Prof Andrea Townsend-Nicholson, University College London
Panelists
Dr Jeremy Yates, University College London
Dr Anneke Seller, Health Education England
Dr Nikolas Maniatis, University College London
Symposium Chair
Andrea Townsend-Nicholson, Professor of Biochemistry & Molecular Biology
Symposium Description
The ability to use computational methodologies to make explicit predictions of the behaviour of biomedical systems that are rapid, precise and accurate will make huge advances in personalised and precision medicine, ultimately leading to the ability to inform clinical decision-making on the timescale needed for patient care. To achieve this, future generations of scientists and medical practitioners need to be trained in the basic medical and clinical contexts of high-performance computing simulation.
Training of existing researchers in academia, industry and clinical practice, when complemented by education of medical, science and engineering students affords a means of providing the computational biomedicine expertise needed to realise this goal. In this symposium, we shall explore ways in which training and education strategies can be developed and delivered to a very heterogeneous set of end users at varying career stages, how these educational templates can be scaled and delivered to a large number of end users and how communities of practice for the next generation of computational biomedicine practitioners can be established.
In my role as UCL lead for CompBioMed, a H2020 Centre of Excellence in Computational Biomedicine (compbiomed.eu), and as Head of Teaching for Molecular Biosciences at UCL, I have integrated research and teaching to lead the development of HPC-based education targeting medical students and undergraduate students studying biosciences in a way that has explicitly designed to be integrated into their existing university programmes as credit bearing courses. This innovation has not been replicated in any other university in the world. One version of the taught course has been designed for medical students in Years 1 and 2 of study (SSC334) and one of the unique features of the course is the integration of experimental and computational aspects, with students obtaining and processing biological samples, using state of the art Next Generation Sequencing and then interrogating the DNA sequences computationally using code that was ported to high performance computing (HPC) facilities of CompBioMed’s HPC Facility core partners (EPCC (UK), SURFsara (Netherlands) and the Barcelona Supercomputing Centre (Spain)). Another version of the taught course (BIOC0023) replaces the final year research project course for undergraduate biomedical science students, providing them with the opportunity to design and complete an entire research project from developing experimental hypotheses to investigating these in a way that involves the integration of experimental and computational methodologies. In the past 18 months, these UCL courses have successfully run with a total of ~250 students participating (60 medical students and 195 biomedical science students). Full Abstract
The realisation that biological processes can be explained in terms of the interactions between a limited set of fundamental chemical building blocks has driven progress in biomedical science for more than a century, and has resulted in the molecular biology revolution. The key interdisciplinary boundary during this period was the frontier between medicine and chemistry. Today, however, a new set of challenges is changing the frontier of biomedicine. Technological advances in every area of biology have resulted in an exponential increase in the rate of data acquisition, and in its complexity. There is a growing consensus that the rate at which we are acquiring new hyper-dimensional biological data now outstrips our ability to analyse, integrate, interpret and eventually exploit it to drive progress in medicine and improve health. In order to tackle these challenges, biomedical scientists need new sets of skills. Specifically, mathematics offers a framework with the potential to simplify the increasingly complex data which is being produced, and provide fundamental rules which capture the behaviour of physiological systems in health and disease, and allow us to predict how they may respond to different types of therapeutic intervention. In parallel, artificial intelligence provides a means to implement this mathematics, using the increasingly sophisticated algorithms of machine learning. Full Abstract
13:40
Othmane Bouhali
Promoting a Research-Based Education through Undergraduate Research Experience for Students
Involving undergraduate students in research has proven to be an essential experience that enhances the learning outcome of students [1,2]. When exposed to research world at an early stage of their career, they acquire new skills that also guide them in tailoring their experience and choosing their future career. The High Energy and Medical Physics Group at Texas A&M University at Qatar (TAMUQ) has been supporting and engaging undergraduate students in different research projects in the areas of High Energy Physics and Medical Physics for the past six years. It attracted students from all four majors offered at TAMUQ, Electrical, Mechanical, Chemical and Petroleum Engineering shortly after the launching of its research activities. Many projects conducted within the group were awarded by the Qatar National Research Fund which is a governmental funding body that provides funding to highly competitive projects that address national priorities and contribute to capacity building [3]. Students were trained to use a high performance computing facility, different programming languages, software and Monte Carlo based platforms for their simulation. As for the outcomes, they participated and presented at international conferences, many of them attended CERN summer internship program. Some of the published journals in international peer-reviewed scientific journals.
In this paper, we present some of the projects that our students completed, the different tools that were used, as well as the research outcomes. Then, we will discuss the impact of this experience on their learning and undergraduate education as well as their career path, especially their postgraduate studies. Full Abstract
In this talk, I will describe an emerging initiative at Argonne National Laboratory to advance the concept of Artificial Intelligence (AI) aimed at addressing challenge problems in science. We call this initiative “AI for Science”. The basic concept is threefold: (1) to identify those scientific problems where existing AI and machine learning methods can have an immediate impact (and organize teams and efforts to realize that impact); (2) identify areas of where new AI methods are needed to meet the unique needs of science research (frame the problems, develop test cases, and outline work needed to make progress); and (3) to develop the means to automate scientific experiments, observations, and data generation to accelerate the overall scientific enterprise. Science offers plenty of hard problems to motivate and drive AI research, from complex multimodal data analysis, to integration of symbolic and data intensive methods, to coupling large-scale simulation and machine learning to drive improved training to control and accelerate simulations. A major sub-theme is the idea of working toward the automation of scientific discovery through integration of machine learning (active learning and reinforcement learning) with simulation and automated high-throughput experimental laboratories. I will provide some examples of projects underway and lay out a set of long-term driver problems. Full Abstract
14:15
Mariana Pereira da Costa
Integrating Computational Biology and Soil Metagenomics: an Undergraduate study
