Reducing complexity in stochastic biochemical networks © Christoph Zechner, CSBD
Randomness plays an important role in many biological processes such as signaling, gene expression or cell fate determination. Stochastic simulations provide a powerful means to study how randomness affects the behavior of living systems in a quantitative fashion. However, performing such simulations on realistic temporal and spatial scales is computationally very demanding and quickly becomes too time-consuming using existing methods.
Lorenzo Duso and Christoph Zechner, researchers at the CSBD and the Max Planck Institute of Molecular Cell Biology and Genetics, have developed a novel simulation algorithm enabling accurate stochastic simulations of biochemical networks while dramatically reducing their computational cost. Using the theory of stochastic processes, the scientists show how an arbitrary (and possibly complicated) network can be transformed into a reduced network - comprising much fewer stochastic components and interactions - without losing the crucial information. Thanks to this transformation, the simulation of a large fraction of biochemical events can be bypassed – sometimes more than 98%. Lorenzo, the first author of the recently published paper in The Journal of Chemical Physics says, “Instead of rolling a dice 100 times, we only have to roll it twice in order to get the same answer.”
With their new algorithm, the authors can now analyze more complex and realistic models of stochastic phenomena in living organisms. “It will thus play an important role to understand the complex mechanisms driving cell and tissue behavior,” says Christoph.