Biology subjects

Öcal, K.

Publications and source records attributed to Öcal, K..

5 recordsLinked to original sources

bioRxiv2026

Probing bacterial fitness in virtual chemostats

The growth rate of a bacterial population is a fundamental quantity in microbiology, but hard to predict from mechanistic single-cell models. Simulating exponentially growing cell populations is computationally infeasible, and single-lineage estimates are biased as they do not account for phenotypic selection between cells. We present computational algorithms to efficiently estimate growth rates with arbitrary accuracy by combining ideas from branching processes and particle filtering. These can be used with general agent-based cell models at a cost quadratic in the desired accuracy, bypassing the exponential complexity of naive approaches. Applying these methods to single-cell models of Escherichia coli, we study how single-cell heterogeneity in antibiotic responses shapes population dynamics, and how bacterial locomotion and chemotaxis in fluctuating environments affect their long-term growth.

systems biology↗

bioRxiv2023

Incorporating extrinsic noise into mechanistic modelling of single-cell transcriptomics

A mechanistic understanding of single-cell transcriptomics data requires differentiating between intrinsic, extrinsic and technical noise, but an abundance of the latter often obscures underlying biological patterns. Accurately modelling such data in the presence of large cell-to-cell heterogeneity due to factors such as cell size and cell cycle stage is a challenging task. We propose a tractable, fully Bayesian framework for mechanistic modelling of single-cell RNA sequencing data in the presence of cellular heterogeneity. Applied to murine transcriptomics data, we show that cell-specific effects can significantly alter previously inferred dynamics of individual genes. Our implementation is statistically exact and readily extensible, and we demonstrate how it can be combined with Bayesian model selection to compare various models of gene expression and measurement noise.

bioinformatics↗

bioRxiv2023

The timing of cellular events: a stochastic vs deterministic perspective

Changes in cell state are driven by key molecular events whose timing can often be measured experimentally. Of particular interest is the time taken for the levels of RNA or protein molecules to reach a critical threshold defining the triggering of a cellular event. While this mean trigger time can be estimated by numerical integration of deterministic models, these ignore intrinsic noise and hence their predictions may be inaccurate. Here we study the differences between deterministic and stochastic model predictions for the mean trigger times using simple models of gene expression, post-transcriptional feedback control, and enzyme-mediated catalysis. By comparison of the two predictions, we show that when promoter switching is present there exists a transition from a parameter regime where deterministic models predict a longer trigger time than stochastic models to a regime where the opposite occurs. Furthermore, the ratio of the trigger times of the two models can be large, particularly for auto-regulatory genetic feedback loops. Our theory provides intuitive insight into the origin of these effects and shows that deterministic predictions for cellular event timing can be highly inaccurate when molecule numbers are within the range known for many cells.

systems biology↗

bioRxiv2022

Approximating Solutions of the Chemical Master Equation using Neural Networks

The Chemical Master Equation (CME) provides an accurate description of stochastic biochemical reaction networks in well-mixed conditions, but it cannot be solved analytically for most systems of practical interest. While Monte Carlo methods provide a principled means to probe the system dynamics, their high computational cost can render the estimation of molecule number distributions and other numerical tasks infeasible due to the large number of repeated simulations typically required. In this paper we aim to leverage the representational power of neural networks to approximate the solutions of the CME and propose a framework for Neural Estimation of Stochastic Simulations for Inference and Exploration (Nessie). Our approach is based on training a neural network to learn the distributions predicted by the CME from a relatively small number of stochastic simulations, thereby accelerating computationally intensive tasks such as parameter exploration and inference. We show on biologically relevant examples that simple neural networks with one hidden layer are able to capture highly complex distributions across parameter space. We provide a detailed discussion of the neural network implementation and code for easy reproducibility.

systems biology↗

bioRxiv2019

Training deep neural density estimators to identify mechanistic models of neural dynamics

Mechanistic modeling in neuroscience aims to explain observed phenomena in terms of underlying causes. However, determining which model parameters agree with complex and stochastic neural data presents a significant challenge. We address this challenge with a machine learning tool which uses deep neural density estimators-- trained using model simulations-- to carry out Bayesian inference and retrieve the full space of parameters compatible with raw data or selected data features. Our method is scalable in parameters and data features, and can rapidly analyze new data after initial training. We demonstrate the power and flexibility of our approach on receptive fields, ion channels, and Hodgkin-Huxley models. We also characterize the space of circuit configurations giving rise to rhythmic activity in the crustacean stomatogastric ganglion, and use these results to derive hypotheses for underlying compensation mechanisms. Our approach will help close the gap between data-driven and theory-driven models of neural dynamics.

neuroscience↗