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1.
BMC Bioinformatics ; 21(1): 69, 2020 Feb 24.
Artigo em Inglês | MEDLINE | ID: mdl-32093622

RESUMO

BACKGROUND: In this paper, we explore the concept of multi-objective optimization in the field of metabolic engineering when both continuous and integer decision variables are involved in the model. In particular, we propose a multi-objective model that may be used to suggest reaction deletions that maximize and/or minimize several functions simultaneously. The applications may include, among others, the concurrent maximization of a bioproduct and of biomass, or maximization of a bioproduct while minimizing the formation of a given by-product, two common requirements in microbial metabolic engineering. RESULTS: Production of ethanol by the widely used cell factory Saccharomyces cerevisiae was adopted as a case study to demonstrate the usefulness of the proposed approach in identifying genetic manipulations that improve productivity and yield of this economically highly relevant bioproduct. We did an in vivo validation and we could show that some of the predicted deletions exhibit increased ethanol levels in comparison with the wild-type strain. CONCLUSIONS: The multi-objective programming framework we developed, called MOMO, is open-source and uses POLYSCIP (Available at http://polyscip.zib.de/). as underlying multi-objective solver. MOMO is available at http://momo-sysbio.gforge.inria.fr.

2.
BMC Bioinformatics ; 21(1): 59, 2020 Feb 18.
Artigo em Inglês | MEDLINE | ID: mdl-32070274

RESUMO

BACKGROUND: Understanding cellular and molecular heterogeneity in glioblastoma (GBM), the most common and aggressive primary brain malignancy, is a crucial step towards the development of effective therapies. Besides the inter-patient variability, the presence of multiple cell populations within tumors calls for the need to develop modeling strategies able to extract the molecular signatures driving tumor evolution and treatment failure. With the advances in single-cell RNA Sequencing (scRNA-Seq), tumors can now be dissected at the cell level, unveiling information from their life history to their clinical implications. RESULTS: We propose a classification setting based on GBM scRNA-Seq data, through sparse logistic regression, where different cell populations (neoplastic and normal cells) are taken as classes. The goal is to identify gene features discriminating between the classes, but also those shared by different neoplastic clones. The latter will be approached via the network-based twiner regularizer to identify gene signatures shared by neoplastic cells from the tumor core and infiltrating neoplastic cells originated from the tumor periphery, as putative disease biomarkers to target multiple neoplastic clones. Our analysis is supported by the literature through the identification of several known molecular players in GBM. Moreover, the relevance of the selected genes was confirmed by their significance in the survival outcomes in bulk GBM RNA-Seq data, as well as their association with several Gene Ontology (GO) biological process terms. CONCLUSIONS: We presented a methodology intended to identify genes discriminating between GBM clones, but also those playing a similar role in different GBM neoplastic clones (including migrating cells), therefore potential targets for therapy research. Our results contribute to a deeper understanding on the genetic features behind GBM, by disclosing novel therapeutic directions accounting for GBM heterogeneity.

3.
Genome Biol ; 20(1): 144, 2019 07 25.
Artigo em Inglês | MEDLINE | ID: mdl-31345254

RESUMO

BACKGROUND: Alignment-free (AF) sequence comparison is attracting persistent interest driven by data-intensive applications. Hence, many AF procedures have been proposed in recent years, but a lack of a clearly defined benchmarking consensus hampers their performance assessment. RESULTS: Here, we present a community resource (http://afproject.org) to establish standards for comparing alignment-free approaches across different areas of sequence-based research. We characterize 74 AF methods available in 24 software tools for five research applications, namely, protein sequence classification, gene tree inference, regulatory element detection, genome-based phylogenetic inference, and reconstruction of species trees under horizontal gene transfer and recombination events. CONCLUSION: The interactive web service allows researchers to explore the performance of alignment-free tools relevant to their data types and analytical goals. It also allows method developers to assess their own algorithms and compare them with current state-of-the-art tools, accelerating the development of new, more accurate AF solutions.


