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1.
Pharm Res ; 37(11): 212, 2020 Oct 06.
Artigo em Inglês | MEDLINE | ID: mdl-33025261

RESUMO

PURPOSE: Coronavirus disease 2019 (COVID-19) is expected to continue to cause worldwide fatalities until the World population develops 'herd immunity', or until a vaccine is developed and used as a prevention. Meanwhile, there is an urgent need to identify alternative means of antiviral defense. Bacillus Calmette-Guérin (BCG) vaccine that has been recognized for its off-target beneficial effects on the immune system can be exploited to boast immunity and protect from emerging novel viruses. METHODS: We developed and employed a systems biology workflow capable of identifying small-molecule antiviral drugs and vaccines that can boast immunity and affect a wide variety of viral disease pathways to protect from the fatal consequences of emerging viruses. RESULTS: Our analysis demonstrates that BCG vaccine affects the production and maturation of naïve T cells resulting in enhanced, long-lasting trained innate immune responses that can provide protection against novel viruses. We have identified small-molecule BCG mimics, including antiviral drugs such as raltegravir and lopinavir as high confidence hits. Strikingly, our top hits emetine and lopinavir were independently validated by recent experimental findings that these compounds inhibit the growth of SARS-CoV-2 in vitro. CONCLUSIONS: Our results provide systems biology support for using BCG and small-molecule BCG mimics as putative vaccine and drug candidates against emergent viruses including SARS-CoV-2.


Assuntos
Vacina BCG/administração & dosagem , Materiais Biomiméticos/administração & dosagem , Infecções por Coronavirus/tratamento farmacológico , Infecções por Coronavirus/prevenção & controle , Reposicionamento de Medicamentos/métodos , Pandemias/prevenção & controle , Pneumonia Viral/tratamento farmacológico , Pneumonia Viral/prevenção & controle , Bibliotecas de Moléculas Pequenas/administração & dosagem , Vacinas Virais/administração & dosagem , Vacina BCG/imunologia , Betacoronavirus/imunologia , Infecções por Coronavirus/imunologia , Infecções por Coronavirus/mortalidade , Humanos , Imunidade Inata , Pneumonia Viral/imunologia , Pneumonia Viral/mortalidade , Biologia de Sistemas/métodos , Vacinas Virais/imunologia , Fluxo de Trabalho
2.
BMC Bioinformatics ; 21(1): 424, 2020 Sep 29.
Artigo em Inglês | MEDLINE | ID: mdl-32993482

RESUMO

BACKGROUND: Genome-scale metabolic models are increasingly employed to predict the phenotype of various biological systems pertaining to healthcare and bioengineering. To characterize the full metabolic spectrum of such systems, Fast Flux Variability Analysis (FFVA) is commonly used in parallel with static load balancing. This approach assigns to each core an equal number of biochemical reactions without consideration of their solution complexity. RESULTS: Here, we present Very Fast Flux Variability Analysis (VFFVA) as a parallel implementation that dynamically balances the computation load between the cores in runtime which guarantees equal convergence time between them. VFFVA allowed to gain a threefold speedup factor with coupled models and up to 100 with ill-conditioned models along with a 14-fold decrease in memory usage. CONCLUSIONS: VFFVA exploits the parallel capabilities of modern machines to enable biological insights through optimizing systems biology modeling. VFFVA is available in C, MATLAB, and Python at https://github.com/marouenbg/VFFVA .


Assuntos
Interface Usuário-Computador , Escherichia coli/metabolismo , Humanos , Redes e Vias Metabólicas , Modelos Biológicos , Biologia de Sistemas/métodos
3.
PLoS Comput Biol ; 16(9): e1007646, 2020 09.
Artigo em Inglês | MEDLINE | ID: mdl-32925899

RESUMO

In this study we analyze the growth-phase dependent metabolic states of Bdellovibrio bacteriovorus by constructing a fully compartmented, mass and charge-balanced genome-scale metabolic model of this predatory bacterium (iCH457). Considering the differences between life cycle phases driving the growth of this predator, growth-phase condition-specific models have been generated allowing the systematic study of its metabolic capabilities. Using these computational tools, we have been able to analyze, from a system level, the dynamic metabolism of the predatory bacteria as the life cycle progresses. We provide computational evidences supporting potential axenic growth of B. bacteriovorus's in a rich medium based on its encoded metabolic capabilities. Our systems-level analysis confirms the presence of "energy-saving" mechanisms in this predator as well as an abrupt metabolic shift between the attack and intraperiplasmic growth phases. Our results strongly suggest that predatory bacteria's metabolic networks have low robustness, likely hampering their ability to tackle drastic environmental fluctuations, thus being confined to stable and predictable habitats. Overall, we present here a valuable computational testbed based on predatory bacteria activity for rational design of novel and controlled biocatalysts in biotechnological/clinical applications.


