The UK Innovation, Universities and Skills Committee, established by the UK Parliament, has launched an enquiry into engineering here http://www.parliament.uk/parliamentary_committees/ius/ius_290108.cfm The following terms of reference have been agreed for this inquiry:
It turns out that a relevant point has been made in a document recently circulated by the RAE (Royal Academy of Engineering), which states: The RAE has identified Synthetic Biology as an area of scientific and national importance and Professor Richard Kitney will be chairing the RAE Working Group to look in to this. What follows is an attempt to explain that a particular aspect of synthetic biology, namely investigation and development of means of replicating biological information processing, is potentially of profound scientific and engineering importance.
The Wikipedia entry starts: The term synthetic biology has long been used to describe an approach to biology that attempts to integrate (or "synthesize") different areas of research in order to create a more holistic understanding of life. More recently the term has been used in a different way, signaling a new area of research that combines science and engineering in order to design and build ("synthesize") novel biological functions and systems. Compare the definition in: http://syntheticbiology.org/FAQ.html Synthetic Biology is A) the design and construction of new biological parts, devices, and systems, and B) the re-design of existing, natural biological systems for useful purposes. Most people in the field of computer science and engineering regard computation as being by definition something done by computers roughly as we know them (e.g. built on principles formulated by people like Turing and von Neumann). But that is a very narrow view of computation. Biological evolution produced a far greater variety of forms and mechanisms of computation (information-processing) than human scientists and engineers have dreamed of yet. It is usually forgotten that Turing machines were invented as a biologically inspired model of a particular capability of humans, namely mathematical thinking. Turing was trying to capture, in the simplest possible form, the essence of mathematical modes of reasoning. However, there are some modes of mathematical reasoning that do not fit naturally into Turing's framework, namely certain kinds of reasoning about geometry and topology that seem to make use of human understanding of spatial structures and processes. For more on that see this presentation: http://www.cs.bham.ac.uk/research/projects/cogaff/talks/#math-robot Could a Child Robot Grow Up To be A Mathematician And Philosopher?
Some people think that all biological computation can be subsumed under evolutionary computation and neural computation, but this ignores two facts: (a) the vast majority of organisms do not have brains or neurons (b) the vast majority of what goes on in individual organisms is chemical/molecular computation. E.g. human brains could not be built but for that. We are only just beginning to understand what such mechanisms can do and to model their operation. By the end of this century the fruits of such understanding could dominate artificial information processing systems. In any case there is still much that is not understood, both about evolution (e.g. the role of epigenesis) and how brains work (e.g. what different sorts of neurones and configurations of neurones are for, what they do and how they do it).
One of the common mistakes is to assume that we know what humans can do and the only problem is to find out how they do it and then replicate that. Unfortunately we then focus on mechanisms, and fail to study the requirements properly. For that, we need to look closely at the environments and competences of whole organisms to investigate the information processing demands. Some critical comments on ambitious artificial intelligence projects that focus primarily on mechanisms: Bill Gates in Scientific American The Numenta project of Jeff Hawkins When we can specify in much more detail the functions of whole organisms, including the many problems they have to overcome in coping with complex environments that are sometimes hostile and sometimes supportive, then we shall be in a better position to design working systems that explain they do it. The potential engineering applications will be of enormous significance, e.g. in building far more intelligent and robust artificial systems of many more kinds than we can build now. (There are understandable concerns about trusting such systems, but that is a topic for another occasion.) There are many problems of automation that people hope can be solved by extensions of current techniques, but which seem to require entirely new biologically inspired approaches. E.g. what the human visual system can do, as demonstrated in these two little experiments http://www.cs.bham.ac.uk/research/projects/cogaff/misc/multipic-challenge.pdf http://www.cs.bham.ac.uk/research/projects/cogaff/challenge.pdf is far beyond anything on the horizon in AI/Machine vision (many of whose practitioners confuse perception with recognition).
Human and some animal information-processing systems do not start off fully programmed but grow their information processing architectures, including developing their own ontologies and some new forms of representation. In part this process is driven by finding out from the environment what the problems are and what works. However innate mechanisms of suitable power and flexiblity are required to use the opportunities provided by the environment. For a more detailed analysis of the tradeoffs involved see http://www.cs.bham.ac.uk/research/projects/cosy/papers/#tr0609 (PDF) Natural and artificial meta-configured altricial information-processing systems Jackie Chappell and Aaron Sloman, IJUC, 2007 At present, the biological information processing mechanisms that drive and enable that process are still not understood. It may well be that many future artificial systems will have to be developed in a similar way, because human designers will have no way of determining in advance in sufficient detail what the requirements are for the systems -- e.g. systems that have to operate in unfamiliar, hostile environments. Moreover the requirements can change, as happens during the life of humans.
In 2002, the (UKCRC) UK Computing Research Committee sponsored a discussion, led by Tony Hoare and Robin Milner leading to the adoption of a number of computing research grand challenges described here. At least three of the UKCRC grand challenges are concerned with issues within the proposed Synthetic biology grand challenge: GC 1: http://fizz.cmp.uea.ac.uk/Research/ivis/ In Vivo -- In Silico The Virtual Worm, Weed and Bug Breathing Life into the Biological DataMountain A Grand Challenge for computational systems biology GC 5: http://www.cs.bham.ac.uk/research/cogaff/gc/ The Architecture of Brain & Mind Integrating high level cognitive processes with brain mechanisms and functions in a working robot. GC 7: http://www.cs.york.ac.uk/nature/gc7/ Journeys in Nonclassical Computation The Challenge: to produce a fully mature science of all forms of computation, that unifies the classical and non-classical paradigms This contrasts with many analyses by computer scientists and software engineers in academe and industry of important future trends: very often the words 'biology' 'biological' 'neural' 'natural' 'evolution' 'intelligence' 'brain' 'perception' 'learning' and 'grand challenge' do not occur in their documents about future trends, which focus mainly on developing current research in computing systems and formalisms and their applications.
One of the potentially important long term engineering developments, in which this country is already among the research leaders, is modelling, developing and applying information-processing mechanisms inspired by those produced by biological evolution, including far more intelligent systems than we currently know how to build. The potential importance of this is recognised in the Synthetic Biology Study of the Royal Academy of Engineering led by Professor Richard Kitney. The UK computing research community has already begun to address problems in this area in its 'Grand Challenges' and in related research projects funded by EPSRC and the EU.
Unfortunately, since the swing away from teaching programming and design of working systems in schools, replacing those activities with learning to use tools thought to be required by future employers, the opportunities in schools for young people to learn about and be inspired by the exciting new problems and opportunities related to understanding and designing information processig systems have not only diminished, but what replaced them taught students to regard computers as useful but essentially boring tools, like cookers and washing machines. If we are to grasp the new opportunities, computing-related educational practices will need drastic re-thinking. The need to produce researchers and engineers able to develop and apply new results in synthetic biology will require new initiatives in schools and universities preparing students for a combination of cross-disciplinary thinking and techniques for analysing, designing, building, testing, comparing and explaining complex information processing systems of many kinds, not just systems based on current hardware and software technologies. Students will also need to learn how to investigate and think about ethical and other implications. NB: that problem is not addressed by educational proposals that teach children to regard computing as "fun". Rather, the brighest ones need to learn to appreciate it as deep and challenging with the potential to continue changing our lives. A sample educational proposal for the last few years at school can be found here. That sort of initiative would, ideally, be supported by new forms of learning starting much earlier. Alas, it seems to me too unlikely that enough people in this country will understand the problems and support appropriate action.
School of Computer Science
The University of Birmingham