Quantifying Cellular Processes

Catalyst Volume 8

Molecular biology research has generated unprecedented amounts of information about the cell. One of the largest molecular databases called Kyoto Encyclopedia of Genes and Genomes (KEGG) stores 9,736 reactions and 17,321 compounds.¹ This information provides rich opportunities for application in synthetic biology, a field that engineers cells to produce large quantities of valuable compounds. Despite its strengths, synthetic biology can still be implemented more systematically and efficiently. To integrate all the cell’s reactions into a coherent picture, researchers have developed computational models of the cell’s biochemical pathways. These models ultimately aim to simulate the pathways of a real cell so that researchers can isolate the set of reactions that produce a compound of interest.

To express all the cell’s compounds and reactions, research on metabolic networks has utilized a concept called a graph. A graph consists of two primary structures: nodes, each representing a single compound or reaction, and edges, which connect related compound and reaction nodes. For example, as shown in Figure 1, the cell’s compounds can be treated as nodes, and the reactions that transform the compounds can be treated as the edges. By simplifying chemicals into symbolic representations, graphs can analyze networks relatively quickly and with minimal computational memory. This low computational cost enables analysis not only of specific pathways but also of the entire cell.

While graphs provide intuitive symbols for the reactions in metabolic networks, they were not arbitrarily invented. Current metabolic network graphs are derived from a well-studied mathematical field. Euler created graph theory in 1735, and the theorems discovered since then have enabled different methods for solving a number of mathematical problems.¹ Metabolic networks research depends specifically on shortest-path algorithms, which search for the most efficient ways to reach a target node from a starting node. Shortest-path algorithms accomplish several tasks that simplify analysis of metabolic networks. They constrain the output to a finite number of pathways, and they enable output of biologically realistic pathways, which evolve to conserve energy and tend to minimize the number of intermediate compounds. Excluding convoluted pathways means avoiding unreasonably complicated and costly production methods.

However, current shortest-path algorithms must be extensively modified to generate meaningful results for metabolic networks. In a simplistic application of the shortestpath algorithm, the only parameter is the distance itself between two nodes, that is, the literal shortness of the path. Such simplicity generates multiple pathways that are biochemically impossible and in fact make little sense. This problem can be illustrated by glycolysis, a nine-step reaction pathway in the metabolism of glucose. During glycolysis, ATP, a small molecule that provides energy for many cellular reactions, is required to prime intermediates. ATP is generated in the following overall reaction: glucose + 2 NAD+ + 2 ADP + 2 Pi → 2 pyruvate + 2 ATP + 2 NADH. A simplistic graphical algorithm would suggest glucose is converted directly into ATP in a short process as indicated by the overall reaction equation. The reality is that glycolysis is a nine-step process with a vast number of enzymes, cofactors, allosteric regulators, covalent regulators, and environmental conditions, all of which must be encoded into the algorithm. The challenge in synthetic biology is considering all of these factors and more for the gamut of simultaneous reactions ongoing in a cell.

Research has produced modifications to the simplistic shortest-path algorithm to better model biological reality. Croes et al. reduced the influence of currency metabolites by constructing a weighted graph: metabolites that had many connections throughout the graph were weighted with a greater cost.² The algorithm, searching for pathways with least total cost, would avoid pathways that incorporated costly component metabolites. This approach correctly replicated 80% of a test set of 160 metabolic pathways known to exist in cells, a noticeable improvement over the unweighted graph.

Several years later, although the weighting scheme of Croes et al. had considerable success, Blum and Kohlbacher created an algorithm that combined weighting with a systematic atom-tracking algorithm.³ The researchers mapped the correspondence of atoms between every substrate and product, recording which atoms are conserved in a chemical transformation. The algorithm deleted pathways containing reactions that did not conserve a minimum number of carbon atoms during the transformation. More so than a simple weighting scheme, atomtracking directly targeted pathways such as the glucose to ATP to ADP to pyruvate pathway, which structurally cannot occur. When tested, this new algorithm replicated actual biological pathways with more sensitivity and specificity than those using atom-tracking or weighting techniques alone.