Assuntos
Bdellovibrio bacteriovorus/genética , Bdellovibrio bacteriovorus/metabolismo , Genoma Bacteriano/genética , Redes e Vias Metabólicas , Modelos Biológicos , Redes e Vias Metabólicas/genética , Redes e Vias Metabólicas/fisiologia , Biologia de Sistemas/métodos
4.
Anticancer Res ; 40(9): 5097-5106, 2020 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-32878798

RESUMO

BACKGROUND/AIM: Accumulating evidence has shown therapeutic effects of herbals on breast cancer, a commonly diagnosed malignancy in women worldwide. However, their underlying mechanisms remain unclear. We aimed to explore the mode of action of a recently developed herbal combination at system-level. MATERIALS AND METHODS: We employed network pharmacological approaches to study the mechanism of a combination of three herbals, Astragalus membranaceus, Angelica gigas and Trichosanthes kirilowii by investigating active compounds and performing functional enrichment analysis for the interacting targets. RESULTS: For in silico pharmacokinetic evaluation, ten active ingredients interacted with fifty-six breast cancer-associated therapeutic targets. Functional enrichment analysis revealed that TNF, estrogen, PI3K-Akt and MAPK signaling pathways were involved in tumorigenesis and development of breast cancer. The pharmacological mechanisms might be associated with cellular effects on proliferation, cell cycle process and apoptosis. CONCLUSION: The present study provides novel insights into the system-level pharmacological mechanisms underlying a herbal combination used for breast cancer therapies.


Assuntos
Antineoplásicos Fitogênicos/farmacologia , Medicamentos de Ervas Chinesas/farmacologia , Redes Neurais de Computação , Biologia de Sistemas/métodos , Tecnologia Farmacêutica/métodos , Antineoplásicos Fitogênicos/química , Astragalus propinquus , Neoplasias da Mama , Linhagem Celular Tumoral , Biologia Computacional/métodos , Ensaios de Seleção de Medicamentos Antitumorais , Medicamentos de Ervas Chinesas/química , Feminino , Humanos , Medicina Tradicional Chinesa , Fluxo de Trabalho
5.
PLoS Comput Biol ; 16(7): e1007884, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-32614821

RESUMO

Motivated by growing evidence for pathway heterogeneity and alternative functions of molecular machines, we demonstrate a computational approach for investigating two questions: (1) Are there multiple mechanisms (state-space pathways) by which a machine can perform a given function, such as cotransport across a membrane? (2) How can additional functionality, such as proofreading/error-correction, be built into machine function using standard biochemical processes? Answers to these questions will aid both the understanding of molecular-scale cell biology and the design of synthetic machines. Focusing on transport in this initial study, we sample a variety of mechanisms by employing Metropolis Markov chain Monte Carlo. Trial moves adjust transition rates among an automatically generated set of conformational and binding states while maintaining fidelity to thermodynamic principles and a user-supplied fitness/functionality goal. Each accepted move generates a new model. The simulations yield both single and mixed reaction pathways for cotransport in a simple environment with a single substrate along with a driving ion. In a "competitive" environment including an additional decoy substrate, several qualitatively distinct reaction pathways are found which are capable of extremely high discrimination coupled to a leak of the driving ion, akin to proofreading. The array of functional models would be difficult to find by intuition alone in the complex state-spaces of interest.


