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1.
Methods Mol Biol ; 1883: 49-94, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-30547396

RESUMO

A challenging problem in systems biology is the reconstruction of gene regulatory networks from postgenomic data. A variety of reverse engineering methods from machine learning and computational statistics have been proposed in the literature. However, deciding on the best method to adopt for a particular application or data set might be a confusing task. The present chapter provides a broad overview of state-of-the-art methods with an emphasis on conceptual understanding rather than a deluge of mathematical details, and the pros and cons of the various approaches are discussed. Guidance on practical applications with pointers to publicly available software implementations are included. The chapter concludes with a comprehensive comparative benchmark study on simulated data and a real-work application taken from the current plant systems biology.


Assuntos
Ciência de Dados/métodos , Redes Reguladoras de Genes , Modelos Genéticos , Biologia de Sistemas/métodos , Algoritmos , Arabidopsis/genética , Teorema de Bayes , Ciência de Dados/instrumentação , Perfilação da Expressão Gênica/instrumentação , Perfilação da Expressão Gênica/métodos , Distribuição Normal , Software , Biologia de Sistemas/instrumentação
2.
Methods Mol Biol ; 1883: 111-142, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-30547398

RESUMO

Biological networks are a very convenient modeling and visualization tool to discover knowledge from modern high-throughput genomics and post-genomics data sets. Indeed, biological entities are not isolated but are components of complex multilevel systems. We go one step further and advocate for the consideration of causal representations of the interactions in living systems. We present the causal formalism and bring it out in the context of biological networks, when the data is observational. We also discuss its ability to decipher the causal information flow as observed in gene expression. We also illustrate our exploration by experiments on small simulated networks as well as on a real biological data set.


Assuntos
Redes Reguladoras de Genes , Modelos Genéticos , Biologia de Sistemas/métodos , Algoritmos , Teorema de Bayes , Perfilação da Expressão Gênica/instrumentação , Perfilação da Expressão Gênica/métodos , Software , Biologia de Sistemas/instrumentação
3.
Methods Mol Biol ; 1883: 217-233, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-30547402

RESUMO

Inference of gene regulatory networks (GRNs) from time series data is a well-established field in computational systems biology. Most approaches can be broadly divided in two families: model-based and model-free methods. These two families are highly complementary: model-based methods seek to identify a formal mathematical model of the system. They thus have transparent and interpretable semantics but rely on strong assumptions and are rather computationally intensive. On the other hand, model-free methods have typically good scalability. Since they are not based on any parametric model, they are more flexible than model-based methods, but also less interpretable.In this chapter, we describe Jump3, a hybrid approach that bridges the gap between model-free and model-based methods. Jump3 uses a formal stochastic differential equation to model each gene expression but reconstructs the GRN topology with a nonparametric method based on decision trees. We briefly review the theoretical and algorithmic foundations of Jump3, and then proceed to provide a step-by-step tutorial of the associated software usage.


Assuntos
Árvores de Decisões , Redes Reguladoras de Genes , Aprendizado de Máquina , Modelos Genéticos , Biologia de Sistemas/métodos , Software , Estatísticas não Paramétricas , Biologia de Sistemas/instrumentação
4.
Methods Mol Biol ; 1883: 235-249, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-30547403

RESUMO

Recent technological breakthroughs in single-cell RNA sequencing are revolutionizing modern experimental design in biology. The increasing size of the single-cell expression data from which networks can be inferred allows identifying more complex, non-linear dependencies between genes. Moreover, the inter-cellular variability that is observed in single-cell expression data can be used to infer not only one global network representing all the cells, but also numerous regulatory networks that are more specific to certain conditions. By experimentally perturbing certain genes, the deconvolution of the true contribution of these genes can also be greatly facilitated. In this chapter, we will therefore tackle the advantages of single-cell transcriptomic data and show how new methods exploit this novel data type to enhance the inference of gene regulatory networks.


