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Modeling Arsenic Accumulation in Plants
Högskolan i Skövde, Institutionen för vård och natur. Högskolan i Skövde, Forskningscentrum för Systembiologi.ORCID-id: 0000-0002-6319-4055
Högskolan i Skövde, Institutionen för vård och natur. Högskolan i Skövde, Forskningscentrum för Systembiologi.ORCID-id: 0000-0002-6549-086x
Högskolan i Skövde, Institutionen för vård och natur. Högskolan i Skövde, Forskningscentrum för Systembiologi.
Högskolan i Skövde, Institutionen för vård och natur.
Vise andre og tillknytning
2011 (engelsk)Inngår i: Proceedings Second International Conference on Emerging Applications of Information Technology / [ed] Debasish Jana & Pinakpani Pal, IEEE Computer Society, 2011, s. 133-137Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Rice growing regions plagued by arsenic-contaminated soils and irrigation water do not have a viable option for producing arsenic-free crops. For instance, in Bangladesh every year more than 30 million people are affected from rice-derived arsenic contamination that contributes to arsenic levels known to cause health-related illnesses. Our strategy is to genetically-modify molecular mechanisms involved in the localization of arsenic to divert it to the non-edible parts of the plant. To identify viable candidate genes, we employed data mining, an in silico analysis based on searching existing genomic databases and in the genetic model plant Arabidopsis thaliana. To assist our investigation, we constructed a kinetic model to outline strategies for developing genetically-modified plants exhibiting a significant reduction in arsenic concentration in the edible parts (straw and grain). This model contains equations for uptake, metabolism and sequestration of different types of arsenic (As (V), As (III), MMAA and DMAA). The model was implemented using XPP and validated against existing data from the literature. From these analysis, we identified four candidate genes that are involved either in uptake, transport or cellular localization of arsenic in plants. But we found only one gene implicated in arsenic metabolism in rice. In parallel, we identified available T-DNA insertion mutants to determine the effects of these genes on arsenic accumulation. Results obtained from in silico data-mining, kinetic modeling, and assays with T-DNA insertion mutants will be used to design gene cloning experiments to study the target genes in yeast, E. coli and Arabidopsis heterologous systems. Upon confirmation of the effectiveness of these candidates, vectors containing the target genes will be constructed for transformation into rice. The new rice varieties produced will be tested under field conditions to assess their effectiveness at reducing or eliminating arsenic from the edible parts of the rice plant.

sted, utgiver, år, opplag, sider
IEEE Computer Society, 2011. s. 133-137
Emneord [en]
Arabidopsis, data-mining, rice, arsenic accumulation, arsenic metabolism, arsenic uptake
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Forskningsprogram
Naturvetenskap
Identifikatorer
URN: urn:nbn:se:his:diva-5199DOI: 10.1109/EAIT.2011.91Scopus ID: 2-s2.0-79953867832ISBN: 978-1-4244-9683-9 (tryckt)ISBN: 978-0-7695-4329-1 (digital)OAI: oai:DiVA.org:his-5199DiVA, id: diva2:429251
Konferanse
Second International Conference on Emerging Applications of Information Technology Kolkata, India, 19 – 20 February 2011
Tilgjengelig fra: 2011-07-04 Laget: 2011-07-04 Sist oppdatert: 2020-01-29bibliografisk kontrollert

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