Hello, and welcome to my online portfolio! Here's a link to my Linkedin and Scholar Profile. I am a data science group leader at the International Centre for Cancer Vaccine Science, working between the University of Gdansk and the University of Edinburgh and focused on the development of computational tools in the immunotherapy space.
My interest is in how a patient's own immune-system can be leveraged in the treatment of disease.
Our ability to mount protective immune responses relies on a variety of immune receptors that recognize ongoing infections and act to trigger the immune response. Innate immunity encompasses several non-specific protective mechanisms against infection. Key among these are macrophages and neutrophils that detect and attack other cells carrying pathogen-associated molecular patterns, small proteins that signal pathogen invasion called cytokines and chemokines and short peptides that directly attach to and restrict microbial pathogens.
The innate immune-response works in conjunction with a highly specialized and adaptive immune response, that actively recognizes foreign entities for a hand-tailored immune-response. The adaptive immune system comprises specialized cells called B and T-cells that recognize and eliminate specific pathogens and also use cytokine/chemokine signaling to recruit additional immune cells. A key feature of adaptive immunity is immune-memory, where specialized cells can store memory of past pathogenic invasions leveraged for accelerated future response. The complement system, along with natural killer cells and dendritic cells, straddles both innate and adaptive immunity.
Regardless of the disease, the immune-system is implicated and a long-standing medical goal has been to develop a toolkit to modulate the immune response to better treat patients. Vaccination has been a long-standing and important aspect in the treatment of pathogen-caused diseases, and an emerging list of added immunotherapies have been developed to modulate the immune response in response to pathologies that involve immune-dysfunction like auto-immunity and cancer. Recent successes have been the development of drugs that block immune checkpoints, which are often leveraged in cancer to disguise the immune-system. Hundreds of ongoing clinical trials have tested, and are testing, the safety and efficacy of these immunotherapies in combination with other therapies and even vaccination strategies.
Building and benchmarking pipelines for personalized and generalized vaccines leveraging the T-Cell response
Multi-omic integration to better understand cancer
The cross-species in-silico design of new antibody-therapeutics
Building and benchmarking pipelines to facilitate the high-throughput analysis of sequencing and mass-spectrometry data.
Application of ML/AI towards prediction of immunotherapy efficacy and neoantigen prioritization
Identifying emerging technologies in genomics and proteomics and supporting their initial application to cancer vaccine science.
Javier's interest in immunotherapy is driven by method development leveraging multi-omic interrogation of mass-spectrometry and high-throughput sequencing data as well as structural bioinformatics. From this technological and computational stand point he chooses to work in projects related to immunology and diseases spanning cancer, emerging diseases and auto-immune disorders. Since September 2018, Javier has been group leader of the immuno-informatics stream at ICCVS. He has a PhD in Medical Biophysics (University of Toronto), specializing in computational proteomics, a Masters in Biochemistry specializing in evolutionary bioinformatics (Dalhousie University) and a double major in Biochemistry and Computer Science (University of Victoria).
The bioinformatics team at ICCVS has broad expertise spanning machine-learning and Artificial intelligence, software engineering, data scieence, statistics, and bioinformatics expertise across the central dogma. Everyone has expertise in data-science and software engineering best-practices. This is at the heart of what we do. But to develop a good vaccine and immunotherapy discovery platform, we cover a bredth of computational and biological expertise, described below. We also have a focus on using integrative biology to study immunity in general.
At the intersection of metabolomics, proteomics and genomics and transcriptomics, integrative multi-omics represents one of the last major challenges in bioinformatics. There is an abundance of fresh questions in this field, all relating to how technological improvements in genomics and transcriptomics can improve our understanding of the expressed human proteome and metabolome. The key challenge in the field is the need to deeply understand two distinct technologies, high-throughput sequencing technologies and mass-spectrometry. The way these instruments work and the type of data they generate is very different. Particularly, diverse expertise is needed to process the data from it's raw form to expression levels, or for the detection of aberrations. Keeping infront of the latest innovtions and applications of these two technologies is hard work, requiring a diverse team of experts in different fields. Software development, data wrangling and machine learning play key roles in the bioinformatics discovery process. From Developing new pipelines and adopting existing pipelines, a strong background in datascience is essential.
