(5j) Tackling Energy and Health Issues with Systems Biology

Systems biology is a versatile field that promises to revolutionize our understanding of and ability to engineer biological systems. The power of systems biology lies in its network-driven approaches that are capable of rendering complex phenotypes more tractable. My work has focused on both the development of these network-based techniques (1-6) and application of systems biology to energy and health issues (1).

With the ever-rising cost and unstable supply of petroleum, biology has become a prospective source of alternative energy (biofuels). However, major obstacles to the economic feasibility of biofuels exist, including the slow and expensive conversion of biomass into fermentable compounds and low product titer due to end-product or intermediate toxicity. Solutions to these problems most likely lie in the organisms we use to harvest solar energy and convert it into biofuel. Since the processes these organisms use are complex, involving multiple cellular systems (e.g., metabolism, signal transduction, etc.), systems biology would provide a greater understanding of what influences these processes and how we can engineer them to increase production. An example of this is from my work on biofuel stress (1), in which we discovered that membrane disruption from biofuels (isobutanol and n-butanol) leads to quinone malfunction (membrane electron carrier) and subsequent restructuring of metabolism through the action of the transcription factors ArcA, Fur, and PhoB. This information allowed us to predict and confirm that certain carbon sources were better growth substrates than others in the presence of isobutanol, which has important implications for feedstock selection for isobutanol production.

Systems biology also has many potential applications related to health. Cancer, bacterial persistence, and drug synergy or antagonism are all examples of where systems biology can be applied to improve treatment. Recently, my work has focused on the application of systems biology to bacterial infections in efforts to eradicate bacterial persistence, and predict how bacterial metabolism can synergize or antagonize the action of antibiotics.

In addition to these applications of systems biology, my research has addressed fundamental network biology questions important to systems biology. These include how transcription networks are structured, what capabilities these structures entail, how these networks respond to environmental stress, and how can we identify the direct targets of stimuli from gene expression data and other available sources. To this end, we characterized the ability of transcription networks to generate gene expression (2), developed a method to cope with uncertainty in transcription networks caused by network dynamics and noise (3), designed a method to identify transcription networks solely from gene expression data (4), developed techniques to infer functional roles for transcription regulators (5, 6), and identified the isobutanol response network of Escherichia coli (1).

The potential for systems biology to address energy and health concerns is great, and driven by the development of inventive network-based techniques, and the application of creative methods to approach these complex problems.

1. Brynildsen MP, Liao JC. (2009) An integrated network approach identifies the isobutanol response network of Escherichia coli. Mol Syst Biol. in press

2. Brynildsen MP, Tran LM, Liao JC. (2006) Versatility and connectivity efficiency of bipartite transcription networks. Biophys J., Oct 15;91(8):2749-59. (Epub 2006 Jun 30)

3. Brynildsen MP, Tran LM, Liao JC. (2006) A Gibbs sampler for the identification of gene expression and network connectivity consistency. Bioinformatics, Dec 15;22(24):3040-6 (Epub 2006 Oct 23)

4. Brynildsen MP, Wu TY, Jang SS, Liao JC. (2007) Biological network mapping and source signal deduction. Bioinformatics, (Epub May 15)

5. Yang YL, Suen J, Brynildsen MP, Galbraith S, Liao JC. (2005) Inferring yeast cell cycle regulators and interactions using transcription factor activities. BMC Genomics, Jun 10;6(1):90.

6. Tran LM, Brynildsen MP, Kao KC, Suen JK, Liao JC. (2005) gNCA: a framework for determining transcription factor activity based on transcriptome: identifiability and numerical implementation. Metab Eng., Mar;7(2):128-41.