Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.

OCR for page 105
Appendix A Contributed Manuscripts A1 COMMERCIAL APPLICATIONS OF SYNTHETIC BIOLOGY David A. Berry1,2 An Overview of Venture Capital Venture capital is financial capital invested into high-potential companies. The role of venture capital is to support the entrepreneurial talent that takes ba- sic science and breakthrough ideas to market by building companies. This risk capital ultimately supports some of the most innovative and promising compa - nies—those that have gone on to change existing industries or create new ones altogether (Thompson Reuters, 2011). Venture capital is a distinct asset class. Venture capital firms, which are professional, institutional capital managers, make investments by purchasing eq - uity in a company. The stock acquired is an illiquid investment that requires the growth of the company for the investors to ultimately reap any potential return. It is this inability of venture capitalists to rapidly enter and exit investments, or “flip” them, that aligns their goals with those of the entrepreneurs. Venture capital is intrinsically a long-term investment (Thompson Reuters, 2011). 1 Affiliation: Flagship VentureLabs, Cambridge, MA, USA. 2 Key words: venture capital, biological engineering, synthetic biology, microbiome, diesel, pho - tosynthesis, genome engineering. 105

OCR for page 105
106 SYNTHETIC AND SYSTEMS BIOLOGY Venture capitalists invest out of a fund, a vehicle that deploys capital on be- half of third-party investors. The investors in these funds, called limited partners, are often pension funds, foundations, corporations, endowments, and wealthy in - dividuals, among others. Given the low liquidity associated with their investment into venture capital funds, limited partners expect large returns—better than those in the stock market—from the funds in which they invest. The funds represent a commitment of capital with a fixed life, typically 10 years. The general partner, a group of partners with fiduciary responsibility for the firm with the legal form of a partnership, manages the capital in the fund. The committed capital is called by the general partner from the limited partners to make a portfolio of invest - ments. Ultimately, when investments mature and become liquid, the profits are shared, with the majority going back to the limited partners and the rest shared by the general partner. Funding provided by venture capitalists typically takes the form of “rounds,” where a given amount of money is invested into a company at a valuation agreed upon between the management and the investors. Prior to an investment, the equity ownership is divided among the founders, management, and others. The valuation sets a share price against which the venture capital firm buys shares. At each round, the earlier investors and management team strive to increase the valuation for the subsequent round(s) of investment. The higher the valuation of a round, the less dilution (reduction in ownership) the existing shareholders take. While each round contemplates a share price that defines a paper value for an investor’s or an employee’s shares, little actual value is created. Only at a sale event or initial public offering do investors and the management team see a tangible financial return, which can take 5-8 years, if not longer. Venture capital firms statistically see 100 business plans, take a deep look at 10 of these proposals, and invest in one. This process involves an assessment of the management team, the proposed business, its potential to exclude competi - tion, the market being pursued, and how well the opportunity fits with the firm’s goals. With an investment, a partner will typically get involved with a company by taking a seat on the board of directors, where he or she works closely with the management team on company strategy and growth. The venture capital industry plays an important role in the economy. Companies supported by early venture capital account for 21 percent of the U.S. gross domestic product by revenue, and 11 percent of private-sector jobs despite the fact that fewer than 1,000 new businesses get venture capital funding any given year (National Venture Capital Association, 2009). A Brief History of the Venture Capital Industry Venture capital is said to have originated in 1946 with the founding of the first two firms: American Research and Development Corporation (ARDC) and

OCR for page 105
107 APPENDIX A J. H. Whitney & Company (Wilson, 1985). Georges Doriot, referred to as the “father of venture capitalism,” the former dean of Harvard Business School and founder of INSEAD, founded ARDC along with Karl Compton, the former presi- dent of MIT, as well as Ralph Flanders (Ante, 2008). ARDC sought to invest in businesses run by soldiers returning from World War II. The firm is most famous for investing $70,000 in Digital Equipment Corporation in 1957—a company that when it had its initial public offering in 1968 was valued at $355 million for a return to ARDC of over 1,200-fold. Employees of ARDC went on to found leading venture funds including Greylock Partners and Flagship Ventures, among others (Kirsner, 2008). Two major government changes allowed venture capital to emerge as a fully fledged industry. First, the Small Business Investment Act of 1958 enabled the Small Business Administration to license Small Business Investment Companies to help finance and manage small entrepreneurial businesses. Second, in 1978, the Employee Retirement Income Security Act was altered to allow corporate pension funds to invest in venture capital. These two acts together supported the framework for venture capital and facilitated substantial investment in it. Successes of the venture capital industry in the 1970s and 1980s, with com - panies including Digital Equipment Corporation, Apple, and Genentech resulting, led to rapid growth of the industry. With rapid growth came diminished returns. In the early 1990s the numbers of firms and managers shrank in response to declining investment performance. At the same time, the more successful firms retrenched, starting a wave of increased returns that began in 1995 and continued through the Internet bubble in 2000 (Metrick, 2007). Once again, with grossly increased returns, the investment into the sector and the number of funds skyrock- eted. Beginning in March 2000, the NASDAQ crashed, and many funds suffered from a second contraction. After the Internet bubble, the funds raised by venture firms shrank substan - tially. Amounts of committed capital increased through 2005 to a level much less than in 2000, and they remained flat until the economic meltdown in 2008. Dur- ing the decade from 2000 to 2010, venture capital returns also fell dramatically to the point that the median 10-year return of all U.S. funds was less than the stock market (Thomson Reuters, 2011). These events resulted in another substantial contraction in the industry. The industry is currently responding to this most recent contraction. The number of funds has decreased as the average fund size has risen. This dynamic has caused venture funds to focus on either earlier-stage investments, later-stage investments (similar to private equity), or a combination. Other funds have started focusing on flipping assets by investing before or to induce specific value-creation events. This has created a new environment where only a small number of funds are focused on the earliest stage—that which ven - ture capital is most associated with and most successful at—with several others focused on a more transactional business. This evolution is still in process, but it has been changing the nature of companies that receive investment as well.

