(Abstract)Cellular development has traditionally been described as a series of transitions between discrete cell states, such as the sequence of double negative, double positive and single positive stages in Tcell development. Recent advances in single cell transcriptomics suggest an alternative description of development, in which cells follow continuous transcriptomic trajectories. A cell's state along such a trajectory can be captured with pseudotemporal ordering, which however is not able to predict development of the system in real time. We present pseudodynamics, a mathematical framework that integrates timeseries and genetic knockout information with such transcriptomebased descriptions in order to describe and analyze the realtime evolution of the system. Pseudodynamics models the distribution of a cell population across a continuous cell state coordinate over time based on a stochastic differential equation along developmental trajectories and random switching between trajectories in branching regions. To illustrate feasibility, we use pseudodynamics to estimate cellstatedependent growth and differentiation of thymic Tcell development. The model approximates a developmental potential function (Waddington's landscape) and suggests that thymic Tcell development is biphasic and not strictly deterministic before betaselection. Pseudodynamics generalizes classical discrete population models to continuous states and thus opens possibilities such as probabilistic model selection to single cell genomics.not peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission. (Fig. 1b). Here, we present pseudodynamics (Fig. 1a), a mathematical framework tailored to developmental trajectories which accounts both for the continuous population structure and the nonsteady state dynamics of the system to understand population growth and differentiation characteristics along trajectories.As an example, we establish an unbiased view of Tcell development based on a branching pseudotemporal ordering of cells observed with singlecell RNAseq data and validate it with previous findings. Secondly, we show that the population growth rate can be fit as a transcriptomic state dependent function which maps out selection pressure during Tcell maturation on specific transcriptomic states. Thirdly, we show how pseudodynamics facilitates the integration of wildtype and mutant data to annotate developmental trajectories with developmental checkpoints using the example of developmental arrest of Tcells at betaselection in Rag1 and Rag2 knockout mice. Our model extends previous efforts on modelling gene expression distributions in time 10 by population size dynamics and by the notion of developmental trajectories in transcriptome space. Pseudodynamics is independent of the method with which the pseudotemporal ordering is generated. In summary, pseudodynamics adds the following layers of information to a lineage trajectory:(1) population growth dynamics such as population bursts and selection, (2) an approxima...