Estimation of biochemical network parameter distributions in cell populations

TL;DR

This paper introduces a heuristic method to estimate the distribution of parameters in biochemical network models of heterogeneous cell populations using flow cytometry data, enabling insights into differential cellular responses.

Contribution

It presents a novel approach combining simulation and convex optimization to infer parameter distributions from experimental data in cell population models.

Findings

Successfully estimated bimodal TNF response distribution.

Identified factors underlying differential cell responses.

Validated approach with artificial data from TNF signaling model.

Abstract

Populations of heterogeneous cells play an important role in many biological systems. In this paper we consider systems where each cell can be modelled by an ordinary differential equation. To account for heterogeneity, parameter values are different among individual cells, subject to a distribution function which is part of the model specification. Experimental data for heterogeneous cell populations can be obtained from flow cytometric fluorescence microscopy. We present a heuristic approach to use such data for estimation of the parameter distribution in the population. The approach is based on generating simulation data for samples in parameter space. By convex optimisation, a suitable probability density function for these samples is computed. To evaluate the proposed approach, we consider artificial data from a simple model of the tumor necrosis factor (TNF) signalling pathway. Its main characteristic is a bimodality in the TNF response: a certain percentage of cells undergoes apoptosis upon stimulation, while the remaining part stays alive. We show how our modelling approach allows to identify the reasons that underly the differential response.