OCR for page 105
108 SYNTHETIC AND SYSTEMS BIOLOGY At the earliest stage of investments, venture capitalists have returned to investing in outstanding teams and under the assumption that they can create great companies. A number of approaches have been taken to inspire innova- tion and support a new era of breakthrough companies. Various firms have taken different approaches. CMEA, for example, invests in proven entrepreneurs “pre- napkin” (before the idea), on the belief that they will come up with ideas. Polaris’ Dogpatch Labs has created an environment where multiple entrepreneurs share a common environment and with light money attempt to prove out their concepts. Y Combinator gives entrepreneurs an education and a small amount of money to try out their ideas. Andreessen Horowitz has similarly created an infrastruc - ture to support the earliest stages of companies and to allow them to focus their capital on the company. “Super Angels” such as Peter Thiel have also emerged to provide important early stage funding. Several companies produced from some of these efforts have emerged as important venture-backed companies. Flagship VentureLabs has created an internal infrastructure of serial entrepreneurs to co- iterate its own innovations and use that as a basis to build companies. Flagship VentureLabs Flagship VentureLabs was built with a focus on increasing the efficiencies of innovation and entrepreneurship. In the broadest context, both traditional entrepreneurship and venture capital have intrinsic benefits and inefficiencies. Entrepreneurs, for example, typically perform well when capital constrained, but, by the same token, avoid asking critical questions because if an undesirable answer results, they are unemployed. Venture capital has the advantage of large sample sizes and substantial funding, but it is limited in its investments to only those which it can see, and all of its investments must fundamentally go through a common set of efforts (i.e., financial infrastructure, legal, etc.). Fundamentally, Flagship VentureLabs removes the constraints from the typical elements of tradi - tional ecosystems; that is, by harnessing the key constituents and requirements all under one roof, with the common goal of the betterment of humankind through innovation and entrepreneurship. The focus of Flagship VentureLabs is to develop breakthrough technologies to match large unmet needs in life sciences and sustainability through the vehicle of startup companies. New companies come from a breakthrough innovation without a set utility or from work within Flagship VentureLabs identifying the intersection between the potential for technology solution and market pull. In the former case, a team is nucleated around the technology, including the inventor, to heavily iterate the concept and pressure test it against markets, intellectual property opportunities, team-building potential, and other features with the at - tempt at nonrationally identifying the “sweet spot.” In certain cases, this process results in the pseudolinear formation of a company focused on commercializing the technology. In others, however, through a progressive set of explorations,

OCR for page 105
109 APPENDIX A the company may end up far from its origins, potentially not including the base technology. In the latter case, the defined intersection creates a hypothesis. If the hypothesis has already been manifest by others in a company or in academia (either singularly or through a combination of efforts), a simple investment may be warranted. In the absence of such a proof point, the concept then goes through heavy conceptual iteration with the attempt to prove the hypothesis wrong, and in the combination of not being able to make it fail and the generation of sig - nificant key stakeholder support (including industry and key opinion leaders), a company will be launched. Ultimately, this approach results in taking new ideas and forming companies several years before such an opportunity is likely to be compelling. The following discusses efforts in three such technology-based companies originating from Flagship VentureLabs covering both life sciences and sustainability. Seres Therapeutics: Rethinking Drug Development In an effort to reduce side effects, drug development has focused its efforts on target specificity, particularly on features including affinity, low off-target effects, pharmacokinetics, pharmacodynamics, and others. The Human Genome Project and systematic understandings of the functions of kinases have helped to drive this increasing target specificity. Nonetheless, the biology of diseases is complex and multi-factorial. Focusing drugs to single actors may reduce side effects but it also limits the spectrum of efficacy. The growing recognition of the nature of disease is driving the understanding of more complex biology and the develop - ment of drugs focused on the multitude of key factors. One particular example is with the human microbiome. Microorganisms have long been thought of as independently functioning pathogens. Recently, however, the commensal and mutualistic natures of various microorganisms that inhabit the body have started to be characterized (Dethlefsen, 2011). The interac - tions between the multitude of organisms, as well as between the organisms and the host, play an important role in normal physiology broadly (Reid et al., 2011). Accordingly, disruptions in the microbiome, whether by antibiotics, diet, infec - tion, or other means, can alter the microbiome and induce or simply increase the likelihood of a wide range of diseases, ranging from Clostridium difficile infec- tion and inflammatory bowel disease to obesity and diabetes (Kau et al., 2011). The complexity of the microbiome, including not only the interrelation between a number of species and the host but also the physical formation of the communities in specific niches (Rickard et al., 2003), is important to take into account when developing therapeutics aimed at diseases where the microbiome plays an important role. Seres Therapeutics was founded specifically to develop drugs based on the complexity of the microbiome. Probiotics and single biolog - ics affect a limited scope of disease and, thus, have limited efficacy in complex diseases such as those involving the microbiome (Shen et al., 2009). By creat-

OCR for page 105
110 SYNTHETIC AND SYSTEMS BIOLOGY ing synthetic microbiomes aimed at disrupting pathogenic communities, Seres provides a therapeutics means by which a normal microbiome can be restored. Understanding biology and synthetically recapitulating conditions that can re - cover from a disease-associated insult enables a new class of therapeutics to be designed and developed that are focused on the etiology of underlying disease. Sustainability Persistently high fossil fuel prices, increasing dependency on foreign fuel supplies, and insecurity relating to the sources of petroleum have created sub - stantial market pull for alternative solutions in the $6 trillion petrochemical industry. Outside of government-mandated markets, such as ethanol of late, fuels and chemicals are fungible products driven by price and purity, as well as supply and demand. Markets therefore require products with a known utility that meet certain industrial specifications while doing so at a competitive cost point. Consumers have not shown a willingness to pay for benefits such as greenhouse gas mitigation or domestic sourcing, so products made as alternatives must do so while competing head-to-head with the incumbents using the same metrics. Fuels have traditionally originated from biology in some form or another. Fossil fuels are thought to ultimately derive from processing of ancient biomass through a process that takes millions to hundreds of millions of years. Histori - cally, humans have also found faster cycle time sources of energy, namely the burning of trees for heat and energy, as well as the removal of spermaceti from whales as a source of wax. All of these resources have limited renewal potential and substantial environmental impact (Tertzakian, 2009). Given the central role biology has had in fossil fuels historically, it stands to reason that biology would be well positioned to be at the forefront of the future of sustainable fuels. Biological engineering has evolved rapidly over the last 50-plus years. Break- throughs in genomics research, increased genetic manipulation potential, and more complete knowledge of the inner workings of cells have set a stage for cells to be engineered to achieve desired functionalities. Moreover, the time from conception to proof of concept, and that from proof of concept to commercial viability, has reduced substantially. Historically, these periods have decreased threefold every 10 years. Given the technological potential enabled, the market needs can now drive the technological direction, thus leading toward an intersec- tion between market pull and the potential for technology solution. LS9: Ultraclean Renewable Diesel In 2005, the U.S. government had built a robust market demand for ethanol by outlining a replacement timeline for methyl tert-butyl ether (MTBE), a fuel oxygenate that had been associated with groundwater contamination and poten - tial increased cancer risks, with ethanol (U.S. Environmental Protection Agency,

