varunr14/newsummary huggingface dataset:
Although image based phenotypic assays are considered a powerful tool for siRNA library screening the reproducibility and biological implications of various image based assays are not well characterized in a systematic manner . Here we compared the resolution of high throughput assays of image based cell count and typical cell viability measures for cancer samples . It was found that the optimal plating density of cells was important to obtain maximal resolution in both types of assays . In general cell counting provided better resolution than the cell viability measure in diverse batches of siRNAs . In addition to cell count diverse image based measures were simultaneously collected from a single screening and showed good reproducibility in repetitions . They were classified into a few functional categories according to biological process based on the differential patterns of hit prioritization from the same screening data . The presented systematic analyses of image based parameters provide new insight to a multitude of applications and better biological interpretation of high content cell based assays . 


Summary: Image based multiplexing assay was attempted for siRNA library screening. Diverse orthogonal phenotypic parameters were retrieved from a single screening. Image based cell count provided better resolution than the viability measure. Geneset analysis revealed biological implications of various image based parameters.
Mammalian target of rapamycin a key mediator of PI3K Akt mTOR signaling pathway has recently emerged as a compelling molecular target in glioblastoma . The mTOR is a member of serine threonine protein kinase family that functions as a central controller of growth proliferation metabolism and angiogenesis but its signaling is dysregulated in various human diseases especially in certain solid tumors including the glioblastoma . Here considering that there are various kinase inhibitors being approved or under clinical or preclinical development it is expected that some of them can be re exploited as new potent agents to target mTOR for glioblastoma therapy . To achieve this a synthetic pipeline that integrated molecular grafting consensus scoring virtual screening kinase assay and structure analysis was described to systematically profile the binding potency of various small molecule inhibitors deposited in the protein kinase inhibitor database against the kinase domain of mTOR . Consequently a number of structurally diverse compounds were successfully identified to exhibit satisfactory inhibition profile against mTOR with IC50 values at nanomolar level . In particular few sophisticated kinase inhibitors as well as a flavonoid myricetin showed high inhibitory activities which could thus be considered as potential lead compounds to develop new potent selective mTOR inhibitors . Structural examination revealed diverse nonbonded interactions such as hydrogen bonds hydrophobic forces and van der Waals contacts across the complex interface of mTOR with myricetin conferring both stability and specificity for the mTOR inhibitor binding . 


Summary: A protocol is described to graft inhibitors from their cognate kinases to non cognate mTOR. The grafted inhibitor mTOR affinity is virtually evaluated using a consensus scoring strategy. A number of identified inhibitors are assayed to determine their inhibition against mTOR. Diverse nonbonded interactions are found at mTOR inhibitor complex interface.
To analyze the evolutionary dynamics of a mutant population in an evolutionary experiment it is necessary to sequence a vast number of mutants by high throughput sequencing technologies which enable rapid and parallel analysis of multikilobase sequences . However the observed sequences include many errors of base call . Therefore if next generation sequencing is applied to analysis of a heterogeneous population of various mutant sequences it is necessary to discriminate between true bases as point mutations and errors of base call in the observed sequences and to subject the sequences to error correction processes . To address this issue we have developed a novel method of error correction based on the Potts model and a maximum a posteriori probability estimate of its parameters corresponding to the true sequences . Our method of error correction utilizes the quality scores which are assigned to individual bases in the observed sequences and the neighborhood relationship among the observed sequences mapped in sequence space . The computer experiments of error correction of artificially generated sequences supported the effectiveness of our method showing that 50 90 of errors were removed . Interestingly this method is analogous to a probabilistic model based method of image restoration developed in the field of information engineering . 


