Modeling microbes: New methods for integrated metabolic and regulatory network reconstruction

PhD Thesis in Bioengineering

Metabolic engineering with multi-objective optimization of kinetic models

Kinetic models have a great potential for metabolic engineering applications. They can be used for testing which genetic and regulatory modifications can increase the production of metabolites of interest, while simultaneously monitoring other key functions of the host organism. This work presents a methodology for increasing productivity in biotechnological processes exploiting dynamic models. It uses multi-objective dynamic optimization to identify the combination of targets (enzymatic modifications) and the degree of up- or down-regulation that must be performed in order to optimize a set of pre-defined performance metrics subject to process constraints. The capabilities of the approach are demonstrated on a realistic and computationally challenging application: a large-scale metabolic model of Chinese Hamster Ovary cells (CHO), which are used for antibody production in a fed-batch process. The proposed methodology manages to provide a sustained and robust growth in CHO cells, increasing productivity while simultaneously increasing biomass production, product titer, and keeping the concentrations of lactate and ammonia at low values. The approach presented here can be used for optimizing metabolic models by finding the best combination of targets and their optimal level of up/down-regulation. Furthermore, it can accommodate additional trade-offs and constraints with great flexibility.

Tapping the wealth of microbial data in high-throughput metabolic model reconstruction

Genome-scale metabolic models are valuable tools in the metabolic engineering process, based on the ability of these models to integrate diverse sources of data to produce global predictions of organism behavior. At the most basic level, these models require only a genome sequence to construct, and once built, they may be used to predict essential genes, culture conditions, pathway utilization, and the modifications required to enhance a desired organism behavior. In this chapter, we address two key challenges associated with the reconstruction of metabolic models: (a) leveraging existing knowledge of microbiology, biochemistry, and available omics data to produce the best possible model; and (b) applying available tools and data to automate the reconstruction process. We consider these challenges as we progress through the model reconstruction process, beginning with genome assembly, and culminating in the integration of constraints to capture the impact of transcriptional regulation. We divide the reconstruction process into ten distinct steps: (1) genome assembly from sequenced reads; (2) automated structural and functional annotation; (3) phylogenetic tree-based curation of genome annotations; (4) assembly and standardization of biochemistry database; (5) genome-scale metabolic reconstruction; (6) generation of core metabolic model; (7) generation of biomass composition reaction; (8) completion of draft metabolic model; (9) curation of metabolic model; and (10) integration of regulatory constraints. Each of these ten steps is documented in detail.

Equity offerings, stock price crash risk, and the impact of securities regulation: international evidence

We examine whether earnings manipulation around seasoned equity offerings (SEOs) is associated with an increase in the likelihood of a stock price crash post-issue and test whether the enactment of securities regulations attenuate the relation between SEOs and crash risk. Empirical evidence documents that managerial tendency to conceal bad news increases the likelihood of a stock price crash (Jin and Myers, 2006; Hutton, Marcus, and Tehranian, 2009). We test this hypothesis using a sample of firms from 29 EU countries that enacted the Market Abuse Directive (MAD). Consistent with our hypothesis, we find that equity issuers that engage in earnings management experience a significant increase in crash risk post-SEO relative to control groups of non-issuers; this effect is stronger for equity issuers with poor information environments. In addition, our findings show a significant decline in crash risk post-issue after the enactment of MAD that is stronger for firms that actively manage earnings. This decline in post-issue crash risk is more effective in countries with high ex-ante institutional quality and enforcement. These results suggest that the implementation of MAD helps to mitigate managers’ ability to manipulate earnings around SEOs.

Exploring chemical conversions in metabolic networks by tracing atoms transitions

Dissertação de mestrado em Bioinformática

Hair coloration by gene regulation: fact or fiction?

The unravelling of hair pigmentation genetics and robust delivery systems to the hair follicle (HF) will allow the development of a new class of colouring products. The challenge will be changing hair colour from inside out by safely regulating the activity of target genes through the specific delivery of synthetic/natural compounds, proteins, genes, or small RNAs.