Assuntos
Análise de Sequência , Benchmarking , Transferência Genética Horizontal , Internet , Filogenia , Sequências Reguladoras de Ácido Nucleico , Alinhamento de Sequência , Análise de Sequência de Proteína , Software
4.
BMC Bioinformatics ; 20(1): 356, 2019 Jun 25.
Artigo em Inglês | MEDLINE | ID: mdl-31238876

RESUMO

BACKGROUND: Breast and prostate cancers are typical examples of hormone-dependent cancers, showing remarkable similarities at the hormone-related signaling pathways level, and exhibiting a high tropism to bone. While the identification of genes playing a specific role in each cancer type brings invaluable insights for gene therapy research by targeting disease-specific cell functions not accounted so far, identifying a common gene signature to breast and prostate cancers could unravel new targets to tackle shared hormone-dependent disease features, like bone relapse. This would potentially allow the development of new targeted therapies directed to genes regulating both cancer types, with a consequent positive impact in cancer management and health economics. RESULTS: We address the challenge of extracting gene signatures from transcriptomic data of prostate adenocarcinoma (PRAD) and breast invasive carcinoma (BRCA) samples, particularly estrogen positive (ER+), and androgen positive (AR+) triple-negative breast cancer (TNBC), using sparse logistic regression. The introduction of gene network information based on the distances between BRCA and PRAD correlation matrices is investigated, through the proposed twin networks recovery (twiner) penalty, as a strategy to ensure similarly correlated gene features in two diseases to be less penalized during the feature selection procedure. CONCLUSIONS: Our analysis led to the identification of genes that show a similar correlation pattern in BRCA and PRAD transcriptomic data, and are selected as key players in the classification of breast and prostate samples into ER+ BRCA/AR+ TNBC/PRAD tumor and normal tissues, and also associated with survival time distributions. The results obtained are supported by the literature and are expected to unveil the similarities between the diseases, disclose common disease biomarkers, and help in the definition of new strategies for more effective therapies.


Assuntos
Perfilação da Expressão Gênica/métodos , Neoplasias da Próstata/genética , Transcriptoma , Neoplasias de Mama Triplo Negativas/genética , Estrogênios/metabolismo , Feminino , Redes Reguladoras de Genes , Humanos , Modelos Logísticos , Masculino , Análise de Componente Principal , Neoplasias da Próstata/mortalidade , Neoplasias da Próstata/patologia , Receptores Androgênicos/metabolismo , Análise de Sobrevida , Neoplasias de Mama Triplo Negativas/mortalidade , Neoplasias de Mama Triplo Negativas/patologia
5.
BMC Med Inform Decis Mak ; 19(1): 13, 2019 01 17.
Artigo em Inglês | MEDLINE | ID: mdl-30654776

RESUMO

BACKGROUND: Joint models (JM) have emerged as a promising statistical framework to concurrently analyse survival data and multiple longitudinal responses. This is particularly relevant in clinical studies where the goal is to estimate the association between time-to-event data and the biomarkers evolution. In the context of oncological data, JM can indeed provide interesting prognostic markers for the event under study and thus support clinical decisions and treatment choices. However, several problems arise when dealing with this type of data, such as the high-dimensionality of the covariates space, the lack of knowledge about the function structure of the time series and the presence of missing data, facts that may hamper the accurate estimation of the JM. METHODS: We propose to apply JM for the analysis of bone metastatic patients and infer the association of their survival with several covariates, in particular the N-Telopeptide of Type I Collagen (NTX) dynamics. This biomarker has been identified as a relevant prognostic factor in patients with metastatic cancer, but only using static information in some specific time points. RESULTS: We extended this analysis using the full NTX time series for a larger cohort of patients with bone metastasis, and compared the results obtained by the JM and the extended Cox regression model. Imputation based on fuzzy clustering was used to deal with missing values and several functions for NTX evolution were compared, such as rational, exponential and cubic splines. CONCLUSIONS: The JM obtained confirm the association between NTX values and patients' response, attesting the importance of this time series, and additionally provide a deep understanding of the key survival covariates.


