Página 9 dos resultados de 6430 itens digitais encontrados em 0.017 segundos

‣ Gene Expression Complex Networks: Synthesis, Identification, and Analysis

LOPES, Fabricio M.; CESAR JR., Roberto M.; COSTA, Luciano Da F.
Fonte: MARY ANN LIEBERT INC Publicador: MARY ANN LIEBERT INC
Tipo: Artigo de Revista Científica
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Thanks to recent advances in molecular biology, allied to an ever increasing amount of experimental data, the functional state of thousands of genes can now be extracted simultaneously by using methods such as cDNA microarrays and RNA-Seq. Particularly important related investigations are the modeling and identification of gene regulatory networks from expression data sets. Such a knowledge is fundamental for many applications, such as disease treatment, therapeutic intervention strategies and drugs design, as well as for planning high-throughput new experiments. Methods have been developed for gene networks modeling and identification from expression profiles. However, an important open problem regards how to validate such approaches and its results. This work presents an objective approach for validation of gene network modeling and identification which comprises the following three main aspects: (1) Artificial Gene Networks (AGNs) model generation through theoretical models of complex networks, which is used to simulate temporal expression data; (2) a computational method for gene network identification from the simulated data, which is founded on a feature selection approach where a target gene is fixed and the expression profile is observed for all other genes in order to identify a relevant subset of predictors; and (3) validation of the identified AGN-based network through comparison with the original network. The proposed framework allows several types of AGNs to be generated and used in order to simulate temporal expression data. The results of the network identification method can then be compared to the original network in order to estimate its properties and accuracy. Some of the most important theoretical models of complex networks have been assessed: the uniformly-random Erdos-Renyi (ER)...

‣ Identification of alternative 5′/3′ splice sites based on the mechanism of splice site competition

Xia, Huiyu; Bi, Jianning; Li, Yanda
Fonte: Oxford University Press Publicador: Oxford University Press
Tipo: Artigo de Revista Científica
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Alternative splicing plays an important role in regulating gene expression. Currently, most efficient methods use expressed sequence tags or microarray analysis for large-scale detection of alternative splicing. However, it is difficult to detect all alternative splice events with them because of their inherent limitations. Previous computational methods for alternative splicing prediction could only predict particular kinds of alternative splice events. Thus, it would be highly desirable to predict alternative 5′/3′ splice sites with various splicing levels using genomic sequences alone. Here, we introduce the competition mechanism of splice sites selection into alternative splice site prediction. This approach allows us to predict not only rarely used but also frequently used alternative splice sites. On a dataset extracted from the AltSplice database, our method correctly classified ∼70% of the splice sites into alternative and constitutive, as well as ∼80% of the locations of real competitors for alternative splice sites. It outperforms a method which only considers features extracted from the splice sites themselves. Furthermore, this approach can also predict the changes in activation level arising from mutations in flanking cryptic splice sites of a given splice site. Our approach might be useful for studying alternative splicing in both computational and molecular biology.

‣ Integrating Computational Biology and Forward Genetics in Drosophila

Aerts, Stein; Vilain, Sven; Hu, Shu; Tranchevent, Leon-Charles; Barriot, Roland; Yan, Jiekun; Moreau, Yves; Hassan, Bassem A.; Quan, Xiao-Jiang
Fonte: Public Library of Science Publicador: Public Library of Science
Tipo: Artigo de Revista Científica
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Genetic screens are powerful methods for the discovery of gene–phenotype associations. However, a systems biology approach to genetics must leverage the massive amount of “omics” data to enhance the power and speed of functional gene discovery in vivo. Thus far, few computational methods for gene function prediction have been rigorously tested for their performance on a genome-wide scale in vivo. In this work, we demonstrate that integrating genome-wide computational gene prioritization with large-scale genetic screening is a powerful tool for functional gene discovery. To discover genes involved in neural development in Drosophila, we extend our strategy for the prioritization of human candidate disease genes to functional prioritization in Drosophila. We then integrate this prioritization strategy with a large-scale genetic screen for interactors of the proneural transcription factor Atonal using genomic deficiencies and mutant and RNAi collections. Using the prioritized genes validated in our genetic screen, we describe a novel genetic interaction network for Atonal. Lastly, we prioritize the whole Drosophila genome and identify candidate gene associations for ten receptor-signaling pathways. This novel database of prioritized pathway candidates...

