We consider the problem of dynamic pricing by sellers in an online market economy using software agents called pricebots. In previous research on dynamic pricing algorithms, each seller's pricebot employs either heuristics-based or learning-based techniques to determine and update the profit maximizing price for itself at certain intervals in response to changes in market dynamics. In these dynamic pricing techniques, each seller's pricebot uses only its private information such as past prices and profits to update its price in successive intervals. In this paper, we posit that the profits obtained by a pricebot can be improved if each pricebot incorporates its competitors' pricing information along with its private price and profit information in its price-update calculations. However, incorporating competitors' pricing information accurately into a pricebot's dynamic pricing algorithm is a challenging problem because competing sellers (pricebots) update their prices asynchronously and by an amount determined by each seller's private pricing strategy. Our contribution in this paper is a novel dynamic pricing algorithm that uses a distributed synchronization model observed in nature to align each seller's price with its competitors' prices. Our analytical and simulation results show that the combination of a heuristics-based pricing mechanism that uses only a seller's private information and the synchronization-based mechanism that aligns its prices with its competitors, enables a seller's pricebot improve its profits by as much as 78% as compared to previous dynamic pricing algorithms.