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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.
Nat Commun ; 11(1): 5206, 2020 10 15.
Artigo em Inglês | MEDLINE | ID: mdl-33060586

RESUMO

Variation in the human gut microbiome can reflect host lifestyle and behaviors and influence disease biomarker levels in the blood. Understanding the relationships between gut microbes and host phenotypes are critical for understanding wellness and disease. Here, we examine associations between the gut microbiota and ~150 host phenotypic features across ~3,400 individuals. We identify major axes of taxonomic variance in the gut and a putative diversity maximum along the Firmicutes-to-Bacteroidetes axis. Our analyses reveal both known and unknown associations between microbiome composition and host clinical markers and lifestyle factors, including host-microbe associations that are composition-specific. These results suggest potential opportunities for targeted interventions that alter the composition of the microbiome to improve host health. By uncovering the interrelationships between host diet and lifestyle factors, clinical blood markers, and the human gut microbiome at the population-scale, our results serve as a roadmap for future studies on host-microbe interactions and interventions.


Assuntos
Biomarcadores , Doença , Microbioma Gastrointestinal/fisiologia , Saúde , Interações entre Hospedeiro e Microrganismos/fisiologia , Adulto , Biodiversidade , Dieta , Feminino , Firmicutes , Microbioma Gastrointestinal/genética , Humanos , Estilo de Vida , Masculino , Pessoa de Meia-Idade , RNA Ribossômico 16S/genética , Biologia de Sistemas
3.
BMC Bioinformatics ; 21(1): 432, 2020 Oct 02.
Artigo em Inglês | MEDLINE | ID: mdl-33008309

RESUMO

BACKGROUND: In systems biology, it is of great interest to identify previously unreported associations between genes. Recently, biomedical literature has been considered as a valuable resource for this purpose. While classical clustering algorithms have popularly been used to investigate associations among genes, they are not tuned for the literature mining data and are also based on strong assumptions, which are often violated in this type of data. For example, these approaches often assume homogeneity and independence among observations. However, these assumptions are often violated due to both redundancies in functional descriptions and biological functions shared among genes. Latent block models can be alternatives in this case but they also often show suboptimal performances, especially when signals are weak. In addition, they do not allow to utilize valuable prior biological knowledge, such as those available in existing databases. RESULTS: In order to address these limitations, here we propose PALMER, a constrained latent block model that allows to identify indirect relationships among genes based on the biomedical literature mining data. By automatically associating relevant Gene Ontology terms, PALMER facilitates biological interpretation of novel findings without laborious downstream analyses. PALMER also allows researchers to utilize prior biological knowledge about known gene-pathway relationships to guide identification of gene-gene associations. We evaluated PALMER with simulation studies and applications to studies of pathway-modulating genes relevant to cancer signaling pathways, while utilizing biological pathway annotations available in the KEGG database as prior knowledge. CONCLUSIONS: We showed that PALMER outperforms traditional latent block models and it provides reliable identification of novel gene-gene associations by utilizing prior biological knowledge, especially when signals are weak in the biomedical literature mining dataset. We believe that PALMER and its relevant user-friendly software will be powerful tools that can be used to improve existing pathway annotations and identify novel pathway-modulating genes.


Assuntos
Algoritmos , Mineração de Dados , Modelos Teóricos , Anotação de Sequência Molecular , Publicações , Simulação por Computador , Ontologia Genética , Redes Reguladoras de Genes , Humanos , Família Multigênica , Biologia de Sistemas
4.
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
5.
PLoS One ; 15(9): e0237975, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32960892

RESUMO

The swift rise of omics-approaches allows for investigating microbial diversity and plant-microbe interactions across diverse ecological communities and spatio-temporal scales. The environment, however, is rapidly changing. The introduction of invasive species and the effects of climate change have particular impact on emerging plant diseases and managing current epidemics. It is critical, therefore, to take a holistic approach to understand how and why pathogenesis occurs in order to effectively manage for diseases given the synergies of changing environmental conditions. A multi-omics approach allows for a detailed picture of plant-microbial interactions and can ultimately allow us to build predictive models for how microbes and plants will respond to stress under environmental change. This article is designed as a primer for those interested in integrating -omic approaches into their plant disease research. We review -omics technologies salient to pathology including metabolomics, genomics, metagenomics, volatilomics, and spectranomics, and present cases where multi-omics have been successfully used for plant disease ecology. We then discuss additional limitations and pitfalls to be wary of prior to conducting an integrated research project as well as provide information about promising future directions.


