1,169 research outputs found
A method for achieving prolonged nutrient limited growth of Neurospora mycelium.
A method for achieving prolonged nutrient-limited growth of Neurospora mycelium
The Evolution of Connectivity in Metabolic Networks
Processes in living cells are the result of interactions between biochemical compounds in highly complex biochemical networks. It is a major challenge in biology to understand causes and consequences of the specific design of these networks. A characteristic design feature of metabolic networks is the presence of hub metabolites such as ATP or NADH that are involved in a high number of reactions. To study the emergence of hub metabolites, we implemented computer simulations of a widely accepted scenario for the evolution of metabolic networks. Our simulations indicate that metabolic networks with a large number of highly specialized enzymes may evolve from a few multifunctional enzymes. During this process, enzymes duplicate and specialize, leading to a loss of biochemical reactions and intermediary metabolites. Complex features of metabolic networks such as the presence of hubs may result from selection of growth rate if essential biochemical mechanisms are considered. Specifically, our simulations indicate that group transfer reactions are essential for the emergence of hubs
The Effect of Recombination on the Neutral Evolution of Genetic Robustness
Conventional population genetics considers the evolution of a limited number
of genotypes corresponding to phenotypes with different fitness. As model
phenotypes, in particular RNA secondary structure, have become computationally
tractable, however, it has become apparent that the context dependent effect of
mutations and the many-to-one nature inherent in these genotype-phenotype maps
can have fundamental evolutionary consequences. It has previously been
demonstrated that populations of genotypes evolving on the neutral networks
corresponding to all genotypes with the same secondary structure only through
neutral mutations can evolve mutational robustness [Nimwegen {\it et al.}
Neutral evolution of mutational robustness, 1999 PNAS], by concentrating the
population on regions of high neutrality. Introducing recombination we
demonstrate, through numerically calculating the stationary distribution of an
infinite population on ensembles of random neutral networks that mutational
robustness is significantly enhanced and further that the magnitude of this
enhancement is sensitive to details of the neutral network topology. Through
the simulation of finite populations of genotypes evolving on random neutral
networks and a scaled down microRNA neutral network, we show that even in
finite populations recombination will still act to focus the population on
regions of locally high neutrality.Comment: Accepted for publication in Math. Biosci. as part of the proceedings
of BIOCOMP 200
Selfishness versus functional cooperation in a stochastic protocell model
How to design an "evolvable" artificial system capable to increase in complexity? Although Darwin's theory of evolution by natural selection obviously offers a firm foundation, little hope of success seems to be expected from the explanatory adequacy of modern evolutionary theory, which does a good job at explaining what has already happened but remains practically helpless at predicting what will occur. However, the study of the major transitions in evolution clearly suggests that increases in complexity have occurred on those occasions when the conflicting interests between competing individuals were partly subjugated. This immediately raises the issue about "levels of selection" in evolutionary biology, and the idea that multi-level selection scenarios are required for complexity to emerge. After analyzing the dynamical behaviour of competing replicators within compartments, we show here that a proliferation of differentiated catalysts and/or improvement of catalytic efficiency of ribozymes can potentially evolve in properly designed artificial cells. Experimental evolution in these systems will likely stand as beautiful examples of artificial adaptive systems, and will provide new insights to understand possible evolutionary paths to the evolution of metabolic complexity
Positive selection for elevated gene expression noise in yeast
It is well known that the expression noise is lessened by natural selection for genes that are important for cell growth or are sensitive to dosage. In theory, expression noise can also be elevated by natural selection when noisy gene expression is advantageous. Here we analyze yeast genome-wide gene expression noise data and show that plasma-membrane transporters show significantly elevated expression noise after controlling all confounding factors. We propose a model that explains why and under what conditions elevated expression noise may be beneficial and subject to positive selection. Our model predicts and the simulation confirms that, under certain conditions, expression noise also increases the evolvability of gene expression by promoting the fixation of favorable expression level-altering mutations. Indeed, yeast genes with higher noise show greater between-strain and between-species divergences in expression, even when all confounding factors are excluded. Together, our theoretical model and empirical results suggest that, for yeast genes such as plasma-membrane transporters, elevated expression noise is advantageous, is subject to positive selection, and is a facilitator of adaptive gene expression evolution
