Accelerating Bioprocess Optimization - A series of advancements has changed the way bioprocesses are developed and optimized. - BioPharm International


Accelerating Bioprocess Optimization
A series of advancements has changed the way bioprocesses are developed and optimized.

BioPharm International
Volume 24, Issue 4, pp. 38-44

In a single RNA-Seq experiment, one can derive not only an accurate, quantitative measure of transcriptome-wide gene expression levels (as with real–time quantitative polymerase chain reaction technologies), but also discover novel transcribed regions (new exons/genes) in an unbiased manner (as with a whole genome tiling microarray approach), map their boundaries, and identify the 5' and 3' ends of the genes (9,10). In addition, this methodology enables a global survey of the usage of the alternative splice sites (similar to a custom designed splicing microarray). It allows for the identification of transcription start sites, the identification of new splicing variants, and the monitoring of allele expression (9,10). Based on the power of the RNA–Seq approach, it is clear, that at least for comprehensive studies in higher eukaryotes where surveys of differential splicing activity, antisense transcription, and discovery of novel regions of transcription are desired, high throughput sequencing of RNA has augmented and is beginning to supersede microarray-based methods (9,10). Not only do the economics of faster development and better optimization support it, but it also allows for a host of new quality control and bioprocess monitoring capabilities after the research and optimization is completed. All of this dovetails perfectly with the intent of FDA's QbD guidelines.

Figure 4: Integration of NGG technologies to develop a species specific genomics platform that can be used to optimize bioprocesses, while building a solid understanding of the biology of the particular production species.
We have developed methods to use various current and next generation genomics tools to increase the performance and cost effectiveness of bioprocesses during manufacturing. NGS technologies, in particular, have several potential applications in this scenario. These include the generation of valuable genomics resources, development of molecular fingerprints for improved product yield and quality and bioprocess monitoring, quantitative expression analysis (RNA–seq), and identification of metabolic bottlenecks, all leading to evidence–based bioprocess optimization (see Figure 4). For example:

(1) Sequencing genomic DNA, mRNA, micro/smallRNAs, and immunoprecipitated DNA fragments from production strains or cell lines provides genomic resources that have a direct impact on understanding the overall biology of an organism. Specifically, it enables the understanding of gene regulation (e.g., the role of noncoding regulatory RNA elements and transcription factors/sigma factors in gene regulation) and genome structure and dynamics (chromosomal rearrangements, alternative splicing events, etc.). These resources have a direct impact on understanding the genome architecture of the production species, laying the foundation for intelligent, biology-guided process development.

(2) To develop functional gene-based markers, NGS of mRNA of contrasting phenotypes for the biomolecule of interest (for example, yield and quality) can be used to identify candidate genes involved in or associated with the production phenotype. These genetic markers can then be used as molecular fingerprints to assist with the selection of production lines, and to monitor process development and optimization with the goal of guiding early stage bioprocesses. At later stages, these molecular fingerprints can also be used to monitor process scaleup and manufacturing. Active monitoring throughout the entire process life cycle maximizes product yield and quality while minimizing associated costs.(3) Coupled to metabolic pathway analysis, RNA–seq of production strains/cell lines in different growth and environmental conditions sheds light on key metabolic pathways controlling biomolecule production and identifies potential metabolic bottlenecks. The ability to zero in on the control points governing metabolic flow towards increased production allows for process manipulation based on empirical knowledge instead of large DOE fishing expeditions and brute force methods.


Intelligently designed NGG experiments have become the hallmark of research and manufacturing design and optimization. With the advent of NGG technologies, the old and the new can be combined for very powerful results. Short term gains in production (in our case, sometimes more than 300%) were recognized from DNA microarray and/or mRNA-seq experiments that have provided foundational information for our NGG initiatives. We have dramatically improved our ability to rapidly optimize both growth media and fermentation conditions associated with the production of a key protein used in the manufacturing of a major commercial product. The technology has enabled us to better understand the overall bioprocess, as well as the physiology of the production cells themselves. We have incorporated various current and next generation genomics tools to form the basis of a bioprocessing genomics platform that will enable us to ultimately support FDA's QbD initiative. Not only will this improve our understanding of bioprocesses and the effect of all CPPs on protein yield, quality and process stability, but it will also make flexible bioprocessing possible and safe.

Len van Zyl*, PhD, is the CEO and CSO of ArrayXpress and a faculty member at NC State University. Michael Zapata III is the chairman of the board at ArrayXpress Inc.


1. A. Kantardjieff et. al., Biotechnol. Adv. 27, 1028–1035 (2009).

2. N.M. Jacob et. al., Chemical Engineering Progress 105 (11), 35–42 (2009).

3. M. Kanehisa, Trends Genet. 13 (9), 375–376 (1997).

4. P. Brodersen and O. Vionnet, Nat. Rev. Mol. Cell. Biol. 10, 141–148 (2009).

5. L.S. Water and G. Storz, Cell 136, 615–628 (2009).

6. N.M. Jacob et. al., Biotechnol. Bioeng. 105, 1002–1009 (2009).

7. D.J. Turner et. al., Mamm. Genome. 20, 327–338 (2009).

8. P.K. Wall et. al., BMC Genomics. 10, 347–366 (2009).

9. Z. Wang, M. Gerstein, and M. Snyder, Nat. Rev. Genet. 10, 57–63 (2009).

10. B.T. Wilhelm and J.R. Landry, Methods. 48, 249–257 (2009).

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