Assuntos
Transporte Biológico/fisiologia , Simulação por Computador , Computadores Moleculares , Biologia de Sistemas/métodos , Algoritmos , Cadeias de Markov , Proteínas de Membrana Transportadoras/química , Proteínas de Membrana Transportadoras/metabolismo , Método de Monte Carlo , Termodinâmica
6.
BMC Infect Dis ; 20(1): 480, 2020 Jul 06.
Artigo em Inglês | MEDLINE | ID: mdl-32631335

RESUMO

BACKGROUND: Influenza A virus (IAV) infection is a serious public health problem not only in South East Asia but also in European and African countries. Scientists are using network biology to dig deep into the essential host factors responsible for regulation of virus infections. Researchers can explore the virus invasion into the host cells by studying the virus-host relationship based on their protein-protein interaction network. METHODS: In this study, we present a comprehensive IAV-host protein-protein interaction network that is obtained based on the literature-curated protein interaction datasets and some important interaction databases. The network is constructed in Cytoscape and analyzed with its plugins including CytoHubba, CytoCluster, MCODE, ClusterViz and ClusterOne. In addition, Gene Ontology and KEGG enrichment analyses are performed on the highly IAV-associated human proteins. We also compare the current results with those from our previous study on Hepatitis C Virus (HCV)-host protein-protein interaction network in order to find out valuable information. RESULTS: We found out 1027 interactions among 829 proteins of which 14 are viral proteins and 815 belong to human proteins. The viral protein NS1 has the highest number of associations with human proteins followed by NP, PB2 and so on. Among human proteins, LNX2, MEOX2, TFCP2, PRKRA and DVL2 have the most interactions with viral proteins. Based on KEGG pathway enrichment analysis of the highly IAV-associated human proteins, we found out that they are enriched in the KEGG pathway of basal cell carcinoma. Similarly, the result of KEGG analysis of the common host factors involved in IAV and HCV infections shows that these factors are enriched in the infection pathways of Hepatitis B Virus (HBV), Viral Carcinoma, measles and certain other viruses. CONCLUSION: It is concluded that the list of proteins we identified might be used as potential drug targets for the drug design against the infectious diseases caused by Influenza A Virus and other viruses.


Assuntos
Interações Hospedeiro-Patógeno/genética , Vírus da Influenza A/metabolismo , Influenza Humana/metabolismo , Mapas de Interação de Proteínas/genética , Biologia de Sistemas/métodos , Proteínas de Transporte/genética , Proteínas de Ligação a DNA/genética , Hepacivirus/metabolismo , Hepatite C/metabolismo , Hepatite C/virologia , Humanos , Influenza Humana/virologia , Fatores de Transcrição/genética , Proteínas do Core Viral/genética , Proteínas não Estruturais Virais/genética , Replicação Viral
7.
PLoS One ; 15(7): e0233755, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32628677

RESUMO

Systems biology aims at holistically understanding the complexity of biological systems. In particular, nowadays with the broad availability of gene expression measurements, systems biology challenges the deciphering of the genetic cell machinery from them. In order to help researchers, reverse engineer the genetic cell machinery from these noisy datasets, interactive exploratory clustering methods, pipelines and gene clustering tools have to be specifically developed. Prior methods/tools for time series data, however, do not have the following four major ingredients in analytic and methodological view point: (i) principled time-series feature extraction methods, (ii) variety of manifold learning methods for capturing high-level view of the dataset, (iii) high-end automatic structure extraction, and (iv) friendliness to the biological user community. With a view to meet the requirements, we present AGCT (A Geometric Clustering Tool), a software package used to unravel the complex architecture of large-scale, non-necessarily synchronized time-series gene expression data. AGCT capture signals on exhaustive wavelet expansions of the data, which are then embedded on a low-dimensional non-linear map using manifold learning algorithms, where geometric proximity captures potential interactions. Post-processing techniques, including hard and soft information geometric clustering algorithms, facilitate the summarizing of the complete map as a smaller number of principal factors which can then be formally identified using embedded statistical inference techniques. Three-dimension interactive visualization and scenario recording over the processing helps to reproduce data analysis results without additional time. Analysis of the whole-cell Yeast Metabolic Cycle (YMC) moreover, Yeast Cell Cycle (YCC) datasets demonstrate AGCT's ability to accurately dissect all stages of metabolism and the cell cycle progression, independently of the time course and the number of patterns related to the signal. Analysis of Pentachlorophenol iduced dataset demonstrat how AGCT dissects data to identify two networks: Interferon signaling and NRF2-signaling networks.


