Agent-Based Modeling of Intracellular Transport

We develop an agent-based model of the motion and pattern formation of vesicles. These intracellular particles can be found in four different modes of (undirected and directed) motion and can fuse with other vesicles. While the size of vesicles follows a log-normal distribution that changes over time due to fusion processes, their spatial distribution gives rise to distinct patterns. Their occurrence depends on the concentration of proteins which are synthesized based on the transcriptional activities of some genes. Hence, differences in these spatio-temporal vesicle patterns allow indirect conclusions about the (unknown) impact of these genes. By means of agent-based computer simulations we are able to reproduce such patterns on real temporal and spatial scales. Our modeling approach is based on Brownian agents with an internal degree of freedom, $\theta$, that represents the different modes of motion. Conditions inside the cell are modeled by an effective potential that differs for agents dependent on their value $\theta$. Agent's motion in this effective potential is modeled by an overdampted Langevin equation, changes of $\theta$ are modeled as stochastic transitions with values obtained from experiments, and fusion events are modeled as space-dependent stochastic transitions. Our results for the spatio-temporal vesicle patterns can be used for a statistical comparison with experiments. We also derive hypotheses of how the silencing of some genes may affect the intracellular transport, and point to generalizations of the model

Time and space efficient RNA-RNA interaction prediction via sparse folding

In the past few years, a large set of new regulatory ncRNAs have been identified, but the number of experimentally verified targets is considerably low. Thus, computational target prediction methods are on high demand. Whereas all previous approaches for predicting a general joint structure have a complexity of O(n 6) running time and O(n 4) space, a more time and space efficient interaction prediction that is able to handle complex joint structures is necessary for genome-wide target prediction problems. In this paper we show how to reduce both the time and space complexity of RNA-RNA interaction prediction problem as described by Alkan et al. [1] by a linear factor via dynamic programming sparsification- which allows to safely discard large portions of DP tables. Applying sparsification techniques reduces the complexity of the original algorithm to O(n 4 ψ(n)) in time and O(n 2 ψ(n) + n 3) in space for some function ψ(n), which turns out to have small values for the range of n that we encounter in practice. By the use of polymer-zeta property for RNA-structures, we demonstrate that ψ(n) = O(n) on average. We evaluate our sparsified algorithm for RNA-RNA interaction prediction through total free energy minimization, based on the energy model of Chitsaz et al. [11], on a set of known interactions. Our results confirm the significant reduction of time and space requirements in practice