OCR for page 105
111 APPENDIX A 2011a). Twelve billion gallons were mandated by 2012, effectively defining a market growth. This mandate was soon supplemented with the renewable fuel standard (RFS), and subsequently RFS2, ultimately requiring 36 billion gallons produced per year by 2022 (U.S. Environmental Protection Agency, 2011b). Corn ethanol was thus given ample runway to launch, and blenders were incorporat - ing biologically based products into fuel nationwide. The intent of the MTBE replacement with ethanol, however, was replacing an oxygenate, not deeming ethanol a fuel. Nonetheless, outspoken investors were enthusiastically supportive of building the future of American renewable fuel on ethanol, asserting that it could be cheaper than and as efficient as petrochemically derived fuels (Khosla, 2006) despite the disadvantaged domestic cost structure and intrinsic lower en - ergy density. The market was becoming well positioned for a viable alternative. LS9 originated by asking the question, “If you could make any fuel from biology, what would you make?” The ideal fuel to be produced from biology would be diesel, given its high energy density and its use throughout the world as a primary transportation fuel. A market-acceptable biologically produced diesel must compete in a low-cost commodity market without subsidies, requiring an efficient biological pathway and process. Translating these needs into specific technological tasks required that the cell be engineerable to be feedstock agnostic (i.e., able to use any form of sugar), that the most efficient pathway of producing the product was available, that the product was to be made directly and secreted, and that it entailed both straightforward separations (a feature of the product) and no downstream processing (the final product is made by the cell). Using the defined market constraints, various pathways to produce a straight- chain hydrocarbon were defined and evaluated. The fatty acid biosynthesis path- way not only has exceptionally high energy efficiency at over 90 percent but also produces a molecule that is chemically identical to diesel, requiring potentially fewer biological steps. Fatty acids are activated with coenzyme A (CoA) or acyl carrier protein (ACP) to make fatty acyl-CoA or fatty acyl-ACP (Zhang and Rock, 2008), which serve as the biological precursor products for fuel synthe - sis. These products are then modified to make a desired end product. The same pathways can be leveraged to make a series of other petrochemicals including fatty acid methyl esters, olefins, fatty alcohols, and others, in addition to alkanes (diesel) (Rude et al., 2011). The product itself is insufficient for a commercial host and process. The iden- tified market constraints require that the cell chassis has flexibility in feedstock, be optimized to maximize carbon flux to end product, and secrete the end product. Feedstock costs, driven by sugar prices, have risen dramatically over the past 6 years. Alternatives require the liberation of sugar from cellulosic biomass, which is done through exogenous enzymes at present. The expression of hemicellulases into the host already engineered to produce alkanes or other derivatives can enable consolidated bioprocessing, thereby reducing process costs (Magnuson et al., 1993). This is achievable, for example, with the endogenous production

OCR for page 105
112 SYNTHETIC AND SYSTEMS BIOLOGY of glycosyl hydrolases such as xylanase (Xsa) from Bacteroides ovatus and an endoxylanase catalytic domain (Xyn10B) from Clostridium stercorarium, which together hydrolyze hemicelluloses to xylose, which is usable in E. coli central metabolism (Adelsberger et al., 2004; Steen et al., 2010; Whitehead and Hespell, 1990). Optimizing the host requires focusing the flux of the sugar input through central metabolism to the product. Specifically, fadD and fadE knockouts block the first two steps of the ß-oxidation pathway, increasing end-product produc - tion three- to fourfold (Steen et al., 2010). Secreting the end product eliminates end-product inhibition and streamlines bioprocessing, thus increasing flux and reducing operating costs by supporting continuous operations (Berry, 2010). Expressing a leaderless version of TesA eliminates end-product inhibition, drives secretion, and notably also positively affects chain length with a natural prefer- ence for C14 fatty acids (Cho and Cronan, 1995; Jiang and Cronan, 1994; Steen et al., 2010). Through this approach, an industrial chassis has been rationally developed to systematically meet commercial needs. By specifically including features neces - sary to ensure diverse feedstock utility, drop-in product synthesis, and lowest cost processing, LS9’s technology has been designed specifically to drive market pull. Joule Unlimited: Renewable Solar Fuels An intrinsic challenge of using a sugar-based feedstock is the price volatility associated with the commodity. Joule Unlimited was founded to develop a plat - form that could eliminate dependence on sugar feedstocks while still producing fuels in a way that meets market needs. A systematic exploration of sources of carbon that can be routed into central metabolism rapidly identifies photosynthe - sis, nature’s solution to carbon dioxide assimilation driven by solar energy, as a compelling, though insufficient, pathway. The Department of Energy’s Aquatic Species program, based on explorations of algal biofuels between 1976 and 1996, concluded that photosynthesis could support viable fuel processes, but it requires a set of key innovations to do so (Sheehan et al., 1998; Weyer et al., 2010; Zhu et al., 2008). At the outset of Joule Unlimited, the fundamental limita - tions of algal fuels were examined and coupled with market needs to design an entirely new and distinct approach, whose only similarity to algae was the use of photosynthesis. A thorough exploration of market needs identified that an ideal solution would directly produce secreted fungible fuel directly from sunlight and carbon dioxide without a dependency on arable land, freshwater, or other costly reagents, while having a cost that could meet or beat fossil fuel equivalents in the absence of subsidies and at the same time scale modularly such that smaller-scale plants could be used to validate large-scale deployments. The simultaneous technological solution to meet all of these needs demands a genetically tractable cyano bacteria engineered to not need exogenous factors and to produce secreted fuel grown in a

OCR for page 105
113 APPENDIX A modular bioreactor leveraging the two-dimensional scaling of light and incorporat- ing fundamental process needs including proper mixing. A schematic of the Joule Unlimited approach, Helioculture™, is provided in Figure A1-1. Cyanobacteria had previously been engineered to express recombinant pro - teins, but it had not been systematically engineered on a genome scale owing primarily to a lack of engineering tools (Alvey et al., 2011). A concerted effort using E. coli engineering over 50 years serves as a roadmap of the needs for genome engineering in cyanobacteria. Using these tools coupled with a systemic genome engineering effort allows one to overcome a theoretical maximum light use and net productivity for algal biofuels of ~2,000 gallons per acre per year by enabling photoautotrophs, for the first time, to function like industrialized heterotrophs, whose phases of growth and production are separated (Berry, 2010; Robertson et al., 2011). Systematic re-regulation of central metabolism directs 95 percent of photosynthetic activity to specific product synthesis versus up to 50 percent in un-engineered organisms, allowing for productivity ~95 percent of the light-exposed time through continuous operations versus substantial down time with batch processes, minimizing maintenance energy requirements, and limiting the energy lost to photorespiration. At the same time, the engineered cells are grown in a reactor system designed to be low cost and linearly scalable. Low-cost product separation, cell mixing, and proper gas transfer to the cells are all incorporated into the reactor design. Coupled together, this systems approach allows for ~12 percent theoretical maxi - mal photonic energy conversion versus 1.5 percent for traditional algal processes (Figure A1-2), which translates to unprecedented areal productivities of 25,000 gallons of ethanol per acre per year or 15,000 gallons of diesel per acre per year. The high productivities achieved through the Joule Unlimited approach al - low for cost points as low as $20/barrel for fungible diesel. Maximizing solar energy capture, carbon dioxide fixation as a replacement for sugar use, and organ- ism productivity creates a system that can be market competitive while providing for the environmental benefits that have been sought in fossil fuel replacements. Specifically, Joule Unlimited’s technology eliminates the need for arable land, requires no freshwater, and reduces life-cycle greenhouse gases by over 90 per- cent by using carbon dioxide as a feedstock. By coupling the technical needs for a total solution with a market need, Joule Unlimited has uniquely developed a process that can produce a sugar-independent diesel in a highly scalable manner, overcoming the challenges of alternative approaches. Conclusions Systematic changes in venture capital have altered the entrepreneurial eco - system. Flagship VentureLabs is pioneering a new approach of technology de - velopment through companies by building technologies that specifically address the intersection between the potential for technology solution with market pull

OCR for page 105
114 FIGURE A1-1 Schematic of the Joule Unlimited Helioculture systems approach. Specific cyanobacteria are engineered to convert sunlight, Figure A1-1.eps CO2, and nonfreshwater to diesel (or other fuels and chemicals). The engineered organisms are housed within a Solar Converter, a single reactor bitmap, landscape unit designed to interconnect with others and therefore scale linearly.