Metabolic pathway algorithms received yet another improvement through modifications that enabled them identify branching pathways. Rather than proceeding linearly from the first to the final compound, pathways often split and converge again during intermediate steps. Pitkanen et al. introduced their branched-path algorithm Retrace, which also incorporated atom-tracking data.⁴ Heath, Kavraki, and Bennett later utilized atom-tracking data to create algorithms with improved search time. These branched algorithms reproduced the pathways leading toward several antibiotic compounds such as penicillin, cephalosporin, and streptomycin.⁵

Improvements in computational models will not merely replicate a cell’s biochemistry. By generating feasible alternative pathways, algorithms should predict undiscovered reactions that the cell could perform. For this reason, graphical algorithms, after constructing a skeleton of a cell’s metabolites, should integrate methods that account for biochemical properties. Constraint-based modelling is an alternative approach to metabolic networks research that ensures that the necessary reactants for a pathway are present in the correct proportions. Such models enable researchers to test how removing an enzyme or regulating a gene can impact the quantity of the desired compound. However, unlike graphical methods, the computational complexity of constraint-based modelling gives it a limited scale. Future research would focus on incorporating more biochemical properties into graphical methods such as atom-tracking, simplifying the constraint-based methods, or integrating the benefits of the two approaches into a comprehensive model.

Although still incomplete, the development of a fully effective computational model to guide the cellular engineering process will have critical implications. For example, the process of drug target identification to molecule optimization to approval currently takes 10 to 15 years to reach the market.⁶ Computational models that fully emulate a real cell will make synthetic biology rapid and systematic, accelerating the discovery and testing of the important compounds with important medical applications. Evidently, the integration of biology with mathematics will be critical to the future advancement of synthetic biology.

References

1. Graph Theory. KEGG: Kyoto Encyclopedia of Genes and Genomes. http://www.britannica. com/EBchecked/topic/242012/graph-theory (accessed Oct. 31, 2014). 
2. Croes, D. et al. J. Mol. Bio. 2006, 356, 222–236. 
3. Blum, T.; Kohlbacher, O. J. Comp. Bio. 2008, 15, 565-576. 
4. Pitkänen, E. et al. BMC Syst. Biol. 2009, 3, doi:10.1186/1752-0509-3-103. 
5. Heath, A. P. Computational discovery and analysis of metabolic pathways. Doctoral Thesis, Rice University. 2010 
6. Drug Discovery and Development. http:// www.phrma.org/sites/default/files/pdf/rd_ brochure_022307.pdf (accessed Feb. 14, 2015).

A Cup of Tea Against Cancer

Catalyst Volume 8

Green tea, made from the leaves of Camellia sinensis, has come a long way from its humble origins in China to its current status as the second most popular beverage worldwide. According to Chinese mythology, Shennong, the legendary ruler of China in approximately 2370 BC, drank the first cup of green tea that was brewed when a tea leaf fell into his boiled water.¹ Despite his title as the divine healer, Shennong could not have possibly realized the numerous health benefits contained in the little cup. Green tea benefits health in various ways including cognitive enhancement, improvement of mental ability and alertness,² and increased reward learning through modulation of dopamine transmission.³ Tea also helps with dieting through increased fat oxidation and prevents cardiovascular disease and diabetes.⁴ Recently, several studies have also credited green tea for its ability to prevent cancer development.^1,4-6

When harvested from the tree, leaves of Camellia sinensis contain a high concentration of flavonoids. Flavonoids are members of the polyphenol group and have demonstrated anti-inflammatory, anti-allergic, and anti-mutagenic effects. In green tea, a group called catechin constitutes a large percentage of the flavonoids. This specific type of flavonoid, especially epigallocatechin gallate (EGCG), prevents the formation and growth of tumors.⁴ Normal cells take both complex and varying pathways to develop into malignant cells, but there are three crucial stages in the path to malignancy. In the initiation stage, undesirable mutations in the chromosome form due to exposure to carcinogenic substances or radiation. In the second stage of promotion, the mutation is translated and transcribed to the cytoplasm and cell membrane. The last stage is progression, during which cancer cells proliferate. By this point, accumulated mutations in chromosomes produce many genetic alterations that promote uncontrollable growth. While the numerous stages of cancer progression may complicate the search of one specific cure, they provide equal number of opportunities for regulation of carcinogenesis.⁵

