mitoODE

mitoODE models population-level cell cycle dynamics from high-throughput perturbation assays and time-lapse imaging by combining automated nuclear morphology classification with dynamic differential equation modeling and the Mitocheck project approach to quantify siRNA-induced cell cycle disruptions.

Key Features:

• Population-Level Analysis: Analyzes population-level behavior from sparsely sampled time-lapse data instead of tracking individual cells.
• Dynamic Differential Equation Modeling: Uses dynamic differential equations to model cellular state populations over time.
• Model Fitting for Parameter Estimation: Fits models to time course data to estimate penetrance and timing of disruptions and durations of interphase and mitosis.
• siRNA Impact Quantification: Identifies siRNAs that reproducibly induce quiescence, mitotic arrest, polynucleation, or cell death and quantifies their dynamic effects.
• Resource Compilation: Compiles estimates of siRNA-induced dynamic effects into a dataset for downstream analysis of genes involved in division, death, and migration.

Scientific Applications:

• Large-scale cell-based temporal assays: Interprets time-course data from high-throughput perturbation screens where temporal tracking of individuals is limited.
• Genetic perturbation screens (siRNA): Quantifies dynamic phenotypes induced by siRNA to aid characterization of gene function in cell cycle regulation.
• Population-level phenotyping: Dissects roles of specific genes in cellular phenotypes associated with division, death, and migration using population frequencies of nuclear morphologies.

Methodology:

Automated image classification of nuclear morphologies at each time point; construction of event-order maps describing population frequencies and temporal distributions of cellular states; formulation and fitting of dynamic differential equation models to estimate parameters including penetrance, timing of disruptions, and durations of interphase and mitosis, following the Mitocheck project approach.