‣ An Integrated Strategy for Analyzing the Unique Developmental Programs of Different Myoblast Subtypes

Estrada, Beatriz; Choe, Sung E; Gisselbrecht, Stephen S; Michaud, Sebastien; Busser, Brian W; Halfon, Marc S; Raj, Lakshmi; Church, George McDonald; Michelson, Alan D
Fonte: Public Library of Science Publicador: Public Library of Science
Tipo: Artigo de Revista Científica
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An important but largely unmet challenge in understanding the mechanisms that govern the formation of specific organs is to decipher the complex and dynamic genetic programs exhibited by the diversity of cell types within the tissue of interest. Here, we use an integrated genetic, genomic, and computational strategy to comprehensively determine the molecular identities of distinct myoblast subpopulations within the Drosophila embryonic mesoderm at the time that cell fates are initially specified. A compendium of gene expression profiles was generated for primary mesodermal cells purified by flow cytometry from appropriately staged wild-type embryos and from 12 genotypes in which myogenesis was selectively and predictably perturbed. A statistical meta-analysis of these pooled datasets—based on expected trends in gene expression and on the relative contribution of each genotype to the detection of known muscle genes—provisionally assigned hundreds of differentially expressed genes to particular myoblast subtypes. Whole embryo in situ hybridizations were then used to validate the majority of these predictions, thereby enabling true-positive detection rates to be estimated for the microarray data. This combined analysis reveals that myoblasts exhibit much greater gene expression heterogeneity and overall complexity than was previously appreciated. Moreover...

‣ Advancing Cancer Systems Biology: Introducing the Center for the Development of a Virtual Tumor, CViT

Zhang, Le; Martin, Sean; Deisboeck, Thomas Steve
Fonte: Libertas Academica Publicador: Libertas Academica
Tipo: Artigo de Revista Científica
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Integrative cancer biology research relies on a variety of data-driven computational modeling and simulation methods and techniques geared towards gaining new insights into the complexity of biological processes that are of critical importance for cancer research. These include the dynamics of gene-protein interaction networks, the percolation of sub-cellular perturbations across scales and the impact they may have on tumorigenesis in both experiments and clinics. Such innovative ‘systems’ research will greatly benefit from enabling Information Technology that is currently under development, including an online collaborative environment, a Semantic Web based computing platform that hosts data and model repositories as well as high-performance computing access. Here, we present one of the National Cancer Institute’s recently established Integrative Cancer Biology Programs, i.e. the Center for the Development of a Virtual Tumor, CViT, which is charged with building a cancer modeling community, developing the aforementioned enabling technologies and fostering multi-scale cancer modeling and simulation.

‣ Computing Complex Visual Features with Retinal Spike Times

Gütig, Robert; Gollisch, Tim; Sompolinsky, Haim I; Meister, Markus
Fonte: Public Library of Science Publicador: Public Library of Science
Tipo: Artigo de Revista Científica
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Neurons in sensory systems can represent information not only by their firing rate, but also by the precise timing of individual spikes. For example, certain retinal ganglion cells, first identified in the salamander, encode the spatial structure of a new image by their first-spike latencies. Here we explore how this temporal code can be used by downstream neural circuits for computing complex features of the image that are not available from the signals of individual ganglion cells. To this end, we feed the experimentally observed spike trains from a population of retinal ganglion cells to an integrate-and-fire model of post-synaptic integration. The synaptic weights of this integration are tuned according to the recently introduced tempotron learning rule. We find that this model neuron can perform complex visual detection tasks in a single synaptic stage that would require multiple stages for neurons operating instead on neural spike counts. Furthermore, the model computes rapidly, using only a single spike per afferent, and can signal its decision in turn by just a single spike. Extending these analyses to large ensembles of simulated retinal signals, we show that the model can detect the orientation of a visual pattern independent of its phase...