OCR for page 105
115 APPENDIX A FIGURE A1-2 A summation of the accumulated photon losses for algal and direct fuel Figure A1-2.eps processes, as well as a theoretical maximum photonic energy conversion. The losses are shown through individual contributions bitmap serially and illustrated on a logarith - accumulated mic scale, beginning with the percent of photosynthetically active radiation empirically measured at the ground. Total energy conversion efficiency as a function of the losses is indicated by the green arrows. SOURCE: Adapted from Robertson et al. (2011). driving invention and innovation toward market needs. The resultant companies are designed by exploring cutting-edge capabilities and iterating against market needs—not just from an evolutionary standpoint, but additionally identifying the true needs of an industry across multiple facets. Synthetic biology is a new and rapidly developing tool that has particular utility in meeting broad-based and distinct market needs, particularly through its ability to create functional mod - ules in a cell-based system. By leveraging the potential of synthetic biology with market-driven needs, Flagship VentureLabs has been able to spearhead a set of breakthrough innovations in both life sciences and sustainability. This approach now takes market potential before traditional research approaches have made for a compelling and investable technology-driven opportunity and, through heavy iteration, can bring it to bear ahead of time through broad-based collaborations with industry and academia. This approach can be broadly leveraged to develop

OCR for page 105
484 SYNTHETIC AND SYSTEMS BIOLOGY FIGURE A22-1 The glycolytic pathway in Trypanosoma brucei. Reaction numbers indicate: 1. glucose transport; 2. hexokinase;Figure A22-1.eps 3. phosphoglucose isomerase; 4. phosphofructokinase; 5. aldolase; 6. triose-phosphate isomerase; 7. glyceraldehyde-3-phosphate dehydrogenase; bitmap 8. phosphoglycerate kinase; 9. phosphoglycerate mutase; 10. enolase; 11. pyruvate kinase; 12. pyruvate transport; 13. glycerol-3-phosphate dehydrogenase; 14. glycerol-3-phosphate oxidase (a combined process of mitochondrial glycerol-3-phosphate dehydrogenase and trypanosome alterative oxidase; 15. glycerol kinase; 16. combined ATP utilization; 17. glycosomal adenylate kinase; 18. cytosolic adenylate kinase. Question marks indicate uncharacterized transport processes. Abbreviations of metabolite names: Glc-6-P: glu - cose 6-phosphate; Fru-6-P: fructose 6-phosphate; Fru-1,6-BP: fructose 1,6-bisphosphate; DHAP: dihydroxyacetone phosphate; Gly-3-P: glycerol 3-phosphate; GA-3-P: glyceral - dehyde 3-phosphate; 1,3-BPGA: 1,3-bisphosphoglycerate; 3-PGA: 3-phosphoglycerate; 2-PGA: 2-phosphoglycerate; PEP: phospho-enolpyruvate.

OCR for page 105
485 APPENDIX A cultivation. Key missing pieces of information remain the mechanism and kinet - ics of the transport of glycolytic metabolites across the glycosomal membrane. The identification of semi-selective pores in peroxisomal membranes suggests that the smaller metabolites equilibrate across the glycosomal membrane, while bulkier molecules like ATP or NADH require specific transporters (Grunau et al., 2009; Rokka et al., 2009). This idea justifies, with hindsight, the choice to model the transport of a number of small intermediates as rapid-equilibrium processes. A number of basic and applied biological questions have been addressed us - ing the glycolysis model. For example, it was predicted and then experimentally confirmed (Bakker et al., 1999a,b) that the uptake of glucose across the plasma membrane was a major flux controlling step and therefore an interesting drug target. Enzymes that have been suggested to control glycolysis in mammalian cells, like hexokinase, phosphofructokinase and pyruvate kinase (Schuster and Holzhütter, 1995), exerted little control in trypanosomes, according to the model (Bakker et al., 1999a; Albert et al., 2005). Experiments, in which the expression of these enzymes was knocked down, confirmed this prediction qualitatively. However, the enormous overcapacity of some enzymes, which was predicted by the model, was shown to be exaggerated (Albert et al., 2005). This suggests that there are in vivo regulation mechanisms affecting these enzymes in a cur- rently unknown fashion. Protein phosphorylation may contribute, since a glyco - somal phosphatase has been identified in developmental signalling (Szoor and Matthews, unpublished data). The inhibition of anaerobic glycolysis by glycerol was also reproduced by the model, first qualitatively and then quantitatively (Bakker et al., 1997; Albert et al., 2005). An interesting biological feature that was revealed by the model was the re - lationship between compartmentation of glycolysis in glycosomes and the virtual absence of allosteric regulation of the glycolytic enzymes. Glycolysis models predict that glycolytic intermediates accumulate readily due to the investment of ATP at the beginning of the pathway (Teusink et al., 1998; Bakker et al., 2000). This risky ‘turbo’ effect can be avoided either by allosteric feedback regulation of hexokinase or by compartmentation of the pathway in glycosomes (Fig. A22-2). Compartmentation prevents the accumulation of intermediates, because the net ATP production occurs outside the glycosome and this excess of ATP cannot activate the first enzymes of glycolysis. This model prediction was recently confirmed experimentally (Haanstra et al., 2008a), providing a clear example of model-driven experimental design and hypothesis-driven systems biology. According to model predictions the glycolytic intermediates glucose 6-phos - phate, fructose 6-phosphate and fructose-1,6-bisphosphate should accumulate on addition of glucose if the glycolytic enzymes are not properly located in the glycosome. Indeed, accumulation of glucose 6-phosphate could be measured in a PEX14-RNAi mutant in which protein import into the glycosomes is disturbed. A similar phenotype was observed on glycerol addition, which led to accumula - tion of glycerol 3-phosphate, both in the model and in the PEX14-RNAi cells.