Assuntos
Expressão Gênica , Software , Biologia de Sistemas/métodos , Análise de Ondaletas , Algoritmos , Animais , Ciclo Celular/genética , Biologia Computacional/métodos , Conjuntos de Dados como Assunto , Regulação da Expressão Gênica/efeitos dos fármacos , Fígado/efeitos dos fármacos , Fígado/metabolismo , Cadeias de Markov , Camundongos , Pentaclorofenol/farmacologia , Pentaclorofenol/envenenamento , Distribuição Aleatória , Saccharomyces cerevisiae/citologia , Saccharomyces cerevisiae/genética , Saccharomyces cerevisiae/metabolismo , Biologia de Sistemas/estatística & dados numéricos
8.
Adv Exp Med Biol ; 1207: 699-706, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32671787

RESUMO

As a classical form of programmed cell death, autophagy is widely involved in cellular metabolism and vital for the maintenance of homeostasis in physiological and pathological states. With multiple levels of regulation and signaling integrated in, autography presents complicated relevance with various diseases, such as cancer and neurological diseases. The emerging subject, systems biology, along with multi-omics approaches, offers a new strategy to investigate these interactive processes from a holistic perspective. In this chapter, we focus on the systems biology method for autophagy research and introduce essential research skills and procedures. The critical step of systematic study is to explore interplay between biological molecules based on massive biological data, which requires construction of networks in different biological levels, modification, and identification of key pathways and targets via optimized algorithm and experimental verification. Guided by systems biology research, drug design can thus be strengthened by efficient screening and accurate evaluation. Overall, systems biology promises to act as a powerful tool which both helps to clarify the profound mechanism and to develop efficacious medicine.


Assuntos
Algoritmos , Autofagia , Pesquisa Biomédica/métodos , Biologia de Sistemas/métodos , Humanos , Neoplasias , Doenças do Sistema Nervoso , Transdução de Sinais
9.
Proc Natl Acad Sci U S A ; 117(31): 18869-18879, 2020 08 04.
Artigo em Inglês | MEDLINE | ID: mdl-32675233

RESUMO

Metabolic modeling and machine learning are key components in the emerging next generation of systems and synthetic biology tools, targeting the genotype-phenotype-environment relationship. Rather than being used in isolation, it is becoming clear that their value is maximized when they are combined. However, the potential of integrating these two frameworks for omic data augmentation and integration is largely unexplored. We propose, rigorously assess, and compare machine-learning-based data integration techniques, combining gene expression profiles with computationally generated metabolic flux data to predict yeast cell growth. To this end, we create strain-specific metabolic models for 1,143 Saccharomyces cerevisiae mutants and we test 27 machine-learning methods, incorporating state-of-the-art feature selection and multiview learning approaches. We propose a multiview neural network using fluxomic and transcriptomic data, showing that the former increases the predictive accuracy of the latter and reveals functional patterns that are not directly deducible from gene expression alone. We test the proposed neural network on a further 86 strains generated in a different experiment, therefore verifying its robustness to an additional independent dataset. Finally, we show that introducing mechanistic flux features improves the predictions also for knockout strains whose genes were not modeled in the metabolic reconstruction. Our results thus demonstrate that fusing experimental cues with in silico models, based on known biochemistry, can contribute with disjoint information toward biologically informed and interpretable machine learning. Overall, this study provides tools for understanding and manipulating complex phenotypes, increasing both the prediction accuracy and the extent of discernible mechanistic biological insights.


Assuntos
Aprendizado de Máquina , Análise do Fluxo Metabólico/métodos , Saccharomyces cerevisiae , Biologia de Sistemas/métodos , Modelos Biológicos , Saccharomyces cerevisiae/citologia , Saccharomyces cerevisiae/genética , Saccharomyces cerevisiae/metabolismo , Transcriptoma
11.
Nat Commun ; 11(1): 2966, 2020 06 11.
Artigo em Inglês | MEDLINE | ID: mdl-32528075

RESUMO

The need to understand cell developmental processes spawned a plethora of computational methods for discovering hierarchies from scRNAseq data. However, existing techniques are based on Euclidean geometry, a suboptimal choice for modeling complex cell trajectories with multiple branches. To overcome this fundamental representation issue we propose Poincaré maps, a method that harness the power of hyperbolic geometry into the realm of single-cell data analysis. Often understood as a continuous extension of trees, hyperbolic geometry enables the embedding of complex hierarchical data in only two dimensions while preserving the pairwise distances between points in the hierarchy. This enables the use of our embeddings in a wide variety of downstream data analysis tasks, such as visualization, clustering, lineage detection and pseudotime inference. When compared to existing methods - unable to address all these important tasks using a single embedding - Poincaré maps produce state-of-the-art two-dimensional representations of cell trajectories on multiple scRNAseq datasets.


