Identification of models of heterogeneous cell populations from population snapshot data

Abstract

BackgroundMost of the modeling performed in the area of systems biology aims at achieving a quantitative description of the intracellular pathways within a "typical cell". However, in many biologically important situations even clonal cell populations can show a heterogeneous response. These situations require study of cell-to-cell variability and the development of models for heterogeneous cell populations.ResultsIn this paper we consider cell populations in which the dynamics of every single cell is captured by a parameter dependent differential equation. Differences among cells are modeled by differences in parameters which are subject to a probability density. A novel Bayesian approach is presented to infer this probability density from population snapshot data, such as flow cytometric analysis, which do not provide single cell time series data. The presented approach can deal with sparse and noisy measurement data. Furthermore, it is appealing from an application point of view as in contrast to other methods the uncertainty of the resulting parameter distribution can directly be assessed.ConclusionsThe proposed method is evaluated using artificial experimental data from a model of the tumor necrosis factor signaling network. We demonstrate that the methods are computationally efficient and yield good estimation result even for sparse data sets.

Structural identification of biochemical reaction networks from population snapshot data

“…The paper is a first investigation of the problem of stochastic network reconstruction from so-called population snapshot data (statistics of the process variables measured from independent samples taken at different time points of a dynamical experiment (Hasenauer et al, 2011)). Randomness is assumed to be primarily due to random occurrence of reactions (intrinsic noise), whereas other sources of noise such as parameter variability (extrinsic noise) are admittedly not accounted for.…”

What Population Reveals About Individual Cell Identity: Single-Cell Parameter Estimation of Models of Gene Expression in Yeast

“…Such models assume that the different observations arise from different realizations of the same stochastic process and, therefore, are still based on the notion of a virtual mean-although noisy-cell. In comparison, and despite recent methodological developments [27,28], few attempts have been made to infer extrinsic noise models from data, see [4,10,23,29,30] and our previous work [31]. We refer the reader to Karlsson et al [24] for a detailed discussion of these works.…”

Concept, Principles, and Objectives of Population Hematology

“…Интересно, что и биологическая наука о клетке сталкивается с потребностью оперировать понятием «гетерогенность» при анализе динамики клеточных популяций [3].…”

“…Для большинства онкогематологических заболеваний само наличие кластеров, подгрупп и характеристики их гетероген-ности в клеточных популяциях несут значительно больше полезной информации, чем значения усред-ненных показателей. В молекулярно-биологических, генетических, иммунологических, цитогенетических методах исследований выходным измерением явля-ется именно мера гетерогенности -соотношение субфракций клеток и биологических структур [3,5].…”