Assuntos
Biomarcadores Tumorais/metabolismo , Neoplasias Ósseas/metabolismo , Colágeno Tipo I/metabolismo , Modelos Teóricos , Peptídeos/metabolismo , Análise de Sobrevida , Neoplasias Ósseas/secundário , Humanos , Estudos Longitudinais
6.
Stat Methods Med Res ; 28(10-11): 3042-3056, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-30146936

RESUMO

Correct classification of breast cancer subtypes is of high importance as it directly affects the therapeutic options. We focus on triple-negative breast cancer which has the worst prognosis among breast cancer types. Using cutting edge methods from the field of robust statistics, we analyze Breast Invasive Carcinoma transcriptomic data publicly available from The Cancer Genome Atlas data portal. Our analysis identifies statistical outliers that may correspond to misdiagnosed patients. Furthermore, it is illustrated that classical statistical methods may fail to identify outliers due to their heavy influence, prompting the need for robust statistics. Using robust sparse logistic regression we obtain 36 relevant genes, of which ca. 60% have been previously reported as biologically relevant to triple-negative breast cancer, reinforcing the validity of the method. The remaining 14 genes identified are new potential biomarkers for triple-negative breast cancer. Out of these, JAM3, SFT2D2, and PAPSS1 were previously associated to breast tumors or other types of cancer. The relevance of these genes is confirmed by the new DetectDeviatingCells outlier detection technique. A comparison of gene networks on the selected genes showed significant differences between triple-negative breast cancer and non-triple-negative breast cancer data. The individual role of FOXA1 in triple-negative breast cancer and non-triple-negative breast cancer, and the strong FOXA1-AGR2 connection in triple-negative breast cancer stand out. The goal of our paper is to contribute to the breast cancer/triple-negative breast cancer understanding and management. At the same time it demonstrates that robust regression and outlier detection constitute key strategies to cope with high-dimensional clinical data such as omics data.

7.
BMC Med Inform Decis Mak ; 19(1): 289, 2019 12 30.
Artigo em Inglês | MEDLINE | ID: mdl-31888660

RESUMO

BACKGROUND: Patient stratification is a critical task in clinical decision making since it can allow physicians to choose treatments in a personalized way. Given the increasing availability of electronic medical records (EMRs) with longitudinal data, one crucial problem is how to efficiently cluster the patients based on the temporal information from medical appointments. In this work, we propose applying the Temporal Needleman-Wunsch (TNW) algorithm to align discrete sequences with the transition time information between symbols. These symbols may correspond to a patient's current therapy, their overall health status, or any other discrete state. The transition time information represents the duration of each of those states. The obtained TNW pairwise scores are then used to perform hierarchical clustering. To find the best number of clusters and assess their stability, a resampling technique is applied. RESULTS: We propose the AliClu, a novel tool for clustering temporal clinical data based on the TNW algorithm coupled with clustering validity assessments through bootstrapping. The AliClu was applied for the analysis of the rheumatoid arthritis EMRs obtained from the Portuguese database of rheumatologic patient visits (Reuma.pt). In particular, the AliClu was used for the analysis of therapy switches, which were coded as letters corresponding to biologic drugs and included their durations before each change occurred. The obtained optimized clusters allow one to stratify the patients based on their temporal therapy profiles and to support the identification of common features for those groups. CONCLUSIONS: The AliClu is a promising computational strategy to analyse longitudinal patient data by providing validated clusters and by unravelling the patterns that exist in clinical outcomes. Patient stratification is performed in an automatic or semi-automatic way, allowing one to tune the alignment, clustering, and validation parameters. The AliClu is freely available at https://github.com/sysbiomed/AliClu.