Assuntos
Biologia Computacional/métodos , Algoritmos , Aprendizado de Máquina , Biologia de Sistemas/métodos
12.
PLoS One ; 15(6): e0233296, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32555729

RESUMO

Chronic medical conditions show substantial heterogeneity in their clinical features and progression. We develop the novel data-driven, network-based Trajectory Profile Clustering (TPC) algorithm for 1) identification of disease subtypes and 2) early prediction of subtype/disease progression patterns. TPC is an easily generalizable method that identifies subtypes by clustering patients with similar disease trajectory profiles, based not only on Parkinson's Disease (PD) variable severity, but also on their complex patterns of evolution. TPC is derived from bipartite networks that connect patients to disease variables. Applying our TPC algorithm to a PD clinical dataset, we identify 3 distinct subtypes/patient clusters, each with a characteristic progression profile. We show that TPC predicts the patient's disease subtype 4 years in advance with 72% accuracy for a longitudinal test cohort. Furthermore, we demonstrate that other types of data such as genetic data can be integrated seamlessly in the TPC algorithm. In summary, using PD as an example, we present an effective method for subtype identification in multidimensional longitudinal datasets, and early prediction of subtypes in individual patients.


Assuntos
Doença de Parkinson/diagnóstico , Biologia de Sistemas/métodos , Algoritmos , Análise por Conglomerados , Estudos de Coortes , Progressão da Doença , Humanos , Modelos Estatísticos , Índice de Gravidade de Doença
13.
PLoS One ; 15(4): e0230599, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32353072

RESUMO

Systems biology applies concepts from engineering in order to understand biological networks. If such an understanding was complete, biologists would be able to design ad hoc biochemical components tailored for different purposes, which is the goal of synthetic biology. Needless to say that we are far away from creating biological subsystems as intricate and precise as those found in nature, but mathematical models and high throughput techniques have brought us a long way in this direction. One of the difficulties that still needs to be overcome is finding the right values for model parameters and dealing with uncertainty, which is proving to be an extremely difficult task. In this work, we take advantage of ensemble modeling techniques, where a large number of models with different parameter values are formulated and then tested according to some performance criteria. By finding features shared by successful models, the role of different components and the synergies between them can be better understood. We will address some of the difficulties often faced by ensemble modeling approaches, such as the need to sample a space whose size grows exponentially with the number of parameters, and establishing useful selection criteria. Some methods will be shown to reduce the predictions from many models into a set of understandable "design principles" that can guide us to improve or manufacture a biochemical network. Our proposed framework formulates models within standard formalisms in order to integrate information from different sources and minimize the dimension of the parameter space. Additionally, the mathematical properties of the formalism enable a partition of the parameter space into independent subspaces. Each of these subspaces can be paired with a set of criteria that depend exclusively on it, thus allowing a separate sampling/screening in spaces of lower dimension. By applying tests in a strict order where computationally cheaper tests are applied first to each subspace and applying computationally expensive tests to the remaining subset thereafter, the use of resources is optimized and a larger number of models can be examined. This can be compared to a complex database query where the order of the requests can make a huge difference in the processing time. The method will be illustrated by analyzing a classical model of a metabolic pathway with end-product inhibition. Even for such a simple model, the method provides novel insight.


Assuntos
Dinâmica não Linear , Biologia de Sistemas/métodos , Fenótipo
14.
Adv Exp Med Biol ; 1194: 303-314, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32468546

RESUMO

MOTIVATION: In the last years, systems-level network-based approaches have gained ground in the research field of systems biology. These approaches are based on the analysis of high-throughput sequencing studies, which are rapidly increasing year by year. Nowadays, the single-cell RNA-sequencing, an optimized next-generation sequencing (NGS) technology that offers a better understanding of the function of an individual cell in the context of its microenvironment, prevails. RESULTS: Toward this direction, a method is developed in which active molecular subpathways are recorded during the time evolution of the disease under study. This method operates for expression profiling by high-throughput sequencing data. Its capability is based on capturing the temporal changes of local gene communities that form a disease-perturbed subpathway. The aforementioned methods are applied to real data from a recent study that uses single-cell RNA-sequencing data related with the progression of neurodegeneration. More specific, microglia cells were isolated from the hippocampus of a mouse model with Alzheimer's disease-like phenotypes and severe neurodegeneration and of control mice at multiple time points during progression of neurodegeneration. Our analysis offers a different view for neurodegeneration progression under the perspective of systems biology. CONCLUSION: Our approach into the molecular perspective using a temporal tracking of active pathways in neurodegeneration at single-cell resolution may offer new insights for designing new efficient strategies to treat Alzheimer's and other neurodegenerative diseases.


