Abstract
Heterogeneity in cell populations originates from two fundamentally different sources: the uneven distribution of intracellular content during cell division, and the stochastic fluctuations of regulatory molecules existing in small amounts. Discrete stochastic models can incorporate both sources of cell heterogeneity with sufficient accuracy in the description of an isogenic cell population; however, they lack efficiency when a systems level analysis is required, due to substantial computational requirements. In this work, we study the effect of cell heterogeneity in the behaviour of isogenic cell populations carrying the genetic network of lac operon, which exhibits solution multiplicity over a wide range of extracellular conditions. For such systems, the strategy of performing solely direct temporal solutions is a prohibitive task, since a large ensemble of initial states needs to be tested in order to drive the system—through long time simulations—to possible co-existing steady state solutions. We implement a multiscale computational framework, the so-called “equation-free” methodology, which enables the performance of numerical tasks, such as the computation of coarse steady state solutions and coarse bifurcation analysis. Dynamically stable and unstable solutions are computed and the effect of intrinsic noise on the range of bistability is efficiently investigated. The results are compared with the homogeneous model, which neglects all sources of heterogeneity, with the deterministic cell population balance model, as well as with a stochastic model neglecting the heterogeneity originating from intrinsic noise effects. We show that when the effect of intrinsic source of heterogeneity is intensified, the bistability range shifts towards higher extracellular inducer concentration values.
Highlights
The phenotype of a cellular population is not exclusively the result of single-cell level complex chemical networks; cells interact with each other leading to phenotypic variations amongst the individual members of isogenic populations, a phenomenon commonly known as cellularPLOS ONE | DOI:10.1371/journal.pone.0132946 July 17, 2015Intrinsic Noise Effects on Heterogeneous Cell Populations heterogeneity
The paper is organized as follows: we present a simplified single-cell reaction rate expression, which describes the dynamics of the lac operon genetic network
We briefly describe the homogeneous model, which neglects all sources of heterogeneity, the deterministic cell population balance model, which incorporates extrinsic heterogeneity, and the stochastic Constant-Number Monte Carlo (CNMC) algorithm, which takes into account both extrinsic and intrinsic source of heterogeneity
Summary
Intrinsic Noise Effects on Heterogeneous Cell Populations heterogeneity. The unevenly distributed regulatory molecules lead to different phenotypes, and the phenomenon is repeated due to the operation of the cell cycle. This type of heterogeneity is called extrinsic [19]. The regulatory molecules, which control the network of intracellular reactions and determine the cells phenotype exist in small amounts [22,23,24], and even small fluctuations can lead to an uncontrolled-uncertain outcome (phenotype). Cells with approximately the same amount of regulatory molecules can feature utterly different phenotypic behaviour; this type of heterogeneity is called intrinsic [19]
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