8.
Orthop J Sports Med ; 6(11): 2325967118808242, 2018 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-30505873

RESUMO

Background: Painful dysfunctional shoulders with irreparable rotator cuff tears (IRCTs) in active patients are a challenge. Arthroscopic superior capsular reconstruction (ASCR) is a new treatment option originally described using a fascia lata autograft harvested through an open approach. However, concerns about donor site morbidity have discouraged surgeons from using this type of graft. Hypothesis: ASCR using a minimally invasive harvested fascia lata autograft produces good 6-month and 2-year shoulder outcomes in IRCTs, with low-impact thigh morbidity at 2 years. Study Design: Case series; Level of evidence, 4. Methods: From 2015 to 2016, a total of 22 consecutive patients (mean age, 64.8 ± 8.6 years) with chronic IRCTs (Hamada grade 1-2; Goutallier cumulative grade ≥3; Patte stage 1: 2 patients; Patte stage 2: 6 patients; Patte stage 3: 14 patients) underwent ASCR using a minimally invasive harvested fascia lata autograft. All patients completed preoperative and 6-month evaluations consisting of the Simple Shoulder Test (SST), subjective shoulder value (SSV), Constant score (CS), range of motion (ROM), acromiohumeral interval (AHI), and magnetic resonance imaging. Twenty-one patients completed the 2-year shoulder and donor site morbidity assessments. Results: The mean active ROMs improved significantly (P < .001): elevation, from 74.8° ± 55.5° to 104.5° ± 41.9° (6 months) and 143.8° ± 31.7° (2 years); abduction, from 53.2° ± 43.3° to 86.6° ± 32.9° (6 months) and 120.7° ± 37.7° (2 years); external rotation, from 13.2° ± 18.4° to 27.0° ± 16.1° (6 months) and 35.6° ± 17.3° (2 years); and internal rotation, from 1.2 ± 1.5 points to 2.6 ± 1.5 points (6 months) and 3.8 ± 1.2 points (2 years). The mean functional shoulder scores improved significantly (P < .001): SST, from 2.1 ± 2.9 to 6.8 ± 3.5 (6 months) and 8.6 ± 3.5 (2 years); SSV, from 33.0% ± 17.4% to 55.7% ± 25.6% (6 months) and 70.0% ± 23.0% (2 years); CS, from 17.5 ± 13.4 to 42.5 ± 14.9 (6 months) and 64.9 ± 18.0 (2 years). The mean shoulder abduction strength improved significantly (P < .001) from 0.0 to 1.1 ± 1.4 kg (6 months) and 2.8 ± 2.6 kg (2 years). The mean AHI improved from 6.4 ± 3.3 mm to 8.0 ± 2.5 mm (6 months) and decreased to 7.1 ± 2.5 mm (2 years). This 0.7 ± 1.5-mm overall decrease was statistically significant (P = .042). At 6 months, 20 of 22 patients (90.9%) had no graft tears. At 2 years, 12 of 21 patients (57.1%) were bothered by their harvested thigh, 16 (76.2%) noticed donor site changes, 16 (76.2%) considered that the shoulder surgery's end result compensated for the thigh's changes, and 18 (85.7%) would undergo the same surgery again. Conclusion: ASCR using a minimally invasive harvested fascia lata autograft produced good 6-month and 2-year shoulder outcomes in IRCTs, with low-impact thigh morbidity at 2 years.

9.
Biotechnol Prog ; 34(6): 1344-1354, 2018 11.
Artigo em Inglês | MEDLINE | ID: mdl-30294889

RESUMO

Over the last years, several genome-scale metabolic models (GEMs) and kinetic models of Escherichia coli were published. Their predictive performance varies according to the evaluation metric considered, the computational simulation methods used, and the type/quality of experimental data available. However, the GEM approach is often not compared with the kinetic modeling framework. Also, the different genome-scale reconstruction versions and simulation methods of mutant phenotypes are usually not validated to predict intracellular fluxes using large experimental datasets. Here, we intended to (i) systematically evaluate the prediction performance of three E. coli GEMs (iJR904, iAF1260, and iJO1366) available in the literature according to predictive growth metrics (intracellular flux distribution); (ii) assess the reliability of a E. coli GEM in the prediction of gene knockout phenotypes when different simulation methods (parsimonious flux balance analysis, Minimization of Metabolic Adjustment, linear version of MoMA, Regulatory on/off minimization, and Minimization of Metabolites Balance) are used; and finally (iii) investigate the flux distribution predictive power of the constrained-based modeling approach (selected stoichiometric GEM) and compare it with the kinetic modeling approach (two published kinetic models) for E. coli central metabolism, in order to assess their accuracy. Results show that the phenotype predictions were not significantly sensitive to the metabolic models, although the GEM iAF1260 was more accurate in the prediction of central carbon fluxes at low dilution rates. Furthermore, we observed that the choice of the appropriate simulation method of mutant phenotypes depends on the biological question to be addressed. In terms of the two modeling approaches, none outperformed the other for all the tested scenarios. © 2018 American Institute of Chemical Engineers Biotechnol. Prog., 34:1344-1354, 2018.


