Posts mit dem Label automation werden angezeigt. Alle Posts anzeigen
Posts mit dem Label automation werden angezeigt. Alle Posts anzeigen

Mittwoch, 7. September 2016

New low-cost opportunities for science

Competition for funding in science is stiff and tends to get stiffer as the number of PhD graduates explodes, together with reduction in government research funding when crises strike, for example. While crowdfunding is a way to overcome this problem, even this alternative is likely to become very competitive. Scientists need to find low-cost alternatives in order to continue making good science. Fortunately, prospects have never been better in this field. Here I examine two new technologies that can help scientists to conduct their research at much lower cost than inside the traditional pathway. They are open-source hardware and scripting.

Open-source hardware

Open-source hardware consists in machines that have their blueprints freely available. Therefore, they can be reproduced by anyone with the means to build them. Nowadays, this has been more accessible than ever. Microcontrollers, like Arduino and Raspberry pi, (which are themselves open-source, by the way) allow people with elementary knowledge on electronics to build devices. At the same time, 3D printers allow low-cost fabrication of customized parts. Joining microcontrollers and customized 3D-printed parts, production costs can be really low. As a consequence, there is a plethora of devices which have their blueprints openly available on the internet, and that can be built at a very low cost. Many of such devices are specifically designed for use in the laboratory, like syringe pumps and spectrophotometers. Others not necessarily, like robotic arms. In any case, they can all be used in laboratory contexts.

The main advantage of using open-source hardware compared to commercial instruments is the cost: open-source hardware can cost less than 10% of similar commercial instruments while performing exactly the same task. Thus, for some cases even crowd funding may not be necessary: a few hundreds of dollars can be enough to fund a research project. It must be understood that the lower cost of open-source devices does not mean that their quality is inferior to their commercial counterparts. Especially in science, devices are vastly overpriced. This is not necessarily greed: the market scale in science is small when compared to that of the general public, and thus manufacturers need to charge high prices or their business collapses. This means that a device with the same technology of a common home printer, like many autosamplers, for example, can cost a hundred times or more than the printer. What happens many times is that some commercial devices can only be used together with others from the same manufacturer by design. In such case, scientists are hostage of the situation and need to purchase the expensive accessory even knowing that more affordable alternatives exist, like those based on open-source hardware. This brings us to the second technology that helps scientists to save money in the laboratory: scripting.

Scripting

Scripting is a kind of programming. Without delving into technical details, scripting can be seen as an easy variety of programming, easy enough to be promptly accessible to people without time to devote to learn complex subjects, which is the case of most laboratory technicians or scientists. The main way by which scripting can help scientists save money in the laboratory is by enabling compatibility between devices from different manufactures, regardless of their originally designed purpose. Scripting allows that because it acts at the software, and not hardware, level. From a very simplistic angle, scripting is a kind of sophisticated scheduling: the tasks performed by a given instrument can be synchronized with those performed by another, provided that both are being controlled by a single computer (or group of connected computers). Scripting power resides in its simplicity: with scripting, it is possible to control mouse clicks and keyboard inputs. Therefore, scripting effectively substitute a person controlling the computer, and repeat her actions over and over.

The problem of lack of compatibility between scientific instruments is an old one. The response of the industry has been the proposal of standardization of scientific instruments, but this has never come true. The main reason for the failure of standardization is that, differently from scripting, it acts mostly at the hardware level by modifying the instruments themselves. Beyond being a complex task, which involves professionals from diverse backgrounds, standardization costs money both for manufacturers and for users. In contrast, scripting is entirely free (AutoIt, for example, is a free scripting language that allows control of mouse clicks and keyboard shortcuts) and easily accessible. In addition to that, standardization necessarily creates a “club”, where insiders have the control of who can enter or not. This is a grave obstacle that hampers the adoption of the amazing devices developed in the explosion of creativity enabled by recent developments in open-source hardware, for example.

Democratization of science

These are exciting times for scientists. Crowd funding enables anyone with a good idea to make it real. Open-source hardware lowers the cost of devices by more than 90% of what they cost just a few years ago. Scripting makes these devices work together, and also together with commercial devices. More than ever, stunning technological advances can be a part of scientific discovery even in the most modest laboratories. These are times of true democratization of science.

New low-cost opportunities for science

Montag, 21. März 2016

Robot scientists and automation: is this the lab of the future?

A team of researchers recently published promising findings on ‘Eve’ – an artificially intelligent ‘robot scientist’ capable of screening potential drugs with almost no human input needed. Meanwhile, IBM’s artificially intelligent (AI) Watson system is seeing take-up among pharmaceutical companies. Is this how we can expect the lab of the future to materialize?



As technology continues evolving and the laboratory becomes more automated, these types of systems will increasingly pool knowledge and data together to discover unique links – between otherwise seemingly unconnected observations. This couldn’t be timelier, with research and development (R&D) teams under intense pressure to develop new products faster and cheaper.


This shift towards automation has been a long time coming. In the 1990s, many cited AI and total laboratory automation as the ‘next big thing’ in R&D. But while some industries are seeing complete job displacement brought about by new technologies, the pharma sector hasn’t yet seen similar levels of ‘human replacement’. Drug discovery labs, for example, will still need a human element for the foreseeable future.


robot scientists and automation is this the lab of the future


The role these AI systems play lies in helping explore fresh ideas, such as a new pathway or target for a drug, and they can offer this coupled with predictive analysis to supplement the work of scientists in the lab. These predictive analyses have limitations – the volume of data and computing power required to simulate the effects of a drug on the whole body are enormous, for example. But their empirical basis means they can now get quite close to simulating ‘nature’ when the system is well defined and understood.


Perfect simulation may still be some way off, but our ability to manage and analyze more and more data has increased exponentially. When these new systems are performing any kind of analysis, they will have to feed back the derived information and integrate it with other new and potentially unrelated data. In that sense, a gradual adoption of more intelligent lab technologies will make it even more important to be able to securely manage multiple streams of data, from one central point of knowledge and IP.


Will the introduction of AI herald a new era for life sciences? Not quite – Eve is the latest evolution of this kind of technology, rather than a brand new concept. This is not a silver bullet but, as science evolves and the data availability changes around it, AI technology will play a role in the discovery and development of new drugs and disease understanding. As with all new tech and tools in sciences, it will help support the scientist – but it will not replace them.


Want to find out more about how laboratories will evolve over the coming years? Download our free whitepaper on the lab of the future here.




Robot scientists and automation: is this the lab of the future?