‣ Reconstruction and Validation of a Genome-Scale Metabolic Model for the Filamentous Fungus Neurospora crassa Using FARM

Dreyfuss, Jonathan M.; Zucker, Jeremy D.; Hood, Heather M.; Ocasio, Linda R.; Sachs, Matthew S.; Galagan, James E.
Fonte: Public Library of Science Publicador: Public Library of Science
Tipo: Artigo de Revista Científica
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The filamentous fungus Neurospora crassa played a central role in the development of twentieth-century genetics, biochemistry and molecular biology, and continues to serve as a model organism for eukaryotic biology. Here, we have reconstructed a genome-scale model of its metabolism. This model consists of 836 metabolic genes, 257 pathways, 6 cellular compartments, and is supported by extensive manual curation of 491 literature citations. To aid our reconstruction, we developed three optimization-based algorithms, which together comprise Fast Automated Reconstruction of Metabolism (FARM). These algorithms are: LInear MEtabolite Dilution Flux Balance Analysis (limed-FBA), which predicts flux while linearly accounting for metabolite dilution; One-step functional Pruning (OnePrune), which removes blocked reactions with a single compact linear program; and Consistent Reproduction Of growth/no-growth Phenotype (CROP), which reconciles differences between in silico and experimental gene essentiality faster than previous approaches. Against an independent test set of more than 300 essential/non-essential genes that were not used to train the model, the model displays 93% sensitivity and specificity. We also used the model to simulate the biochemical genetics experiments originally performed on Neurospora by comprehensively predicting nutrient rescue of essential genes and synthetic lethal interactions...

‣ Computational comparative genomics : genes, regulation, evolution

Kamvysselis, Manolis, 1977-
Fonte: Massachusetts Institute of Technology Publicador: Massachusetts Institute of Technology
Tipo: Tese de Doutorado Formato: 100 p.; 9290582 bytes; 9290340 bytes; application/pdf; application/pdf
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Understanding the biological signals encoded in a genome is a key challenge of computational biology. These signals are encoded in the four-nucleotide alphabet of DNA and are responsible for all molecular processes in the cell. In particular, the genome contains the blueprint of all protein-coding genes and the regulatory motifs used to coordinate the expression of these genes. Comparative genome analysis of related species provides a general approach for identifying these functional elements, by virtue of their stronger conservation across evolutionary time. In this thesis we address key issues in the comparative analysis of multiple species. We present novel computational methods in four areas (1) the automatic comparative annotation of multiple species and the determination of orthologous genes and intergenic regions (2) the validation of computationally predicted protein-coding genes (3) the systematic de-novo identification of regulatory motifs (4) the determination of combinatorial interactions between regulatory motifs. We applied these methods to the comparative analysis of four yeast genomes, including the best-studied eukaryote, Saccharomyces cerevisiae or baker's yeast. Our results show that nearly a tenth of currently annotated yeast genes are not real...

‣ To Supplement or Not to Supplement: A Metabolic Network Framework for Human Nutritional Supplements

Nogiec, Christopher D.; Kasif, Simon
Fonte: Public Library of Science Publicador: Public Library of Science
Tipo: Artigo de Revista Científica
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Flux balance analysis and constraint based modeling have been successfully used in the past to elucidate the metabolism of single cellular organisms. However, limited work has been done with multicellular organisms and even less with humans. The focus of this paper is to present a novel use of this technique by investigating human nutrition, a challenging field of study. Specifically, we present a steady state constraint based model of skeletal muscle tissue to investigate amino acid supplementation's effect on protein synthesis. We implement several in silico supplementation strategies to study whether amino acid supplementation might be beneficial for increasing muscle contractile protein synthesis. Concurrent with published data on amino acid supplementation's effect on protein synthesis in a post resistance exercise state, our results suggest that increasing bioavailability of methionine, arginine, and the branched-chain amino acids can increase the flux of contractile protein synthesis. The study also suggests that a common commercial supplement, glutamine, is not an effective supplement in the context of increasing protein synthesis and thus, muscle mass. Similar to any study in a model organism, the computational modeling of this research has some limitations. Thus...