Summary: Sequences analyzed by next generation sequencing contain many errors. We proposed a method of error correction of heterogeneous sequences. We performed a computer experiment of error correction. We confirmed the effectiveness of our method.
The modulation of the properties and function of cell membranes by small volatile substances is important for many biomedical applications . Despite available experimental results molecular mechanisms of action of inhalants and organic solvents such as acetone on lipid membranes remain not well understood . To gain a better understanding of how acetone interacts with membranes we have performed a series of molecular dynamics simulations of a POPC bilayer in aqueous solution in the presence of acetone whose concentration was varied from 2.8 to 11.2mol . The MD simulations of passive distribution of acetone between a bulk water phase and a lipid bilayer show that acetone favors partitioning into the water free region of the bilayer located near the carbonyl groups of the phospholipids and at the beginning of the hydrocarbon core of the lipid membrane . Using MD umbrella sampling we found that the permeability barrier of 0.5kcal mol exists for acetone partitioning into the membrane . In addition a Gibbs free energy profile of the acetone penetration across a bilayer demonstrates a favorable potential energy well of 3.6kcal mol located at 15 16 from the bilayer center . The analysis of the structural and dynamics properties of the model membrane revealed that the POPC bilayer can tolerate the presence of acetone in the concentration range of 2.8 5.6mol . The accumulation of the higher acetone concentration of 11.2mol results however in drastic disordering of phospholipid packing and the increase in the membrane fluidity . The acetone molecules push the lipid heads apart and hence act as spacers in the headgroup region . This effect leads to the increase in the average headgroup area per molecule . In addition the acyl tail region of the membrane also becomes less dense . We suggest therefore that the molecular mechanism of acetone action on the phospholipid bilayer has many common features with the effects of short chain alcohols DMSO and chloroform . 


Summary: Acetone favors partitioning into the water free region of the bilayer. The bilayer can tolerate the presence of acetone in the concentration range 2.8 5.6mol . Drastic disordering of phospholipid packing was observed above the critical acetone concentration.
An attempt was made to develop a computational model based on artificial neural network and ant colony optimization to estimate the composition of medium components for maximizing the productivity of Penicillin G Acylase enzyme from Escherichia coli DH5 strain harboring the plasmid pPROPAC . As a first step an artificial neural network model was developed to predict the PGA activity by considering the concentrations of seven important components of the medium . Design of experiments employing central composite design technique was used to obtain the training samples . In the second step ant colony optimization technique for continuous domain was employed to maximize the PGA activity by finding the optimal inputs for the developed ANN model . Further the effect of a combination of ant colony optimization for continuous domain with a preferential local search strategy was studied to analyze the performance . For a comparative study the training samples were fed into the response surface methodology optimization software to maximize the PGA production . The obtained PGA activity by the proposed approach was found to be higher than that of the obtained value with the response surface methodology . The optimum solution obtained computationally was experimentally verified . The observed PGA activity exhibited a close agreement with the model predictions . 


Summary: A computational model to maximize Penicillin G Acylase production is proposed. Bioprocess is modeled using ANN and optimized using ACO. Effect of applying local search techniques within global ACO process is analyzed. Computationally obtained optimized solution was successfully verified in the lab. Proposed method can be employed for modeling and optimization of other bioprocesses.
Gene silencing is an important function as it keeps newly acquired foreign DNA repressed thereby avoiding possible deleterious effects in the host organism . Known transcriptional regulators associated with this process are called xenogeneic silencers and belong to either the H NS Lsr2 MvaT or Rok families . In the work described here we looked for XS like regulators and their distribution in prokaryotic organisms was evaluated . Our analysis showed that putative XS regulators similar to H NS Lsr2 MvaT or Rok are present only in bacteria . This does not exclude the existence of alternative XS in the rest of the organisms analyzed . Additionally of the four XS groups evaluated in this work those from the H NS family have diversified more than the other groups . In order to compare the distribution of these putative XS regulators we also searched for other nucleoid associated proteins not included in this group such as Fis EbfC YbaB HU IHF and Alba . Results showed that NAPs from the Fis EbfC YbaB HU IHF and Alba families are widely distributed among prokaryotes . These NAPs were found in multiple combinations with or without XS like proteins . In regard with XS regulators results showed that only XS proteins from one family were found in those organisms containing them . This suggests specificity for this type of regulators and their corresponding genomes . 


Summary: Xenogeneic silencer like regulators were searched in fully sequenced prokaryote genomes. Putative xenogeneic silencer regulators are limited to some bacteria and are specific for each division. Xenogeneic silencer regulators from the H NS family are the most diversified. Other nucleoid associated proteins were also searched and their distribution was also assessed. Our search updates and extends xenogeneic silencer regulators database. Results showed that xenogeneic silencer like regulators are specific for the bacteria they are found in and exclusive as members of the other families are excluded.
Reconstructions of genome scale metabolic networks from different organisms have become popular in recent years . Metabolic engineering can simulate the reconstruction process to obtain desirable phenotypes . In previous studies optimization algorithms have been implemented to identify the near optimal sets of knockout genes for improving metabolite production . However previous works contained premature convergence and the stop criteria were not clear for each case . Therefore this study proposes an algorithm that is a hybrid of the ant colony optimization algorithm and flux balance analysis to predict near optimal sets of gene knockouts in an effort to maximize growth rates and the production of certain metabolites . Here we present a case study that uses Baker s yeast also known as Saccharomyces cerevisiae as the model organism and target the rate of vanillin production for optimization . The results of this study are the growth rate of the model organism after gene deletion and a list of knockout genes . The ACOFBA algorithm was found to improve the yield of vanillin in terms of growth rate and production compared with the previous algorithms . 


