If you've eaten vegan burgers that taste like meat or used synthetic collagen in your beauty routine--both products that are "grown" in the lab--then you've benefited from synthetic biology. It's a field rife with potential, as it allows scientists to design biological systems to specification, such as engineering a microbe to produce a cancer-fighting agent. Yet conventional methods of bioengineering are slow and laborious, with trial and error being the main approach. Now scientists at the Department of Energy's Lawrence Berkeley National Laboratory (Berkeley Lab) have developed a new tool that adapts machine learning algorithms to the needs of synthetic biology to guide development systematically. The innovation means scientists will not have to spend years developing a meticulous understanding of each part of a cell and what it does in order to manipulate it; instead, with a limited set of training data, the algorithms are able to predict how changes in a cell's DNA or biochemistry will affect its behavior, then make recommendations for the next engineering cycle along with probabilistic predictions for attaining the desired goal.
Dr. Sander kindly agreed to give us this interview at the Idorsia headquarters in Basel, Switzerland. Asking the questions from CDD are Neil Chapman and Mariana Vaschetto. By education I am organic chemist. During my seventh year at school we started to have chemistry classes and soon I had made up my mind to study chemistry. Four years later while still at school I had an opportunity to access the local University's Tectronix graphics computers.
Patents are used to grant exclusive property rights to an inventor and prevent their discovery from being copied by others. The main requirements for a patent are that the invention must be novel, non-obvious and be useful or have an industrial application. Patents are a central part of how pharma does business. Pharma products require longer and more complex research and development (R&D) cycles than products in other industries. Consequently, companies invest significant amounts of money into their new products early on in their development.
A collaboration between the Norwich Bioscience Institutes and The Alan Turing Institute will enhance the ways machine learning and artificial intelligence are applied to life science research. With biological research becoming increasingly data rich, the collaboration will help identify new ways to exploit this wealth of information and accelerate advances in understanding. The Norwich Bioscience Institutes – including the Earlham Institute, the John Innes Centre, Quadram Institute and The Sainsbury Laboratory – have teamed up with The Alan Turing Institute in a £600,000 project to kickstart collaborations that will employ machine learning and artificial intelligence. Half of the funding comes from The Alan Turing Institute's'AI for Science and Government' Strategic Priorities Fund award, with the other half coming from a strategic award from the Biotechnology and Biological Sciences Research Council (UKRI-BBSRC).The funding will support up to six year-long research posts who will work together in a cross-institute cohort to expand the application of machine learning and artificial intelligence to several key areas, which may include: As technologies for capturing information – from DNA sequences through to high resolution images – become ever cheaper and more widely available, so do the reams of data associated with that. Making sense of huge datasets can create bottlenecks in research projects and, importantly, discoveries.
I.S.K. is on the scientific advisory boards of Pulse Data and Medaware, both companies involved in predictive analytics. S.S. is a founder of, and holds equity in, Bayesian Health. The results of the study discussed in this publication could affect the value of Bayesian Health. This arrangement has been reviewed and approved by Johns Hopkins University in accordance with its conflict-of-interest policies. S.S. is a member of the scientific advisory board for PatientPing.
As you know, we recently announced the new $1M AAAI Squirrel AI Award for AI for the Benefit of Humanity. The award recognizes positive impacts of AI to protect, enhance, and improve human life in meaningful ways with long-lived effects. I am thrilled to share the news that Regina Barzilay of MIT is the inaugural recipient of the award. Regina is recognized for fundamental advances in AI for healthcare, being a proactive community builder and role model, and demonstrating significant impact in people's lives through cancer diagnosis, drug-resistant microbe antibiotics, and drug discovery. The award will be given at the AAAI conference in February, and the associated prize of $1 million will be provided by the online education company Squirrel AI.
Precision medicine is a medical model, which proposes customization of the healthcare to a subgroup of patients, based on a genetics, lifestyle and environment. This technique allows doctors and researchers to prognosis treatment and prevention strategies for a specific disease which can work on a group of people. It is opposed to a one-size-fits-all approach, in which disease treatment and prevention techniques are advanced for the average individual with much less attention for the variations among individuals. There is an overlap between the terms "precision medication" and "personalized medicine." As per the National Research Council, "personalized medicine" is a traditional word with a meaning close to "precision medication."
When you're a young male, you spend your money on cheap booze, hard drugs, and fast women – and the rest you waste. As you enter old age, you increase your spending on drugs, the type needed to treat the chronic problems you developed from abusing your body so much during your youth. The end result is more than $1.2 trillion spent last year on drugs produced by the pharmaceutical industry. Drugs are big business, but investing in biotech stocks is extremely risky due to the regulatory risks involved, the uncertainty around a drug's efficacy and safety, and the cost of taking a drug candidate to market which a pharmaceutical company incurs whether the drug gets approved or not. From an investor's perspective, it's best to find a "pick and shovel" business model somewhere in the pharma food chain that makes money regardless of whether or not drugs make it past human trials.
As technologies for capturing information – from DNA sequences through to high resolution images – become ever cheaper and more widely available, so do the reams of data associated with that. Making sense of huge datasets can create bottlenecks in research projects and, importantly, discoveries.Machine learning offers a promising route into not only exploring that data, but also helping us to find hidden patterns and new hypotheses that we had never previously considered.The Alan Turing Institute believes that data science and artificial intelligence will change the world, and part of that vision includes their Data Science At Scale research programme. This aligns strongly with the mission of the Earlham Institute, who are contributing to solving global challenges by applying data driven methods, and host the UK National Capability in computational infrastructure."Data "Over the last ten years our ability to generate vast datasets has increased rapidly, and this collaboration will allow us to better use that information for public good. To work alongside The AlanTuring Institute, with all their expertise in machine learning, is fantastic for the future of UK life science research."Professor Jonathan Rowe, Programme Director of Data Science For Science at The Alan Turing Institute, said that "this is a significant new collaboration for the Turing which offers new opportunities for advancing data-driven science.