Assuntos
Escherichia coli/genética , Cinética , Engenharia Metabólica/métodos , Redes e Vias Metabólicas/genética , Mutação/genética
10.
BMC Bioinformatics ; 19(1): 168, 2018 05 04.
Artigo em Inglês | MEDLINE | ID: mdl-29728051

RESUMO

BACKGROUND: Learning accurate models from 'omics data is bringing many challenges due to their inherent high-dimensionality, e.g. the number of gene expression variables, and comparatively lower sample sizes, which leads to ill-posed inverse problems. Furthermore, the presence of outliers, either experimental errors or interesting abnormal clinical cases, may severely hamper a correct classification of patients and the identification of reliable biomarkers for a particular disease. We propose to address this problem through an ensemble classification setting based on distinct feature selection and modeling strategies, including logistic regression with elastic net regularization, Sparse Partial Least Squares - Discriminant Analysis (SPLS-DA) and Sparse Generalized PLS (SGPLS), coupled with an evaluation of the individuals' outlierness based on the Cook's distance. The consensus is achieved with the Rank Product statistics corrected for multiple testing, which gives a final list of sorted observations by their outlierness level. RESULTS: We applied this strategy for the classification of Triple-Negative Breast Cancer (TNBC) RNA-Seq and clinical data from the Cancer Genome Atlas (TCGA). The detected 24 outliers were identified as putative mislabeled samples, corresponding to individuals with discrepant clinical labels for the HER2 receptor, but also individuals with abnormal expression values of ER, PR and HER2, contradictory with the corresponding clinical labels, which may invalidate the initial TNBC label. Moreover, the model consensus approach leads to the selection of a set of genes that may be linked to the disease. These results are robust to a resampling approach, either by selecting a subset of patients or a subset of genes, with a significant overlap of the outlier patients identified. CONCLUSIONS: The proposed ensemble outlier detection approach constitutes a robust procedure to identify abnormal cases and consensus covariates, which may improve biomarker selection for precision medicine applications. The method can also be easily extended to other regression models and datasets.


Assuntos
Neoplasias de Mama Triplo Negativas/genética , Sequenciamento Completo do Genoma/métodos , Feminino , Humanos , Tamanho da Amostra , Neoplasias de Mama Triplo Negativas/patologia
11.
Front Microbiol ; 9: 321, 2018.
Artigo em Inglês | MEDLINE | ID: mdl-29599757

RESUMO

Pyrimidine nucleotides play an important role in the biosynthesis of activated nucleotide sugars (NDP-sugars). NDP-sugars are the precursors of structural polysaccharides in bacteria, including capsule, which is a major virulence factor of the human pathogen S. pneumoniae. In this work, we identified a spontaneous non-reversible mutant of strain D39 that displayed a non-producing capsule phenotype. Whole-genome sequencing analysis of this mutant revealed several non-synonymous single base modifications, including in genes of the de novo synthesis of pyrimidines and in the -10 box of capsule operon promoter (Pcps). By directed mutagenesis we showed that the point mutation in Pcps was solely responsible for the drastic decrease in capsule expression. We also demonstrated that D39 subjected to uracil deprivation shows increased biomass and decreased Pcps activity and capsule amounts. Importantly, Pcps expression is further decreased by mutating the first gene of the de novo synthesis of pyrimidines, carA. In contrast, the absence of uracil from the culture medium showed no effect on the spontaneous mutant strain. Co-cultivation of the wild-type and the mutant strain indicated a competitive advantage of the spontaneous mutant (non-producing capsule) in medium devoid of uracil. We propose a model in that uracil may act as a signal for the production of different capsule amounts in S. pneumoniae.