Over two decades of practical design and development of advanced AI tools. Most of them went into he.real production mostly in the healthcare and biotech domain. Among the interesting projects I've had the opportunity to work with are: (1) image processing software including complete Optical Character Recognition module, (2) A medical insurance claims recognition system, (3) A diagnosis Related Group prediction toolkit, (4) mortality and hospital readmission risk prediction, (5) predicting ideal hospital discharge times for patients (6) optimal treatment trajectory prediction (7) medication adherence prediction (8) automated identification and classification of protein patterns that is used in the process of fast building of protein structures’ models. I now help guide AI and ML applications in immunotherapy, with a focus on predicting immunogenicity for the development of T-Cell stimulating vaccines to fight cancer and emerging disease.
I finished Białystok University of Technology with a master's degree in software engineering. During my academic career I developed a few scientific threads connected with Orienteering Problems and extensions therein, which were developed in a few publications. Since 2017 I have develop my skills as a Python and Data Science developer including university and industrial experience. My main interests in the area of software development, machine learning, web development and data engineering. I have been and am now involved in many medical and biotechnological problems connected with web applications development, especially the whole stuff close to the backend side of development and some of frontend side, creating and improving existing data processing workflows, creating predicting models, preparing efficient and suitable working environemnts, data encoding and embedding, creating tools improving data science work and various data visualizations. I'm always looking for opportunities to expand my knowledge, learn best practices and improve my skills to become more valuable and fluent in my area of interests. Currently i'm working as a Software Engineer and AI specialist interacting with scientists at ICCVS supporting a variety of projects with an immunology and immunotherapy focus.
dr. inz Umesh Kalathiya is currently a postdoc at ICCVS. He obtained his PhD degree in 2018 in chemical sciences from the Gdansk University of Technology, and graduated in bioinformatics from Wroclaw University of Technology. dr. Kalathiya received training in bioinformatics field from Indian Institute of Technology (IIT), New Delhi. His PhD study was funded by Polish National Agency for Academic Exchange (NAWA), and he was awarded as the best PhD student in the year 2014 and 2015 from the rector of Gdansk University of Technology. In 2015, he received 'InterPhD prize' for scientific achievements from Gdańsk University of Technology. dr. Kalathiya has experience in characterizing of proteins/peptides from mass spectrometry datasets, and has a keen interest in immunopeptidomics research. He believes that the network between the NMD components or PTCs (protein-RNA / protein or RNA-RNA) could be explored using the structural informatics, with the use of mass spectrometry (MS)-based cross-linking (XL-MS) technique. He has routine use of different programming languages such as Python, R, Tcl, C++, etc., and has expertise applying different molecular dynamics algorithms to study biomolecular systems. Applying proteomics and theoretical methods (molecular modelling/docking/dynamics, quantum calculations, protein-protein/drug interactions in silico virtual screening) to understand biomolecular systems is his area of expertise. Moreover, he is always active to take opportunities in learning new skills/methods in the field of bioinformatics. His scientific work includes publication of 23 research papers and appearance in several conferences symposium/workshop. Recently applying informatics techniques he identified novel cavity in SARS-CoV-2 spike protein: J Clin Med. 2020;9(5):1473.
dr. inz Monikaben Padariya, is currently working as a post doc researcher at ICCVS, University of Gdansk. dr. Padariya has experience in applying different bioinformatics techniques to understand biological systems, as well as she acquires interest in mining different large datasets. She has a emerging interests in genomics and immunopeptidomics research directions, relating to this field, her current research work is focused on understanding the peptide loading complex and the effects of cancer mutations as well as polymorphism on each components of PLC. Having knowledge of programming languages such as C, C++, Python, HTML, XML, PHP, MySQL, and Wamp Server, describes her expertise in the informatics field. Considering her education qualification, dr. Padariya obtained her PhD (dr inż.) degree from Faculty of Chemistry, Gdańsk University of Technology and Master in engineering (mgr inż.) from Wrocław University of Science and Technology. She received her training from Indian Institute of Technology (IIT), New Delhi. During her PhD she was awarded for three consecutive years the best PhD students, as well as received pro-quality doctoral stipends from Gdańsk University of Technology. In addition, her entire PhD study was funded by Polish National Agency for Academic Exchange (NAWA), Poland. She is a author of 22 scientific publications, and in the year 2015 and 2017, she has been awarded for the "Young scientists with best creative work published" by Polish Academy of Sciences (PAN). Here recent work published includes (below figure); Padariya M et al. Recognition Dynamics of Cancer Mutations on the ERp57-Tapasin Interface. Cancers (Basel). 2020;12(3):737.