‣ The Fitness Landscape of HIV-1 Gag: Advanced Modeling Approaches and Validation of Model Predictions by In Vitro Testing

Mann, Jaclyn K.; Barton, John P.; Ferguson, Andrew L.; Omarjee, Saleha; Walker, Bruce D.; Chakraborty, Arup; Ndung'u, Thumbi
Fonte: Public Library of Science Publicador: Public Library of Science
Tipo: Artigo de Revista Científica
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Viral immune evasion by sequence variation is a major hindrance to HIV-1 vaccine design. To address this challenge, our group has developed a computational model, rooted in physics, that aims to predict the fitness landscape of HIV-1 proteins in order to design vaccine immunogens that lead to impaired viral fitness, thus blocking viable escape routes. Here, we advance the computational models to address previous limitations, and directly test model predictions against in vitro fitness measurements of HIV-1 strains containing multiple Gag mutations. We incorporated regularization into the model fitting procedure to address finite sampling. Further, we developed a model that accounts for the specific identity of mutant amino acids (Potts model), generalizing our previous approach (Ising model) that is unable to distinguish between different mutant amino acids. Gag mutation combinations (17 pairs, 1 triple and 25 single mutations within these) predicted to be either harmful to HIV-1 viability or fitness-neutral were introduced into HIV-1 NL4-3 by site-directed mutagenesis and replication capacities of these mutants were assayed in vitro. The predicted and measured fitness of the corresponding mutants for the original Ising model (r = −0.74...

‣ Cancer systems biology in the genome sequencing era: Part 2, evolutionary dynamics of tumor clonal networks and drug resistance

Wang, Edwin; Zou, Jinfeng; Zaman, Naif; Beitel, Lenore K.; Trifiro, Mark; Paliouras, Miltiadis
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 05/09/2014 Português
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A tumor often consists of multiple cell subpopulations (clones). Current chemo-treatments often target one clone of a tumor. Although the drug kills that clone, other clones overtake it and the tumor reoccurs. Genome sequencing and computational analysis allows to computational dissection of clones from tumors, while singe-cell genome sequencing including RNA-Seq allows to profiling of these clones. This opens a new window for treating a tumor as a system in which clones are evolving. Future cancer systems biology studies should consider a tumor as an evolving system with multiple clones. Therefore, topics discussed in Part 2 of this review include evolutionary dynamics of clonal networks, early-warning signals for formation of fast-growing clones, dissecting tumor heterogeneity, and modeling of clone-clone-stroma interactions for drug resistance. The ultimate goal of the future systems biology analysis is to obtain a whole-system understanding of a tumor and therefore provides a more efficient and personalized management strategies for cancer patients.; Comment: 2 figs. Related papers can be found at http://www.cancer-systemsbiology.org, Seminar in Cancer Biology, 2013

‣ Developing and applying heterogeneous phylogenetic models with XRate

Westesson, Oscar; Holmes, Ian
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 16/02/2012 Português
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Modeling sequence evolution on phylogenetic trees is a useful technique in computational biology. Especially powerful are models which take account of the heterogeneous nature of sequence evolution according to the "grammar" of the encoded gene features. However, beyond a modest level of model complexity, manual coding of models becomes prohibitively labor-intensive. We demonstrate, via a set of case studies, the new built-in model-prototyping capabilities of XRate (macros and Scheme extensions). These features allow rapid implementation of phylogenetic models which would have previously been far more labor-intensive. XRate's new capabilities for lineage-specific models, ancestral sequence reconstruction, and improved annotation output are also discussed. XRate's flexible model-specification capabilities and computational efficiency make it well-suited to developing and prototyping phylogenetic grammar models. XRate is available as part of the DART software package: http://biowiki.org/DART .; Comment: 34 pages, 3 figures, glossary of XRate model terminology

‣ A scalable computational framework for establishing long-term behavior of stochastic reaction networks

Gupta, Ankit; Briat, Corentin; Khammash, Mustafa
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
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Reaction networks are systems in which the populations of a finite number of species evolve through predefined interactions. Such networks are found as modeling tools in many biological disciplines such as biochemistry, ecology, epidemiology, immunology, systems biology and synthetic biology. It is now well-established that, for small population sizes, stochastic models for biochemical reaction networks are necessary to capture randomness in the interactions. The tools for analyzing such models, however, still lag far behind their deterministic counterparts. In this paper, we bridge this gap by developing a constructive framework for examining the long-term behavior and stability properties of the reaction dynamics in a stochastic setting. In particular, we address the problems of determining ergodicity of the reaction dynamics, which is analogous to having a globally attracting fixed point for deterministic dynamics. We also examine when the statistical moments of the underlying process remain bounded with time and when they converge to their steady state values. The framework we develop relies on a blend of ideas from probability theory, linear algebra and optimization theory. We demonstrate that the stability properties of a wide class of biological networks can be assessed from our sufficient theoretical conditions that can be recast as efficient and scalable linear programs...