12.
BioData Min ; 11: 1, 2018.
Artigo em Inglês | MEDLINE | ID: mdl-29456628

RESUMO

Background: Survival analysis is a statistical technique widely used in many fields of science, in particular in the medical area, and which studies the time until an event of interest occurs. Outlier detection in this context has gained great importance due to the fact that the identification of long or short-term survivors may lead to the detection of new prognostic factors. However, the results obtained using different outlier detection methods and residuals are seldom the same and are strongly dependent of the specific Cox proportional hazards model selected. In particular, when the inherent data have a high number of covariates, dimensionality reduction becomes a key challenge, usually addressed through regularized optimization, e.g. using Lasso, Ridge or Elastic Net regression. In the case of transcriptomics studies, this is an ubiquitous problem, since each observation has a very high number of associated covariates (genes). Results: In order to solve this issue, we propose to use the Rank Product test, a non-parametric technique, as a method to identify discrepant observations independently of the selection method and deviance considered. An example based on the The Cancer Genome Atlas (TCGA) ovarian cancer dataset is presented, where the covariates are patients' gene expressions. Three sub-models were considered, and, for each one, different outliers were obtained. Additionally, a resampling strategy was conducted to demonstrate the methods' consistency and robustness. The Rank Product worked as a consensus method to identify observations that can be influential under survival models, thus potential outliers in the high-dimensional space. Conclusions: The proposed technique allows us to combine the different results obtained by each sub-model and find which observations are systematically ranked as putative outliers to be explored further from a clinical point of view.

13.
BMC Syst Biol ; 11(1): 143, 2017 12 21.
Artigo em Inglês | MEDLINE | ID: mdl-29268790

RESUMO

BACKGROUND: We propose OptPipe - a Pipeline for Optimizing Metabolic Engineering Targets, based on a consensus approach. The method generates consensus hypotheses for metabolic engineering applications by combining several optimization solutions obtained from distinct algorithms. The solutions are ranked according to several objectives, such as biomass and target production, by using the rank product tests corrected for multiple comparisons. RESULTS: OptPipe was applied in a genome-scale model of Corynebacterium glutamicum for maximizing malonyl-CoA, which is a valuable precursor for many phenolic compounds. In vivo experimental validation confirmed increased malonyl-CoA level in case of ΔsdhCAB deletion, as predicted in silico. CONCLUSIONS: A method was developed to combine the optimization solutions provided by common knockout prediction procedures and rank the suggested mutants according to the expected growth rate, production and a new adaptability measure. The implementation of the pipeline along with the complete documentation is freely available at https://github.com/AndrasHartmann/OptPipe .


Assuntos
Engenharia Metabólica/métodos , Redes e Vias Metabólicas , Actinomycetales/genética , Algoritmos , Software
14.
Genome Biol ; 18(1): 186, 2017 10 03.
Artigo em Inglês | MEDLINE | ID: mdl-28974235

RESUMO

Alignment-free sequence analyses have been applied to problems ranging from whole-genome phylogeny to the classification of protein families, identification of horizontally transferred genes, and detection of recombined sequences. The strength of these methods makes them particularly useful for next-generation sequencing data processing and analysis. However, many researchers are unclear about how these methods work, how they compare to alignment-based methods, and what their potential is for use for their research. We address these questions and provide a guide to the currently available alignment-free sequence analysis tools.


