for efficient yet accurate simulation of wave propagation in an ideal membrane. Nevertheless, real membranes exhibit a different behaviour, due to the environmental conditions and to the material they are made of. In this work we consider some aspects, crucial for the audio signal, of the physical phenomena concerning real membranes, and we will develop a 2-D waveguide model encompassing the effects of these aspects. In order to excite the simulated membrane, we will consider a hammer model previously developed for piano strings, and here adapted to fit the hammer-membrane interaction.

Shape modeling with point-sampled geometry

Mark Pauly — 2005-07-25T13:41:02-00:00

ACM Trans. Graph., Vol. 22, No. 3. (July 2003), pp. 641-650.

Data assimilation (4D-VAR) to forecast flood in shallow-waters with sediment erosion

Eric Belanger — 2006-11-27T13:13:11-00:00

Journal of Hydrology, Vol. 300, No. 1-4. (10 January 2005), pp. 114-125.

In this paper, the four-dimensional variational data assimilation technique (4D-VAR) is presented as a tool to forecast floods. Our study is limited to purely hydrological flows and supposes that the weather, here a big rain, has been already forecasted by meteorological services. The technique consists in minimizing, in the sense of Lagrange, the cost function: a measure of the difference between calculated data and available observations, here the water level. This is done under constraints that are the equations of the physical model. In our case, we modified the shallow-water equations to include a simplified sediment transport model. The steepest descent algorithm is then used to find the minimum. This is made possible because we can compute analytically the gradient of the cost function by using the adjoint equations of the model. As an application of the 4D-VAR technique, the overflowing of the Chicoutimi River at the Chute-Garneau dam, during the 1996 flood, is investigated. It is found that the 4D-VAR method reduces the error in the water height forecast even when the erosion model is not activated. In terms of Lyapunov exponents, we estimate the predictability horizon of such an event to be about half-an-hour after a big rain. However, this limit of predictability can be increased by using more observations or by using a finer computational grid.

Climate Cycles and Forecasts of Cutaneous Leishmaniasis, a Nonstationary Vector-Borne Disease

Luis Chaves — 2008-01-26T18:18:15-00:00

PLoS Medicine, Vol. 3, No. 8. (1 August 2006), e295.

BackgroundCutaneous leishmaniasis (CL) is one of the main emergent diseases in the Americas. As in other vector-transmitted diseases, its transmission is sensitive to the physical environment, but no study has addressed the nonstationary nature of such relationships or the interannual patterns of cycling of the disease.Methods and FindingsWe studied monthly data, spanning from 1991 to 2001, of CL incidence in Costa Rica using several approaches for nonstationary time series analysis in order to ensure robustness in the description of CL's cycles. Interannual cycles of the disease and the association of these cycles to climate variables were described using frequency and time-frequency techniques for time series analysis. We fitted linear models to the data using climatic predictors, and tested forecasting accuracy for several intervals of time. Forecasts were evaluated using “out of fit” data (i.e., data not used to fit the models). We showed that CL has cycles of approximately 3 y that are coherent with those of temperature and El Niño Southern Oscillation indices (Sea Surface Temperature 4 and Multivariate ENSO Index).ConclusionsLinear models using temperature and MEI can predict satisfactorily CL incidence dynamics up to 12 mo ahead, with an accuracy that varies from 72% to 77% depending on prediction time. They clearly outperform simpler models with no climate predictors, a finding that further supports a dynamical link between the disease and climate.

From classical genetics to quantitative genetics to systems biology: modeling epistasis.

DL Aylor — 2008-04-17T11:57:23-00:00

PLoS genetics, Vol. 4, No. 3. (March 2008)

Gene expression data has been used in lieu of phenotype in both classical and quantitative genetic settings. These two disciplines have separate approaches to measuring and interpreting epistasis, which is the interaction between alleles at different loci. We propose a framework for estimating and interpreting epistasis from a classical experiment that combines the strengths of each approach. A regression analysis step accommodates the quantitative nature of expression measurements by estimating the effect of gene deletions plus any interaction. Effects are selected by significance such that a reduced model describes each expression trait. We show how the resulting models correspond to specific hierarchical relationships between two regulator genes and a target gene. These relationships are the basic units of genetic pathways and genomic system diagrams. Our approach can be extended to analyze data from a variety of experiments, multiple loci, and multiple environments.

