Different chemotherapeutic strategies like Maximum Tolerable Dosing (MTD), Metronomic Chemotherapy (MCT), and Antiangiogenic (AAG) drug are available; however, the selection of the best therapeutic strategy for an individual patient remains uncertain till now. Several analytical models are proposed for each of the chemotherapeutic strategies; however, no single analytical model is available which can make a comparative assessment regarding the long-term therapeutic efficacy among these strategies. This, in turn, may limit the clinical application of such analytical models. To address this issue here we developed a composite synergistic system (CSS) model. Through this CSS model, comparative assessment among the MTD, MCT, and AAG drug therapy can be assessed. Moreover, these chemotherapeutic strategies along with different supportive therapies like Hematopoietic Stem Cell transplantation (HSC), cellular immunotherapy as well as different combinations among these therapeutic strategies can be assessed. Fitting of initial clinical data of individual clinical cases to this analytical model followed by simulation runs may help in making such decision. Analytical assessments suggest that with the considered tumor condition MCT alone could be more effective one than any other therapeutics and/or their combinations for controlling the long-term tumor burden.

Probir Kumar Dhar

Durjoy Majumder

A Composite Synergistic Systems Model for Exploring the Efficacies of Different Chemotherapeutic Strategies in Cancer

Current genomic analyses often require the managing and comparison of big data using desktop bioinformatic software that was not developed regarding multicore distribution.  The task-farm SCBI&#x5f;M<small class="sc">AP</small>R<small class="sc">EDUCE</small> is intended to simplify the trivial parallelisation and distribution of new and legacy software and scripts for biologists who are interested in using computers but are not skilled programmers. In the case of legacy applications, there is no need of modification or rewriting the source code. It can be used from multicore workstations to heterogeneous grids. Tests have demonstrated that speed-up scales almost linearly and that distribution in small chunks increases it. It is also shown that SCBI&#x5f;M<small class="sc">AP</small>R<small class="sc">EDUCE</small> takes advantage of shared storage when necessary, is fault-tolerant, allows for resuming aborted jobs, does not need special hardware or virtual machine support, and provides the same results than a parallelised, legacy software. The same is true for interrupted and relaunched jobs. As proof-of-concept, distribution of a compiled version of B<small class="sc">LAST+</small> in the SCBI&#x5f;D<small class="sc">ISTRIBUTED</small>&#x5f;B<small class="sc">LAST</small> gem is given, indicating that other blast  binaries can be used while maintaining the same SCBI&#x5f;D<small class="sc">ISTRIBUTED</small>&#x5f;B<small class="sc">LAST</small> code. Therefore, SCBI&#x5f;M<small class="sc">AP</small>R<small class="sc">EDUCE</small> suits most parallelisation and distribution needs in, for example, gene and genome studies.

Dar&#xed;o Guerrero-Fern&#xe1;ndez

Juan Falgueras

M. Gonzalo Claros

SCBI&#x5f;MapReduce, a New Ruby Task-Farm Skeleton for Automated Parallelisation and Distribution in Chunks of Sequences: The Implementation of a Boosted Blast+

We deal with the problem of protein superfamily classification in which the family membership of newly discovered amino acid sequence is predicted. Correct prediction is a matter of great concern for the researchers and drug analyst which helps them in discovery of new drugs. As this problem falls broadly under the category of pattern classification problem, we have made all efforts to optimize feature extraction in the first stage and classifier design in the second stage with an overall objective to maximize the performance accuracy of the classifier. In the feature extraction phase, Genetic Algorithm- (GA-) based wrapper approach is used to select few eigenvectors from the principal component analysis (PCA) space which are encoded as binary strings in the chromosome. On the basis of position of 1&#x2019;s in the chromosome, the eigenvectors are selected to build the transformation matrix which then maps the original high-dimension feature space to lower dimension feature space. Using PCA-NSGA-II (non-dominated sorting GA), the nondominated solutions obtained from the Pareto front solve the trade-off problem by compromising between the number of eigenvectors selected and the accuracy obtained by the classifier. In the second stage, recursive orthogonal least square algorithm (ROLSA) is used for training radial basis function network (RBFN) to select optimal number of hidden centres as well as update the output layer weighting matrix. This approach can be applied to large data set with much lower requirements of computer memory. Thus, very small architectures having few number of hidden centres are obtained showing higher level of performance accuracy.

