WHole Animal Modelling (WHAM): Toward the integrated understanding of sensory motor control in C. elegans

Animals are remarkable creatures. No man-made machine even comes close in its ability to navigate complex environments, respond to rich sensory cues, learn and adapt its behaviour when encountering completely novel scenarios and much much more. But even the simplest animals, ruled by even the simplest nervous systems, can achieve this. Simple animals may not be able to play chess or balance bank statements, but there is much we can learn from them about their robust mechanisms for sensory-integration and motor control which may be of use to us in control engineering, bio-robotics and even in future brain-machine interfaces that are being developed for neuro-prosthetic applications.

An excellent starting point for understanding animal behaviour is a tiny, free living 1mm long roundworm, called C. elegans. By comparison to our 100 billion nerve cells, or even a fly's 100 thousand nerve cells, this worm's entire nervous system consists of a mere 302 nerve cells. Unlike our nervous system, the worm's circuitry is hard wired and identical across individuals of the species, making it possible to study rigorously and reproducibly. Due in part to its simplicity, and in part to its ease of manipulation in the lab, this is the only animal for which this entire nervous system has been mapped in exquisite detail (to sub-cellular resolution). But despite its relative simplicity, this worm possesses many of the functions that are attributed to more complex animals, including feeding, mating, complex sensory abilities, memory and learning. It is not surprising, therefore, that the modelling of this worm has captured the imagination of physicists, computer scientists and engineers alike. The integrated modelling of C. elegans has even been proposed as one of the UK's ``Grand Challenges for Computing Research.''

In this Fellowship, I will begin to integrate our understanding of C. elegans sensory motor behaviour in a single computational model. The challenge is to bridge the gap between the effectively static neural circuit architecture and the dynamic neural computation it sustains. This fellowship will enable me to deliver a step change, not only in our understanding of an important model organism, but also in advancing the science and engineering of complex systems, whether in the context of reverse engineering real world networks, or in the context of designing them.

The research proposed will generate new understanding and models of a complex (biological) system. The outcomes will inform a number of academic disciplines, which themselves impact on a number of different application areas. This is fundamental research, so the societal and economic impacts are envisaged as long term, with quantifiable benefits being realised within 10-20 years.

The key application areas identified at present as having potential societal and economic impact are:

1. Engineering design processes - Modelling frameworks and methodologies that address difficulties inherent in complex systems may be applied in industrial applications that involve the design of complex systems. Examples could range from automated control systems (that are embedded in cars or other vehicles) to cognitive robotics (with applications for more efficient/effective manufacturing processes and autonomous robots for search and rescue, navigation in toxic or difficult environments, space exploration and biomedical applications).

2. 3D vision and imaging technologies are increasingly important in a range of applications, in particular in entertainment (films and games). This technology will almost certainly feed into education within 5-10 years. In addition, the advancement of 3D technologies may have applications in manufacturing (such as more efficient and more miniaturised automation, quality control, sorting or manipulation).

3. Medical - knowledge of the C.elegans genetics and neurobiology will enable medical researchers to tease out principles of nerve function and neurological pathologies. Such knowledge will in turn be used develop improved diagnostics and treatment. Examples are better drug therapies and improved design of brain-machine interfaces for neuro-prostheses. There would be both economic impacts to companies developing such products and societal impact to patients receiving improved treatments.

The approach taken to the modelling of a complete animal has already attracted interest from the press and public engagement is a key objective. The central idea of deriving lessons from studying animals as examples of complex adaptive systems and applying the principles and methodologies to model complex processes and organisations and to design products and systems is accessible and appealing. The improvement of understanding by the public of the complex systems approach will enrich our culture, contribute to a better awareness of the importance of University research and inform future research directions here and with collaborators. In turn, this can also be used to inform public policy.