Assuntos
Perfilação da Expressão Gênica/métodos , Redes Reguladoras de Genes , Modelos Genéticos , Análise de Célula Única/métodos , Biologia de Sistemas/métodos , Algoritmos , Perfilação da Expressão Gênica/instrumentação , Sequenciamento de Nucleotídeos em Larga Escala/instrumentação , Sequenciamento de Nucleotídeos em Larga Escala/métodos , Humanos , Análise de Sequência de RNA , Análise de Célula Única/instrumentação , Biologia de Sistemas/instrumentação
5.
Methods Mol Biol ; 1883: 251-282, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-30547404

RESUMO

Gaussian process dynamical systems (GPDS) represent Bayesian nonparametric approaches to inference of nonlinear dynamical systems, and provide a principled framework for the learning of biological networks from multiple perturbed time series measurements of gene or protein expression. Such approaches are able to capture the full richness of complex ODE models, and can be scaled for inference in moderately large systems containing hundreds of genes. Related hierarchical approaches allow for inference from multiple datasets in which the underlying generative networks are assumed to have been rewired, either by context-dependent changes in network structure, evolutionary processes, or synthetic manipulation. These approaches can also be used to leverage experimentally determined network structures from one species into another where the network structure is unknown. Collectively, these methods provide a comprehensive and flexible platform for inference from a diverse range of data, with applications in systems and synthetic biology, as well as spatiotemporal modelling of embryo development. In this chapter we provide an overview of GPDS approaches and highlight their applications in the biological sciences, with accompanying tutorials available as a Jupyter notebook from https://github.com/cap76/GPDS .


Assuntos
Conjuntos de Dados como Assunto , Redes Reguladoras de Genes , Modelos Genéticos , Biologia de Sistemas/métodos , Algoritmos , Teorema de Bayes , Perfilação da Expressão Gênica/instrumentação , Perfilação da Expressão Gênica/métodos , Distribuição Normal , Análise Espaço-Temporal , Biologia de Sistemas/instrumentação
6.
Methods Mol Biol ; 1883: 283-302, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-30547405

RESUMO

Inferring gene regulatory networks from expression data is a very challenging problem that has raised the interest of the scientific community. Different algorithms have been proposed to try to solve this issue, but it has been shown that different methods have some particular biases and strengths, and none of them is the best across all types of data and datasets. As a result, the idea of aggregating various network inferences through a consensus mechanism naturally arises. In this chapter, a common framework to standardize already proposed consensus methods is presented, and based on this framework different proposals are introduced and analyzed in two different scenarios: Homogeneous and Heterogeneous. The first scenario reflects situations where the networks to be aggregated are rather similar because they are obtained with inference algorithms working on the same data, whereas the second scenario deals with very diverse networks because various sources of data are used to generate the individual networks. A procedure for combining multiple network inference algorithms is analyzed in a systematic way. The results show that there is a very significant difference between these two scenarios, and that the best way to combine networks in the Heterogeneous scenario is not the most commonly used. We show in particular that aggregation in the Heterogeneous scenario can be very beneficial if the individual networks are combined with our new proposed method ScaleLSum.


Assuntos
Redes Reguladoras de Genes , Modelos Genéticos , Biologia de Sistemas/métodos , Aprendizado de Máquina não Supervisionado , Conjuntos de Dados como Assunto , Biologia de Sistemas/instrumentação
7.
Methods Mol Biol ; 1883: 347-383, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-30547408

RESUMO

Modelling gene regulatory networks requires not only a thorough understanding of the biological system depicted, but also the ability to accurately represent this system from a mathematical perspective. Throughout this chapter, we aim to familiarize the reader with the biological processes and molecular factors at play in the process of gene expression regulation. We first describe the different interactions controlling each step of the expression process, from transcription to mRNA and protein decay. In the second section, we provide statistical tools to accurately represent this biological complexity in the form of mathematical models. Among other considerations, we discuss the topological properties of biological networks, the application of deterministic and stochastic frameworks, and the quantitative modelling of regulation. We particularly focus on the use of such models for the simulation of expression data that can serve as a benchmark for the testing of network inference algorithms.


Assuntos
Redes Reguladoras de Genes , Modelos Genéticos , Modelos Estatísticos , Biologia de Sistemas/métodos , Algoritmos , Perfilação da Expressão Gênica , Regulação da Expressão Gênica , Processos Estocásticos , Biologia de Sistemas/instrumentação
8.
Methods Mol Biol ; 1883: 385-422, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-30547409

RESUMO

Ordinary differential equation models have become a standard tool for the mechanistic description of biochemical processes. If parameters are inferred from experimental data, such mechanistic models can provide accurate predictions about the behavior of latent variables or the process under new experimental conditions. Complementarily, inference of model structure can be used to identify the most plausible model structure from a set of candidates, and, thus, gain novel biological insight. Several toolboxes can infer model parameters and structure for small- to medium-scale mechanistic models out of the box. However, models for highly multiplexed datasets can require hundreds to thousands of state variables and parameters. For the analysis of such large-scale models, most algorithms require intractably high computation times. This chapter provides an overview of the state-of-the-art methods for parameter and model inference, with an emphasis on scalability.