Assuntos
Ecologia , Genômica/métodos , Metabolômica/métodos , Metagenômica/métodos , Doenças das Plantas/etiologia , Plantas/imunologia , Proteômica/métodos , Microbiota , Plantas/metabolismo , Biologia de Sistemas
6.
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
7.
PLoS Comput Biol ; 16(9): e1008185, 2020 09.
Artigo em Inglês | MEDLINE | ID: mdl-32925942

RESUMO

Cells adjust their metabolism in response to mutations, but how this reprogramming depends on the genetic context is not well known. Specifically, the absence of individual enzymes can affect reprogramming, and thus the impact of mutations in cell growth. Here, we examine this issue with an in silico model of Saccharomyces cerevisiae's metabolism. By quantifying the variability in the growth rate of 10000 different mutant metabolisms that accumulated changes in their reaction fluxes, in the presence, or absence, of a specific enzyme, we distinguish a subset of modifier genes serving as buffers or potentiators of variability. We notice that the most potent modifiers refer to the glycolysis pathway and that, more broadly, they show strong pleiotropy and epistasis. Moreover, the evidence that this subset depends on the specific growing condition strengthens its systemic underpinning, a feature only observed before in a toy model of a gene-regulatory network. Some of these enzymes also modulate the effect that biochemical noise and environmental fluctuations produce in growth. Thus, the reorganization of metabolism induced by mutations has not only direct physiological implications but also transforms the influence that other mutations have on growth. This is a general result with implications in the development of cancer therapies based on metabolic inhibitors.


Assuntos
Redes Reguladoras de Genes/genética , Redes e Vias Metabólicas , Mutação , Redes e Vias Metabólicas/genética , Redes e Vias Metabólicas/fisiologia , Mutação/genética , Mutação/fisiologia , Fenótipo , Saccharomyces cerevisiae/genética , Saccharomyces cerevisiae/metabolismo , Biologia de Sistemas
8.
PLoS One ; 15(9): e0238838, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32915842

RESUMO

Computational systems biology provides multiple formalisms for modelling of biochemical processes among which the rule-based approach is one of the most suitable. Its main advantage is a compact and precise mechanistic description of complex processes. However, state-of-the-art rule-based languages still suffer several shortcomings that limit their use in practice. In particular, the elementary (low-level) syntax and semantics of rule-based languages complicate model construction and maintenance for users outside computer science. On the other hand, mathematical models based on differential equations (ODEs) still make the most typical used modelling framework. In consequence, robust re-interpretation and integration of models are difficult, thus making the systems biology paradigm technically challenging. Though several high-level languages have been developed at the top of rule-based principles, none of them provides a satisfactory and complete solution for semi-automated description and annotation of heterogeneous biophysical processes integrated at the cellular level. We present the second generation of a rule-based language called Biochemical Space Language (BCSL) that combines the advantages of different approaches and thus makes an effort to overcome several problems of existing solutions. BCSL relies on the formal basis of the rule-based methodology while preserving user-friendly syntax of plain chemical equations. BCSL combines the following aspects: the level of abstraction that hides structural and quantitative details but yet gives a precise mechanistic view of systems dynamics; executable semantics allowing formal analysis and consistency checking at the level of the language; universality allowing the integration of different biochemical mechanisms; scalability and compactness of the specification; hierarchical specification and composability of chemical entities; and support for genome-scale annotation.


