Dominique Chu

Translation elongation can control translation initiation on eukaryotic mRNAs

Synonymous codons encode the same amino acid, but differ in other biophysical properties. The evolutionary selection of codons whose properties are optimal for a cell generates the phenomenon of codon bias. Although recent studies have shown strong effects of codon usage changes on protein expression levels and cellular physiology, no translational control mechanism is known that links codon usage to protein expression levels. Here, we demonstrate a novel translational control mechanism that responds to the speed of ribosome movement immediately after the start codon. High initiation rates are only possible if start codons are liberated sufficiently fast, thus accounting for the observation that fast codons are overrepresented in highly expressed proteins. In contrast, slow codons lead to slow liberation of the start codon by initiating ribosomes, thereby interfering with efficient translation initiation. Codon usage thus evolved as a means to optimise translation on individual mRNAs, as well as global optimisation of ribosome availability.

Models of transcription factor binding : Sensitivity of activation functions to model assumptions

We present three models of how transcription factors (TFs) bind to their specific binding sites on the DNA: a model based on statistical physics, a Markov-chain model and a computational simulation. Comparison of these models suggests that the effect of non-specific binding can be significant. We also investigate possible mechanisms for cooperativity. The simulation model suggests that direct interactions between TFs are unlikely to be the main source of cooperativity between specific binding sites, because such interactions tend to lead to the formation of clusters on the DNA with undesirable side-effects.

The role of tRNA and ribosome competition in coupling the expression of different mRNAs in Saccharomyces cerevisiae

Protein synthesis translates information from messenger RNAs into functional proteomes. Because of the finite nature of the resources required by the translational machinery, both the overall protein synthesis activity of a cell and activity on individual mRNAs are controlled by the allocation of limiting resources. Upon introduction of heterologous sequences into an organism—for example for the purposes of bioprocessing or synthetic biology—limiting resources may also become overstretched, thus negatively affecting both endogenous and heterologous gene expression. In this study, we present a mean-field model of translation in Saccharomyces cerevisiae for the investigation of two particular translational resources, namely ribosomes and aminoacylated tRNAs. We firstly use comparisons of experiments with heterologous sequences and simulations of the same conditions to calibrate our model, and then analyse the behaviour of the translational system in yeast upon introduction of different types of heterologous sequences. Our main findings are that: competition for ribosomes, rather than tRNAs, limits global translation in this organism; that tRNA aminoacylation levels exert, at most, weak control over translational activity; and that decoding speeds and codon adaptation exert strong control over local (mRNA specific) translation rates.

A Category Theoretical Argument against the Possibility of Artificial Life: Robert Rosen's Central Proof Revisited

One of Robert Rosens main contributions to the scientific community is summarized in his book Life itself. There Rosen presents a theoretical framework to define living systems; given this definition, he goes on to show that living systems are not realizable in computational universes. Despite being well known and often cited, Rosens central proof has so far not been evaluated by the scientific community. In this article we review the essence of Rosens ideas leading up to his rejection of the possibility of real artificial life in silico. We also evaluate his arguments and point out that some of Rosens central notions are ill defined. The conclusion of this article is that Rosens central proof is wrong.

The architecture of eukaryotic translation

Translation in baker’s yeast involves the coordinated interaction of 200 000 ribosomes, 3 000 000 tRNAs and between 15 000 and 60 000 mRNAs. It is currently unknown whether this specific constellation of components has particular relevance for the requirements of the yeast proteome, or whether this is simply a frozen accident. Our study uses a computational simulation model of the genome-wide translational apparatus of yeast to explore quantitatively which combinations of mRNAs, ribosomes and tRNAs can produce viable proteomes. Surprisingly, we find that if we only consider total translational activity over time without regard to composition of the proteome, then there are many and widely differing combinations that can generate equivalent synthesis yields. In contrast, translational activity required for generating specific proteomes can only be achieved within a much more constrained parameter space. Furthermore, we find that strongly ribosome limited regimes are optimal for cells in that they are resource efficient and simplify the dynamics of the system.

Complexity: against systems

This article assumes a specific intuitive notion of complexity as a difficulty to generate and/or assess the plausibility of models. Based on this intuitive understanding of complexity, it identifies two main causes of complexity, namely, radical openness and contextuality. The former is the idea that there are no natural systems. The modeler always needs to draw artificial boundaries around phenomena to generate feasible models. Contextuality is intimately connected to the requirement to simplify models and to leave out most aspects. Complexity occurs when contextuality and radical openness cannot be contained that is when it is not clear where the boundaries of the system are and which abstractions are the correct ones. This concept of complexity is illustrated using a number of example from evolution.

Evolving genetic regulatory networks for systems biology

Recently there has been significant interest in evolving genetic regulatory networks with a user-determined behaviour. It is unclear whether or not artificial evolution of biochemical networks can be of direct benefit for or biological relevance to systems biology. This article highlights some pitfalls when concluding from artificially evolved genetic regulatory networks to real networks. This article also gives a (brief) review of some previous attempts to evolve genetic regulatory networks with oscillatory behaviour; it also describes a new system to evolve networks and describes the networks that have been evolved. These networks seem to be very diverse sharing no apparent common motifs either with one another or with their real-life counterparts.

Computational limits to binary genes

We analyse the trade-off between the speed with which a gene can propagate information, the noise of its output and its metabolic cost. Our main finding is that for any given level of metabolic cost there is an optimal trade-off between noise and processing speed. Any system with a non-vanishing leak expression rate is suboptimal, i.e. it will exhibit higher noise and/or slower speed than leak-free systems with the same metabolic cost. We also show that there is an optimal Hill coefficient h which minimizes noise and metabolic cost at fixed speeds, and an optimal threshold K which minimizes noise.

The Localization Hypothesis and Machines

In a recent article in Artificial Life, Chu and Ho suggested that Rosens central result about the simulability of living systems might be flawed. This argument was later declared null and void by Louie. In this article the validity of Louies objections are examined.

Computational Realizations of Living Systems

Robert Rosens central theorem states that organisms are fundamentally different from machines, mainly because they are closed with respect to effcient causation. The proof for this theorem rests on two crucial assumptions. The first is that for a certain class of systems (mechanisms) analytic modeling is the inverse of synthetic modeling. The second is that aspects of machines can be modeled using relational models and that these relational models are themselves refined by at least one analytic model. We show that both assumptions are unjustified. We conclude that these results cast serious doubts on the validity of Rosens proof.