Assuntos
Algoritmos , Fenômenos Bioquímicos , Modelos Biológicos , Biologia de Sistemas/métodos , Interpretação Estatística de Dados , Conjuntos de Dados como Assunto , Biologia de Sistemas/instrumentação
9.
São Paulo; s.n; s.n; 2019. 110 p. graf, tab.
Tese em Inglês | LILACS | ID: biblio-1023378

RESUMO

Metabolic Syndrome (MetS) is a combination of diseases interrelated and associated with increased mortality and risk of cardiovascular events. Among the elucidated molecular mechanisms of MetS, there are several genes regulated by miRNAs - small non-coding RNAs. A large number of transcriptomic studies in public databases integrated with new analysis methods can generate new insights. Therefore, this study aimed to identify circulating miRNAs and their target genes in MetS using a Systems Biology approach. For this, we used GEO-NCBI to download and analyse 26 microarray transcriptome studies of MetS and obesity. After preprocessing, the data underwent differential expression (LIMMA method), gene co-expression (CEMiTool), and enrichment (GSEA, Reactome) analyses. We retrieved a gene expression signature for subcutaneous adipose tissue (SAT) for obese individuals that included 291 consistent differentially expressed genes (DEG). This signature had a positive normalized enrichment score (NES) for adaptive immune system activation responses, and negative NES for metabolic pathways. The consensus co-expression network of SAT revealed 3 communities (CM) of densely interconnected genes. These CMs had a high number of up regulated genes and a consistent positive NES among the studies. The co-expressed genes of these 3 CMs were related to neutrophil degranulation, infiltration of immune system cells, and inflammatory processes. Also, a small brazillian cohort (6 individuals with MetS and 6 controls) underwent a seric miRNA profiling using PCR array. From the 222 miRNAs detected in serum, the differential expression analysis identified 4 upregulated miRNAs (miR-30c-5p, miR-421, miR-542-5p and miR-574) in MetS patients (p<0.01). The integrative miRNAs-mRNAs analysis revealed that the circulating upregulated miRNAs had 12 targets in the SAT, 3 targets in the liver; and no targets in the muscle and blood. Many of these target genes are known modulators of proinflammatory pathways. In conclusion, the use of Systems Biology in the analysis of gene networks and circulating miRNAs identified some potential molecular and pathophysiological mechanisms of the Metabolic Syndrome. The circulating miRNAs identified in this study are potential biomarkers and/or therapeutic targets. However, further studies are needed to validate these miRNAs and their target mRNA


A Síndrome Metabólica (MetS) é um conjunto de doenças inter-relacionadas e associadas ao aumento de mortalidade e risco de eventos cardiovasculares. Entre os mecanismos moleculares elucidados da MetS, existem muitos genes regulados por miRNAs - RNAs pequenos não codificadores. O grande número de estudos transcriptômicos em banco dados públicos integrado a novos métodos de análise podem gerar novas descobertas. Deste modo, o objetivo deste estudo foi identificar miRNAs circulantes e genes alvos na MetS usando a abordagem de Biologia de Sistemas. Para isso, GEO-NCBI foi usado para obter e analisar 26 estudos de transcriptoma por microarray de MetS e obesidade. Após o pré-processamento, realizamos análises de expressão diferencial (método LIMMA), co-expressão gênica (CEMiTool), e enriquecimento (GSEA, Reactome). Identificamos uma assinatura de expressão gênica do tecido adiposo subcutâneo (SAT) de indivíduos obesos, composta por 291 genes consistentemente diferencialmente expressos (DEG). Essa assinatura teve um escore de enriquecimento normalizado (NES) positivo para ativação de respostas do sistema imune adaptativo, e NES negativo para vias de metabolismo. A rede consenso de co-expressão do SAT revelou 3 comunidades (CM) de genes densamente interconectadas. Essas CMs continham muitos genes regulados positivamente e com consistência de NES positivo entre os estudos. Os genes co-expressos dessas 3 comunidades pertenciam a vias de a degranulação de neutrófilos, infiltração de células do sistema imune e processos inflamatórios. Além disso, uma pequena coorte brasileira (6 indivíduos com MetS e 6 controles) foi submetida à dosagem sérica de miRNAs por PCR array. Dos 222 miRNAs detectados no soro, a análise de expressão diferencial identificou 4 miRNAs regulados positivamente (miR-30c-5p, miR-421, miR-542-5p e miR-574) nos pacientes com MetS (p<0.01). A análise integrativa miRNAs-mRNAs revelou que osmiRNAs circulantes superexpressos tinham 12 alvos no SAT, 3 alvos no fígado; e nenhum alvo no músculo e no sangue. Muitos desses alvos são moduladores de vias ró-inflamatórias. Em conclusão, a utilização da Biologia de Sistemas na análise de redes gênicas e miRNAs circulantes identificou alguns potenciais mecanismos moleculares e fisiopatológicos da Síndrome Metabólica. Os miRNAs circulantes identificados neste trabalho são potenciais biomarcadores e/ou alvos terapêuticos. Entretanto, mais estudos são necessários para validar esses miRNAs e seus mRNAs alvos