Assuntos
Fenômenos Bioquímicos , Simulação por Computador , Modelos Biológicos , Proteínas/classificação , Proteínas/metabolismo , Software , Biologia de Sistemas , Algoritmos , Humanos , Idioma , Modelos Teóricos , Linguagens de Programação , Proteínas/química
9.
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
10.
Science ; 369(6508): 1210-1220, 2020 09 04.
Artigo em Inglês | MEDLINE | ID: mdl-32788292

RESUMO

Coronavirus disease 2019 (COVID-19) represents a global crisis, yet major knowledge gaps remain about human immunity to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We analyzed immune responses in 76 COVID-19 patients and 69 healthy individuals from Hong Kong and Atlanta, Georgia, United States. In the peripheral blood mononuclear cells (PBMCs) of COVID-19 patients, we observed reduced expression of human leukocyte antigen class DR (HLA-DR) and proinflammatory cytokines by myeloid cells as well as impaired mammalian target of rapamycin (mTOR) signaling and interferon-α (IFN-α) production by plasmacytoid dendritic cells. By contrast, we detected enhanced plasma levels of inflammatory mediators-including EN-RAGE, TNFSF14, and oncostatin M-which correlated with disease severity and increased bacterial products in plasma. Single-cell transcriptomics revealed a lack of type I IFNs, reduced HLA-DR in the myeloid cells of patients with severe COVID-19, and transient expression of IFN-stimulated genes. This was consistent with bulk PBMC transcriptomics and transient, low IFN-α levels in plasma during infection. These results reveal mechanisms and potential therapeutic targets for COVID-19.


Assuntos
Betacoronavirus/imunologia , Infecções por Coronavirus/imunologia , Pneumonia Viral/imunologia , Citocinas/sangue , DNA Bacteriano/sangue , Células Dendríticas/imunologia , Células Dendríticas/metabolismo , Feminino , Citometria de Fluxo , Antígenos HLA-DR/análise , Humanos , Imunidade , Imunidade Inata , Imunoglobulinas/sangue , Imunoglobulinas/imunologia , Mediadores da Inflamação/sangue , Interferon Tipo I/metabolismo , Leucócitos Mononucleares/imunologia , Leucócitos Mononucleares/metabolismo , Lipopolissacarídeos/sangue , Masculino , Células Mieloides/imunologia , Células Mieloides/metabolismo , Pandemias , Transdução de Sinais , Análise de Célula Única , Biologia de Sistemas , Serina-Treonina Quinases TOR/metabolismo , Transcrição Genética , Transcriptoma
12.
PLoS One ; 15(8): e0234539, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32756554

RESUMO

Diabetes Mellitus (DM) accelerates coronary artery disease (CAD) and atherosclerosis, the causes of most heart attacks. The biomolecules involved in these inter-related disease processes are not well understood. This study analyzes biomolecules in the sera of patients with CAD, with and without type (T) 2DM, who are about to undergo coronary artery bypass graft (CABG) surgery. The goal is to develop methodology to help identify and monitor CAD patients with and without T2DM, in order to better understand these phenotypes and to glean relationships through analysis of serum biomolecules. Aorta, fat, muscle, and vein tissues from CAD T2DM patients display diabetic-related histologic changes (e.g., lipid accumulation, fibrosis, loss of cellularity) when compared to non-diabetic CAD patients. The patient discriminatory methodology utilized is serum biomolecule mass profiling. This mass spectrometry (MS) approach is able to distinguish the sera of a group of CAD patients from controls (p value 10-15), with the CAD group containing both T2DM and non-diabetic patients. This result indicates the T2DM phenotype does not interfere appreciably with the CAD determination versus control individuals. Sera from a group of T2DM CAD patients however are distinguishable from non-T2DM CAD patients (p value 10-8), indicating it may be possible to examine the T2DM phenotype within the CAD disease state with this MS methodology. The same serum samples used in the CAD T2DM versus non-T2DM binary group comparison were subjected to MS/MS peptide structure analysis to help identify potential biochemical and phenotypic changes associated with CAD and T2DM. Such peptide/protein identifications could lead to improved understanding of underlying mechanisms, additional biomarkers for discriminating and monitoring these disease conditions, and potential therapeutic targets. Bioinformatics/systems biology analysis of the peptide/protein changes associated with CAD and T2DM suggested cell pathways/systems affected include atherosclerosis, DM, fibrosis, lipogenesis, loss of cellularity (apoptosis), and inflammation.


