Dynamic genome-scale metabolic modeling of Pichia pastoris by integrating transcriptomics using TRFBA algorithm

  • سال انتشار: 1400
  • محل انتشار: چهارمین کنفرانس زیست شناسی سامانه های ایران
  • کد COI اختصاصی: ICSB04_037
  • زبان مقاله: انگلیسی
  • تعداد مشاهده: 546
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نویسندگان

Mohammad Amin Boojari

Department of Chemical Engineering, Tarbiat Modares University, Tehran, Iran

Fatemeh Rajabi Ghaledari

Department of Chemical Engineering, Tarbiat Modares University, Tehran, Iran

Ehsan Motamedian

Department of Chemical Engineering, Tarbiat Modares University, Tehran, Iran

Seyed Abbas Shojaosadati

Department of Chemical Engineering, Tarbiat Modares University, Tehran, Iran

چکیده

The yeast Pichia pastoris turned out to be an outstanding host for high-level production of recombinant proteins. To increase the productivity of recombinant proteins in P. pastoris, different approaches have been suggested including design of expression vectors metabolic engineering (Saitua et al., ۲۰۱۷), efficient fermentation protocols. Such strategies are usually investigated by empirical means. However, it has been demonstrated that model-based approaches are key to uncovering strategies leading to improved production of biopharmaceuticals. Nevertheless, this is a complex problem since the characteristics and process variables of the strain often take considerable time and money. So, a framework for integration of various levels of information from P. pastoris during cultivation can be used to elaborate rational hypotheses for increasing process efficiency. Systems biology uses large databases and simulates system behavior using mathematical models. This allows integrative study of various data types and thus offers a new perspective on complex biological systems (Nielsen, ۲۰۱۷). Genome-Scale dynamic Flux Balance Analysis is a modeling framework that enables the simulation of metabolism during fed-batch cultures (Saitua et al., ۲۰۱۷). In this research, biomass profile of the recombinant Pichia pastoris producing human growth hormone production during induction phase was simulated using dynamic flux balance integrated with transcriptomics data using TRFBA algorithm Pearson correlation between estimated and experimental data was ۰.۹۹ after finding optimal value of the algorithm parameter (C). This result indicates a significant improvement in the quantitative prediction of growth by integrating transcriptomics with the genome-scale model.

کلیدواژه ها

Fed-batch, dynamic model, Genome-scale model, TRFBA, Transcriptomics, Pichia pastoris

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