Using Likelihood-Free Inference to Compare Evolutionary Dynamics of the Protein Networks of H. pylori and P. falciparum

Oliver Ratmann — 2007-12-01T08:59:40-00:00

PLoS Computational Biology, Vol. 3, No. 11. (1 November 2007), e230.

Gene duplication with subsequent interaction divergence is one of the primary driving forces in the evolution of genetic systems. Yet little is known about the precise mechanisms and the role of duplication divergence in the evolution of protein networks from the prokaryote and eukaryote domains. We developed a novel, model-based approach for Bayesian inference on biological network data that centres on approximate Bayesian computation, or likelihood-free inference. Instead of computing the intractable likelihood of the protein network topology, our method summarizes key features of the network and, based on these, uses a MCMC algorithm to approximate the posterior distribution of the model parameters. This allowed us to reliably fit a flexible mixture model that captures hallmarks of evolution by gene duplication and subfunctionalization to protein interaction network data of Helicobacter pylori and Plasmodium falciparum. The 80% credible intervals for the duplication–divergence component are [0.64, 0.98] for H. pylori and [0.87, 0.99] for P. falciparum. The remaining parameter estimates are not inconsistent with sequence data. An extensive sensitivity analysis showed that incompleteness of PIN data does not largely affect the analysis of models of protein network evolution, and that the degree sequence alone barely captures the evolutionary footprints of protein networks relative to other statistics. Our likelihood-free inference approach enables a fully Bayesian analysis of a complex and highly stochastic system that is otherwise intractable at present. Modelling the evolutionary history of PIN data, it transpires that only the simultaneous analysis of several global aspects of protein networks enables credible and consistent inference to be made from available datasets. Our results indicate that gene duplication has played a larger part in the network evolution of the eukaryote than in the prokaryote, and suggests that single gene duplications with immediate divergence alone may explain more than 60% of biological network data in both domains.

Chaos Theory, Optimal Embedding and Evolutionary Algorithms

Viadan Babovic — 2006-09-06T13:18:15-00:00

ABSTRACT: Constructing models from time series with non-trivial dynamics is a difficult problem. The classical approach is to build a model from first principles and use it to forecast on the basis of the initial conditions. Unfortunately, this is not always possible. For example, in fluid dynamics a perfect model in the form of the Navier-Stokes equations exists, but initial conditions are difficult to obtain. In other cases, a good model may not exist. In either case, alternative approaches...

Integrative molecular concept modeling of prostate cancer progression

Scott Tomlins — 2006-12-28T00:50:03-00:00

Nature Genetics, Vol. 39, No. 1. (17 December 2006), pp. 41-51.

Gene Network Inference via Structural Equation Modeling in Genetical Genomics Experiments.

Bing Liu — 2008-02-06T11:38:22-00:00

Genetics (3 February 2008)

Our goal is gene network inference in Genetical Genomics or Systems Genetics experiments. For species where sequence information is available, we first perform expression QTL mapping by jointly utilizing cis, cistrans and trans regulation. After using local structural models to identify regulator-target pairs for each eQTL, we construct an encompassing directed network (EDN) by assembling all retained regulator-target relationships. The EDN has nodes corresponding to expressed genes and eQTLs, and directed edges from eQTLs to cis-regulated target genes, from cis-regulated genes to cistrans regulated target genes, from trans-regulator genes to target genes and from trans-eQTLs to target genes. For network inference within the strongly constrained search space defined by the EDN, we propose Structural Equation Modeling (SEM), because it can model cyclic networks and the EDN indeed contains feedback relationships. Based on a factorization of the likelihood and the constrained search space, our SEM algorithm infers networks involving several hundred genes and eQTL. Structure inference is based on a penalized likelihood ratio and an adaptation of Occam's Window model selection. The SEM algorithm was evaluated using data simulated with nonlinear ordinary differential equations and known cyclic network topologies and was applied to a real yeast data set.