Assuntos
Doença de Alzheimer , Biologia de Sistemas , Doença de Alzheimer/fisiopatologia , Animais , Modelos Animais de Doenças , Progressão da Doença , Humanos , Camundongos , Microglia/patologia , Análise de Sequência de RNA , Análise de Célula Única/normas , Biologia de Sistemas/métodos
15.
NPJ Syst Biol Appl ; 6(1): 8, 2020 04 03.
Artigo em Inglês | MEDLINE | ID: mdl-32245958

RESUMO

Some biological networks exhibit oscillations in their components to convert stimuli to time-dependent responses. The eukaryotic cell cycle is such a case, being governed by waves of cyclin-dependent kinase (cyclin/Cdk) activities that rise and fall with specific timing and guarantee its timely occurrence. Disruption of cyclin/Cdk oscillations could result in dysfunction through reduced cell division. Therefore, it is of interest to capture properties of network designs that exhibit robust oscillations. Here we show that a minimal yeast cell cycle network is able to oscillate autonomously, and that cyclin/Cdk-mediated positive feedback loops (PFLs) and Clb3-centered regulations sustain cyclin/Cdk oscillations, in known and hypothetical network designs. We propose that Clb3-mediated coordination of cyclin/Cdk waves reconciles checkpoint and oscillatory cell cycle models. Considering the evolutionary conservation of the cyclin/Cdk network across eukaryotes, we hypothesize that functional ("healthy") phenotypes require the capacity to oscillate autonomously whereas dysfunctional (potentially "diseased") phenotypes may lack this capacity.


Assuntos
Relógios Biológicos/fisiologia , Ciclina B/metabolismo , Ciclinas/metabolismo , Proteínas de Saccharomyces cerevisiae/metabolismo , Ciclo Celular/fisiologia , Pontos de Checagem do Ciclo Celular/genética , Divisão Celular , Ciclina B/genética , Ciclina B/fisiologia , Quinases Ciclina-Dependentes/genética , Quinases Ciclina-Dependentes/metabolismo , Ciclinas/genética , Modelos Biológicos , Saccharomyces cerevisiae/genética , Proteínas de Saccharomyces cerevisiae/genética , Proteínas de Saccharomyces cerevisiae/fisiologia , Biologia de Sistemas/métodos
16.
Transl Res ; 220: 57-67, 2020 06.
Artigo em Inglês | MEDLINE | ID: mdl-32272094

RESUMO

Clostridioides difficile infection (CDI) is an urgent threat to global public health. Patient susceptibility to C. difficile is highly dependent on host immune status and gut dysbiosis resulting in loss of protective microbiota consortia that prevent spore germination, pathogen colonization, and disease pathogenesis. Recent clinical studies highlight the problems of differentiating symptomatic CDI from asymptomatic C. difficile carriage in patients with diarrhea. In this review, we consider how integration of microbiome and host immune systems biology data may aid in the clinical diagnosis of CDI when validated against gold standard testing and combined with standard microbiology laboratory assays.


Assuntos
Infecções por Clostridium/diagnóstico , Biologia de Sistemas/métodos , Infecções por Clostridium/imunologia , Infecções por Clostridium/microbiologia , Microbioma Gastrointestinal , Humanos
17.
Curr Opin Chem Biol ; 54: 76-84, 2020 02.
Artigo em Inglês | MEDLINE | ID: mdl-32146330

RESUMO

Recent advances in -omic profiling technologies have ushered in an era where we no longer want to merely measure the presence or absence of a biomolecule of interest, but instead hope to understand its function and interactions within larger signaling networks. Here, we review several emerging proteomic technologies capable of detecting protein interaction networks in live cells and their integration to draft holistic maps of proteins that respond to diverse stimuli, including bioactive small molecules. Moreover, we provide a conceptual framework to combine so-called 'top-down' and 'bottom-up' interaction profiling methods and ensuing proteomic profiles to directly identify binding targets of small molecule ligands, as well as for unbiased discovery of proteins and pathways that may be directly bound or influenced by those first responders. The integrated, interaction-based profiling methods discussed here have the potential to provide a unique and dynamic view into cellular signaling networks for both basic and translational biological studies.


