14 papers · ranked by Valyu relevance
Olesya Melnichenko, Venkat S. Malladi
In the field of genomics, bioinformatics pipelines play a crucial role in processing and analyzing vast biological datasets. These pipelines, consisting of interconnected tasks, can be optimized for efficiency and scalability by leveraging cloud platforms such as Microsoft Azure. The choice of compute resources…
Mengzhen Guo, Stefan Grünewald
We present Lpnet, a variant of the widely used Neighbor-net method that approximates pairwise distances between taxa by a circular phylogenetic network. We first apply standard methods to construct a binary phylogenetic tree and then use integer linear programming to compute an optimal circular orderings that agrees…
Kirill Sechkar, Zoltan A. Tuza, Guy-Bart Stan
Laboratory automation and mathematical optimisation are key to improving the efficiency of synthetic biology research. While there are algorithms optimising the construct designs and synthesis strategies for DNA assembly, the optimisation of how DNA assembly reaction mixes are prepared remains largely unexplored. Here…
Robert Wakhata, Sudi Balimuttajjo, Védaste Mutarutinya
The study explored the direct and indirect relationship between students’ attitude towards, and performance in mathematics word problems (MWTs), mediated by the active learning heuristic problem solving (ALHPS) approach. Specifically, this study investigated the correlation between students’ performance and their…
Mustafa Ozen, Ali Abdi, Effat S. Emamian
Analysis of intracellular molecular networks has many applications in understanding of the molecular bases of some complex diseases and finding the effective therapeutic targets for drug development. To perform such analyses, the molecular networks need to be converted into computational models. In general, network…
Conor F. Hayes, Steven A. Magana-Zook, Andre Gonçalves, Ahmet Can Solak + 2 more
We propose a novel approach for antibody library design that combines deep learning and multi-objective linear programming with diversity constraints. Our method leverages recent advances in sequence and structure-based deep learning for protein engineering to predict the effects of mutations on antibody properties.…
A.J.R. Cotter
A simulator, ‘ECOLPS’ in R, is developed and trialed for ecological studies of closed aquatic ecosystems. Its constraint-based approach contrasts with function-based models widely applied in ecology. Total gross production (ΣGP) by ‘wild components’ (= species/life stages, grouped by ecological roles) is maximized…
Fernando H. C. Dias, Alexandru I. Tomescu
Minimum flow decomposition (MFD) is a common problem across various fields of Computer Science, where a flow is decomposed into a minimum set of weighted paths. However, in Bioinformatics applications, such as RNA transcript or quasi-species assembly, the flow is erroneous, since is obtained from noisy read coverages.…
Daniel Machado
Genome-scale metabolic modeling is a powerful framework for predicting metabolic phenotypes of any organism with an annotated genome. For two decades, this framework has been used for rational design of microbial cell factories. In the last decade, the range of applications has exploded, and new frontiers have emerged…
Yanica Said, Dipali Singh, Cristiana Sebu, Mark Poolman
We describe a novel algorithm, ‘LPEM’, that given a steady-state flux vector from a (possibly genome-scale) metabolic model, decomposes that vector into a set of weighted elementary modes such that the sum of these elementary modes is equal to the original flux vector. We apply the algorithm to a genome scale metabolic…
Wynand S. Verwoerd, Longfei Mao
The solution space of an FBA-based model of cellular metabolism, can be characterized by extraction of a bounded, low dimensional kernel (the SSK) that facilitates perceiving it as a geometric object in multidimensional flux space. The aim is to produce an amenable description, intermediate between the single feasible…
Josh A. Taylor, Alain Rapaport, Denis Dochain
Polyhedral models of metabolic networks are computationally tractable and can predict some cellular functions. A longstanding challenge is incorporating metabolites without losing tractability. In this paper, we do so using a new second-order cone representation of the Michaelis-Menten kinetics. The resulting model…
Pouya Ahadi, Balabhaskar Balasundaram, Juan S. Borrero, Charles Chen
In this study, we address the mate selection problem in the hybridization stage of a breeding pipeline, which constitutes the multi-objective breeding goal key to the performance of a variety development program. The solution framework we formulate seeks to ensure that individuals with the most desirable genomic…
Prasad U. Bandodkar, Razeen R. Shaikh, Gregory T. Reeves
Model development is essential to gain a mathematical understanding of the underlying phenomena in systems biology. In most models, it is typically hard to estimate the values of the biophysical/phenomenological parameters that characterize the model. The parameters are estimated by minimizing a function that reduces a…