OCR for page 105
486 SYNTHETIC AND SYSTEMS BIOLOGY FIGURE A22-2 A. The positive feedback from the ATP produced by glycolysis to the ini- Figure A22-2.eps tial kinase reactions can lead to toxic accumulation of hexose phosphates. In many organ - bitmap isms this is prevented by a negative feedback from the hexose phosphates to hexokinase. B. In trypanosomes, the compartmentation of glycolysis prevents the positive feedback. This renders the negative feedback unnecessary, and indeed there is no evidence for such feedback in trypanosomes. Also in accordance with model predictions, a down-regulation of the expression of the genes encoding hexokinase and glycerol kinase rescues the PEX14-RNAi cells on glucose and glycerol, respectively (Kessler and Parsons, 2005; Haanstra et al., 2008a). More recently, a model of the gene-expression cascade, based on quanti- tative knowledge of transcription, RNA precursor degradation, trans-splicing and mRNA degradation for phosphoglycerate kinase (PGK) has been generated (Haanstra et al. 2008b). The model allowed a quantitative analysis of the con - trol and regulation of the expression of the PGK isoenzymes. It was shown that regulation of mRNA degradation explains 80–90% of the regulation of mature mRNA levels, while precursor degradation and trans-splicing make only minor contributions. In spite of the success of the model, it covers to date only a small part of trypanosome metabolism. This relates, for instance, to the fact that even the

OCR for page 105
487 APPENDIX A compartmentalised glycolysis does branch into other pathways, for example towards the biosynthesis of glycoconjugates and the pentose phosphate pathway. Although the fluxes into these branches may be small, they are vital for trypano - somes. Sufficient kinetic data have become available to enable extension of the model to include the pentose phosphate pathway which provides NADPH for reductive biosyntheses and also reducing equivalents to sustain cellular redox balance. Since redox balance is intimately related to the biosynthesis of try - panothione (from polyamine and glutathione precursors), a natural next step in a bottom-up systems biology approach to trypanosome metabolism would be the inclusion of the trypanothione–pentose phosphate pathway and related areas of redox metabolism (Fig. A22-3). Growth Stages of Building a Silicon Trypanosome Our current level of knowledge of trypanosome redox metabolism, as well as its biological importance (Krauth-Siegel and Comini, 2008), render it a natural choice for a next model extension (Fig. A22-3). The inclusion of redox metabo - lism is particularly interesting as trypanosome redox metabolism is sufficiently different from its human counterpart to offer perspectives for drug discovery. The unusual polyamine–glutathione conjugate trypanothione or bis(glutathionyl) spermidine (Fairlamb and Cerami, 1992) takes on the majority of roles served by glutathione in most other cell types. In addition, work in the last few years re - vealed that the enzymes involved in the synthesis and reduction of trypanothione are essential for the parasite (Krauth-Siegel and Comini, 2008). The trypanocidal drug eflornithine exerts its trypanocidal activity as an ir- reversible inhibitor of the enzyme ornithine decarboxylase (Bacchi et al., 1980), an enzyme involved in trypanothione biosynthesis (enzyme 10 in Fig. A22-3). A significant amount of information is available on kinetic parameters of that pathway, too. Preliminary attempts to model trypanothione metabolism have been made (Xu Gu, University of Glasgow PhD thesis, unpublished). Informa - tion available on the abundance of key metabolites measured in bloodstream form T. brucei grown in vitro (Fairlamb et al., 1987) and in vivo (Xiao et al., 2009), before and after exposure to eflornithine, was used to determine whether predicted behaviour under those perturbed conditions emulated the measured behaviour. The scarcity of kinetic data describing the whole pathway, however, has presented many challenges to constructing a model that captures observed behaviour. The acquisition of new kinetic data and the implementation of new mathematical tools to fill gaps in the data (Nikerel et al., 2006; Smallbone et al., 2007; Resendis-Antonio, 2009) should improve this. An extension of the glycolysis model to include the pentose phosphate path - way (Hanau et al., 1996; Barrett, 1997; Duffieux et al., 2000) and trypanothione metabolism should be a suitable next step in the modular approach that we envis - age towards a complete Silicon Trypanosome. Initial efforts in this direction (not

OCR for page 105
488 SYNTHETIC AND SYSTEMS BIOLOGY FIGURE A22-3 The glycolytic and trypanothione pathways are linked through the Figure A22-3.eps oxidative pentose phosphate pathway. Metabolites are presented in abbreviated form within rectangles. Enzymes and transporters are circles. Kinetic data is available for bitmap those shaded grey. Met = methionine; Arg = arginine; Gly = glycine; Glu = glutamate; Cys = cysteine; ATP = adenosine triphosphate; SAM = S-adenosylmethionine; dcSAM = decarboxylated S-adenosylmethionine; MTA = methylthioadenosine; Orn = ornithine; Put = putrescine; Spd = spermidine; c-GC = c-glutamylcysteine; GSH = glutathione; GSpd = glutathionylspermidine; T(SH2) = reduced trypanothione; TS2 = oxidised trypanothione; NADP = nicotinamide adenine dinucleotide phosphate; NADPH = reduced nicotinamide adenine dinucleotide phosphate; Glc = glucose; G6P = glucose 6-phosphate; Ru5P = ribulose 5-phosphate; Ox = oxidised cellular metabolites; Red = reduced cellular metabolites. 1. = methionine transport; 2. = arginine transport; 3. = glycine transport; 4. = glutamate trans - port; 5. = cysteine transport; 6. = methionine adenosyltransferase; 7. = arginase (N.B., a robust arginase gene orthologue has not been annotated in the T. brucei genome project, raising the possibility that arginine does not serve as a source of ornithine in these cells); 8. = S-adenosylmethionine decarboxylase; 9. = prozyme; 10. = ornithine decarboxylase; 11. = spermidine synthase; 12. = c-glutamylcysteine synthetase; 13. = glutathione synthetase; 14. = c-glutathionylspermidine synthetase; 15. = trypanothione synthetase/ amidase (in T. brucei 14. & 15. are catalysed by a single protein); 16. = trypanothione reductase; 17. oxidative pentose phosphate pathway (glucose 6-phosphate dehydrogenase, 6-phopshogluconolactonase & 6-phosphoglconate dehydrogenase); 18. = glucose trans - porter; 19. = hexokinase (this enzyme links the redox pathway to glycolysis); 20. = pyruvate transporter; 21. = methionine cycle enzymes; 22. The pathway of electrons from reduced trypanothione for final acceptance on oxidised cellular metabolites or macromolecules is complex, involving transfers via other redox active intermediates including tryparedoxin (thioredoxin-like proteins) and peroxidoxin.

