Posts by Collection


An in silico -in vitro pipeline identifying an HLA-A*02:01+ KRAS G12V+ spliced epitope candidate for a broad tumor-immune response in cancer patients

Published in , 1900

Recommended citation: Mishto, M.,Mansurkhodzhaev, A.,Ying, G.,Bitra, A.,Cordfunke, A R.,Henze, S., Paul, D.,Sidney, J.,Urlaub, H.,Neefjes, J., Sette, A.,Dirk, Z.,Liepe, J., 2019. An in silico-in vitro pipeline identifying an HLA-A*02:01+ KRAS G12V+spliced epitope candidate for a broad tumor-immune response in cancer patients. Frontiers in Immunology. Accepted for publication.


Comparison between combinatorial and spectral approaches in identifying the largest bipartite subgraphs of a graph



In this work, we compare the performances between a combinatorial approach by Erdös [1] and approaches based on the non-zero entries of eigenvectors corresponding to the extremal eigenvalues of four different matrices: adjacency, Laplacian, normalized Laplacian and signless Laplacian matrix, in identifying the largest bipartite subgraphs of a simple connected graph. We employ these approaches to one scale-free and three random graph models, which cover a wide range of real-world networks. It is observed that the normalized Laplacian and the adjacency matrix based approaches yield slightly better but comparable results to that of the combinatorial approach. Using these two matrices, we subsequently formulate a new index, analogous to the edge bipartivity index of Estrada and Rodríguez-Velázquez [2]. Our results indicate that the application of this new index not only significantly outperforms combinatorial approach but also performs better than the edge bipartivity index in identifying the largest bipartite subgraphs.

Stochastic sequestration dynamics can act as intrinsic noise filter in signaling network motifs



The relation between design principles of signaling network motifs and their robustness against intrinsic noise still remains illusive. In this talk, I will summarize our recent investigation on the role of cascading for coping with intrinsic noise due to stochasticity in molecular reactions. We use stochastic modeling approaches to quantify fluctuations in the terminal kinase of phosphorylation-dephosphorylation cascade motifs and demonstrate that cascading highly affects these fluctuations. We show that this purely stochastic effect can be explained by time-varying sequestration of upstream kinase molecules. In particular, we discuss conditions on time scales and parameter regimes which lead to a reduction of output fluctuations. Our results are put into biological context by adapting rate parameters of our modeling approach to biologically feasible ranges for general binding-unbinding and phosphorylation-dephosphorylation mechanisms. Overall, this study reveals a novel role of stochastic sequestration for dynamic noise filtering in ubiquitous signaling motifs.