A proposition based on the fluctuation theorem in thermodynamics is formulated to quantitatively describe molecular evolution processes in biology.Although we cannot give full proof of its generality,we demonstrate via computer simulation its applicability in an example of DNA in vitro evolution.According to this theorem,the evolution process is a series of exponentially rare fluctuations fixed by the force of natural selection.
It is of significance for splice site prediction to develop novel algorithms that combine the sequence patterns of regulatory elements such as enhancers and silencers with the patterns of splicing signals.In this paper,a statistical model of splicing signals was built based on the entropy density profile(EDP) method,weight array method(WAM) and κ test;moreover,the model of splicing regulatory elements was developed by an unsupervised self-learning method to detect motifs associated with regulatory elements.With two models incorporated,a multi-level support vector machine(SVM) system was de-vised to perform ab initio prediction for splice sites originating from DNA sequence in eukaryotic ge-nome.Results of large scale tests on human genomic splice sites show that the new method achieves a comparative high performance in splice site prediction.The method is demonstrated to be with at least the same level of performance and usually better performance than the existing SpliceScan method based on modeling regulatory elements,and shown to have higher accuracies than the traditional methods with modeling splicing signals such as the GeneSplicer.In particular,the method has evident advantage over splice site prediction for the genes with lower GC content.
Flux balance analysis, based on the mass conservation law in a cellular organism, has been extensively employed to study the interplay between structures and functions of cellular metabolic networks. Consequently, the phenotypes of the metabolism can be well elucidated. In this paper, we introduce the Expanded Flux Variability Analysis (EFVA) to characterize the intrinsic nature of metabolic reactions, such as flexibility, modularity and essentiality, by exploring the trend of the range, the maximum and the minimum flux of reactions. We took the metabolic network of Escherichia coli as an example and analyzed the variability of reaction fluxes under different growth rate constraints. The average variabil-ity of all reactions decreases dramatically when the growth rate increases. Consider the noise effect on the metabolic system, we thus argue that the microorganism may practically grow under a suboptimal state. Besides, under the EFVA framework, the reactions are easily to be grouped into catabolic and anabolic groups. And the anabolic groups can be further assigned to specific biomass constitute. We also discovered the growth rate dependent essentiality of reactions.
Constraint-based models such as flux balance analysis (FBA) are a powerful tool to study biological metabolic networks.Under the hypothesis that cells operate at an optimal growth rate as the result of evolution and natural selection,this model successfully predicts most cellular behaviours in growth rate.However,the model ignores the fact that cells can change their cellular metabolic states during evolution,leaving optimal metabolic states unstable.Here,we consider all the cellular processes that change metabolic states into a single term 'noise',and assume that cells change metabolic states by randomly walking in feasible solution space.By simulating a state of a cell randomly walking in the constrained solution space of metabolic networks,we found that in a noisy environment cells in optimal states tend to travel away from these points.On considering the competition between the noise effect and the growth effect in cell evolution,we found that there exists a trade-off between these two effects.As a result,the population of the cells contains different cellular metabolic states,and the population growth rate is at suboptimal states.