OCR for page 105
489 APPENDIX A published) have indicated the importance of the compartmentation of the pentose phosphate pathway. Although most of the enzymes of the pathway have a peroxi- some targeting sequence (PTS1), a significant fraction of their activity is often found in the cytosol (Michels et al., 2006; Heise and Opperdoes, 1999; Duffieux et al., 2000). A correct localisation of the enzymes as well as good estimates of the transport of intermediates across the glycosomal membrane will be key to a good model of the pentose phosphate pathway. Challenges of Trypanosome Systems Biology The first initiatives in systems biology of trypanosomes as well as of other organisms dealt with enzymatic sub-systems, such as glycolysis. The models have depended largely on kinetic data for isolated enzymes. However, the abundance of these enzymes can, in principle, be controlled by the rates of transcription, RNA processing, translation, protein modification and turnover. These processes themselves may be regulated through complex signalling networks in response to both internal and external conditions (Westerhoff et al. 1990). To include gene expression in a Silicon Trypanosome requires a dramatic in - crease in model complexity – as well as the acquisition of new types of data on a large scale. Fortunately, the absence of transcriptional control of most individual open reading frames makes trypanosome gene expression simpler than that of yeast or even E. coli, rendering it uniquely amenable to mathematical modelling. It may well be possible to describe much of trypanosome mRNA metabolism using the following parameters: the rate constant of processing of the precursor RNA, i.e. of trans-splicing; the rate constant of degradation of the precursor (which competes with its trans-splicing); the rate constant of polyadenylation; and the rate constant of mRNA degradation. The rates of degradation of the precursor and the mature mRNA can be measured by inhibiting splicing and tran- scription. To measure the rate of mRNA processing two approaches are possible. First, one can inhibit transcription alone, and assay precursor decay; this approach is, however, compromised by practical constraints since splicing is very rapid. Second, the splicing rate can be calculated based on the steady-state abundance of the precursor mRNA, and the half-life and abundance of the products. This methodology has already been applied to the mRNA encoding PGK and it was demonstrated that splicing occurred within less than one minute; mRNA decay was the primary determinant of mRNA abundance (Haanstra et al., 2008b). Previous microarray studies with yeast have yielded estimates of the half- lives and polysomal loading of many mRNAs (e.g. Grigull et al., 2004). Deep sequencing technology – being more sensitive – should allow measurement of the abundances of all mRNAs and precursors on a genome-wide scale and to the accuracy required for the modelling; from these data, it should be possible to derive the steady-state abundances and half-lives of all RNAs, revealing regu- lated or inefficient processing. This – combined with global polysome profiling

OCR for page 105
490 SYNTHETIC AND SYSTEMS BIOLOGY – will provide quantitative data which allow quantifying the regulation of the processing, degradation and translation of each mRNA (Daran-Lapujade et al. 2007). The next challenge would then be to integrate such measurements with metabolic modelling in order to provide a complete model of pathways, from DNA to metabolic end-products. Anticipated Outcomes from a Silicon Trypanosome So far, the systems biology approach to trypanosomes has contributed to a fundamental understanding of cellular regulation (Bakker et al., 1999a; Haanstra et al., 2008a), as well as to improvements in the drug-target selection process (Bakker et al., 1999a,b; Albert et al., 2005; Hornberg et al., 2007). Since the initial systems biology analysis only addressed processes associated with less that 1% of the organism’s genome, we would expect many more new insights to lie ahead. Drugs currently used against human African trypanosomiasis are unsatis - factory for a number of reasons, including their extreme adverse effects in the patient and the emergence of resistant parasites. New drugs are urgently needed and there is hope that a better understanding of the control points of the metabolic network can guide the selection of optimal drug targets. This has already been achieved for enzymes of the glycolytic pathway (Hornberg et al., 2007). This information can be used alongside enhanced chemoinformatics (Frearson et al., 2007) in order to determine those components of the trypanosome that are most amenable to drug targeting. As a consortium we have embarked on the construction of a Silicon Trypano- some. In this review we have discussed the current status and future directions of trypanosome systems biology that form the context of this endeavour. Our ambition is to achieve a comprehensive, quantitative description of the flow of information from gene, through transcript and protein, to metabolism and back. This will allow prediction of how the parasite responds to changes in its environ - ment, with respect to nutrients, temperature and/or chemical inhibitors. It will also assist the deciphering of complex phenotypes generated by genetic perturba- tions in the laboratory or in the field. Thus, model predictions will improve our biological understanding of the differentiation and adaptation of the parasite as well as stimulate the discovery of inhibitors that attack processes which control trypanosome growth. The latter should contribute to the development of new optimised drugs for trypanocidal chemotherapy. Pioneering efforts have focused on energy metabolism and recently started to include adaptations of the parasite via gene expression (Haanstra, 2009). The construction of a complete Silicon Trypanosome, which integrates me - tabolism, gene expression and signal transduction is an ambitious project. Clearly the route towards this objective will be long, and many challenges will emerge as the datasets required to build such a model are collected and analysed. How-

OCR for page 105
491 APPENDIX A ever, the emergence of methods to allow collection of massive datasets, at every level, suggests that we may, in time, be able to generate a reasonably complete mathematical description of trypanosome cellular biology. Even if completion is not feasible, the evolving description will always represent the best conceivable dynamic representation of our knowledge of trypanosome biology. As a result, drug development programmes will have at their disposal a predictive model of the trypanosome to help identify those parts of metabolism most amenable to targeting by novel drugs and to controlling vital functions of the parasite. The project will be strengthened by parallel world-wide systems biology projects of human metabolism, in which some of us will be involved. After all, killing try - panosomes is easy. The difficulty is to kill the trypanosome without harming its host (Bakker et al., 2002). A careful comparison of the behaviour of our Silicon Trypanosome to quantitative knowledge of the control of human metabolism, will allow the identification of selective targets. Acknowledgements The work of BMB was funded by NWO Vernieuwingsimpuls and by a Rosa- lind Franklin Fellowship. RB was supported by an NWO-Vidi fellowship. HVW thanks BBSRC and EPSC for support through the MCISB grant (http://www. systembiology.net/ support/). MPB is grateful to the BBSRC for their support of the BBSRC-ANR “Systryp” consortium. The Silicon Trypanosome consortium is supported by a grant from SysMO2 (www.sysmo.net). References Albert, M. A., Haanstra, J. R., Hannaert, V., Van Roy, J., Opperdoes, F. R., Bakker, B. M., and Michels, P. A. M. (2005). Experimental and in silico analyses of glycolytic flux control in bloodstream form Trypanosoma brucei. Journal of Biological Chemistry 280, 28306–28315. Archer, S. K., Luu, V. D., de Queiroz, R., Brems, S. and Clayton, C. E. (2009). Trypanosoma brucei PUF9 regulates mRNAs for proteins involved in replicative processes over the cell cycle. PLoS Pathogens 5, e1000565. Bacchi, C. J., Nathan, H. C., Hutner, S. H., McCann, P. P. and Sjoerdsma, A. (1980). Polyamine metabolism: a potential therapeutic target in trypanosomes. Science 210, 332–334. Bakker, B. M., Aßmus, H. E., Bruggeman, F., Haanstra, J., Klipp, E. and Westerhoff, H. V. (2002). Network-based selectivity of antiparasitic inhibitors. Molecular Biology Reports 29, 1–5. Bakker, B. M., Mensonides, F. I. C., Teusink, B., Michels, P. A. M. and Westerhoff, H. V. (2000). Compartmentation protects trypanosomes from the dangerous design of glycolysis. Proceedings of the National Academy of Sciences, USA 97, 2087–2092. Bakker, B. M., Michels, P. A. M., Opperdoes, F. R. and Westerhoff, H. V. (1997). Glycolysis in blood- stream form Trypanosoma brucei can be understood in terms of the kinetics of the glycolytic enzymes. Journal of Biological Chemistry 272, 3207–3215. ——. (1999a). What controls glycolysis in bloodstream form Trypanosoma brucei? Journal of Bio- logical Chemistry 274, 14551–14559.