Assuntos
Doença da Artéria Coronariana/sangue , Doença da Artéria Coronariana/complicações , Diabetes Mellitus Tipo 2/sangue , Diabetes Mellitus Tipo 2/complicações , Angiopatias Diabéticas/sangue , Adulto , Idoso , Biomarcadores/sangue , Proteínas Sanguíneas/metabolismo , Estudos de Casos e Controles , Ponte de Artéria Coronária , Doença da Artéria Coronariana/cirurgia , Estudos Transversais , Angiopatias Diabéticas/cirurgia , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Fenótipo , Estudos Retrospectivos , Espectrometria de Massas por Ionização por Electrospray , Biologia de Sistemas , Espectrometria de Massas em Tandem
13.
PLoS One ; 15(8): e0232384, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32750052

RESUMO

We propose a process graph (P-graph) approach to develop ecosystem networks from knowledge of the properties of the component species. Originally developed as a process engineering tool for designing industrial plants, the P-graph framework has key advantages over conventional ecological network analysis techniques based on input-output models. A P-graph is a bipartite graph consisting of two types of nodes, which we propose to represent components of an ecosystem. Compartments within ecosystems (e.g., organism species) are represented by one class of nodes, while the roles or functions that they play relative to other compartments are represented by a second class of nodes. This bipartite graph representation enables a powerful, unambiguous representation of relationships among ecosystem compartments, which can come in tangible (e.g., mass flow in predation) or intangible form (e.g., symbiosis). For example, within a P-graph, the distinct roles of bees as pollinators for some plants and as prey for some animals can be explicitly represented, which would not otherwise be possible using conventional ecological network analysis. After a discussion of the mapping of ecosystems into P-graph, we also discuss how this framework can be used to guide understanding of complex networks that exist in nature. Two component algorithms of P-graph, namely maximal structure generation (MSG) and solution structure generation (SSG), are shown to be particularly useful for ecological network analysis. These algorithms enable candidate ecosystem networks to be deduced based on current scientific knowledge on the individual ecosystem components. This method can be used to determine the (a) effects of loss of specific ecosystem compartments due to extinction, (b) potential efficacy of ecosystem reconstruction efforts, and (c) maximum sustainable exploitation of human ecosystem services by humans. We illustrate the use of P-graph for the analysis of ecosystem compartment loss using a small-scale stylized case study, and further propose a new criticality index that can be easily derived from SSG results.


Assuntos
Ecossistema , Algoritmos , Animais , Gráficos por Computador , Heurística Computacional , Cadeia Alimentar , Heurística , Humanos , Conceitos Matemáticos , Modelos Biológicos , Biologia de Sistemas , Teoria de Sistemas
14.
Nat Commun ; 11(1): 4256, 2020 08 26.
Artigo em Inglês | MEDLINE | ID: mdl-32848126

RESUMO

Predicting biological systems' behaviors requires taking into account many molecular and genetic elements for which limited information is available past a global knowledge of their pairwise interactions. Logical modeling, notably with Boolean Networks (BNs), is a well-established approach that enables reasoning on the qualitative dynamics of networks. Several dynamical interpretations of BNs have been proposed. The synchronous and (fully) asynchronous ones are the most prominent, where the value of either all or only one component can change at each step. Here we prove that, besides being costly to analyze, these usual interpretations can preclude the prediction of certain behaviors observed in quantitative systems. We introduce an execution paradigm, the Most Permissive Boolean Networks (MPBNs), which offers the formal guarantee not to miss any behavior achievable by a quantitative model following the same logic. Moreover, MPBNs significantly reduce the complexity of dynamical analysis, enabling to model genome-scale networks.


