Robust mutant strain design by pessimistic optimization

Itaconic acid production by co-feeding of Ustilago maydis: A combined approach of experimental data, design of experiments, and metabolic modeling

Itaconic acid is a platform chemical with a range of applications in polymer synthesis and is also discussed for biofuel production. While produced in industry from glucose or sucrose, co-feeding of glucose and acetate was recently discussed to increase itaconic acid production by the smut fungus Ustilago maydis. In this study, we investigate the optimal co-feeding conditions by interlocking experimental and computational methods. Flux balance analysis indicates that acetate improves the itaconic acid yield up to a share of 40% acetate on a carbon molar basis. A design of experiment results in the maximum yield of 0.14 itaconic acid per carbon source from 100 g L - 1 $\,\text{g L}{}^{-1}$ glucose and 12 g L - 1 $\,\text{g L}{}^{-1}$ acetate. The yield is improved by around 22% when compared to feeding of glucose as sole carbon source. To further improve the yield, gene deletion targets are discussed that were identified using the metabolic optimization tool OptKnock. The study contributes ideas to reduce land use for biotechnology by incorporating acetate as co-substrate, a C2-carbon source that is potentially derived from carbon dioxide.

TRIMER: Transcription Regulation Integrated with Metabolic Regulation

There has been extensive research in predictive modeling of genome-scale metabolic reaction networks. Living systems involve complex stochastic processes arising from interactions among different biomolecules. For more accurate and robust prediction of target metabolic behavior under different conditions, not only metabolic reactions but also the genetic regulatory relationships involving transcription factors (TFs) affecting these metabolic reactions should be modeled. We have developed a modeling and simulation pipeline enabling the analysis of Transcription Regulation Integrated with Metabolic Regulation: TRIMER. TRIMER utilizes a Bayesian network (BN) inferred from transcriptomes to model the transcription factor regulatory network. TRIMER then infers the probabilities of the gene states relevant to the metabolism of interest, and predicts the metabolic fluxes and their changes that result from the deletion of one or more transcription factors at the genome scale. We demonstrate TRIMER’s applicability to both simulated and experimental data and provide performance comparison with other existing approaches.

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TRIMER models transcription-regulated metabolism using Bayesian network modeling;

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TRIMER integrates prior knowledge (regulatory interaction) with data (expression);

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TRIMER enables metabolic behavior prediction for general knockout strategies;

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TRIMER includes a simulator as an evaluation platform for similar hybrid models;

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TRIMER reliably predicts metabolite yields for both simulated and experimental data.

The International Conference on Intelligent Biology and Medicine (ICIBM) 2016: summary and innovation in genomics

In this editorial, we first summarize the 2016 International Conference on Intelligent Biology and Medicine (ICIBM 2016) that was held on December 8–10, 2016 in Houston, Texas, USA, and then briefly introduce the ten research articles included in this supplement issue. ICIBM 2016 included four workshops or tutorials, four keynote lectures, four conference invited talks, eight concurrent scientific sessions and a poster session for 53 accepted abstracts, covering current topics in bioinformatics, systems biology, intelligent computing, and biomedical informatics. Through our call for papers, a total of 77 original manuscripts were submitted to ICIBM 2016. After peer review, 11 articles were selected in this special issue, covering topics such as single cell RNA-seq analysis method, genome sequence and variation analysis, bioinformatics method for vaccine development, and cancer genomics.