Ozdemir, F. | bioRxiv Science

bioRxiv2020

Root and canopy traits and adaptability genes explain drought tolerance mechanism in winter wheat.

Bread wheat (Triticum aestivum L) is one of main staple food crops worldwide contributing 20% calories in human diet. Drought stress is the main factor limiting yields and threatening to food security, with climate change resulting in more frequent and intense drought. Developing drought-tolerant wheat cultivars is a promising way forward. The use of a holistic approaches that include high-throughput phenotyping and genetic makers in selection could help in accelerating genetic gains. Fifty advanced breeding lines were selected from the CIMMYT Turkey winter wheat breeding program and studied under irrigated and semiarid conditions for two years. High-throughput phenotyping were done for wheat crown root traits using shovelomics techniques and canopy green area and senescence dynamics using vegetation indices (green area using RGB images and Normalized Difference Vegetation Index using spectral reflectance). In addition, genotyping by KASP markers for adaptability genes was done. Overall, under semiarid conditions compared to irrigated conditions yield reduced by 3.09 t ha-1 (-46.8%). Significant difference between the treatment and genotype was observed for grain yield and senescence traits. Genotypes responded differently under drought stress. Root traits including shallower nodal root angle under irrigated conditions and root number per shoot under semiarid conditions were associated with increased grain yield. RGB based vegetation index measuring canopy green area at anthesis was more strongly associated with GY than NDVI under drought. Five established functional genes (PRR73.A1 - flowering time, TEF-7A - grain size and weight, TaCwi.4A - yield under drought, Dreb1-drought tolerance, and ISBW11.GY.QTL.CANDIDATE- grain yield) were associated with different drought-tolerance traits in this experiment. We conclude that a combination of high-throughput phenotyping and selection for genetic markers can help to develop drought-tolerant wheat cultivars.

plant biology↗

bioRxiv2019

Personalized Tumor Growth Prediction with Multiscale Tumor Modeling

BackgroundCancer is one of the most complex phenomena in biology and medicine. Extensive attempts have been made to work around this complexity. In this study, we try to take a selective approach; not modeling each particular facet in detail but rather only the pertinent and essential parts of the tumor system are simulated and followed by optimization, revealing specific traits. This leads us to a pellucid personalized model which is noteworthy as it closely approximates existing experimental results.\n\nMethodFor years, research has focused on modeling tumor growth but not many studies have put forward a framework for the personalization of models. In the present study, a hybrid modeling approach which consists of cellular automata for discrete cell state representation and diffusion equations to calculate distribution of relevant substances in the tumor micro-environment is favored. Moreover, naive Bayesian decision making with weighted stochastic equations and a Bayesian network to model the temporal order of mutations is presented. The model is personalized according to the evidence using Markov Chain Monte Carlo. Ultimately, this way of thinking about tumor modeling leads us to a vascular multi-scale model of tumor growth.\n\nResultsTo validate the tumor model, a data set belonging to the A549 cell line is used. The data represents the growth of a tumor for 30 days. We optimize the coefficients of the stochastic decision making equations using first half of the timeline. Then we predict next 15 days of growth without any other supervision. Results are promising with their low error margin and simulated growth data is in line with laboratory results.\n\nConclusionThere are many subsystems which have an effect in the growth of a tumor. A detailed model which includes all of them is currently virtually impossible to implement. We have therefore focused on a system that only includes fundamental components in this study, and have evaluated its predictions. We propose novel probability functions to obtain a personalized model and estimate the individual importance (weights) of each with parameter optimization. Our approach of using simulated annealing for parameter estimation and the subsequent validation of the prediction with in-vitro tumor growth data are, to our knowledge, unique in the literature.

bioinformatics↗