8 research outputs found
Predicting the Distribution of Spiral Waves from Cell Properties in a Developmental-Path Model of Dictyostelium Pattern Formation
The slime mold Dictyostelium discoideum is one of the model systems of biological pattern formation. One of the most successful answers to the challenge of establishing a spiral wave pattern in a colony of homogeneously distributed D. discoideum cells has been the suggestion of a developmental path the cells follow (Lauzeral and coworkers). This is a well-defined change in properties each cell undergoes on a longer time scale than the typical dynamics of the cell. Here we show that this concept leads to an inhomogeneous and systematic spatial distribution of spiral waves, which can be predicted from the distribution of cells on the developmental path. We propose specific experiments for checking whether such systematics are also found in data and thus, indirectly, provide evidence of a developmental path
Adjusting Phenotypes by Noise Control
Genetically identical cells can show phenotypic variability. This is often caused by stochastic events that originate from randomness in biochemical processes involving in gene expression and other extrinsic cellular processes. From an engineering perspective, there have been efforts focused on theory and experiments to control noise levels by perturbing and replacing gene network components. However, systematic methods for noise control are lacking mainly due to the intractable mathematical structure of noise propagation through reaction networks. Here, we provide a numerical analysis method by quantifying the parametric sensitivity of noise characteristics at the level of the linear noise approximation. Our analysis is readily applicable to various types of noise control and to different types of system; for example, we can orthogonally control the mean and noise levels and can control system dynamics such as noisy oscillations. As an illustration we applied our method to HIV and yeast gene expression systems and metabolic networks. The oscillatory signal control was applied to p53 oscillations from DNA damage. Furthermore, we showed that the efficiency of orthogonal control can be enhanced by applying extrinsic noise and feedback. Our noise control analysis can be applied to any stochastic model belonging to continuous time Markovian systems such as biological and chemical reaction systems, and even computer and social networks. We anticipate the proposed analysis to be a useful tool for designing and controlling synthetic gene networks
Evolution under Fluctuating Environments Explains Observed Robustness in Metabolic Networks
A high level of robustness against gene deletion is observed in many organisms. However, it is still not clear which biochemical features underline this robustness and how these are acquired during evolution. One hypothesis, specific to metabolic networks, is that robustness emerges as a byproduct of selection for biomass production in different environments. To test this hypothesis we performed evolutionary simulations of metabolic networks under stable and fluctuating environments. We find that networks evolved under the latter scenario can better tolerate single gene deletion in specific environments. Such robustness is underlined by an increased number of independent fluxes and multifunctional enzymes in the evolved networks. Observed robustness in networks evolved under fluctuating environments was “apparent,” in the sense that it decreased significantly as we tested effects of gene deletions under all environments experienced during evolution. Furthermore, when we continued evolution of these networks under a stable environment, we found that any robustness they had acquired was completely lost. These findings provide evidence that evolution under fluctuating environments can account for the observed robustness in metabolic networks. Further, they suggest that organisms living under stable environments should display lower robustness in their metabolic networks, and that robustness should decrease upon switching to more stable environments
Spontaneous Reaction Silencing in Metabolic Optimization
Metabolic reactions of single-cell organisms are routinely observed to become
dispensable or even incapable of carrying activity under certain circumstances.
Yet, the mechanisms as well as the range of conditions and phenotypes
associated with this behavior remain very poorly understood. Here we predict
computationally and analytically that any organism evolving to maximize growth
rate, ATP production, or any other linear function of metabolic fluxes tends to
significantly reduce the number of active metabolic reactions compared to
typical non-optimal states. The reduced number appears to be constant across
the microbial species studied and just slightly larger than the minimum number
required for the organism to grow at all. We show that this massive spontaneous
reaction silencing is triggered by the irreversibility of a large fraction of
the metabolic reactions and propagates through the network as a cascade of
inactivity. Our results help explain existing experimental data on
intracellular flux measurements and the usage of latent pathways, shedding new
light on microbial evolution, robustness, and versatility for the execution of
specific biochemical tasks. In particular, the identification of optimal
reaction activity provides rigorous ground for an intriguing knockout-based
method recently proposed for the synthetic recovery of metabolic function.Comment: 34 pages, 6 figure