Projects

Lista de serviços

• Systems Vaccinology Vaccination is one of the greatest triumphs of modern medicine, yet we remain largely ignorant of the mechanisms by which successful vaccines stimulate protective immunity. The structure and function of the immune system is governed by complex networks of interactions between cells and molecular components. Vaccination perturbs these networks, triggering specific pathways to induce cellular and humoral immunity. Systems vaccinology studies have generated vast data sets describing the genes related to vaccination, motivating the use of new approaches to identify patterns within the data.
  Systems Vaccinology
• Network & Precision Medicine The structure and function of biological systems are determined by a complex network of interactions among cell components. Network medicine offers a toolset for us to systematically explore perturbations in biological networks and to understand how they can spread and affect other cellular processes. In this way, we can have mechanistic insights underlying diseases and phenotypes, evaluate gene function in the context of their molecular interactions, and identify molecular relationships among apparently distinct phenotypes. These tools have also enabled the interpretation of heterogeneity among biological samples, identification of drug targets and drug repurposing as well as biomarker discovery. As our ability to profile biological samples increases, these network-based approaches are fundamental for Precision Medicine.
  Network & Precision Medicine
• Digital Epidemiology Epidemiology can be defined as the "study of the distribution and determinants of health-related states or events in specified populations, and the application of this study to the control of health problems". Digital data, such as search queries, social media posts, webpage access logs, mobile phone network data, data generated by sensors, and data collected at call centers, are currently being applied to epidemiology. Our lab has access to large datasets and advanced analytics which enable us to perform Digital Epidemiology.
  Digital Epidemiology
• User-friendly computational Tools Life scientists now have access to an unprecedented amount of experimental data. A single laboratory can measure the levels of all transcripts, proteins, or metabolites of an organism under different perturbations or can sequence the entire genome of hundreds of individuals or specimens. Systems biology aims to study the behavior and interaction of these molecules, using advanced mathematical models. Modern data-intensive genetics is also often dependent on statistical tools for identifying signals through population-level measurements. However, according to Sydney Brenner “we are drowning in a sea of data and starving for knowledge. Today, biology is more about gathering data than hunting down new ideas.” This is partly due to the fact that a substantial number of researchers who are capable of thinking about new insights, are not able to deal with the vast amounts of data generated by modern technologies. A fundamental characteristic for a tool to be adopted widely by life scientists is that it should be user-friendly. Our laboratory has developed several user-friendly tools and databases for those that have no bioinformatics background.
  User-friendly computational Tools
• Systems Immunology of Human Diseases With the technological revolutions that occurred in the past decades, we are now able to access and integrate information about all the components within a biological system (e.g., genes, proteins, cells) and use it to compute and predict that system’s behavior. When applied to immunology, systems biology approaches can help us to understand the mechanisms by which pathogens and vaccines stimulate protective immunity. We have several projects on systems immunology that utilize mathematical approaches and computational methods to provide comprehensive and unbiased dissections of the complex interactions between genes and proteins in human diseases.
  Systems Immunology of Human Diseases
• Knowledge-based networks Thousands of new scientific articles are published every day, which pile up exponentially with the other millions of papers already deposited in the literature. Keeping up with the scientific state-of-the-art has become an overwhelming task for researchers even in their own fields and subfields, let alone in other areas of science. In this scenario, computational methods such as text-mining, machine learning, and cognitive computing are becoming invaluable to make the chore of summarising published scientific literature less daunting. In this project, we utilized cognitive computing text-mining application to extract known relationships between genes and a broad range of diseases from the peer-reviewed literature. We built knowledge networks of genes and diseases and tracked the evolution of these relationships yearly.
  Knowledge-based networks
• Drug repositioning Due to the high cost of developing novel drugs, drug repositioning represents a promising alternative method of treatment. In this project, we use a network medicine approach to identify novel drug candidates for repositioning. Using a powerful machine learning text-mining application, we built knowledge networks containing connections between human diseases and genes or drugs mentioned in the scientific literature published in the past decades. This approach reveals several drugs that target key disease-related genes, which have never been used to treat these disorders to date.
  Drug repositioning
• Science Outreach We are committed to promote public awareness and understanding of our scientific projects and to making informal contributions to science education. Follow the activities of CSBL in Facebook (https://www.facebook.com/csbl.usp), Twitter (@CSBL1), Instagram (@CSBL1), and YouTube. Our findings are also covered by news outlets and media.
  Science Outreach
• Machine Learning Machine learning provides computational systems the ability to automatically learn and improve through experience and by the use of data without being explicitly programmed. Machine learning algorithms build a model based on "training data" to make predictions or decisions in novel datasets. Our projects on machine learning are related to transcriptome analyses, image recognition, and advanced text-mining. Using such approaches we can define gene signatures that predict the outcome of human diseases or to identify pathogens in microscopic images.
  Machine Learning
• Long Noncoding RNAs Long non-coding RNAs (lncRNAs) are a type of RNA, defined as being transcripts with lengths exceeding 200 nucleotides that are not translated into protein. These molecules are crucially involved in multiple biological processes, but their role in vaccine-induced immunity and infectious diseases is still being explored. It is estimated that thousands of long non-coding RNAs (lncRNAs) are transcribed in the genome of several organisms. Yet, only a small fraction of them have been functionally characterized. Studying the expression profile of lncRNAs and how their expression correlates with the expression of protein-coding genes in various tissues and conditions will reveal interesting mechanisms of gene regulation.
  Long Noncoding RNAs
• Microbiology or Immunology Immunology is the study of the immune system and is a very important branch of the medical and biological sciences. The immune system protects us from infection through various lines of defence. If the immune system is not functioning as it should, it can result in disease, such as autoimmunity, allergy and cancer. Microbiology is the study of microscopic organisms, such as bacteria, viruses, archaea, fungi and protozoa. Our laboratory has several projects and publications related to both microbiology or immunology.
  Microbiology or Immunology
• Integrative Biology The study of multifactorial and complex interactions in human diseases has been transformed by the omics revolution. The speed and scale of omics analysis have increased exponentially in the past decades, and it is now easier and faster to generate large amounts of biological data. However, extracting meaningful information from this “sea of data” remains a major challenge. The field of integrative biology utilizes a holistic approach to integrate multilayer biological data. Integrative biology is a promising approach for the study of a wide range of human diseases.
  Integrative Biology
• Life Sciences Life Sciences comprises the branches of science that involve the scientific study of life – such as microorganisms, plants, and animals including human beings. This science is one of the two major branches of natural science, the other being physical science, which is concerned with non-living matter. Biology is the overall natural science that studies life, with the other life sciences as its sub-disciplines. Some of our collaborations are not related to human health and therefore are classified as "Life Sciences" projects. 
  Life Sciences