Empathy and heart rate variability: a need and a mechanism of emotion regulation

Dissertação de mestrado integrado em Psicologia

Pharmaceutical regulation and pseudo - generics

Dissertação de mestrado em Economia Industrial e da Empresa

Hemispheric dorsolateral prefrontal cortex lateralization in the regulation of empathy for pain

The dorsolateral prefrontal cortex (DLPFC) is involved in the cognitive appraisal and modulation of the pain experience. In this sham-controlled study, with healthy volunteers, we used bi-hemispheric transcranial direct current stimulation (tDCS) over the DLPFC to assess emotional reactions elicited by pain observation. Left-cathodal/right-anodal tDCS decreased valence and arousal evaluations compared to other tDCS conditions. Compared to sham condition, both left-cathodal/right-anodal and left-anodal/right-cathodal tDCS decreased hostility, sadness and self-pain perception. These decreased sensations after both active tDCS suggest a common role for left and right DLPFC in personal distress modulation. However, the differences in arousal and valence evaluations point to distinct roles of lateralized DLPFC in cognitive empathy, probably through distinct emotion regulation mechanisms.

Challenges and confusion in media regulation: a four-country comparison

[Excerto] ln this chapter we discuss recent developments and challenges in European media and communication policy, focusing on the period following the 2008 global financial crisis. We are especially interested in the implications of the financial crisis and its political repercussions nationally (austerity measures and cuts to public services, growing anti-politics sentiments and widespread dissatisfaction with free-market capitalism and representative democracy) for media and communication policy, understood here in a broad sense, so as to include ali electronic communications, such as the Internet, mobile communications, social media etc. Our overarching concern is with the implications of developments in media and communication policy for the democratic functions of the media in Europe (...).

Metabolic profiling and classification of propolis samples from Southern Brazil: an NMR-based platform coupled with machine learning

The chemical composition of propolis is affected by environmental factors and harvest season, making it difficult to standardize its extracts for medicinal usage. By detecting a typical chemical profile associated with propolis from a specific production region or season, certain types of propolis may be used to obtain a specific pharmacological activity. In this study, propolis from three agroecological regions (plain, plateau, and highlands) from southern Brazil, collected over the four seasons of 2010, were investigated through a novel NMR-based metabolomics data analysis workflow. Chemometrics and machine learning algorithms (PLS-DA and RF), including methods to estimate variable importance in classification, were used in this study. The machine learning and feature selection methods permitted construction of models for propolis sample classification with high accuracy (>75%, reaching 90% in the best case), better discriminating samples regarding their collection seasons comparatively to the harvest regions. PLS-DA and RF allowed the identification of biomarkers for sample discrimination, expanding the set of discriminating features and adding relevant information for the identification of the class-determining metabolites. The NMR-based metabolomics analytical platform, coupled to bioinformatic tools, allowed characterization and classification of Brazilian propolis samples regarding the metabolite signature of important compounds, i.e., chemical fingerprint, harvest seasons, and production regions.

Molecular regulation of flowering induction in Quercus suber

Dissertação de mestrado em Plant Molecular Biology, Biotechnology and Bioentrepeneurship

The influence of kaolin application on key metabolic pathways associated with grape berry quality: a molecular and biochemical analysis

Dissertação de mestrado em Biologia Molecular, Biotecnologia e Bioempreendedorismo em Plantas

Regulation of human mesenchymal stem cell osteogenesis by specific surface density of fibronectin: a gradient study

 The success of synthetic bone implants requires good interface between the material and the host tissue. To study the biological relevance of fi bronectin (FN) density on the osteogenic commitment of human bone marrow mesenchymal stem cells (hBMMSCs), human FN was adsorbed in a linear density gradient on the surface of PCL. The evolution of the osteogenic markers alkaline phosphatase and collagen 1 alpha 1 was monitored by immunohistochemistry, and the cytoskeletal organization and the cell-derived FN were assessed. The functional analysis of the gradient revealed that the lower FN-density elicited stronger osteogenic expression and higher cytoskeleton spreading, hallmarks of the stem cell commitment to the osteoblastic lineage. The identifi cation of the optimal FN density regime for the osteogenic commitment of hBM-MSCs presents a simple and versatile strategy to signifi cantly enhance the surface properties of polycaprolactone as a paradigm for other synthetic polymers intended for bone-related applications.