Assuntos
Modelos Biológicos , Biologia de Sistemas , Animais , Biologia Computacional , Redes Reguladoras de Genes , Humanos , Lógica , Redes e Vias Metabólicas , Modelos Genéticos
15.
BMC Bioinformatics ; 21(1): 346, 2020 Aug 10.
Artigo em Inglês | MEDLINE | ID: mdl-32778050

RESUMO

BACKGROUND: While technological advances have made it possible to profile the immune system at high resolution, translating high-throughput data into knowledge of immune mechanisms has been challenged by the complexity of the interactions underlying immune processes. Tools to explore the immune network are critical for better understanding the multi-layered processes that underlie immune function and dysfunction, but require a standardized network map of immune interactions. To facilitate this we have developed ImmunoGlobe, a manually curated intercellular immune interaction network extracted from Janeway's Immunobiology textbook. RESULTS: ImmunoGlobe is the first graphical representation of the immune interactome, and is comprised of 253 immune system components and 1112 unique immune interactions with detailed functional and characteristic annotations. Analysis of this network shows that it recapitulates known features of the human immune system and can be used uncover novel multi-step immune pathways, examine species-specific differences in immune processes, and predict the response of immune cells to stimuli. ImmunoGlobe is publicly available through a user-friendly interface at www.immunoglobe.org and can be downloaded as a computable graph and network table. CONCLUSION: While the fields of proteomics and genomics have long benefited from network analysis tools, no such tool yet exists for immunology. ImmunoGlobe provides a ground truth immune interaction network upon which such tools can be built. These tools will allow us to predict the outcome of complex immune interactions, providing mechanistic insight that allows us to precisely modulate immune responses in health and disease.


Assuntos
Comunicação Celular , Curadoria de Dados , Espaço Extracelular/metabolismo , Sistema Imunitário/metabolismo , Mapas de Interação de Proteínas , Software , Biologia de Sistemas , Animais , Humanos , Camundongos , Modelos Imunológicos
16.
Expert Opin Drug Discov ; 15(11): 1267-1281, 2020 11.
Artigo em Inglês | MEDLINE | ID: mdl-32662677

RESUMO

INTRODUCTION: A new body of evidence depicts the applications of artificial intelligence and systems biology in vaccine design and development. The combination of both approaches shall revolutionize healthcare, accelerating clinical trial processes and reducing the costs and time involved in drug research and development. AREAS COVERED: This review explores the basics of artificial intelligence and systems biology approaches in the vaccine development pipeline. The topics include a detailed description of epitope prediction tools for designing epitope-based vaccines and agent-based models for immune system response prediction, along with a focus on their potentiality to facilitate clinical trial phases. EXPERT OPINION: Artificial intelligence and systems biology offer the opportunity to avoid the inefficiencies and failures that arise in the classical vaccine development pipeline. One promising solution is the combination of both methodologies in a multiscale perspective through an accurate pipeline. We are entering an 'in silico era' in which scientific partnerships, including a more and more increasing creation of an 'ecosystem' of collaboration and multidisciplinary approach, are relevant for addressing the long and risky road of vaccine discovery and development. In this context, regulatory guidance should be developed to qualify the in silico trials as evidence for intelligent vaccine development.


Assuntos
Inteligência Artificial , Betacoronavirus/imunologia , Infecções por Coronavirus/prevenção & controle , Desenvolvimento de Medicamentos/métodos , Pandemias/prevenção & controle , Pneumonia Viral/prevenção & controle , Biologia de Sistemas , Vacinas Virais/normas , Infecções por Coronavirus/terapia , Humanos , Pneumonia Viral/terapia , Vacinas Virais/uso terapêutico
17.
J Biol Dyn ; 14(1): 590-607, 2020 12.
Artigo em Inglês | MEDLINE | ID: mdl-32696723

RESUMO

In this paper, we apply optimal control theory to a novel coronavirus (COVID-19) transmission model given by a system of non-linear ordinary differential equations. Optimal control strategies are obtained by minimizing the number of exposed and infected population considering the cost of implementation. The existence of optimal controls and characterization is established using Pontryagin's Maximum Principle. An expression for the basic reproduction number is derived in terms of control variables. Then the sensitivity of basic reproduction number with respect to model parameters is also analysed. Numerical simulation results demonstrated good agreement with our analytical results. Finally, the findings of this study shows that comprehensive impacts of prevention, intensive medical care and surface disinfection strategies outperform in reducing the disease epidemic with optimum implementation cost.