OCR for page 105
492 SYNTHETIC AND SYSTEMS BIOLOGY Bakker, B. M., Walsh, M. C., ter Kuile, B. H., Mensonides, F. I., Michels, P. A. M., Opperdoes, F. R. and Westerhoff, H. V. (1999b). Contribution of glucose transport to the control of the glyco - lytic flux in Trypanosoma brucei. Proceedings of the National Academy of Sciences, USA 96, 10098–10103. Barrett, M. P. (1997). The pentose phosphate pathway and parasitic protozoa. Parasitology Today 13, 11–16. Barrett, M. P., Burchmore, R. J., Stich, A., Lazzari, J. O., Frasch, A. C., Cazzulo, J. J. and Krishna, S. (2003). The trypanosomiases. Lancet 362, 1469–1480. Berriman, M., Ghedin, E., Hertz-Fowler, C., Blandin, G., Renauld, H. et al. (2005). The genome of the African trypanosome Trypanosoma brucei. Science 309, 416–422. Bruggeman, F. J. and Westerhoff, H. V. (2007). The nature of systems biology. Trends in Microbiol- ogy 15, 45–50. Chukualim, B., Peters, N., Hertz-Fowler, C. and Berriman, M. (2008). TrypanoCyc – a metabolic pathway database for Trypanosoma brucei. BMC Bioinformatics 9 (Suppl 10), P5. Clayton, C. and Shapira, M. (2007). Post-transcriptional regulation of gene expression in trypano - somes and leishmanias. Molecular and Biochemical Parasitology 156, 93–101. Daran-Lapujade, P., Rossell, S., van Gulik, W. M., Luttik, M. A. H., de Groot, M. J. L., Slijper, M., Heck, A. J. R., Daran, J. M., de Winde, J. H., Westerhoff, H. V., Pronk, J. T. and Bakker, B. M. (2007). The fluxes through glycolytic enzymes in Saccharomyces cerevisiae are predominantly regulated at posttranscriptional levels. Proceedings of the National Academy of Sciences, USA 104, 15753–15758. Duffieux, F., Van Roy, J., Michels, P. A. M., Opperdoes, F. R. (2000). Molecular characterization of the first two enzymes of the pentose-phosphate pathway of Trypanosoma brucei. Glucose-6- phosphate dehydrogenase and 6-phosphogluconolactonase. Journal of Biological Chemistry 275, 27559–27565. Estévez, A. (2008). The RNA-binding protein TbDRBD3 regulates the stability of a specific subset of mRNAs in trypanosomes. Nucleic Acids Research 36, 4573–4586. Fairlamb, A. H. and Cerami, A. (1992). Metabolism and functions of trypanothione in the Kineto - plastida. Annual Review of Microbiology 46, 695–729. Fairlamb, A. H., Henderson, G. B., Bacchi, C. J. and Cerami, A. (1987). In vivo effects of difluoro- methylornithine on trypanothione and polyamine levels in bloodstream forms of Trypanosoma brucei. Molecular and Biochemical Parasitology 24, 185–191. Fairlamb, A. J., Opperdoes, F. R. and Borst, P. (1977). New approach to screening drugs for activity against African trypanosomes. Nature 265, 270–271. Fenn, K. and Matthews, K. R. (2007). The cell biology of Trypanosoma brucei differentiation. Current Opinion in Microbiology 10, 539–546. Flynn, I. W. and Bowman, I. B. (1973). The metabolism of carbohydrate by pleomorphic African trypanosomes. Comparative Biochemistry and Physiology B 45, 25–42. Frearson, J. A., Wyatt, P. G., Gilbert, I. H. and Fairlamb, A. H. (2007). Target assessment for anti - parasitic drug discovery. Trends in Parasitology 23, 589–595. Grigull, J., Mnaimneh, S., Pootoolal, J., Robinson, M. and Hughes, T. (2004). Genome-wide analysis of mRNA stability using transcription inhibitors and microarrays reveals posttranscriptional control of ribosome biogenesis factors. Molecular and Cellular Biology 24, 5534–5547. Grunau, S., Mindtho, S., Rottensteiner, H., Sormunen, R. T., Hiltunen, J. K., Erdmann, R. and An - tonenkov, V. D. (2009) Channel-forming activities of peroxisomal membrane proteins from the yeast Saccharomyces cerevisiae. FEBS Journal 276, 1698–1708. Haanstra, J. (2009). The power of network-based drug design and the interplay between metabolism and gene expression in Trypanosoma brucei. PhD thesis Vrije Universiteit Amsterdam, ISBN 978-90-8659-331-6.