Assuntos
Síndrome Metabólica/diagnóstico , MicroRNAs/análise , Biologia de Sistemas/instrumentação , RNA Mensageiro/análise , Redes Reguladoras de Genes , Obesidade/classificação
10.
PLoS Comput Biol ; 14(6): e1006220, 2018 06.
Artigo em Inglês | MEDLINE | ID: mdl-29906293

RESUMO

The considerable difficulty encountered in reproducing the results of published dynamical models limits validation, exploration and reuse of this increasingly large biomedical research resource. To address this problem, we have developed Tellurium Notebook, a software system for model authoring, simulation, and teaching that facilitates building reproducible dynamical models and reusing models by 1) providing a notebook environment which allows models, Python code, and narrative to be intermixed, 2) supporting the COMBINE archive format during model development for capturing model information in an exchangeable format and 3) enabling users to easily simulate and edit public COMBINE-compliant models from public repositories to facilitate studying model dynamics, variants and test cases. Tellurium Notebook, a Python-based Jupyter-like environment, is designed to seamlessly inter-operate with these community standards by automating conversion between COMBINE standards formulations and corresponding in-line, human-readable representations. Thus, Tellurium brings to systems biology the strategy used by other literate notebook systems such as Mathematica. These capabilities allow users to edit every aspect of the standards-compliant models and simulations, run the simulations in-line, and re-export to standard formats. We provide several use cases illustrating the advantages of our approach and how it allows development and reuse of models without requiring technical knowledge of standards. Adoption of Tellurium should accelerate model development, reproducibility and reuse.


Assuntos
Biologia de Sistemas/métodos , Simulação por Computador , Humanos , Modelos Biológicos , Reprodutibilidade dos Testes , Software , Biologia de Sistemas/instrumentação
11.
Methods Mol Biol ; 1782: 249-265, 2018.
Artigo em Inglês | MEDLINE | ID: mdl-29851004

RESUMO

The advent of "big data" in biology (e.g., genomics, proteomics, metabolomics), holding the promise to reveal the nature of the formidable complexity in cellular and organ makeup and function, has highlighted the compelling need for analytical and integrative computational methods to interpret and make sense of the patterns and changes in those complex networks. Computational models need to be built on sound physicochemical mechanistic principles in order to integrate, interpret, and simulate high-throughput experimental data. Energy transduction processes have been traditionally studied with thermodynamic, kinetic, or thermo-kinetic models, with the latter proving superior to understand the control and regulation of mitochondrial energy metabolism and its interactions with cytoplasmic and other cellular compartments. In this work, we survey the methods to be followed to build a computational model of mitochondrial energetics in isolation or integrated into a network of cellular processes. We describe the use of analytical tools such as elementary flux modes, linear optimization of metabolic models, and control analysis, to help refine our grasp of biologically meaningful behaviors and model reliability. The use of these tools should improve the design, building, and interpretation of steady-state behaviors of computational models while assessing validation criteria and paving the way to prediction.