Assuntos
Betacoronavirus , Infecções por Coronavirus/prevenção & controle , Infecções por Coronavirus/transmissão , Modelos Biológicos , Pandemias/prevenção & controle , Pneumonia Viral/prevenção & controle , Pneumonia Viral/transmissão , Número Básico de Reprodução/estatística & dados numéricos , Simulação por Computador , Infecções por Coronavirus/epidemiologia , Epidemias/prevenção & controle , Epidemias/estatística & dados numéricos , Humanos , Controle de Infecções , Conceitos Matemáticos , Dinâmica não Linear , Pneumonia Viral/epidemiologia , Fatores de Risco , Biologia de Sistemas
18.
Mol Syst Biol ; 16(7): e9628, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-32729248

RESUMO

The COVID-19 pandemic caused by SARS-CoV-2 has is a global health challenge. Angiotensin-converting enzyme 2 (ACE2) is the host receptor for SARS-CoV-2 entry. Recent studies have suggested that patients with hypertension and diabetes treated with ACE inhibitors (ACEIs) or angiotensin receptor blockers have a higher risk of COVID-19 infection as these drugs could upregulate ACE2, motivating the study of ACE2 modulation by drugs in current clinical use. Here, we mined published datasets to determine the effects of hundreds of clinically approved drugs on ACE2 expression. We find that ACEIs are enriched for ACE2-upregulating drugs, while antineoplastic agents are enriched for ACE2-downregulating drugs. Vorinostat and isotretinoin are the top ACE2 up/downregulators, respectively, in cell lines. Dexamethasone, a corticosteroid used in treating severe acute respiratory syndrome and COVID-19, significantly upregulates ACE2 both in vitro and in vivo. Further top ACE2 regulators in vivo or in primary cells include erlotinib and bleomycin in the lung and vancomycin, cisplatin, and probenecid in the kidney. Our study provides leads for future work studying ACE2 expression modulators.


Assuntos
Antagonistas de Receptores de Angiotensina/farmacologia , Inibidores da Enzima Conversora de Angiotensina/farmacologia , Infecções por Coronavirus/tratamento farmacológico , Pneumonia Viral/tratamento farmacológico , Células A549 , Betacoronavirus , Bleomicina/farmacologia , Dexametasona/farmacologia , Desenho de Fármacos , Avaliação Pré-Clínica de Medicamentos , Cloridrato de Erlotinib/farmacologia , Flufenazina/farmacologia , Células HEK293 , Humanos , Rim/efeitos dos fármacos , Pulmão/efeitos dos fármacos , Células MCF-7 , Pandemias , Peptidil Dipeptidase A , Biologia de Sistemas , Regulação para Cima , Vemurafenib/farmacologia
19.
Nat Commun ; 11(1): 3515, 2020 07 14.
Artigo em Inglês | MEDLINE | ID: mdl-32665557

RESUMO

An unmet clinical need in solid tumor cancers is the ability to harness the intrinsic spatial information in primary tumors that can be exploited to optimize prognostics, diagnostics and therapeutic strategies for precision medicine. Here, we develop a transformational spatial analytics computational and systems biology platform (SpAn) that predicts clinical outcomes and captures emergent spatial biology that can potentially inform therapeutic strategies. We apply SpAn to primary tumor tissue samples from a cohort of 432 chemo-naïve colorectal cancer (CRC) patients iteratively labeled with a highly multiplexed (hyperplexed) panel of 55 fluorescently tagged antibodies. We show that SpAn predicts the 5-year risk of CRC recurrence with a mean AUROC of 88.5% (SE of 0.1%), significantly better than current state-of-the-art methods. Additionally, SpAn infers the emergent network biology of tumor microenvironment spatial domains revealing a spatially-mediated role of CRC consensus molecular subtype features with the potential to inform precision medicine.


Assuntos
Neoplasias Colorretais/genética , Recidiva Local de Neoplasia/genética , Biomarcadores/metabolismo , Imunofluorescência , Regulação Neoplásica da Expressão Gênica/genética , Humanos , Análise de Sequência com Séries de Oligonucleotídeos , Medicina de Precisão , Biologia de Sistemas , Microambiente Tumoral/genética
20.
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
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