Evolution of mechanoregulation of bone growth will lead to non-optimal bone phenotypes |
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Authors: | Nowlan Niamh C Prendergast Patrick J |
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Affiliation: | Department of Mechanical Engineering, Centre for Bioengineering, Trinity College, Dublin 2, Ireland |
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Abstract: | Mechanical forces acting on the bones during growth affect their final shape and strength. Mechanoregulation of bone growth may be recognized in embryogenesis, and also in the adaptation of the adult skeleton to changes in mechanical loading. Mechanoregulatory responses for tissues have arisen during evolution, but does evolution give rise to responses that produce optimal skeletal phenotypes? In this paper, we investigate the emergence of an optimal mechanoregulation response in a population. By combining equations describing long bone growth with a genetic algorithm to describe evolutionary change, we created a computational model to simulate the evolution of mechanoregulation in bone growth. A population of individuals is created where each individual is assigned a diploid gene set which controls the growth and remodelling of the bone. At maturity, each bone is assessed and its 'fitness' calculated; fitness is quantified as bone strength relative to bone mass. The simulation continues for many generations, and includes mutations and a varying environment. The genes present in the population are tracked and the evolution of parameters governing mechanoregulation is calculated. The results indicate that a population may converge to one bone growth algorithm but, more usually, a range of mechanoregulation algorithms for different individuals will persist after many generations. Even if the population converges to one mechanoregulation law, convergence to the 'optimum' bone was never found. Although many researchers propose that natural selection has pushed skeletal structure towards an optimum, our computational model suggests that this is unlikely to be the case. |
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Keywords: | Mechanoregulation algorithms Bone growth Optimality Modelling evolution Genetic algorithm |
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