Assuntos
Simulação por Computador , Metabolismo Energético , Mitocôndrias/metabolismo , Modelos Biológicos , Biologia de Sistemas/métodos , Cinética , Redes e Vias Metabólicas , Metabolômica/métodos , Software , Biologia de Sistemas/instrumentação
12.
IEEE Trans Biomed Circuits Syst ; 12(2): 379-389, 2018 04.
Artigo em Inglês | MEDLINE | ID: mdl-29570064

RESUMO

The analysis and simulation of complex interacting biochemical reaction pathways in cells is important in all of systems biology and medicine. Yet, the dynamics of even a modest number of noisy or stochastic coupled biochemical reactions is extremely time consuming to simulate. In large part, this is because of the expensive cost of random number and Poisson process generation and the presence of stiff, coupled, nonlinear differential equations. Here, we demonstrate that we can amplify inherent thermal noise in chips to emulate randomness physically, thus alleviating these costs significantly. Concurrently, molecular flux in thermodynamic biochemical reactions maps to thermodynamic electronic current in a transistor such that stiff nonlinear biochemical differential equations are emulated exactly in compact, digitally programmable, highly parallel analog "cytomorphic" transistor circuits. For even small-scale systems involving just 80 stochastic reactions, our 0.35-µm BiCMOS chips yield a 311× speedup in the simulation time of Gillespie's stochastic algorithm over COPASI, a fast biochemical-reaction software simulator that is widely used in computational biology; they yield a 15 500× speedup over equivalent MATLAB stochastic simulations. The chip emulation results are consistent with these software simulations over a large range of signal-to-noise ratios. Most importantly, our physical emulation of Poisson chemical dynamics does not involve any inherently sequential processes and updates such that, unlike prior exact simulation approaches, they are parallelizable, asynchronous, and enable even more speedup for larger-size networks.


Assuntos
Fenômenos Fisiológicos Celulares/fisiologia , Modelos Biológicos , Semicondutores , Biologia de Sistemas , Algoritmos , Desenho de Equipamento , Razão Sinal-Ruído , Silício/química , Processos Estocásticos , Biologia de Sistemas/instrumentação , Biologia de Sistemas/métodos
13.
Philos Trans R Soc Lond B Biol Sci ; 372(1720)2017 May 19.
Artigo em Inglês | MEDLINE | ID: mdl-28348260

RESUMO

Systems morphodynamics describes a multi-level analysis of mechanical morphogenesis that draws on new microscopy and computational technologies and embraces a systems biology-informed scope. We present a selection of articles that illustrate and explain this rapidly progressing field.This article is part of the themed issue 'Systems morphodynamics: understanding the development of tissue hardware'.


Assuntos
Microscopia/métodos , Morfogênese , Biologia de Sistemas/métodos , Microscopia/instrumentação , Biologia de Sistemas/instrumentação
14.
Ann N Y Acad Sci ; 1387(1): 112-123, 2017 01.
Artigo em Inglês | MEDLINE | ID: mdl-27801987

RESUMO

Big Data is no longer solely the purview of big organizations with big resources. Today's routine tools and experimental methods can generate large slices of data. For example, high-throughput sequencing can quickly interrogate biological systems for the expression levels of thousands of different RNAs, examine epigenetic marks throughout the genome, and detect differences in the genomes of individuals. Multichannel electrophysiology platforms produce gigabytes of data in just a few minutes of recording. Imaging systems generate videos capturing biological behaviors over the course of days. Thus, any researcher now has access to a veritable wealth of data. However, the ability of any given researcher to utilize that data is limited by her/his own resources and skills for downloading, storing, and analyzing the data. In this paper, we examine the necessary resources required to engage Big Data, survey the state of modern data analysis pipelines, present a few data repository case studies, and touch on current institutions and programs supporting the work that relies on Big Data.


Assuntos
Pesquisa Biomédica/métodos , Computação em Nuvem , Redes de Comunicação de Computadores , Biologia de Sistemas/métodos , Acesso à Informação , Animais , Pesquisa Biomédica/tendências , Computação em Nuvem/tendências , Redes de Comunicação de Computadores/instrumentação , Redes de Comunicação de Computadores/tendências , Mineração de Dados/métodos , Mineração de Dados/tendências , Tomada de Decisões Assistida por Computador , Genômica/métodos , Genômica/tendências , Humanos , Processamento de Imagem Assistida por Computador , Internet , Software , Biologia de Sistemas/instrumentação , Biologia de Sistemas/tendências
15.
Proteomics ; 16(3): 437-47, 2016 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-26593131

RESUMO

Histone posttranslational modifications and histone variants control the epigenetic regulation of gene expression and affect a wide variety of biological processes. A complex pattern of such modifications and variants defines the identity of cells within complex organ systems and can therefore be used to characterize cells at a molecular level. However, their detection and identification in situ has been limited so far due to lack of specificity, selectivity, and availability of antihistone antibodies. Here, we describe a novel MALDI imaging MS based workflow, which enables us to detect and characterize histones by their intact mass and their correlation with cytological properties of the tissue using novel statistical and image analysis tools. The workflow allows us to characterize the in situ distribution of the major histone variants and their modification in the mouse brain. This new analysis tool is particularly useful for the investigation of expression patterns of the linker histone H1 variants for which suitable antibodies are so far not available.