Swati Vipsita

Santanu Ku. Rath

Two-Stage Approach for Protein Superfamily Classification

Typhoid fever continues to be a major public health problem in the developing world. Antibiotic therapy has been the main stay of treating typhoid fever for decades. The emergence of drug-resistant typhoid strain in the last two decades has been a major problem in tackling this scourge. A mathematical model for investigating the impact of drug resistance on the transmission dynamics of typhoid fever is developed. The reproductive number for the model has been computed. Numerical results in this study suggest that when a typhoid outbreak occurs with more drug-sensitive cases than drug-resistant cases, then it may take 10&#x2013;15 months for symptomatic drug-resistant cases to outnumber all typhoid cases, and it may take an average of 15&#x2013;20 months for nonsymptomatic drug-resistant cases to outnumber all drug-sensitive cases.

S. Mushayabasa

C. P. Bhunu

E. T. Ngarakana-Gwasira

Assessing the Impact of Drug Resistance on the Transmission Dynamics of Typhoid Fever

In 1996, a trinucleotide circular code which is maximum, self-complementary, and <svg     style="vertical-align:-0.23206pt;width:18.4375px;" id="M1" height="16.125" version="1.1" viewBox="0 0 18.4375 16.125" width="18.4375"  xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg">
	
		
			<g transform="matrix(.017,-0,0,-.017,.062,15.775)"><path id="x1D436" d="M682 629q-1 -16 -1.5 -72t-2.5 -86l-31 -4q-5 92 -51 129t-139 37q-100 0 -177 -49t-116 -125t-39 -162q0 -122 66 -201t182 -79q83 0 137 42.5t112 128.5l26 -15q-12 -31 -42.5 -88t-45.5 -75q-139 -27 -199 -27q-148 0 -243 81.5t-95 226.5q0 173 129.5 274.5&#xA;t325.5 101.5q114 0 204 -38z" /></g>

			<g transform="matrix(.012,-0,0,-.012,12.05,7.613)"><path id="x33" d="M285 378v-2q65 -13 102 -54.5t37 -97.5q0 -57 -30.5 -104.5t-74 -75t-85.5 -42t-72 -14.5q-31 0 -59.5 11t-40.5 23q-19 18 -16 36q1 16 23 33q13 10 24 0q58 -51 124 -51q55 0 88 40t33 112q0 64 -39 96.5t-88 32.5q-29 0 -64 -11l-6 29q77 25 118 57.5t41 84.5&#xA;q0 45 -26.5 69.5t-68.5 24.5q-67 0 -120 -79l-20 20l43 63q51 56 127 56h1q66 0 107 -37t41 -95q0 -42 -31 -71q-22 -23 -68 -54z" /></g>

		
	
</svg>, called <svg     style="vertical-align:-3.3907pt;width:19.924999px;" id="M2" height="15.4" version="1.1" viewBox="0 0 19.924999 15.4" width="19.924999"  xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg">
	
		
			<g transform="matrix(.017,-0,0,-.017,.062,11.112)"><path id="x1D44B" d="M775 650l-6 -28q-60 -6 -81.5 -16t-61.5 -54l-175 -191l125 -243q30 -58 48.5 -71t82.5 -19l-5 -28h-275l7 28l35 4q31 4 37 12t-6 34l-108 216q-140 -165 -177 -219q-16 -22 -10.5 -30.5t41.5 -13.5l22 -3l-7 -28h-244l8 28q52 4 75 15.5t67 52.5q48 46 206 231&#xA;l-110 215q-26 51 -44 63t-72 17l6 28h250l-6 -28l-27 -4q-30 -5 -35 -10t3 -27q17 -43 95 -190q70 78 154 185q15 21 10 29.5t-33 12.5l-30 4l5 28h236z" /></g>

