Type I Interferon Drives a Cellular State Inert to TCR-Stimulation and Could Impede Effective T-Cell Differentiation in Cancer.

Background: Head and neck squamous cell carcinoma (HNSCC) is linked to human papillomavirus (HPV) infection. HPV-positive and HPV-negative HNSCC exhibit distinct molecular and clinical characteristics. Although checkpoint inhibitors have shown efficiency in recurrent/metastatic HNSCC, response variability persists regardless of HPV status.

Benchmarking methods for computing local sensitivities in ordinary differential equation models at dynamic and steady states.

Estimating parameters of dynamic models from experimental data is a challenging, and often computationally-demanding task. It requires a large number of model simulations and objective function gradient computations, if gradient-based optimization is used. In many cases, steady-state computation is a part of model simulation, either due to steady-state data or an assumption that the system is at steady state at the initial time point.

Processivity and specificity of histone acetylation by the male-specific lethal complex.

Acetylation of lysine 16 of histone H4 (H4K16ac) stands out among the histone modifications, because it decompacts the chromatin fiber. The metazoan acetyltransferase MOF (KAT8) regulates transcription through H4K16 acetylation. Antibody-based studies had yielded inconclusive results about the selectivity of MOF to acetylate the H4 N-terminus.

pyPESTO: a modular and scalable tool for parameter estimation for dynamic models.

Summary: Mechanistic models are important tools to describe and understand biological processes. However, they typically rely on unknown parameters, the estimation of which can be challenging for large and complex systems. pyPESTO is a modular framework for systematic parameter estimation, with scalable algorithms for optimization and uncertainty quantification.