Summary: We proposed a hybrid algorithm to increase metabolites production. The hybrid algorithm select best combination of gene knockout. Using 2 microorganisms shows increment in metabolites production.
Naturally inspired evolutionary algorithms prove effectiveness when used for solving feature selection and classification problems . Artificial Bee Colony is a relatively new swarm intelligence method . In this paper we propose a new hybrid gene selection method namely Genetic Bee Colony algorithm . The proposed algorithm combines the used of a Genetic Algorithm along with Artificial Bee Colony algorithm . The goal is to integrate the advantages of both algorithms . The proposed algorithm is applied to a microarray gene expression profile in order to select the most predictive and informative genes for cancer classification . In order to test the accuracy performance of the proposed algorithm extensive experiments were conducted . Three binary microarray datasets are use which include colon leukemia and lung . In addition another three multi class microarray datasets are used which are SRBCT lymphoma and leukemia . Results of the GBC algorithm are compared with our recently proposed technique mRMR when combined with the Artificial Bee Colony algorithm . We also compared the combination of mRMR with GA and Particle Swarm Optimization algorithms . In addition we compared the GBC algorithm with other related algorithms that have been recently published in the literature using all benchmark datasets . The GBC algorithm shows superior performance as it achieved the highest classification accuracy along with the lowest average number of selected genes . This proves that the GBC algorithm is a promising approach for solving the gene selection problem in both binary and multi class cancer classification . 


Summary: We improved the ABC algorithm by adding a uniform crossover operation in the onlooker phase. We increased the number of scout bees to two. We adopted a mutation operation during the replacement process at the scout bee phase.
Cse1p and Xpot are two karyopherin proteins that transport the corresponding cargos during the nucleocytoplasmic transport . We utilized Elastic Network Model and Finite Element Analysis to study their conformational dynamics . These dynamics were interpreted by their intrinsic modes that played key roles in the flexibility of karyopherins which further affected the binding affinities . The findings included that it was the karyopherin s versatile conformations composed of the same superhelices of HEAT repeats that produced different degrees of functional flexibilities . We presented evidence that these coarse grained methods could help to elucidate the biological function behind the structures of the two karyopherins . 


Summary: The intrinsic dynamics of exportins were inherent from their cargo bound and cargo free conformations. Dominant modes were found to be relevant to the functional flexibilities and the binding affinities. ENM and FEA were complementary methods to study conformational dynamics of proteins.
Organisms thriving at extreme cold surroundings are called as psychrophiles and they present a wealth of knowledge about sequence adjustments in proteins that had occurred during the adaptation to low temperatures . In this paper we propose a new cascading model to investigate the basis for psychrophilicity . In this model a superior classifier was used to discriminate psychrophilic from mesophilic protein sequences and then the PART rule generating algorithm was applied on the input instances that are correctly classified by the classifier to generate human interpretable rules . These derived rules were further validated on a structural dataset and finally analyzed to discover the underlying biological basis about the psychrophilicity . In this study we have used one of the key features of psychrophilic proteins accountable for remaining functional in extreme cold temperature surroundings i.e . global patterns of amino acid composition as the input features . The rotation forest classifier outperformed all the other classifiers with maximum accuracy of 70.5 and maximum AUC of 0.78 . The effect of sequence length on the classification accuracy was also investigated . The analysis of the derived rules and interpretation of the analyzed results had revealed some interesting phenomena such as the amino acids A D G F and S are over represented and T is under represented in psychrophilic proteins . These findings augment the existing domain knowledge for psychrophilic sequence features . 


