Журнал компьютерных наук и системной биологии

Evolutionary Algorithms, although powerful, are known to be wasteful and time consuming, requiring the evaluation of a large number of candidates. However the strength of the methodology is their ability to continually optimise the population hopefully ensuring a near optimal final solution. When applied to automatic programming tasks, the same limitations are observed, notably the time taken to develop a solution. An alternate, swarm-based method ‘Grammatical Herding’ suffers from the opposite concerns. Whilst it generates moderate fitness solutions quickly, these candidates often lack the optimisation of solutions generated via an evolutionary approach. This study details a hybrid technique ‘Seeded Grammatical Evolution’ where Grammatical Herding (GH) is used to seed the initial population of a Grammatical Evolution (GE) algorithm, with the result that the final solution is produced faster than one produced by GE alone and more effective (fitter) than one produced by GH. In this paper, we explore the background to the study including the initial work that inspired the approach. We also discuss the design of the algorithm and finally the results. We conclude that the hybrid approach is not only capable of producing a fast solution but also achieves state of the art results on a standard benchmark problem, the Santa Fe Trail.

In systems biology, systems of kinetic reactions are generally used to model and simulate various biochemical pathways. These reactions are translated into ordinary differential equations, which are computationally resolved by numerical algorithms. Computation performance, defined by how fast the algorithm converges to a numerical solution of the system of ordinary differential equations, critically depends on the choice of the appropriate algorithm. In this paper, we compared several algorithms used to solve ordinary differential equations applied to several kinetic models that describe the dynamic behavior of receptors and ion channels found in chemical synapses of the Central Nervous System; we provide a simplified method to determine the performances of these ordinary differential equation solvers, in order to provide a benchmark for algorithm selection. This method will facilitate the choice of the most efficient algorithm for a given kinetic model with a minimum number of tests. Our results provide a tool for identifying optimal solvers for any biological bilinear kinetic models under various experimental conditions. This comparison also underscored the complexity of biological kinetic models and illustrates how their input dependency could interfere with performance. Despite these challenges, our simplified method helps to select the best solvers for any synaptic receptors kinetic models described, with a bilinear system with minimal a priori information on the solver structure and the model.

Nanogenomics, being the interplay of nanobiotechnologies and bioinformatics, is emerging as an intriguing approach in the field of nowadays biomedicine. Microarrays can produce a wealth of data and details, but they need an algorithm for data reduction to be clearly understood and exploited. The Leader Genes approach, integrating the different available databases and genomics tools, enables the user to search for genes linked to a disease or a cellular process, and to visualize the class of the most important genes, that is to say those having the highest number of interconnections. In this manuscript, we will review the algorithm which has been validated with both experimental and clinical studies. We will describe the different steps that lead to its final version, and we will discuss future perspectives and developments.

Oftentimes, we want to discover the genes whose expression levels are associated with a time-to-event endpoint, such as progression free survival or overall survival, through microarray studies. In this case, we need to adjust the false positivity in such discovery procedure for multiplicity of the genes using a multiple testing method. The most popular multiple testing methods used for gene discovery in microarray studies are to control the false discovery rate or the family wise error rate. In this paper, we review a FDR-control method to discover the genes associated with a time-to-event outcome and propose a sample size calculation method for microarray studies designed to discover genes whose expression levels are associated the chosen time-to-event outcome. These methods can be easily modified for other types of high throughput genome projects.

α2-adrenergic receptors play a key role in the regulation of sympathetic system, neurotransmitter release, blood pressure and intraocular pressure. Although α2-adrenergic receptors mediate a number of physiological functions in vivo and have great therapeutic potential, the absence of crystal structure of α2-adrenergic receptor subtypes is a major hindrance in the drug design efforts. The therapeutic efficacy of the available drugs is not selective for subtype specificity (α2a, α2b and α2c) leading to unwanted side effects. We used Homology modelling and docking studies to understand and analyze the residues important for agonist and antagonist binding. We have also analyzed binding site volume, and the residue variations which may play important role in ligand binding. We have identified residues through our modelling and docking studies, which would be critical in giving subtype specificity and may help in the development of future subtype-selective drugs.

Rock-Paper-Scissors is a game played by two players to deter-mine a single winner. Biological relationships of Rock-Paper-Scissors are documented. In this paper, we form a continuous model of Rock-Papers-Scissors in the chemostat that coincides with the biology of such relationships. The basic models that we develop coincide with the observed phenomena. Be-cause the model involves a system of seven nonlinear differential equations, global results are difficult to obtain. We present several numerical studies that are the result of a substantial number of numerical trials to illustrate the various possibilities that might occur in the context of the problem discussed here.

Virtual screening of chemical databases has become an integral part of ligand design. Docking is one of the most important methods in computer assisted screening. If a three dimensional structure of target receptor is available, along with information regarding nature of the ligand-binding site, ligand-binding mode; the interactions between the ligand and receptor can be studied extensively, in order to design and develop target specific new compounds in a short time period. Juvenile Hormone Analogues, sesqui-terpenoid series of compound act as an insect growth regulator, and presently in use as a potential environment friendly pesticide. Juvenile hormone is the molting hormone responsible for each molt, and involved in a wide range of physiological processes in both developing and mature insect. Designing of various juvenile hormone analogues are new and emerging area to counter the insect problem. In this paper, we report protein-ligand interactions using a standard protocol of docking. We perform screening of synthesized (A-B) and proposed (C-D) series of Juvenile Hormone Analogues with hemolymph binding proteins of Galleria mellonella. Further binding energy profile of all the series have been compared with the phenoxy derivatives of juvenile hormone mimics, as well as natural JH III, in order to design targeted JHAs with improved biological activities. Our proposed series of juvenile hormone analogues exhibit better energy profile over in use phenoxy derivatives.

The presence of T-Wave Alternans (TWA) in an electrocardiogram (ECG) has been certified as an important predictor for the risk of sudden cardiac death (SCD). TWA is a beat-to-beat change in the amplitude of a T-wave, but is rarely visible to the naked eye. Thus, automatic detection and quantification of TWA are desirable. While several automatic algorithms, such as the periodogram method or the modified moving average method (MMA), have been developed to detect or quantify TWA, most conventional methods do not effectively measure short-duration TWA (SDTWA) (<16 beats). In this paper, we proposed a fractal dimension based SDTWA quantification method, and evaluated it with simulated ECG signals with SDTWA episodes (<16 beats) based on the European ST-T database. In the evaluation, the proposed method was applied to ECG signals with TWA amplitude of 5, 15, 30, 45, 60 and 75 μV. Sensitivity and positive predictivity of over or closed to 90% were obtained except for the 5 μV SDTWA episodes. Even for 5 μV SDTWA episodes, the sensitivity reached 75%. We believe that proposed s fractal dimension based method is a promising method for SDTWA analysis.

A number of homologous human heat shock proteins (HSPs) are available in plants. Human heat shock proteins (HSPs), which are expressed at higher temperature or other stress, have chaperone activity belong to five conserved families: HSP33, HSP60, HSP70, HSP90 and HSP100. A well known bioinformatics program BLASTp reveals that each of the human HSP families possesses a number of plant homologues except HSP33. Out of the rest four families, HSP70 carries best plant homologue. The closest identified plant homologue of human HSP_7C is a protein of unknown function (NCBI Accession XP_002332067) derived from Populus trichocarpa. In silico comparative studies have showed invigorating similarity between human HSP_7C and the designated plant protein. Secondary and three-dimensional (3D) structure analysis of the predicted plant protein strongly supports its functional relationship to the class of human HSP70.