Journal of Bioinformatics and Computational Biology

Urinary proteins serve as indicators of various conditions in human normal physiology and disease pathology. Using mass spectrometry proteome analysis, the permanent constituent of the urine was examined in the Mars-500 experiment (520 days isolation of healthy volunteers in a terrestrial complex with an autonomous life support system). Seven permanent proteins with predominant distribution in the liver and blood plasma as well as extracellular localization were identified. Analysis of the overrepresentation of the molecular functions and biological processes based on Gene Ontology revealed that the functional association among these proteins was low. The results showed that the identified proteins may be independent markers of the various conditions and processes in healthy humans and that they can be used as standards in determination of the concentration of other proteins in the urine.

In the present work, rotational oscillations of nitrogenous bases in the DNA with the sequence of the gene coding interferon alpha 17 (IFNA17), are investigated. As a mathematical model simulating oscillations of the bases, we use a system of two coupled nonlinear partial differential equations that takes into account effects of dissipation, action of external fields and dependence of the equation coefficients on the sequence of bases. We apply the methods of the theory of oscillations to solve the equations in the linear approach and to construct the dispersive curves determining the dependence of the frequency of the plane waves (ω) on the wave vector (q). In the nonlinear case, the solutions in the form of kink are considered, and the main characteristics of the kink: the rest energy (E₀), the rest mass (m₀), the size (d) and sound velocity (C₀), are calculated. With the help of the energetic method, the kink velocity (υ), the path (S), and the lifetime (τ) are also obtained.

Alternative splicing is a widespread phenomenon in higher eukaryotes, where it serves as a mechanism to increase the functional diversity of proteins. This phenomenon has been described for different classes of proteins, including transcription regulatory proteins. We demonstrated that in the simplest genetic system model the formation of the alternatively spliced isoforms with opposite functions (activators and repressors) could be a cause of transition to chaotic dynamics. Under the simplest genetic system we understand a system consisting of a single gene encoding the structure of a transcription regulatory protein whose expression is regulated by a feedback mechanism. As demonstrated by numerical analysis of the models, if the synthesized isoforms regulate the expression of their own gene acting through different sites and independently of each other, for the generation of chaotic dynamics it is sufficient that the regulatory proteins have a dimeric structure. If regulatory proteins act through one site, the chaotic dynamics is generated in the system only when the repressor protein is either a tetrameric or a higher-dimensional multimer. In this case the activator can be a dimer. It was also demonstrated that if the transcription factor isoforms exhibit either activating or inhibiting activity and are lower-dimensional multimers (< 4), independently of the regulation type the model demonstrates either cyclic or stationary trajectories.

Molecular dynamics (MD) is widely used in computational biology for studying binding mechanisms of molecules, molecular transport, conformational transitions, protein folding, etc. The method is computationally expensive; thus, the demand for the development of novel, much more efficient algorithms is still high. Therefore, the new algorithm designed in 2007 and called interaction sorting (IS) clearly attracted interest, as it outperformed the most efficient MD algorithms. In this work, a new IS modification is proposed which allows the algorithm to utilize SIMD processor instructions. This paper shows that the improvement provides an additional gain in performance, 9% to 45% in comparison to the original IS method.

The epidermis of a linear leaf, as in Poaceae, is established by parallel files of cells originating from the leaf base. Their feature is symplastic growth where neighboring cell walls adhere and do not slide along each other. We developed a simple mechanical cell-based model for symplastic growth of linear leaf blade. The challenge is to determine what restrictions on cell size symplastic growth creates compared to the free growing cells. We assume an unidirectional growing cell ensemble starting from a meristem-like layer of generative cells and then generating parallel cell rows from every cell of the initial layer. Each cell is characterized by its growth function, and growth of the whole leaf blade is accompanied by mutual adjustment between all the cells. Cells divide once they have reached a threshold area. A mathematical model and its implementation are proposed for computational simulation of 1D symplastic growth of tissues. The question analyzed is how a cell grows in a plant tissue if there is a mechanism for regulating the growth of an isolated growing cell and the behavior of the cell wall matter is elastoplastic. The results of the simulation of linear leaf blade growth are compared to those for a free-growing cell population.