Expression Level, Evolutionary Rate, and the Cost of Expression
There is great variation in the rates of sequence evolution among proteins encoded by the same genome. The strongest correlate of evolutionary rate is expression level: highly expressed proteins tend to evolve slowly. This observation has led to the proposal that a major determinant of protein evolutionary rate involves the toxic effects of protein that misfolds due to transcriptional and translational errors (the mistranslation-induced misfolding [MIM] hypothesis). Here, I present a model that explains the correlation of evolutionary rate and expression level by selection for function. The basis of this model is that selection keeps expression levels near optima that reflect a trade-off between beneficial effects of the protein's function and some nonspecific cost of expression (e.g., the biochemical cost of synthesizing protein). Simulations confirm the predictions of the model. Like the MIM hypothesis, this model predicts several other relationships that are observed empirically. Although the model is based on selection for protein function, it is consistent with findings that a protein's rate of evolution is at most weakly correlated with its importance for fitness as measured by gene knockout experiments
Metabolic Futile Cycles and Their Functions: A Systems Analysis of Energy and Control
It has long been hypothesized that futile cycles in cellular metabolism are
involved in the regulation of biochemical pathways. Following the work of
Newsholme and Crabtree, we develop a quantitative theory for this idea based on
open-system thermodynamics and metabolic control analysis. It is shown that the
{\it stoichiometric sensitivity} of an intermediary metabolite concentration
with respect to changes in steady-state flux is governed by the effective
equilibrium constant of the intermediate formation, and the equilibrium can be
regulated by a futile cycle. The direction of the shift in the effective
equilibrium constant depends on the direction of operation of the futile cycle.
High stoichiometric sensitivity corresponds to ultrasensitivity of an
intermediate concentration to net flow through a pathway; low stoichiometric
sensitivity corresponds to super-robustness of concentration with respect to
changes in flux. Both cases potentially play important roles in metabolic
regulation. Futile cycles actively shift the effective equilibrium by expending
energy; the magnitude of changes in effective equilibria and sensitivities is a
function of the amount of energy used by a futile cycle. This proposed
mechanism for control by futile cycles works remarkably similarly to kinetic
proofreading in biosynthesis. The sensitivity of the system is also intimately
related to the rate of concentration fluctuations of intermediate metabolites.
The possibly different roles of the two major mechanisms for cellular
biochemical regulation, namely reversible chemical modifications via futile
cycles and shifting equilibrium by macromolecular binding, are discussed.Comment: 11 pages, 5 figure
Systems approaches to modelling pathways and networks.
Peer reviewedPreprin
Photorespiration: metabolic pathways and their role in stress protection
Photorespiration results from the oxygenase reaction catalysed by ribulose-1,5-bisphosphate carboxylase/
oxygenase. In this reaction glycollate-2-phosphate is produced and subsequently metabolized in the
photorespiratory pathway to form the Calvin cycle intermediate glycerate-3-phosphate. During this metabolic
process, CO2 and NH3 are produced and ATP and reducing equivalents are consumed, thus
making photorespiration a wasteful process. However, precisely because of this ine¤ciency, photorespiration
could serve as an energy sink preventing the overreduction of the photosynthetic electron transport
chain and photoinhibition, especially under stress conditions that lead to reduced rates of photosynthetic
CO2 assimilation. Furthermore, photorespiration provides metabolites for other metabolic processes, e.g.
glycine for the synthesis of glutathione, which is also involved in stress protection. In this review, we
describe the use of photorespiratory mutants to study the control and regulation of photorespiratory pathways.
In addition, we discuss the possible role of photorespiration under stress conditions, such as
drought, high salt concentrations and high light intensities encountered by alpine plants
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