Pharmacokinetic-pharmacodynamic modeling of the antinociceptive effect of buprenorphine and fentanyl in rats: role of receptor equilibration kinetics.

A Yassen — 2007-02-24T02:01:49-00:00

J Pharmacol Exp Ther, Vol. 313, No. 3. (June 2005), pp. 1136-1149.

The objective of this investigation was to characterize the pharmacokinetic/pharmacodynamic correlation of buprenorphine and fentanyl for the antinociceptive effect in rats. Data on the time course of the antinociceptive effect following intravenous administration of buprenorphine or fentanyl was analyzed in conjunction with plasma concentrations by nonlinear mixed-effects analysis. For fentanyl, the pharmacokinetics was described on the basis of a two-compartment pharmacokinetic model. For buprenorphine, a three-compartment pharmacokinetic model best described the concentration time course. To explain time dependencies in pharmacodynamics of buprenorphine and fentanyl, a combined effect compartment/receptor binding model was applied. A log logistic probability distribution model is proposed to account for censored tail-flick latencies. The model converged, yielding precise estimates of the parameters characterizing hysteresis. The results show that onset and offset of the antinociceptive effect of both buprenorphine and fentanyl is mainly determined by biophase distribution. The k(eo) was 0.024 min(-1) [95% confidence interval (CI): 0.018-0.030 min(-1)] and 0.123 min(-1) (95% CI: 0.095-0.151 min(-1)) for buprenorphine and fentanyl, respectively. On the other hand, part of the hysteresis in the buprenorphine pharmacodynamics could be explained by slow receptor association/dissociation kinetics. The k(off) was 0.073 min(-1) (95% CI: 0.042-0.104 min(-1)) and k(on) was 0.023 ml/ng/min (95% CI: 0.013-0.033 ml/ng/min). Fentanyl binds instantaneously to the OP3 receptor because no reasonable values for k(on) and k(off) were obtained with the dynamical receptor model. In contrast to earlier reports in the literature, the findings of this study show that the rate-limiting step in the onset and offset of buprenorphine's antinociceptive effect is distribution to the brain.

Structural systems biology: modelling protein interactions

Patrick Aloy — 2006-02-23T17:07:10-00:00

Nature Reviews Molecular Cell Biology, Vol. 7, No. 3., pp. 188-197.

Chromophore Channeling in the G-Protein Coupled Receptor Rhodopsin.

Ting Wang — 2007-05-21T08:42:19-00:00

J Am Chem Soc (15 May 2007)

Rhodopsin serves as the prototype for studies of the G-protein coupled receptor (GPCR) proteins as it is the only GPCR protein with known crystal structures, and its structure has been used as the template to model a large number of GPCR proteins, including many used as drug targets. Understanding ligand entrance routes is important for designing drugs with improved efficacy. Here we simulated the egress of the retinal chromophore from the protein by applying the random acceleration molecular dynamics (RAMD) method. The interhelical clefts near the extracellular side were identified to be the predominant egress, while the movement of retinal deep into the cytoplasmic side was also observed. These results suggest possible routes for ligands to enter into the binding pockets of GPCR proteins. In addition, the RAMD simulation results revealed the high stability of the interactions between helix 3 and other helices.

Computational prediction of atomic structures of helical membrane proteins aided by EM maps.

Julio A Kovacs — 2007-05-21T08:40:05-00:00

Biophys J (11 May 2007)

Integral membrane proteins pose a major challenge for protein-structure prediction because only approximately 100 high-resolution structures are available currently, thereby impeding the development of rules or empirical potentials to predict the packing of transmembrane alpha-helices. However, when a low-resolution EM map is available, it can be used to provide restraints which, in combination with a suitable computational protocol, make structure prediction feasible. In this work we present such a protocol, which proceeds in three stages: (1) generation of an ensemble of alpha-helices by flexible fitting into each of the density rods in the low-resolution EM map, spanning a range of rotational angles around the main helical axes and translational shifts along the density rods; (2) fast optimization of side chains and scoring of the resulting conformations; and (3) refinement of the lowest-scoring conformations with Internal Coordinate Mechanics, by optimizing the van der Waals, electrostatics, hydrogen bonding, torsional and solvation energy contributions. In addition, our method implements a penalty term through a so-called "tethering map," derived from the EM map, which restrains the positions of the alpha-helices. The protocol was validated on three test cases: GpA, KcsA, and MscL.