Summary: Enhanced classification of psychrophilic proteins by rotation forest. Rule extraction from correctly classified sequences. Validation of generated rules on structural data. Biological interpretation of rules. Ranking of amino acids according to their discriminative ability.
Receptor like kinase is an important member in protein kinase family which is widely involved in plant growth development and defense responses . It is significant to analyze the kinase structure and evolution of pollen RLKs in order to study their mechanisms . In our study 64 and 73 putative pollen RLKs were chosen from maize and Arabidopsis . Phylogenetic analysis showed that the pollen RLKs were conservative and might had existed before divergence between monocot and dicot which were mainly concentrated in RLCK VII and LRR III two subfamilies . Chromosomal localization and gene duplication analysis showed the expansion of pollen RLKs were mainly caused by segmental duplication . By calculating Ka Ks value of extracellular domain intracellular domain and kinase domain in pollen RLKs we found that the pollen RLKs duplicated genes had mainly experienced the purifying selection while maize might have experienced weaker purifying selection . Meanwhile extracellular domain might have experienced stronger diversifying selection than intracellular domain in both species . Estimation of duplication time showed that the duplication events of Arabidopsis have occurred approximately between 18 and 69 million years ago compared to 0.67 170 million years ago of maize . 


Summary: 64 and 73 putative pollen RLKs chosen from maize and Arabidopsis can be divided into 8 subfamilies. Expansion of pollen RLKs were mainly caused by segmental duplication. Maize might have experienced weaker purifying selection as compare to Arabidopsis. Duplication events of Arabidopsis and maize are between 18 69 million years and 0.67 170 million years ago respectively.
Continuous collision detection is a key technique to meet non penetration requirements in many applications . Even though it is possible to perform efficient culling operations in the broad stage of a continuous collision detection algorithm such as bounding volume hierarchies a huge number of potentially colliding triangles still survive and go to the succeeding narrow stage . This heavily burdens the elementary collision tests in a collision detection algorithm and affects the performance of the entire pipeline especially for fast moving or deforming objects . This paper presents a low cost filtering algorithm using algebraic analysis techniques . It can significantly reduce the number of elementary collision tests that occur in the narrow stage . We analyze the root existence during the time interval for a standard cubic equation defining an elementary collision test . We demonstrate the efficiency of the algebraic filter in our experiments . Cubic solvers augmented by our filtering algorithm are able to achieve up to 99 filtering ratios and more than 10 performance improvement against the standard cubic solver without any filters . 


Summary: We present a low cost filtering algorithm using algebraic analysis techniques. Our algorithm can significantly reduce the number of elementary collision tests that occur in the narrow stage of continuous collision detection. We demonstrated that cubic solvers augmented by our filtering algorithm are able to achieve up to 99. We observed more than ten times performance improvement against the standard cubic solver without any filters.
We present algorithms for computing the differential geometry properties of lines of curvature of parametric surfaces . We derive a unit tangent vector curvature vector binormal vector torsion and algorithms to evaluate their higher order derivatives of lines of curvature of parametric surfaces . Among these quantities it is shown that the curvature and its first derivative of the lines of curvature lend a hand for the formation of curved plates in shipbuilding . We also visualize the twist of lines of curvature which enables us to observe how much the osculating plane of the line of curvature turns about the tangent vector . 


Summary: We examine the differential geometry properties of the lines of curvature. We visualize the twist of line of curvature using its Frenet frame. We apply our technique to flatten doubly curved plates used in shipbuilding. We apply our technique to thin plate freeform fabrication.
In recent years computer aided redesigning methods based on genome scale metabolic network models have played important roles in metabolic engineering studies however most of these methods are hindered by intractable computing times . In particular methods that predict knockout strategies leading to overproduction of desired biochemical are generally unable to do high level prediction because the computational time will increase exponentially . In this study we propose a new framework named IdealKnock which is able to efficiently evaluate potentials of the production for different biochemical in a system by merely knocking out pathways . In addition it is also capable of searching knockout strategies when combined with the OptKnock or OptGene framework . Furthermore unlike other methods IdealKnock suggests a series of mutants with targeted overproduction which enables researchers to select the one of greatest interest for experimental validation . By testing the overproduction of a large number of native metabolites IdealKnock showed its advantage in successfully breaking through the limitation of maximum knockout number in reasonable time and suggesting knockout strategies with better performance than other methods . In addition gene reaction relationship is well considered in the proposed framework . 


Summary: IdealKnock can be employed to efficiently identify excellent knockout strategies for the overproduction of various products. IdealKnock breaks through the common bottleneck of knockout number limitation and gene reaction relationship is well considered. The knockout strategies given by IdealKnock are generally robust which means the maximum and minimum production rate are close.