Thursday, June 17 at 06:45am (PDT)Thursday, June 17 at 02:45pm (BST)Thursday, June 17 10:45pm (KST)
SMB2021 FollowWednesday (Thursday) during the "CT09" time block.
"Constrained Optimization Approach to Predictability Analysis in Bio-Mathematical Modeling"
Background: Identifiability analysis is a crucial step in improving reliability and predictability of biological models. Profile Likelihood (PL) is a reliable though computationally expensive approach to identifiability analysis. PL-based algorithm Confidence Intervals by Constraint Optimization (CICO), which was recently published (https://doi.org/10.1371/journal.pcbi.1008495/), reduces computational requirements and increases the accuracy of the estimated parameters' confidence intervals. The CICO algorithm is available in a free software package LikelihoodProfiler based on Julia (https://github.com/insysbio/LikelihoodProfiler.jl). CICO can be potentially extended to predictability analysis and confidence bands estimation.Objectives: The goal of this study is to examine the application of CICO to estimation of confidence and prediction bands. The analysis was performed on a number of published biological models, including STAT5 Dimerization model, Cancer Taxol Treatment model, etc.Results: The original CICO algorithm can be extended to a broader use-case of confidence bands. The analysis demonstrates good performance characteristics for both identifiable and non-identifiable cases. The approach can be used with complex biological models where each likelihood estimation is computationally expensive and some output values are non-identifiable. Detailed analysis of each model can be found on the GitHub repository likelihoodprofiler-cases https://github.com/insysbio/likelihoodprofiler-cases.
ICM SB RAS, Krasnoyarsk Mathematical Center
"Novel alignment-free highly parallel method to compare symbol sequences of an arbitrary length"
Comparison of long symbolic sequences corresponding to various biological macromolecules is the principal tool in bioinformatics, biophysics, and other life sciences. However, symbolic sequence alignment, currently widely used for computing comparison, suffers from multiple downsides. Some of them are subjective parameters' choice, divergence, and high computational complexity. The paper proposes an alignment-free non-parametric, highly efficient novel method to compare symbolic sequences based on a binary multi-channel encoding and their Fourier convolution. Due to the high O(n*log(n)) efficiency of the Fourier convolution, the method can process sequences up to 10^7 symbols long on consumer-level hardware under half an hour. Also, the method lends itself to a straightforward parallelization. The base version of the algorithm determines the number of exact matches in any overlapping configuration of two sequences and provides it in a single run of the convolution calculation. The advanced version determines the number of exactly matching k-mers in those configurations. The insertions and deletions, which present significant challenges for the alignment-based computations, do not affect the proposed method's efficiency.
National Technical University of Athens
"Towards extending the arsenal of cancer immunology modeling with algorithmic asymptotic analysis"
The recent advances in cancer immunotherapy paved the way for the development of mathematical models formulating the complex interactions between the tumor and the immune system, with the aim to indicate more efficient treatment regimes. However, the complexity of such models and their multi-scale character renders them inaccessible for wide utilization and hinders the acquisition of physical understanding. In order to tackle these obstacles, here the algorithmic tools of asymptotic analysis are utilized in a fundamental model formulating the interactions of tumor cells with natural killer cells, CD8+ T cells and circulating lymphocytes. It is firstly revealed that the long-term evolution of the system towards the high-tumor or the tumor-free equilibrium is determined by the dynamics of an initial explosive stage of tumor progression. Focusing on this stage, the algorithmic Computational Singular Perturbation methodology is employed to identify the underlying mechanisms confining the system to evolve towards the high-tumor equilibrium and the governing slow dynamics along them. The results demonstrate the potential of algorithmic asymptotic analysis to simplify the complex, overeparameterized and multi-scale cancer immunology models and to indicate the interactions and cell types to target for more effective treatment development.
Siberian federal university, Institute of fundamental biology and biotechnology
"The prevalence of function over taxonomy for triplet composition of mitochondrial and chloroplast plant genes"
We studied the relation between triplet composition of genes and taxonomy of the bearers in case of plant genes. There are two gene families that are common for mitochondria and chloroplast genomes of plants: atp genes family and nad family. In this study we compared mitochondrial and chloroplast atp genes of the same species. These genes encode subunits of ATP synthase. Totally, 170 (85 mitochondrial and 85 chloroplast) plant genomes were studied. Each gene sequence was transformed into a triplet frequency dictionary, where the reading frame shift was equal to t = 1. Then the points in 64-dimensional space of the triplet frequencies of the genes were clustered with ViDaExpert software. Three types of clustering have been analyzed: for mitochondria genes solely, for chloroplast genes solely, and for the merged set of the genes from both organelles' genomes. It was observed that clusters are formed on a functional basis. To be more precise, the genes encoded different subunits were split into separate clusters. Moreover, each cluster contained genes encoding only one subunit of ATP synthase. Thus, the prevalence of function over the taxonomy for atp genes family of organelles genomes of plants has been proven.