‣ Parameter inference and model selection in signaling pathway models

Toni, Tina; Stumpf, Michael P. H.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 27/05/2009 Português
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To support and guide an extensive experimental research into systems biology of signaling pathways, increasingly more mechanistic models are being developed with hopes of gaining further insight into biological processes. In order to analyse these models, computational and statistical techniques are needed to estimate the unknown kinetic parameters. This chapter reviews methods from frequentist and Bayesian statistics for estimation of parameters and for choosing which model is best for modeling the underlying system. Approximate Bayesian Computation (ABC) techniques are introduced and employed to explore different hypothesis about the JAK-STAT signaling pathway.; Comment: Book chapter for Topics in Computational Biology Methods in Molecular Biology Series, Humana Press, 2009

‣ Ten Simple Rules for Creating Biomolecular Graphics

Mura, Cameron
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 15/07/2014 Português
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One need only compare the number of three-dimensional molecular illustrations in the first (1990) and third (2004) editions of Voet & Voet's "Biochemistry" in order to appreciate this field's profound communicative value in modern biological sciences -- ranging from medicine, physiology, and cell biology, to pharmaceutical chemistry and drug design, to structural and computational biology. The clich\'e about a picture being worth a thousand words is quite poignant here: The information 'content' of an effectively-constructed piece of molecular graphics can be immense. Because biological function arises from structure, it is difficult to overemphasize the utility of visualization and graphics in molding our current understanding of the molecular nature of biological systems. Nevertheless, creating effective molecular graphics is not easy -- neither conceptually, nor in terms of effort required. The present collection of Rules is meant as a guide for those embarking upon their first molecular illustrations.; Comment: 3 pages, 0 figures; see also the full-length article in PLoS Computational Biology (cited below)

‣ Perturbation Biology: inferring signaling networks in cellular systems

Molinelli, Evan J.; Korkut, Anil; Wang, Weiqing; Miller, Martin L.; Gauthier, Nicholas P.; Jing, Xiaohong; Kaushik, Poorvi; He, Qin; Mills, Gordon; Solit, David B.; Pratilas, Christine A.; Weigt, Martin; Braunstein, Alfredo; Pagnani, Andrea; Zecchina, Ric
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 23/08/2013 Português
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We present a new experimental-computational technology of inferring network models that predict the response of cells to perturbations and that may be useful in the design of combinatorial therapy against cancer. The experiments are systematic series of perturbations of cancer cell lines by targeted drugs, singly or in combination. The response to perturbation is measured in terms of levels of proteins and phospho-proteins and of cellular phenotype such as viability. Computational network models are derived de novo, i.e., without prior knowledge of signaling pathways, and are based on simple non-linear differential equations. The prohibitively large solution space of all possible network models is explored efficiently using a probabilistic algorithm, belief propagation, which is three orders of magnitude more efficient than Monte Carlo methods. Explicit executable models are derived for a set of perturbation experiments in Skmel-133 melanoma cell lines, which are resistant to the therapeutically important inhibition of Raf kinase. The resulting network models reproduce and extend known pathway biology. They can be applied to discover new molecular interactions and to predict the effect of novel drug perturbations, one of which is verified experimentally. The technology is suitable for application to larger systems in diverse areas of molecular biology.