			<g transform="matrix(.012,-0,0,-.012,13.525,15.187)"><path id="x30" d="M241 635q53 0 94 -28.5t63.5 -76t33.5 -102.5t11 -116q0 -58 -11 -112.5t-34 -103.5t-63.5 -78.5t-94.5 -29.5t-95 28t-64.5 75t-34.5 102.5t-11 118.5q0 58 11.5 112.5t34.5 103t64.5 78t95.5 29.5zM238 602q-32 0 -55.5 -25t-35.5 -68t-17.5 -91t-5.5 -105&#xA;q0 -76 10 -138.5t37 -107.5t69 -45q32 0 55.5 25t35.5 68.5t17.5 91.5t5.5 105t-5.5 105.5t-18 92t-36 68t-56.5 24.5z" /></g>

		
	
</svg>, was identified statistically on a large gene population of eukaryotes and prokaryotes (Arqu&#xe8;s and Michel (1996)). Transition and transversions I and II are classical molecular evolution processes. A comprehensive computer analysis of these three evolution processes in the code <svg     style="vertical-align:-3.3907pt;width:19.924999px;" id="M3" height="15.4" version="1.1" viewBox="0 0 19.924999 15.4" width="19.924999"  xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg">
	
		
			<g transform="matrix(.017,-0,0,-.017,.062,11.112)"><use xlink:href="#x1D44B"/></g>

			<g transform="matrix(.012,-0,0,-.012,13.525,15.187)"><use xlink:href="#x30"/></g>

		
	
</svg> shows some new results; in particular (i) transversion I on the 2nd position of any subset of trinucleotides of <svg     style="vertical-align:-3.3907pt;width:19.924999px;" id="M4" height="15.4" version="1.1" viewBox="0 0 19.924999 15.4" width="19.924999"  xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg">
	
		
			<g transform="matrix(.017,-0,0,-.017,.062,11.112)"><use xlink:href="#x1D44B"/></g>

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</svg>
 generates trinucleotide circular codes which are always <svg     style="vertical-align:-0.23206pt;width:18.4375px;" id="M5" height="16.125" version="1.1" viewBox="0 0 18.4375 16.125" width="18.4375"  xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg">
	
		
			<g transform="matrix(.017,-0,0,-.017,.062,15.775)"><use xlink:href="#x1D436"/></g>

			<g transform="matrix(.012,-0,0,-.012,12.05,7.613)"><use xlink:href="#x33"/></g>

		
	
</svg> and (ii) transversion II on the three positions of any subset of trinucleotides of <svg     style="vertical-align:-3.3907pt;width:19.924999px;" id="M6" height="15.4" version="1.1" viewBox="0 0 19.924999 15.4" width="19.924999"  xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg">
	
		
			<g transform="matrix(.017,-0,0,-.017,.062,11.112)"><use xlink:href="#x1D44B"/></g>

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</svg>
 yields no trinucleotide circular codes. These new results extend our theory of circular code in genes to its evolution under transition and transversion.

Emmanuel Benard

Christian J. Michel

Transition and Transversion on the Common Trinucleotide Circular Code

Biochemical networks are not only complex but also extremely large. The dynamic biological model of great complexity resulting in a large number of parameters is a main difficulty for optimization and control processes. In practice, it is highly desirable to further simplify the structure of biological models for the sake of reducing computational cost or simplification for the task of system analysis. This paper considers the S-system model used for describing the response of biochemical networks. By introducing the technique of singular value decomposition (SVD), we are able to identify the major state variables and parameters and eliminate unimportant metabolites and the corresponding signal transduction pathways. The model reduction by multiobjective analysis integrates the criteria of reactive weight, sensitivity, and flux analyses to obtain a reduced model in a systematic way. The resultant model is closed to the original model in performance but with a simpler structure. Representative numerical examples are illustrated to prove feasibility of the proposed method.