Macrochaetes (large bristles) are sensor organs of the Drosophila peripheral nervous system with a function of mechanoreceptors. An adult mechanoreceptor comprises four specialized cells: shaft (trichogen), socket (tormogen), neuron, and glial cell (thecogen). All these cells originate from a single cell, the so-called sensor organ precursor (SOP) cell. Separation of the SOP cell from the encompassing cells of the imaginal disc initiates a multistage process of sensory organ development. A characteristic feature of the SOP cell is the highest amount of the proneural proteins AS-C as compared with the encompassing ectodermal cells. The accumulation of proneural proteins and maintenance of their amount in the SOP cell at a necessary level is provided by the gene network with the achaete–scute gene complex (AS-C) as its key component. The activity of this complex is controlled by the central regulatory circuit (CRC). The CRC comprises the genes hairy, senseless (sens), charlatan (chn), scratch (scrt), daughterless (da), extramacrochaete (emc), and groucho (gro), coding for the transcription factors involved in the system of direct links and feedbacks and implementation of activation–repression relationships between the CRC components. The gene phyllopod (phyl), involved in degradation of the AS-C proteins, is also associated with the CRC functioning. In this paper, we propose a mathematical model for the CRC functioning as a regulator of the amount of proneural AS-C proteins in the SOP cell taking into account their degradation. The modeling has demonstrated that a change in the amount of proneural proteins in the SOP cell is stepwise rather than strictly monotonic. This prediction can be tested experimentally.

Drugs are essential for the prevention and treatment of diseases. However, co-administration of multiple drugs may cause serious adverse drug reactions, which are usually known but sometimes unknown. Package inserts of prescription drugs are supposed to contain risks and side effects, but such information is not necessarily complete. At the core of efforts to improve prescription quality, there is reliance on the extent and quality of information used for decision of a medical doctor. To address this on-going need, GraphSAW provides users a comprehensive view on drug-related pharmacological and molecular information. The features of GraphSAW allow users to analyze drug cocktails for adverse drug reactions and drug-induced diseases. Network visualization by drug mapping enables exploring associative networks of drugs, pathways, and diseases to fully understand effects of drugs in an intuitive way. GraphSAW is meant to be a platform and starting point for health professionals and researchers for educational and scientific research in order to achieve substantial improvements in patient safety.

In this paper, we consider the cyclopeptide sequencing problem that arose in computational biology with regard to de novo peptide sequencing in the 2000s. The sequencing problem for cyclic peptides is reduced in mathematics to the one-dimensional beltway problem: given a set of all circular pairwise distances between points, find the coordinates of these points. The beltway problem is one of the few fundamental problems that are neither known to be NP-complete nor solvable by polynomial-time algorithms. We develop an efficient algorithm for the cyclopeptide sequencing problem. The algorithm exploits information on possible elements of sequence and, thus, it makes it possible to restore sequences of lengths up to 160 elements. Numerical simulations sustain the effectiveness of the proposed algorithm.

Auxin is one of the main regulators of growth and development in plants. Prediction of auxin response based on gene sequence is of high importance. We found the TGTCNC consensus of 111 known natural and artificially mutated auxin response elements (AuxREs) with measured auxin-caused relative increase in genes' transcription levels, so-called either "a response to auxin" or "an auxin response." This consensus was identical to the most cited AuxRE motif. Also, we found several DNA sequence features that correlate with auxin-caused increase in genes' transcription levels, namely: number of matches with TGTCNC, homology score based on nucleotide frequencies at the consensus positions, abundances of five trinucleotides and five B-helical DNA features around these known AuxREs. We combined these correlations using a four-step empirical model of auxin response based on a gene's sequence with four steps, namely: (1) search for AuxREs with no auxin; (2) stop at the found AuxRE; (3) repression of the basal transcription of the gene having this AuxRE; and (4) manifold increase of this gene's transcription in response to auxin. Independently measured increases in transcription levels in response to auxin for 70 Arabidopsis genes were found to significantly correlate with predictions of this equation (r = 0.44, p < 0.001) as well as with TATA-binding protein (TBP)'s affinity to promoters of these genes and with nucleosome packing of these promoters (both, p < 0.025). Finally, we improved our equation for prediction of a gene's transcription increase in response to auxin by taking into account TBP-binding and nucleosome packing (r = 0.53, p < 10^-6). Fisher's F-test validated the significant impact of both TBP/promoter-affinity and promoter nucleosome on auxin response in addition to those of AuxRE, F = 4.07, p < 0.025. It means that both TATA-box and nucleosome should be taken into account to recognize transcription factor binding sites upon DNA sequences: in the case of the TATA-less nucleosome-rich promoters, recognition scores must be higher than in the case of the TATA-containing nucleosome-free promoters at the same transcription activity.