How fast could a T-rex run?
And, more importantly, was it fast enough to catch you?
How grid computing is helping palaeontologists to understand better how dinosaurs moved around and what roles they played in their ancient world.
With its sharp teeth and massive jaws, the T-rex is the stuff of nightmares. It’s not surprising that scientists are convinced the T-rex was a carnivorous predator but huge teeth don’t tell the whole story. Was it like the modern cheetah and catch its prey in short burst-like sprints? Or was the T-rex a sneaky stalk-and-ambush hunter like the jaguar? What was its place in the Cretaceous ecosystem?
Image: wikicomnmons / J.M.Luijt
Since we can’t see a real T-rex in action (it disappeared along with the other dinosaurs 65 million years ago), palaeontologists need to look elsewhere to understand its role as a predator. Top running speed offers good clues to solving this mystery – but how do you measure the maximum speed of an extinct animal?
If zebras were to become extinct, the palaeontologists of the future could probably use horses or donkeys as comparisons. People looking at dinosaur behaviour don’t have that luxury because there is nothing alive today quite like a T-rex. The solution is to create a detailed computer simulation of the animal’s skeleton and muscles.
Teaching a T-rex how to run
William Sellers and Phillip Manning, two palaeontologists from the University of Manchester, used a programme called GaitSym to model the top running speeds of five types of bipedal dinosaur – Compsognathus, Velociraptor, Dilophosaurus, Allosaurus and T-rex (officially known as Tyrannosaurus rex). They also modelled three living animals – the ostrich, the emu and humans – with relatively well-known top speeds to use as comparison (see table below).
First, they used the information available from known fossils to reconstruct the animal’s locomotive anatomy and to build a 2D musculoskeletal model. The model specifies, for example, where the joints are, where the muscles are, the weight/mass of the trunk, thighs, feet and other parts of the animal alongside the size and properties of its muscles.
Then, they ‘released’ this virtual robot in GaitSym – a simulation environment that respects the real laws of physics (e.g. gravity, inertia) – and told it to run as fast as possible. The key to the model is that the palaeontologists didn’t specify which muscle activation sequence the dinosaurs should use. This is what GaitSym does – the programme experiments with different combinations of muscle activation patterns and searches for an optimum solution. In this case, GaitSym looked for the muscle activation pattern that allowed the animal to cover the most ground in a given amount of time.
Poor solutions – patterns that caused the animal to stagger, stumble or fall – were abandoned while promising patterns were selected for further investigation. Each individual computation is not complex but the problem is that GaitSym needs to go through thousands of muscle activation patterns. This makes the work computationally demanding and impractical to complete using a single computer. Instead, Sellers and Manning accessed the grid computing services provided by the UK’s NW-Grid and used about 170,000 hours of computing time to complete the project in a few months.