Weijia is a Master student of Biochemistry at the University of Edinburgh and supervised by Javier for the MSc project and dissertation. She obtained the bachelor’s degree at China Pharmaceutical University and completed the bachelor dissertation “Investigation of anti-cancer drug susceptibility to cancer cell lines in the presence of PPARδ modulators” at Seoul National University. With the education background of pharmacy and biochemistry, she also has the keen interest in bioinformatics. Her MSc project is focused on the homology modeling of UPF2 protein structure and the protein-protein interactions related to UPF2 by using bioinformatics tools. Nonsense-mediated mRNA decay (NMD) pathway is an RNA surveillance pathway that can eliminate translation errors during gene expression because of the selective degradation of mRNAs that contain premature termination codons (PTCs). With the presence of PTCs in the mRNA, the NMD pathway degrades the mRNA and prevents the production of proteins with negative function. In diseased conditions like cancer, blocking NMD may boost the synthesis of abnormal proteins to induce natural immune response against the tumour. For this reason I have been actively researching the NMD-complex, it's interacting partners and how to disrupt this process.
Marcos carried out a degree in Biochemistry at the University of Castilla-La Mancha (Spain) and decided to specialize in the bioinformatics area by performing the Master studies in Copenhagen (Denmark). He started to work on benchmarking where he designed RNA sequencing tools, in collaboration with the Danish Cancer Society (DCRC). Currently he is performing his PhD studies as part of the Bioinformatics team within the ICCVS. Currently he is working on integrated proteogenomics, where he performs the analysis of deep sequencing data combined with mass spectrometry in various types of cancer tissues. This aim of his work is to report valuable information of cancer tissue, like the number and type of mutations contained, the state and abundance of transcript isoforms, the identification of novel proteoforms and the correlation between RNA abundances and protein abundances.
At the International Centre for Cancer Vaccine research, I am conducting proteogenomic screens in oral adenocarcinoma, sarcoma, ovarian cancer, Renal Cell Carcinoma. It is my goal to understand how the process of antigen presentation is perturbed in cancer and to find new ways to prioritize neoantigen discovery.
PhD candidate (Foundations of computer science, databases, software and system modelling) in University of Edinburgh. He, with math Olympiad experience before, is graduated from engineering bachelor degree with multiple wet-lab experience in structural biology and cancer biology. During the synthetic biology and biotech master degree, he is well-equipped genomics analysing. Tongjie now is using his multiple education backgrounds to bridge the gaps between clinicians, biologists and data scientists. He is applying various cutting-edge machine learning strategies, in context of prediction of patients’ survival outcomes, meanwhile, improving algorithms accuracy. Also, he is interested in utilising data augmentation in the health digital data and neoantigen discovery.
Davies is a master student at the University of Edinburgh, with a focus on oncogene research, especially in tumour suppressor gene TP53 and TP53 pathway related genes. The project is co-supervised by Dr. Javier Alfaro. With 5 years’ experience in biotechnology, Davies combines techniques in the molecular biology and pharmaceutical data science flexibly to explore how the synonymous mutations in TP53 gene affects the transcription and translation process. He currently gets involved in analysing the mutation data from COSMIC (Catalogue of Somatic Mutations in Cancer) and the effects of synonymous mutation hotspots on the pre-mRNA structure by using the computational tools. Thus, the following research is about two main direction. Firstly, Since the mutation would not change the codon use but mRNA secondary structure, he would discuss whether the synonymous SNVs affect protein folding. On the other hand, he would try to find the RNA structural motifs for the binding site of thymidylate synthase protein that have a close connection with TP53 coding region on the TP53 mRNA. In this way, Davies would have a broad research on the TP53 synonymous mutation effects. The project would ultimately catalogue the different synonymous mutation sites according to its functional impacts and sort by influence, which would provide the support for cancer immunotherapy.
Georges Bedran is a bioinformatician recently graduated from the university of Rouen, France. His previous work was focused on developing a bioinformatic workflow to asses biological mutations considering the flow of information from the genome to the proteome. He recently joined the ICCVS as a PhD student to work on developing computational strategies for neo-epitope discovery.