Assuntos
Análise de Sequência/métodos , Algoritmos , Sequenciamento de Nucleotídeos em Larga Escala , Teoria da Informação , Alinhamento de Sequência , Homologia de Sequência , Software
15.
Sci Rep ; 6: 29182, 2016 07 04.
Artigo em Inglês | MEDLINE | ID: mdl-27373593

RESUMO

Synthetic biology has boomed since the early 2000s when it started being shown that it was possible to efficiently synthetize compounds of interest in a much more rapid and effective way by using other organisms than those naturally producing them. However, to thus engineer a single organism, often a microbe, to optimise one or a collection of metabolic tasks may lead to difficulties when attempting to obtain a production system that is efficient, or to avoid toxic effects for the recruited microorganism. The idea of using instead a microbial consortium has thus started being developed in the last decade. This was motivated by the fact that such consortia may perform more complicated functions than could single populations and be more robust to environmental fluctuations. Success is however not always guaranteed. In particular, establishing which consortium is best for the production of a given compound or set thereof remains a great challenge. This is the problem we address in this paper. We thus introduce an initial model and a method that enable to propose a consortium to synthetically produce compounds that are either exogenous to it, or are endogenous but where interaction among the species in the consortium could improve the production line.


Assuntos
Algoritmos , Consórcios Microbianos , Biologia Sintética/métodos , Acetatos/metabolismo , Bactérias/metabolismo , Biotecnologia , Glicerol/metabolismo , Propilenoglicóis/metabolismo
16.
Math Biosci ; 279: 83-9, 2016 09.
Artigo em Inglês | MEDLINE | ID: mdl-27424949

RESUMO

This article considers a new mathematical model for the description of multiphasic cell growth. A linear hybrid model is proposed and it is shown that the two-parameter logistic model with switching parameters can be represented by a Switched affine AutoRegressive model with eXogenous inputs (SARX). The growth phases are modeled as continuous processes, while the switches between the phases are considered to be discrete events triggering a change in growth parameters. This framework provides an easily interpretable model, because the intrinsic behavior is the same along all the phases but with a different parameterization. Another advantage of the hybrid model is that it offers a simpler alternative to recent more complex nonlinear models. The growth phases and parameters from datasets of different microorganisms exhibiting multiphasic growth behavior such as Lactococcus lactis, Streptococcus pneumoniae, and Saccharomyces cerevisiae, were inferred. The segments and parameters obtained from the growth data are close to the ones determined by the experts. The fact that the model could explain the data from three different microorganisms and experiments demonstrates the strength of this modeling approach for multiphasic growth, and presumably other processes consisting of multiple phases.


Assuntos
Fenômenos Fisiológicos Bacterianos , Ciclo Celular , Modelos Lineares
17.
PLoS One ; 11(3): e0150369, 2016.
Artigo em Inglês | MEDLINE | ID: mdl-26934190

RESUMO

The article focus is the improvement of machine learning models capable of predicting protein expression levels based on their codon encoding. Support vector regression (SVR) and partial least squares (PLS) were used to create the models. SVR yields predictions that surpass those of PLS. It is shown that it is possible to improve the models predictive ability by using two more input features, codon identification number and codon count, besides the already used codon bias and minimum free energy. In addition, applying ensemble averaging to the SVR or PLS models also improves the results even further. The present work motivates the test of different ensembles and features with the aim of improving the prediction models whose correlation coefficients are still far from perfect. These results are relevant for the optimization of codon usage and enhancement of protein expression levels in synthetic biology problems.


Assuntos
Códon/genética , Proteínas de Escherichia coli/genética , Escherichia coli/genética , Análise dos Mínimos Quadrados , Modelos Genéticos , Máquina de Vetores de Suporte , Sequência de Bases , Expressão Gênica , Proteômica
18.
J Biotechnol ; 219: 126-41, 2016 Feb 10.
Artigo em Inglês | MEDLINE | ID: mdl-26724578

RESUMO

Kinetic models of cellular metabolism are important tools for the rational design of metabolic engineering strategies and to explain properties of complex biological systems. The recent developments in high-throughput experimental data are leading to new computational approaches for building kinetic models of metabolism. Herein, we briefly survey the available databases, standards and software tools that can be applied for kinetic models of metabolism. In addition, we give an overview about recently developed ordinary differential equations (ODE)-based kinetic models of metabolism and some of the main applications of such models are illustrated in guiding metabolic engineering design. Finally, we review the kinetic modeling approaches of large-scale networks that are emerging, discussing their main advantages, challenges and limitations.