Yen-Chang Liu

Chun-Liang Lin

Chia-Hua Chuang

An Approach for Model Reduction of Biochemical Networks

Sparse-grid interpolation provides good approximations to smooth functions in high dimensions based on relatively few function evaluations, but in standard form it is expressed in Lagrange polynomials. Here, we give a block-diagonal factorization of the basis-change matrix to give an efficient conversion of a sparse-grid interpolant to a tensored orthogonal polynomial (or gPC) representation. We describe how to use this representation to give an efficient method for estimating Sobol' sensitivity coefficients and apply this method to analyze and efficiently approximate a complex model of T-cell signaling events.

Gregery T. Buzzard

Efficient Basis Change for Sparse-Grid Interpolating Polynomials with Application to T-Cell Sensitivity Analysis

Cystic fibrosis is one of the most common inherited diseases and is caused by a mutation in a membrane protein, the cystic fibrosis transmembrane conductance regulator (CFTR). This protein serves as a chloride channel and regulates the viscosity of mucus lining the ducts of a number of organs. Although much has been learned about the consequences of mutations on the energy landscape and the resulting disrupted folding pathway of CFTR, a level of understanding needed to correct the misfolding has not been achieved. The most common mutations of CFTR are located in one of two nucleotide binding domains, namely, the nucleotide binding domain 1 (NBD1). We model NBD1 using a nested graph model. The vertices in the lowest layer each represent an atom in the structure of an amino acid residue, while the vertices in the mid layer each represent the residue. The vertices in the top layer each represent a subdomain of the nucleotide binding domain. We use this model to quantify the effects of a single point mutation on the protein domain. We compare the wildtype structure with eight of the most common mutations. The graph-theoretic model provides insight into how a single point mutation can have such profound structural consequences.

Debra J. Knisley

Jeff R. Knisley

Andrew Cade Herron

Graph-Theoretic Models of Mutations in the Nucleotide Binding Domain 1 of the Cystic Fibrosis Transmembrane Conductance Regulator

Density functional theory has been used to calculate harmonic normal mode vibrational frequencies for unlabeled and isotope-labeled ubisemiquinones in both the gas phase and in several solvents. It is shown that four methoxy group conformations are likely to be present in solution at room temperature. Boltzmann weighted infrared and Raman spectra for the four conformers were calculated, and composite spectra that are the sum of the Boltzmann weighted spectra were produced. These composite spectra were compared to experimental FTIR and resonance Raman spectra, and it is shown that the calculated band frequencies, relative band intensities, and <svg     style="vertical-align:-0.20473pt;width:23.475px;" id="M1" height="16.1" version="1.1" viewBox="0 0 23.475 16.1" width="23.475"  xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg">
	
		
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</svg> and <svg     style="vertical-align:-0.20473pt;width:24.862499px;" id="M2" height="16.1" version="1.1" viewBox="0 0 24.862499 16.1" width="24.862499"  xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg">
	
		
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			<g transform="matrix(.017,-0,0,-.017,12.112,15.775)"><path id="x4F" d="M381 665q131 0 226.5 -93t95.5 -239q0 -158 -96 -253t-238 -95q-137 0 -231 95t-94 238q0 142 92.5 244.5t244.5 102.5zM359 629q-89 0 -151 -74.5t-62 -208.5q0 -141 69 -233t175 -92q90 0 150.5 74t60.5 211q0 152 -69 237.5t-173 85.5z" /></g>

		
	
</svg> isotope-induced band shifts are in excellent agreement with experiment. The calculations show that the C=O and C=C modes of ubisemiquinone strongly mix with methoxy methyl CH bending vibrations, and that the degree of mixing is altered upon isotope labeling, resulting in complicated changes in mode frequencies, intensities, and composition upon isotope labeling. Upon consideration of the calculated potential energy distributions of the normal modes of ubisemiquinone, and how they change upon isotope labeling, an explanation of some puzzling features in previously published Raman spectra is provided.

Hari P. Lamichhane

Gary Hastings

Calculated Vibrational Properties of Ubisemiquinones

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Computational Biology Journal