‣ Proceedings Second International Workshop on Hybrid Systems and Biology

Dang, Thao; Piazza, Carla
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 26/08/2013 Português
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This volume contains the proceedings of the Second International Workshop Hybrid Systems and Biology (HSB 2013) held in Taormina (Italy), on September 2th, 2013. The workshop is affiliated to the 12th European Conference on Artificial Life (ECAL 2013). Systems biology aims at providing a system-level understanding of biological systems by unveiling their structure, dynamics and control methods. Due to the intrinsic multi-scale nature of these systems in space, in organization levels and in time, it is extremely difficult to model them in a uniform way, e.g., by means of differential equations or discrete stochastic processes. Furthermore, such models are often not easily amenable to formal analysis, and their simulations at the organ or even at the cell levels are frequently impractical. Indeed, an important open problem is finding appropriate computational models that scale well for both simulation and formal analysis of biological processes. Hybrid modeling techniques, combining discrete and continuous processes, are gaining more and more attention in such a context, and they have been successfully applied to capture the behavior of many biological complex systems, ranging from genetic networks, biochemical reactions, signaling pathways...

‣ Mathematical and computational modeling for describing the basic behavior of free radicals and antioxidants within epithelial cells

Garcia, Alvaro Juan Ojeda
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 21/01/2012 Português
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The traditional methods of the biology, based on illustrative descriptions and linear logic explanations, are discussed. This work aims to improve this approach by introducing alternative tools to describe and represent complex biological systems. Two models were developed, one mathematical and another computational, both were made in order to study the biological process between free radicals and antioxidants. Each model was used to study the same process but in different scenarios. The mathematical model was used to study the biological process in an epithelial cells culture; this model was validated with the experimental data of Anne Hanneken's research group from the Department of Molecular and Experimental Medicine, published by the journal Investigative Ophthalmology and Visual Science in July 2006. The computational model was used to study the same process in an individual. The model was made using C++ programming language, supported by the network theory of aging.; Comment: 4 pages, 5 figures. Treball de Recerca, gener de 2012

‣ Converting a Systems Dynamic Model to an Agent-based model for studying the Bicoid morphogen gradient in Drosophila embryo

Kiran, Mariam; Liu, Wei
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 17/12/2014 Português
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The concentration gradient of the Bicoid morphogen, which is established during the early stages of a Drosophila melanogaster embryonic development, determines the differential spatial patterns of gene expression and subsequent cell fate determination. This is mainly achieved by diffusion elicited by the different concentrations of the Bicoid protein in the embryo. Such chemical dynamic progress can be simulated by stochastic models, particularly the Gillespie alogrithm. However, as with various modelling approaches in biology, each technique involves drawing assumptions and reducing the model complexity sometimes limiting the model capability. This is mainly due to the complexity of the software modelling approaches to construct these models. Agent-based modelling is a technique which is becoming increasingly popular for modelling the behaviour of individual molecules or cells in computational biology. This paper attempts to compare these two popular modelling techniques of stochastic and agent-based modelling to show how the model can be studied in detail using the different approaches. This paper presents how to use these techniques with the advantages and disadvantages of using either of these. Through various comparisons, such as computation complexity and results obtained...

‣ BioPreDyn-bench: benchmark problems for kinetic modelling in systems biology

Villaverde, Alejandro F; Henriques, David; Smallbone, Kieran; Bongard, Sophia; Schmid, Joachim; Cicin-Sain, Damjan; Crombach, Anton; Saez-Rodriguez, Julio; Mauch, Klaus; Balsa-Canto, Eva; Mendes, Pedro; Jaeger, Johannes; Banga, Julio R
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 22/07/2014 Português
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Dynamic modelling is one of the cornerstones of systems biology. Many research efforts are currently being invested in the development and exploitation of large-scale kinetic models. The associated problems of parameter estimation (model calibration) and optimal experimental design are particularly challenging. The community has already developed many methods and software packages which aim to facilitate these tasks. However, there is a lack of suitable benchmark problems which allow a fair and systematic evaluation and comparison of these contributions. Here we present BioPreDyn-bench, a set of challenging parameter estimation problems which aspire to serve as reference test cases in this area. This set comprises six problems including medium and large-scale kinetic models of the bacterium E. coli, baker's yeast S. cerevisiae, the vinegar fly D. melanogaster, Chinese Hamster Ovary cells, and a generic signal transduction network. The level of description includes metabolism, transcription, signal transduction, and development. For each problem we provide (i) a basic description and formulation, (ii) implementations ready-to-run in several formats, (iii) computational results obtained with specific solvers, (iv) a basic analysis and interpretation. This suite of benchmark problems can be readily used to evaluate and compare parameter estimation methods. Further...