Assuntos
Encéfalo/metabolismo , Cromatina/química , Epigênese Genética , Histonas/genética , Processamento de Proteína Pós-Traducional , Acetilação , Animais , Encéfalo/ultraestrutura , Química Encefálica , Cromatina/metabolismo , Histonas/metabolismo , Masculino , Metilação , Camundongos , Imagem Molecular/métodos , Fosforilação , Análise de Componente Principal , Espectrometria de Massas por Ionização e Dessorção a Laser Assistida por Matriz/métodos , Biologia de Sistemas/instrumentação , Biologia de Sistemas/métodos
16.
Proteomics ; 15(9): 1486-502, 2015 May.
Artigo em Inglês | MEDLINE | ID: mdl-25545106

RESUMO

Aberrant cell signaling events either drive or compensate for nearly all pathologies. A thorough description and quantification of maladaptive signaling flux in disease is a critical step in drug development, and complex proteomic approaches can provide valuable mechanistic insights. Traditional proteomics-based signaling analyses rely heavily on in vitro cellular monoculture. The characterization of these simplified systems generates a rich understanding of the basic components and complex interactions of many signaling networks, but they cannot capture the full complexity of the microenvironments in which pathologies are ultimately made manifest. Unfortunately, techniques that can directly interrogate signaling in situ often yield mass-limited starting materials that are incompatible with traditional proteomics workflows. This review provides an overview of established and emerging techniques that are applicable to context-dependent proteomics. Analytical approaches are illustrated through recent proteomics-based studies in which selective sample acquisition strategies preserve context-dependent information, and where the challenge of minimal starting material is met by optimized sensitivity and coverage. This review is organized into three major technological themes: (i) LC methods in line with MS; (ii) antibody-based approaches; (iii) MS imaging with a discussion of data integration and systems modeling. Finally, we conclude with future perspectives and implications of context-dependent proteomics.


Assuntos
Proteômica/métodos , Transdução de Sinais , Animais , Cromatografia Líquida/instrumentação , Cromatografia Líquida/métodos , Eletroforese Capilar/instrumentação , Eletroforese Capilar/métodos , Humanos , Proteômica/instrumentação , Biologia de Sistemas/instrumentação , Biologia de Sistemas/métodos
17.
Cell Adh Migr ; 8(5): 468-77, 2014.
Artigo em Inglês | MEDLINE | ID: mdl-25482526

RESUMO

Systems biology has recently achieved significant success in the understanding of complex interconnected phenomena such as cell polarity and migration. In this context, the definition of systems biology has come to encompass the integration of quantitative measurements with sophisticated modeling approaches. This article will review recent progress in live cell imaging technologies that have expanded the possibilities of quantitative in vivo measurements, particularly in regards to molecule counting and quantitative measurements of protein concentration and dynamics. These methods have gained and continue to gain popularity with the biological community. In general, we will discuss three broad categories: protein interactions, protein quantitation, and protein dynamics.


Assuntos
Biologia de Sistemas/métodos , Movimento Celular/fisiologia , Transferência Ressonante de Energia de Fluorescência , Humanos , Microscopia de Fluorescência , Biologia de Sistemas/instrumentação
18.
Biomed Res Int ; 2014: 207041, 2014.
Artigo em Inglês | MEDLINE | ID: mdl-25050327

RESUMO

The paper arguments are on enabling methodologies for the design of a fully parallel, online, interactive tool aiming to support the bioinformatics scientists .In particular, the features of these methodologies, supported by the FastFlow parallel programming framework, are shown on a simulation tool to perform the modeling, the tuning, and the sensitivity analysis of stochastic biological models. A stochastic simulation needs thousands of independent simulation trajectories turning into big data that should be analysed by statistic and data mining tools. In the considered approach the two stages are pipelined in such a way that the simulation stage streams out the partial results of all simulation trajectories to the analysis stage that immediately produces a partial result. The simulation-analysis workflow is validated for performance and effectiveness of the online analysis in capturing biological systems behavior on a multicore platform and representative proof-of-concept biological systems. The exploited methodologies include pattern-based parallel programming and data streaming that provide key features to the software designers such as performance portability and efficient in-memory (big) data management and movement. Two paradigmatic classes of biological systems exhibiting multistable and oscillatory behavior are used as a testbed.