Protein-Protein Docking with Backbone Flexibility

Chu Wang — 2007-09-27T16:39:57-00:00

Journal of Molecular Biology, Vol. 373, No. 2. (19 October 2007), pp. 503-519.

Computational protein-protein docking methods currently can create models with atomic accuracy for protein complexes provided that the conformational changes upon association are restricted to the side chains. However, it remains very challenging to account for backbone conformational changes during docking, and most current methods inherently keep monomer backbones rigid for algorithmic simplicity and computational efficiency. Here we present a reformulation of the Rosetta docking method that incorporates explicit backbone flexibility in protein-protein docking. The new method is based on a "fold-tree" representation of the molecular system, which seamlessly integrates internal torsional degrees of freedom and rigid-body degrees of freedom. Problems with internal flexible regions ranging from one or more loops or hinge regions to all of one or both partners can be readily treated using appropriately constructed fold trees. The explicit treatment of backbone flexibility improves both sampling in the vicinity of the native docked conformation and the energetic discrimination between near-native and incorrect models.

Protein-Protein Docking with Simultaneous Optimization of Rigid-body Displacement and Side-chain Conformations

Jeffrey Gray — 2006-04-25T15:40:03-00:00

Journal of Molecular Biology, Vol. 331, No. 1. (1 August 2003), pp. 281-299.

Protein-protein docking algorithms provide a means to elucidate structural details for presently unknown complexes. Here, we present and evaluate a new method to predict protein-protein complexes from the coordinates of the unbound monomer components. The method employs a low-resolution, rigid-body, Monte Carlo search followed by simultaneous optimization of backbone displacement and side-chain conformations using Monte Carlo minimization. Up to 105 independent simulations are carried out, and the resulting “decoys” are ranked using an energy function dominated by van der Waals interactions, an implicit solvation model, and an orientation-dependent hydrogen bonding potential. Top-ranking decoys are clustered to select the final predictions. Small-perturbation studies reveal the formation of binding funnels in 42 of 54 cases using coordinates derived from the bound complexes and in 32 of 54 cases using independently determined coordinates of one or both monomers. Experimental binding affinities correlate with the calculated score function and explain the predictive success or failure of many targets. Global searches using one or both unbound components predict at least 25% of the native residue-residue contacts in 28 of the 32 cases where binding funnels exist. The results suggest that the method may soon be useful for generating models of biologically important complexes from the structures of the isolated components, but they also highlight the challenges that must be met to achieve consistent and accurate prediction of protein-protein interactions.

Metal Ion Site Engineering Indicates a Global Toggle Switch Model for Seven-transmembrane Receptor Activation.

CE Elling — 2006-07-04T11:16:03-00:00

J Biol Chem, Vol. 281, No. 25. (23 June 2006), pp. 17337-17346.

Much evidence indicates that, during activation of seven-transmembrane (7TM) receptors, the intracellular segments of the transmembrane helices (TMs) move apart with large amplitude, rigid body movements of especially TM-VI and TM-VII. In this study, AspIII:08 (Asp(113)), the anchor point for monoamine binding in TM-III, was used as the starting point to engineer activating metal ion sites between the extracellular segments of thebeta(2)-adrenergic receptor. Cu(II) and Zn(II) alone and in complex with aromatic chelators acted as potent (EC(50) decreased to 0.5 mum) and efficacious agonists in sites constructed between positions III:08 (Asp or His), VI:16 (preferentially Cys), and/or VII:06 (preferentially Cys). In molecular models built over the backbone conformation of the inactive rhodopsin structure, the heavy atoms that coordinate the metal ion were located too far away from each other to form high affinity metal ion sites in both the bidentate and potential tridentate settings. This indicates that the residues involved in the main ligand-binding pocket will have to move closer to each other during receptor activation. On the basis of the distance constraints from these activating metal ion sites, we propose a global toggle switch mechanism for 7TM receptor activation in which inward movement of the extracellular segments of especially TM-VI and, to some extent, TM-VII is coupled to the well established outward movement of the intracellular segments of these helices. We suggest that the pivots for these vertical seesaw movements are the highly conserved proline bends of the involved helices.

