1,315 research outputs found
Network-level dynamics of diffusively coupled cells
We study molecular dynamics within populations of diffusively coupled cells
under the assumption of fast diffusive exchange. As a technical tool, we
propose conditions on boundedness and ultimate boundedness for systems with a
singular perturbation, which extend the classical asymptotic stability results
for singularly perturbed systems. Based on these results, we show that with
common models of intracellular dynamics, the cell population is coordinated in
the sense that all cells converge close to a common equilibrium point. We then
study a more specific example of coupled cells which behave as bistable
switches, where the intracellular dynamics are such that cells may be in one of
two equilibrium points. Here, we find that the whole population is bistable in
the sense that it converges to a population state where either all cells are
close to the one equilibrium point, or all cells are close to the other
equilibrium point. Finally, we discuss applications of these results for the
robustness of cellular decision making in coupled populations
Explicit congruences for mock modular forms
In recent work of Bringmann, Guerzhoy, and the first author, p-adic modular
forms were constructed from mock modular forms. This paper proves explicit
congruences for these p-adic modular forms
Estimation of biochemical network parameter distributions in cell populations
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.Comment: 14 pages, 5 figure
Dynamic optimization of metabolic networks coupled with gene expression
The regulation of metabolic activity by tuning enzyme expression levels is
crucial to sustain cellular growth in changing environments. Metabolic networks
are often studied at steady state using constraint-based models and
optimization techniques. However, metabolic adaptations driven by changes in
gene expression cannot be analyzed by steady state models, as these do not
account for temporal changes in biomass composition. Here we present a dynamic
optimization framework that integrates the metabolic network with the dynamics
of biomass production and composition, explicitly taking into account enzyme
production costs and enzymatic capacity. In contrast to the established dynamic
flux balance analysis, our approach allows predicting dynamic changes in both
the metabolic fluxes and the biomass composition during metabolic adaptations.
We applied our algorithm in two case studies: a minimal nutrient uptake
network, and an abstraction of core metabolic processes in bacteria. In the
minimal model, we show that the optimized uptake rates reproduce the empirical
Monod growth for bacterial cultures. For the network of core metabolic
processes, the dynamic optimization algorithm predicted commonly observed
metabolic adaptations, such as a diauxic switch with a preference ranking for
different nutrients, re-utilization of waste products after depletion of the
original substrate, and metabolic adaptation to an impending nutrient
depletion. These examples illustrate how dynamic adaptations of enzyme
expression can be predicted solely from an optimization principle
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