In 2015 Kamila obtained her bachelor’s degree at Poznan University of Medical Science. The thesis covered the subject of novel therapies targeting cancer stem cell. She continued studies of cancer biology through her Master’s research project, that was focusing on epigenetics of glioblastoma. In 2017, Kamila graduated with a Master’s degree in Medical Biotechnology. From September 2018 she is working on her PhD at the Precision Medicine Doctoral Training Programme at the University of Edinburgh. In her research Kamila is focusing on developing pipelines to identify neoantigens and determinating their sources in human cancers from matched genomic, transcriptomic and proteomic data.
Mikolaj is a PhD student using his understanding of computational approaches, degree in medical biotechnology and experience in biopharmaceutical research and development to explore the functions of immune checkpoints and target them with novel therapeutics. The project he leads merges bioinformatic and laboratory methods to re-engineer antibody sequences for safe therapeutic use in different species. "Caninizing" mAbs of murine origin supports the use of canine model as a superior alternative to murine research with the premise of safer, more efficient and affordable therapeutics for both human and veterinary cancer patients. The team is aiming at an automated algorithm for antibody design based on a proprietary sequence library. Mikolaj is also working on pan-cancer RNAseq analysis, antibody epitope mapping and phage display methods. His other interests include entrepreneurship and science commercialisation.
We have re-analyzed a large number of publicly available immunopeptidomics datasets. In doing so, we have developed a series of projects in neoantigen prediction based on a large re-analysis of publicly available immunopeptidomics data. These projects have focused on understanding the overall motif-space of antigen presentation, as well as
Ashwin's undergraduate studies in Biotechnology were completed at Loyola College, Chennai, India, followed by post-graduation in Microbiology and Immunology at the University of Nottingham, UK and Bioinformatics at the Institute of Bioinformatics and Applied Biotechnology (IBAB) at Bangalore, India. I have over 2 years of research experience in both experimental and computational biology and 3 years of industrial work experience. I was most recently associated with Glaxosmithkline Asia Pvt Ltd as a clinical data programmer, where I worked extensively on data analysis of clinical trials related to a novel drug candidate against Multiple Myeloma. I have presented talks at national level conferences, most notably the Conference of Statistics and Programming in Clinical Research (CONSPIC). My research has been published in PLOS One. I have also received several awards, including the prestigious Developing Solutions Scholarship from the University of Nottingham and the first prize in analytics at CONSPIC 2018, held at Bangalore, India.
Current research: I am currently involved in the development of a systems biology approach towards modelling metabolic flux, structure and immunogenicity of the Lipid A molecule, using Escherichia coli as a model organism. The model is based on the Raetz pathway through which Gram- negative bacteria synthesise Lipid A. Firstly, the optimal Lipid A concentration is determined through a constraint-based optimization model. Secondly, the most probable structure of Lipid A at different temperatures is determined by a probabilistic model. Thirdly, an algorithm will be developed to predict the immunogenicity of the predicted structure.
Ren Bo is an MSc. Biochemistry student at the University of Edinburgh Co-supervised by Javier. Ren's project revolves around the structural underpinnings of metabolite antigen presentation in MR-1. His project focuses on understanding how MR-1 could be leveraged for cancer immunotherapy by trying to understand which cancer metabolites in the aberrant metabolome could be presented to the immune-system. He has a strong background in structural biology and computational metabolomics, as well as a certain understanding of drug discovery. Through the structural analysis of MR1 and its antigens, he successfully screened out some potential MR1 antigens in cancer cells.
To support the next generation of therapeutics, a data driven approach is essential. The computational biology group at ICCVS employs Software Engineers, Data Engineers, Mathematicians and Statisticians to play critical roles in our research and development programs. We employ undergraduates, Masters students and PhD students with both biology and computational expertise. Regardless of your background, we provide an environment to help you develop in pharmaceutical data science with the ultimate goal of curing cancer. Students joining ICCVS will have the opportunity to complete joint PhD degrees between the University of Edinburgh (Scotland) and the University of Gdansk (Poland). Students joining ICCVS Bioinformatics will be co-supervised by Dr. Ted Hupp (University of Edinburgh) and Dr. Javier Alfaro (Gdansk) and can cover any of the projects suggested above. Students with their own project ideas are encouraged to bring up their ideas. Students will have the opportunity to spend a portion of their PhD degrees abroad to network and facilitate their research projects.
We are continuously seeking applications for the following roles:
I encourage all interested candidates to contact me at the contact provided below.
E-mail: Javier.Alfaro AT proteogenomics.ca