Homology search is a means through which DNA double-strand breaks (DSBs) explore the genome for sequences of lossless repair. As this search process is fundamental to the relationship between DNA damage and disease, a better understanding the underlying mechanisms is crucial. In this work, we use an effective entropic bead-spring polymer chain model to simulate the spatiotemporal dynamics of...
Pluripotent stem cells have the capacity to differentiate into all the primary germ layers represented in embryos. Individual cells process information from their environment through biophysical and biochemical cues in order to determine their cell fate (i.e., the germ layer into which they will differentiate). However, cell autonomous decision-making does not fully account for the...
The advent of temporal single-cell RNA sequencing (scRNA-seq) data has enabled in-depth investigation of dynamic processes in heterogeneous multicellular systems. Despite remarkable advancements in computational methods for modeling cellular dynamics, integrating cell-cell interactions (CCIs) into these models remains a major challenge. This is particularly true when dealing with...
Organ growth during development is orchestrated by a combination of patterning, cell
proliferation, and morphogenesis, but the extent in which these contributions are
integrated into a multi-scale process is largely unknown. The developing wing of the fruit
fly, Drosophila melanogaster, offers an excellent experimental model to address this
question because there is a broad knowledge of...
Biological tissues grow under strong mechanical constraints, leading to curvature control of their rate of growth. However, elucidating how this emergent control arises from dynamic cellular processes such as cell proliferation, cell migration, and cell mechanics remains a major challenge. In this talk, I will present recent advances from cell-based mathematical models and their continuum...
Neural ordinary differential equation frameworks, such as Biologically-Informed Neural Networks (BINNs), have shown strong potential for learning mechanistic laws from sparse biological data. However, most existing approaches assume homoscedastic Gaussian noise, overlooking biologically meaningful variability arising from cell-to-cell heterogeneity and experimental measurement processes. In...
Stochastic search is ubiquitous in cell biology, from the propagation of action potentials via synaptic transmission to the spatial regulation of patterning during tissue development via cytoneme-based morphogenesis. In dynamic systems like these, the number of 'searchers' is rarely constant: new agents may be recruited while others can abandon the search. Despite the ubiquity of these...
The self-organization of microtubule (MT) polymers along the inner surface (cortex) of the plant cell membrane is an essential element in facilitating directional cell growth. The key questions are: what gives rise to the ordering and orientation of MT patterns? Mathematical and computational modelling has proven successful in providing insights: the process is distilled into a system of...
Modern cell and developmental biology increasingly relies on the integration of mechanistic modeling, data-driven inference, and advanced computational techniques to generate predictive insight and guide experimental design. State-of-the-art approaches now combine differential equations and stochastic models with modern data analysis and data science methodologies, including machine learning,...
