Studies on computation of pathways connecting two metabolites have been reported. However, they did not intend to find pathways containing cycling, although there are biologically important cycles such as citric acid cycle (CAC). Whilst computation of pathways connecting two atoms, single-atom tracing, would contribute to finding pathways which include those containing cycling, it produces too many pathways to examine. The present article proposes a strategy to select pathways from those obtained by single-atom tracing, where coexistence of reactions on each pathway, specifically coexistence of a reaction and its reverse reaction forming a futile cycle together or reactions regulated in a reciprocal manner, is checked to select pathways based on biochemical meaning of the pathway. Using this strategy, 121 pathways were selected from total 7876 pathways from carbon atoms of glucose to CO2 in a model network of carbohydrate metabolism. The selected pathways included pathways using reactions or metabolites of CAC or pentose phosphate pathway multiple times. These results indicate that the proposed strategy can contribute to listing a limited number of pathways which include those containing cycling as possibly biochemically meaningful pathways.
Protein production in plants is a hot topic because there are many benefits relative to bacteria, yeasts, and animals, but the amount of protein expression in plants is less. It is argued that editing 5'UTRs increases the amount of translated proteins. However, obtaining such 5'UTRs is difficult due to the cost, time and effort required in experiments. To solve this, we predict the amount of translated proteins by machine learning. In this paper, we propose a method, named “R-STEINER, ” that generates 5'UTRs that increase the amount of proteins of a given gene. The proposed process involves building a model for predicting the amount of translated proteins, generating 5'UTRs, selecting them and increasing the proteins according to the model. This method enables us to obtain 5'UTRs that increase the amount of translated proteins without real synthesis experiments, resulting in reduced cost, time and effort. In our study, we built a prediction model for Oryza sativa and synthesized the 5'UTRs generated by R-STEINER. We confirmed that the model can predict the amount of translated proteins with a correlation coefficient of 0.89.
Ca2+ waves propagate through the oocyte during fertilization, activate the oocyte and induce embryonic development. Ca2+-induced Ca2+-release (CICR) is a mechanism of Ca2+ wave formation. We previously quantified the Ca2+ waves in the nematode Caenorhabditis elegans by using high-speed imaging and image analysis. We found that the waves consist of a rapid local rise at the point of sperm entry and a slow global wave. We demonstrated that the Nagumo model, which models the CICR by a reaction-diffusion equation, can produce a similar biphasic waveform. However, the model cannot represent the observed gradual decrease in maximum Ca2+ concentration with increasing distance from the point of sperm entry. In this study, we introduced a linear monotonically decreasing function into the reaction part of the Nagumo model. We demonstrated that our new model can produce the gradual decrease in maximum Ca2+ concentration with increasing distance from the point of sperm entry and a biphasic waveform simultaneously.
Kinetic modeling is a powerful tool to understand how a biochemical system behaves as a whole. To develop a realistic and predictive model, kinetic parameters need to be estimated so that a model fits experimental data. However, parameter estimation remains a major bottleneck in kinetic modeling. To accelerate parameter estimation, we developed a C library for real-coded genetic algorithms (libRCGA). In libRCGA, two real-coded genetic algorithms (RCGAs), viz. the Unimodal Normal Distribution Crossover with Minimal Generation Gap (UNDX/MGG) and the Real-coded Ensemble Crossover star with Just Generation Gap (REX star/JGG), are implemented in C language and paralleled by Message Passing Interface (MPI). We designed libRCGA to take advantage of high-performance computing environments and thus to significantly accelerate parameter estimation. Constrained optimization formulation is useful to construct a realistic kinetic model that satisfies several biological constraints. libRCGA employs stochastic ranking to efficiently solve constrained optimization problems. In the present paper, we demonstrate the performance of libRCGA through benchmark problems and in realistic parameter estimation problems. libRCGA is freely available for academic usage at http://kurata21.bio.kyutech.ac.jp/maeda/index.html.
Lead optimization is an essential step in drug discovery in which the chemical structures of compounds are modified to improve characteristics such as binding affinity, target selectivity, physicochemical properties, and toxicity. We present a concept for a computational compound optimization system that outputs optimized compounds from hit compounds by using previous lead optimization data from a pharmaceutical company. In this study, to predict the drug-likeness of compounds in the evaluation function of this system, we evaluated and compared the ability to correctly predict lead optimization strategies through learning to rank methods.