Automatic atom type and bond type perception in molecular mechanical calculations.

Junmei Wang — 2006-02-13T15:46:20-00:00

J Mol Graph Model (1 February 2006)

In molecular mechanics (MM) studies, atom types and/or bond types of molecules are needed to determine prior to energy calculations. We present here an automatic algorithm of perceiving atom types that are defined in a description table, and an automatic algorithm of assigning bond types just based on atomic connectivity. The algorithms have been implemented in a new module of the AMBER packages. This auxiliary module, antechamber (roughly meaning "before AMBER"), can be applied to generate necessary inputs of leap-the AMBER program to generate topologies for minimization, molecular dynamics, etc., for most organic molecules. The algorithms behind the manipulations may be useful for other molecular mechanical packages as well as applications that need to designate atom types and bond types.

Toward the active conformations of rhodopsin and the beta2-adrenergic receptor.

PR Gouldson — 2006-03-08T14:56:17-00:00

Proteins, Vol. 56, No. 1. (1 July 2004), pp. 67-84.

Using sets of experimental distance restraints, which characterize active or inactive receptor conformations, and the X-ray crystal structure of the inactive form of bovine rhodopsin as a starting point, we have constructed models of both the active and inactive forms of rhodopsin and the beta2-adrenergic G-protein coupled receptors (GPCRs). The distance restraints were obtained from published data for site-directed crosslinking, engineered zinc binding, site-directed spin-labeling, IR spectroscopy, and cysteine accessibility studies conducted on class A GPCRs. Molecular dynamics simulations in the presence of either "active" or "inactive" restraints were used to generate two distinguishable receptor models. The process for generating the inactive and active models was validated by the hit rates, yields, and enrichment factors determined for the selection of antagonists in the inactive model and for the selection of agonists in the active model from a set of nonadrenergic GPCR drug-like ligands in a virtual screen using ligand docking software. The simulation results provide new insights into the relationships observed between selected biochemical data, the crystal structure of rhodopsin, and the structural rearrangements that occur during activation.

Modeling of the complex between transducin and photoactivated rhodopsin, a prototypical G-protein-coupled receptor.

GV Nikiforovich — 2007-07-11T10:04:59-00:00

Biochemistry, Vol. 46, No. 16. (24 April 2007), pp. 4734-4744.

Obtaining a reliable 3D model for the complex formed by photoactivated rhodopsin (R*) and its G-protein, transducin (Gtalphabetagamma), would significantly benefit the entire field of structural biology of G-protein-coupled receptors (GPCRs). In this study, we have performed extensive configurational sampling for the isolated C-terminal fragment of the alpha-subunit of transducin, Gtalpha 340-350, within cavities of photoactivated rhodopsin formed by different energetically feasible conformations of the intracellular loops. Our results suggested a new 3D model of the rhodopsin-transducin complex that fully satisfied all available experimental data on site-directed mutagenesis of rhodopsin and Gtalphabetagamma as well as data from disulfide-linking experiments. Importantly, the experimental data were not used as a priori constraints in model building. We performed a thorough comparison of existing computational models of the rhodopsin-transducin complex with each other and with current experimental data. It was found that different models suggest interactions with different molecules in the rhodopsin oligomer, that providing valuable guidance in design of specific novel experimental studies of the R*-Gtalphabetagamma complex. Finally, we demonstrated that the isolated Gtalpha 340-350 fragment does not necessarily bind rhodopsin in the same binding mode as the same segment in intact Gtalpha.