Assuntos
Modelos Biológicos , Biologia de Sistemas/métodos , Bactérias/metabolismo , Fungos/metabolismo , Cinética , Engenharia Metabólica , Software
19.
BMC Bioinformatics ; 17(Suppl 16): 449, 2016 Dec 13.
Artigo em Inglês | MEDLINE | ID: mdl-28105908

RESUMO

BACKGROUND: Modeling survival oncological data has become a major challenge as the increase in the amount of molecular information nowadays available means that the number of features greatly exceeds the number of observations. One possible solution to cope with this dimensionality problem is the use of additional constraints in the cost function optimization. LASSO and other sparsity methods have thus already been successfully applied with such idea. Although this leads to more interpretable models, these methods still do not fully profit from the relations between the features, specially when these can be represented through graphs. We propose DEGREECOX, a method that applies network-based regularizers to infer Cox proportional hazard models, when the features are genes and the outcome is patient survival. In particular, we propose to use network centrality measures to constrain the model in terms of significant genes. RESULTS: We applied DEGREECOX to three datasets of ovarian cancer carcinoma and tested several centrality measures such as weighted degree, betweenness and closeness centrality. The a priori network information was retrieved from Gene Co-Expression Networks and Gene Functional Maps. When compared with RIDGE and LASSO, DEGREECOX shows an improvement in the classification of high and low risk patients in a par with NET-COX. The use of network information is especially relevant with datasets that are not easily separated. In terms of RMSE and C-index, DEGREECOX gives results that are similar to those of the best performing methods, in a few cases slightly better. CONCLUSIONS: Network-based regularization seems a promising framework to deal with the dimensionality problem. The centrality metrics proposed can be easily expanded to accommodate other topological properties of different biological networks.


Assuntos
Algoritmos , Regulação Neoplásica da Expressão Gênica , Redes Reguladoras de Genes , Neoplasias Ovarianas/genética , Modelos de Riscos Proporcionais , Feminino , Humanos , Modelos Genéticos
20.
J Theor Biol ; 391: 1-12, 2016 Feb 21.
Artigo em Inglês | MEDLINE | ID: mdl-26657065

RESUMO

Bone is a common site for the development of metastasis, as its microenvironment provides the necessary conditions for the growth and proliferation of cancer cells. Several mathematical models to describe the bone remodeling process and how osteoclasts and osteoblasts coupled action ensures bone homeostasis have been proposed and further extended to include the effect of cancer cells. The model proposed here includes the influence of the parathyroid hormone (PTH) as capable of triggering and regulating the bone remodeling cycle. It also considers the secretion of PTH-related protein (PTHrP) by cancer cells, which stimulates the production of receptor activator of nuclear factor kappa-B ligand (RANKL) by osteoblasts that activates osteoclasts, increasing bone resorption and the subsequent release of growth factors entrapped in the bone matrix, which induce tumor growth, giving rise to a self-perpetuating cycle known as the vicious cycle of bone metastases. The model additionally describes how the presence of metastases contributes to the decoupling between bone resorption and formation. Moreover, the effects of anti-cancer and anti-resorptive treatments, through chemotherapy and the administration of bisphosphonates or denosumab, are also included, along with their corresponding pharmacokinetics (PK) and pharmacodynamics (PD). The simulated models, available at http://sels.tecnico.ulisboa.pt/software/, are able to describe bone remodeling cycles, the growth of bone metastases and how treatment can effectively reduce tumor burden on bone and prevent loss of bone strength.


Assuntos
Neoplasias Ósseas , Denosumab/uso terapêutico , Difosfonatos/uso terapêutico , Modelos Biológicos , Hormônio Paratireóideo/metabolismo , Microambiente Tumoral , Animais , Neoplasias Ósseas/tratamento farmacológico , Neoplasias Ósseas/metabolismo , Neoplasias Ósseas/patologia , Neoplasias Ósseas/secundário , Humanos , Metástase Neoplásica , Osteoblastos/metabolismo , Osteoblastos/patologia , Osteoclastos/metabolismo , Osteoclastos/patologia
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