Dae Wook Kim, Neural network aided approximation and parameter inference of stochastic models of gene expression

KAIST E2-1 room 3221 E2-1 building, Daejeon, Daejeon, Korea, Republic of

We will discuss about "Neural network aided approximation and parameter inference of stochastic models of gene expression", Jian et al., bioRxiv (2020). Non-Markov models of stochastic biochemical kinetics often incorporate explicit time delays to effectively model large numbers of intermediate biochemical processes. Analysis and simulation of these models, as well as the inference of their

Seokjoo Chae, Ligand-receptor promiscuity enables cellular addressing

KAIST E2-1 room 3221 E2-1 building, Daejeon, Daejeon, Korea, Republic of

We will discuss about "Ligand-receptor promiscuity enables cellular addressing", Su et al., bioRxiv (2021) In multicellular organisms, secreted ligands selectively activate, or “address,” specific target cell populations to control cell fate decision-making and other processes. Key cell-cell communication pathways use multiple promiscuously interacting ligands and receptors, provoking the question of how addressing specificity can emerge

Seokjoo Chae, Synthetic gene networks recapitulate dynamic signal decoding and differential gene expression

KAIST E2-1 room 3221 E2-1 building, Daejeon, Daejeon, Korea, Republic of

We will discuss about "Synthetic gene networks recapitulate dynamic signal decoding and differential gene expression", Benzinger et al., bioRxiv (2021) Cells live in constantly changing environments and employ dynamic signaling pathways to transduce information about the signals they encounter. However, the mechanisms by which dynamic signals are decoded into appropriate gene expression patterns remain poorly

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