OCR for page 105
493 APPENDIX A Haanstra, J. R., Stewart, M., Luu, V. D., van Tuijl, A., Westerhoff, H. V., Clayton, C. and Bakker, B. M. (2008b). Control and regulation of gene expression: quantitative analysis of the expression of phosphoglycerate kinase in bloodstream form Trypanosoma brucei. Journal of Biological Chemistry 283, 2495–2507. Haanstra, J. R., van Tuijl, A., Kessler, P., Reijnders, W., Michels, P. A. M., Westerhoff, H. V., Parsons, M. and Bakker, B. M. (2008a). Compartmentation prevents a lethal turbo-explosion of glycoly - sis in trypanosomes. Proceedings of the National Academy of Sciences, USA 105, 17718–17723. Hanau, S., Rippa, M., Bertelli, M., Dallocchio, F. and Barrett, M. P. (1996). 6-Phosphogluconate dehydrogenase from Trypanosoma brucei. Kinetic analysis and inhibition by trypanocidal drugs. European Journal of Biochemistry 240, 592–599. Heise, N. and Opperdoes, F. R. (1999). Purification, localisation and characterisation of glucose-6- phosphate dehydrogenase of Trypanosoma brucei. Molecular and Biochemical Parasitology 99, 21–32. Helfert, S., Bakker, B. M., Michels, P. A. M. and Clayton, C. (2001). An essential role of triosephos - phate isomerase and aerobic metabolism in trypanosomes. Biochemical Journal 357, 117–125. Hirumi, H. and Hirumi, K. (1989). Continuous cultivation of Trypanosoma brucei blood stream forms in a medium containing a low concentration of serum protein without feeder cell layers. Journal of Parasitology 75, 985–989. Hornberg, J. J., Bruggeman, F. J., Bakker, B. M. and Westerhoff, H. V. (2007). Metabolic control analysis to identify optimal drug targets. Progress in Drug Research 64, 173–189. Hynne, F., Danø, S. and Sørensen, P. G. (2001). Full-scale model of glycolysis in Saccharomyces cerevisiae. Biophysical Chemistry 94, 121–163. Kabani, S., Fenn, K., Ross, A., Ivens, A., Smith, T. K., Ghazal, P. and Matthews, K. (2009). Genome- wide expression profiling of in vivo-derived bloodstream parasite stages and dynamic analysis of mRNA alterations during synchronous differentiation in Trypanosoma brucei. BMC Genom- ics 10, 427. Kessler, P. S. and Parsons, M. (2005). Probing the role of compartmentation of glycolysis in procyclic form Trypanosoma brucei: RNA interference studies of PEX14, hexokinase and phosphofruc- tokinase. Journal of Biological Chemistry 280, 9030–9036. Krauth-Siegel, R. L. and Comini, M. A. (2008). Redox control in trypanosomatids, parasitic protozoa with trypanothione-based thiol metabolism. Biochimica et Biophysica Acta 1780, 1236–1248 Lee, J. H., Jung, H. S. and Gunzl, A. (2009). Transcriptionally active TFIIH of the early-diverged eukaryote Trypanosoma brucei harbors two novel core subunits but not a cyclin-activating kinase complex. Nucleic Acids Research 37, 3811–3820. Liang, X., Haritan, A., Uliel, S. and Michaeli, S. (2003). Trans and cis splicing in trypanosomatids: mechanism, factors, and regulation. Eukaryotic Cell 2, 830–840. Lustig, Y., Sheiner, L., Vagima, Y., Goldshmidt, H., Das, A., Bellofatto, V. and Michaeli, S. (2007). Spliced-leader RNA silencing: a novel stress-induced mechanism in Trypanosoma brucei. EMBO Reports 8, 408–413. Michels, P. A. M., Bringaud, F., Herman, M. and Hannaert, V. (2006). Metabolic functions of glyco - somes in trypanosomatids. Biochimica et Biophysica Acta 1763, 1463–1477. Nikerel, I. E., van Winden, W. A., van Gulik, W. M. and Heijnen, J. J. (2006). A method for estima - tion of elasticities in metabolic networks using steady state and dynamic metabolomics data and linlog kinetics. BMC Bioinformatics 7, 540. Nikerel, I. E., van Winden, W. A., Verheijen, J. and Heijnen, J. J. (2009). Model reduction and a priori kinetic parameter identifiability analysis using metabolome time series for metabolic reaction networks with linlog kinetics. Metabolic Engineering 11, 20–30. Opperdoes, F. R. and Borst, P. (1977). Localization of nine glycolytic enzymes in a microbody-like organelle in Trypanosoma brucei: the glycosome. FEBS Letters 80, 360–364. Parsons, M. (2004). Glycosomes: parasites and the divergence of peroxisomal purpose. Molecular Microbiology 53, 717–724.

OCR for page 105
494 SYNTHETIC AND SYSTEMS BIOLOGY Palenchar, J. B. and Bellofatto, V. (2006). Gene transcription in trypanosomes. Molecular and Bio- chemical Parasitology 146, 135–141. Paterou, A., Walrad, P., Craddy, P., Fenn, K. and Matthews, K. (2006). Identification and stage-specific association with the translational apparatus of TbZFP3, a ccch protein that promotes trypano - some life cycle development. Journal of Biological Chemistry 281, 39002–39013. Queiroz, R., Benz, C., Fellenberg, K., Hoheisel, J. and Clayton, C. (2009). Transcriptome analysis of differentiating trypanosomes reveals the existence of multiple post-transcriptional regulons. BMC Genomics 10, 495. Resendis-Antonio, O. (2009). Filling kinetic gaps: dynamic modeling of metabolism where detailed kinetic information is lacking. PLoS One 4, e4967. Richard, P., Teusink, B., Hemker, M. B., van Dam, K. and Westerhoff, H. V. (1996). Sustained oscil - lations in free-energy state and hexose phosphates in yeast. Yeast 12, 731–740. Rokka, A., Antonenkov, V. D., Soininen, R., Immonen, H. L., Pirilä, P. L., Bergmann, U., Sormunen, R. T., Weckström, M., Benz, R. and Hiltunen, J. K. (2009). Pxmp2 is a channel-forming protein in Mammalian peroxisomal membrane. PLoS One 4, e5090. Schmitz, J. P., Van Riel, N. A., Nicolay, K., Hilbers, P. A. and Jeneson, J. A. (2009). Silencing of glycolysis in muscle: experimental observation and numerical analysis. Experimental Physiol- ogy 95, 380–397. Schuster, R. and Holzhütter, H. G. (1995). Use of mathematical models for predicting the metabolic effect of large-scale enzyme activity alterations. Application to enzyme deficiencies of red blood cells. European Journal of Biochemistry 229, 403–418. Siegel, T., Hekstra, D., Kemp, L., Figueiredo, L., Lowell, J., Fenyo, D., Wang, X., Dewell, S. and Cross, G. (2009). Four histone variants mark the boundaries of polycistronic transcription units in Trypanosoma brucei. Genes & Development 23, 1063–1076. Smallbone, K., Simeonidis, E., Broomhead, D. S. and Kell, D. B. (2007). Something from noth - ing: bridging the gap between constraint-based and kinetic modelling. FEBS Journal 274, 5576–5585. Snoep, J. L., Bruggeman, F., Olivier, B. G. and Westerhoff, H. V. (2006). Towards building the silicon cell: a modular approach. Biosystems 83, 207–216. Stern, M., Gupta, S., Salmon-Divon, M., Haham, T., Barda, O., Levi, S., Wachtel, C., Nilsen, T. and Michaeli, S. (2009). Multiple roles for polypyrimidine tract binding (PTB) proteins in trypano - some RNA metabolism. RNA 15, 648–665. Teusink, B., Walsh, M. C., Van Dam, K. and Westerhoff, H. V. (1998). The danger of metabolic pathways with turbo design. Trends in Biochemical Sciences 23, 162–169. Walrad, P., Paterou, A., Acosta-Serrano, A. and Matthews, K. R. (2009). Differential trypanosome sur- face coat regulation by a CCCH protein that co-associates with procyclin mRNA cis-elements. PLoS Pathogens 5, e1000317. Westerhoff, H. V., Kolodkin, A., Conradie, R., Wilkinson, S. J., Bruggeman, F. J., Krab, K., Van Schuppen, J. H., Hardin, H., Bakker, B. M., Moné, M. J., Rybakova, K. N., Eijken, M., Van Leeuwen, H. J. and Snoep, J. L. (2009). Sytems biology towards life in silico: mathematics of the control of living cells. Journal of Mathematical Biology 58, 7–34. Westerhoff, H. V., Koster, J. G., Van Workum, M. and Rudd, K. E. (1990). On the control of gene expression. In Control of Metabolic Processes (ed. Cornish-Bowden, A. and Cardenas, M.-L.), pp. 399–412. Plenum Press, New York. Xiao, Y., McCloskey, D. E. and Phillips, M. A. (2009). RNA interference-mediated silencing of or- nithine decarboxylase and spermidine synthase genes in Trypanosoma brucei provides insight into regulation of polyamine biosynthesis. Eukaryotic Cell 8, 747–755.