Assuntos
Mapas de Interação de Proteínas , Proteômica/métodos , Ligantes , Biologia de Sistemas/métodos
18.
PLoS Comput Biol ; 16(3): e1007669, 2020 03.
Artigo em Inglês | MEDLINE | ID: mdl-32150537

RESUMO

Systems Biology models reveal relationships between signaling inputs and observable molecular or cellular behaviors. The complexity of these models, however, often obscures key elements that regulate emergent properties. We use a Bayesian model reduction approach that combines Parallel Tempering with Lasso regularization to identify minimal subsets of reactions in a signaling network that are sufficient to reproduce experimentally observed data. The Bayesian approach finds distinct reduced models that fit data equivalently. A variant of this approach that uses Lasso to perform selection at the level of reaction modules is applied to the NF-κB signaling network to test the necessity of feedback loops for responses to pulsatile and continuous pathway stimulation. Taken together, our results demonstrate that Bayesian parameter estimation combined with regularization can isolate and reveal core motifs sufficient to explain data from complex signaling systems.


Assuntos
Modelos Biológicos , Transdução de Sinais , Biologia de Sistemas/métodos , Teorema de Bayes , Retroalimentação Fisiológica/fisiologia , NF-kappa B/metabolismo
19.
NPJ Syst Biol Appl ; 6(1): 6, 2020 03 13.
Artigo em Inglês | MEDLINE | ID: mdl-32170148

RESUMO

In biotechnology, the emergence of high-throughput technologies challenges the interpretation of large datasets. One way to identify meaningful outcomes impacting process and product attributes from large datasets is using systems biology tools such as metabolic models. However, these tools are still not fully exploited for this purpose in industrial context due to gaps in our knowledge and technical limitations. In this paper, key aspects restraining the routine implementation of these tools are highlighted in three research fields: monitoring, network science and hybrid modeling. Advances in these fields could expand the current state of systems biology applications in biopharmaceutical industry to address existing challenges in bioprocess development and improvement.


Assuntos
Bioengenharia/métodos , Produtos Biológicos/metabolismo , Biologia de Sistemas/métodos , Produtos Biológicos/farmacologia , Biotecnologia/métodos , Biotecnologia/tendências , Indústrias/tendências , Modelos Biológicos
20.
Int J Mol Sci ; 21(4)2020 Feb 24.
Artigo em Inglês | MEDLINE | ID: mdl-32102370

RESUMO

The synaptic cleft has been vastly investigated in the last decades, leading to a novel and fascinating model of the functional and structural modifications linked to synaptic transmission and brain processing. The classic neurocentric model encompassing the neuronal pre- and post-synaptic terminals partly explains the fine-tuned plastic modifications under both pathological and physiological circumstances. Recent experimental evidence has incontrovertibly added oligodendrocytes, astrocytes, and microglia as pivotal elements for synapse formation and remodeling (tripartite synapse) in both the developing and adult brain. Moreover, synaptic plasticity and its pathological counterpart (maladaptive plasticity) have shown a deep connection with other molecular elements of the extracellular matrix (ECM), once considered as a mere extracellular structural scaffold altogether with the cellular glue (i.e., glia). The ECM adds another level of complexity to the modern model of the synapse, particularly, for the long-term plasticity and circuit maintenance. This model, called tetrapartite synapse, can be further implemented by including the neurovascular unit (NVU) and the immune system. Although they were considered so far as tightly separated from the central nervous system (CNS) plasticity, at least in physiological conditions, recent evidence endorsed these elements as structural and paramount actors in synaptic plasticity. This scenario is, as far as speculations and evidence have shown, a consistent model for both adaptive and maladaptive plasticity. However, a comprehensive understanding of brain processes and circuitry complexity is still lacking. Here we propose that a better interpretation of the CNS complexity can be granted by a systems biology approach through the construction of predictive molecular models that enable to enlighten the regulatory logic of the complex molecular networks underlying brain function in health and disease, thus opening the way to more effective treatments.


Assuntos
Matriz Extracelular/fisiologia , Neuroglia/fisiologia , Plasticidade Neuronal , Neurônios/fisiologia , Sinapses/fisiologia , Biologia de Sistemas/métodos , Animais , Sistema Nervoso Central/fisiologia , Epilepsia/fisiopatologia , Humanos , Neuroglia/citologia , Neurônios/citologia
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