Assuntos
Simulação por Computador , Sistemas On-Line/instrumentação , Desenho de Programas de Computador , Estatística como Assunto , Biologia de Sistemas/instrumentação , Bacteriófago lambda/fisiologia , Citosol/metabolismo , Proteínas Fúngicas/metabolismo , Modelos Biológicos , Neurospora/metabolismo , Interface Usuário-Computador
19.
Nat Chem Biol ; 10(7): 502-11, 2014 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-24937068

RESUMO

Systems biologists aim to understand how organism-level processes, such as differentiation and multicellular development, are encoded in DNA. Conversely, synthetic biologists aim to program systems-level biological processes, such as engineered tissue growth, by writing artificial DNA sequences. To achieve their goals, these groups have adapted a hierarchical electrical engineering framework that can be applied in the forward direction to design complex biological systems or in the reverse direction to analyze evolved networks. Despite much progress, this framework has been limited by an inability to directly and dynamically characterize biological components in the varied contexts of living cells. Recently, two optogenetic methods for programming custom gene expression and protein localization signals have been developed and used to reveal fundamentally new information about biological components that respond to those signals. This basic dynamic characterization approach will be a major enabling technology in synthetic and systems biology.


Assuntos
Elétrons , Optogenética/métodos , Biologia Sintética/métodos , Biologia de Sistemas/métodos , Algoritmos , Animais , Linhagem Celular , DNA/genética , DNA/metabolismo , Eletricidade , MAP Quinases Reguladas por Sinal Extracelular/genética , MAP Quinases Reguladas por Sinal Extracelular/metabolismo , Regulação da Expressão Gênica/efeitos da radiação , Luz , Optogenética/instrumentação , Transdução de Sinais/efeitos da radiação , Biologia Sintética/instrumentação , Biologia de Sistemas/instrumentação , Proteínas ras/genética , Proteínas ras/metabolismo
20.
Lab Chip ; 14(7): 1336-47, 2014 Apr 07.
Artigo em Inglês | MEDLINE | ID: mdl-24531367

RESUMO

Accurate spatiotemporal regulation of genetic expression and cell microenvironment are both essential to epithelial morphogenesis during development, wound healing and cancer. In vivo, this is achieved through the interplay between intrinsic cellular properties and extrinsic signals. Amongst these, morphogen gradients induce specific concentration- and time-dependent gene expression changes that influence a target cell's fate. As systems biology attempts to understand the complex mechanisms underlying morphogenesis, the lack of experimental setup to recapitulate morphogen-induced patterning in vitro has become limiting. For this reason, we developed a versatile microfluidic-based platform to control the spatiotemporal delivery of chemical gradients to tissues grown in Petri dishes. Using this setup combined with a synthetic inducible gene expression system, we were able to restrict a target gene's expression within a confluent epithelium to bands of cells as narrow as four cell diameters with a one cell diameter accuracy. Applied to the targeted delivery of growth factor gradients to a confluent epithelium, this method further enabled the localized induction of epithelial-mesenchymal transitions and associated morphogenetic changes. Our approach paves the way for replicating in vitro the morphogen gradients observed in vivo to determine the relative contributions of known intrinsic and extrinsic factors in differential tissue patterning, during development and cancer. It could also be readily used to spatiotemporally control cell differentiation in ES/iPS cell cultures for re-engineering of complex tissues. Finally, the reversibility of the microfluidic chip assembly allows for pre- and post-treatment sample manipulations and extends the range of patternable samples to animal explants.


Assuntos
Técnicas de Cultura de Células/métodos , Regulação da Expressão Gênica , Técnicas Analíticas Microfluídicas/métodos , Animais , Técnicas de Cultura de Células/instrumentação , Cães , Células Madin Darby de Rim Canino , Técnicas Analíticas Microfluídicas/instrumentação , Biologia de Sistemas/instrumentação , Biologia de Sistemas/métodos
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