The polyphenol substituents found in tea can suppress cancer at various stages in its progression. First, tea can prevent initiation by inactivating or eliminating the mutagens that can potentially damage the cell DNA. Potential mutagens are surprisingly common in our environment.⁵ Every day, we are exposed to processes that introduce dangerous reactive oxygen species (ROS) such as hydrogen peroxide and oxygen radicals that can react with DNA and induce detrimental mutations.¹ Common ionizing radiation (UV and X-rays) as well as tobacco are well-documented mutagens as well. The flavonoids contained in tea are natural scavengers that destroy these free oxygen radicals.¹ Catechin, a type of flavonoid, is especially effective at reducing free radicals by binding to ROS as well as to ferric ions, which are required to create ROS.⁶ Polyphenols of green tea can also competitively inhibit intermediates of heterocyclic aromatic amines, a new class of carcinogens, thus reducing the danger of accumulating DNA-damaging material.1 Finally, the chemical structure of the polyphenols in tea has strong affinity toward carcinogens, enabling them to bind to and neutralize the harmful substances.⁶ By blocking common cancer-initiating factors, tea lowers the chance of genetic mutations that may result in a tumor.

Substances in green tea can also prevent cancer by blocking angiogenesis, essentially starving the tumor cells.¹ Angiogenesis is the formation of network of blood vessels through cancerous growths. In smaller tumors, cancer cells can use simple diffusion to transport necessary oxygen and nutrients. However, as the number of accumulated cells increase, tumor cells send signals to surrounding host tissues to produce the proteins necessary for blood vessel generation. These blood vessels supply large amounts of oxygen and nutrients that are unavailable through passive diffusion. Catechins in green tea stop angiogenesis by interfering with the tumor cell signals. EGCG has been shown to inhibit epidermal growth factor receptor, and thus production of vascular endothelial growth factor (VEGF), which is in charge of initiating angiogenic blood vessel formation.¹ Further studies have shown direct inhibition of VEGF transcription and VEGF promoter activity in breast cancer cells by green tea extract (GTE) and EGCG.^4-6 GTE also suppresses production of protein kinase C, which regulates VEGF as well. By inhibiting the signal pathway to blood vessel formation, green tea is able to reduce the progression of angiogenesis.

Another role of tea includes preventing metastasis, which is the most common cause of cancer-related mortality.¹ Metastasis represents the full development of a tumor, in which the boundary that enclosed the cancer is broken and the tumor freely migrates to other parts of the body. Green tea’s flavonoids prevent degradation of membranes and proteins on the cell surface that promotes anchorage.¹ Once base membranes and proteins that anchor cells to specific locations disappear, tumor cells are unfettered. EGCG in green tea has been shown to block metastasis by inhibition of membrane type 1 matrix metalloproteinase (MMP), which in turn restrains MMP-2, an enzyme crucial to degradation of the extracellular matrix. In experiments, a mixture of EGCG and ascorbic acid showed a significant suppression of metastasis by 65.9%.¹

Finally, tea can prevent the unregulated proliferation of cancer cells that drives tumor formation and metastasis. Apoptosis, or the self-destruction of a cell, is actually a common and natural biological process. When a cell loses the ability to undergo apoptosis, it becomes potentially cancerous. Increasing apoptosis in cancer cells should restore balance and eliminate unrequired and harmful cells in the body. The problem lies in specifically inducing apoptosis of cancer cells without harming the normal cells, but research has shown tea’s potential in the selective promotion of apoptosis. In an experiment involving human papillomavirus 16-associated cervical cancer cells, EGCG inhibited cell growth by promoting apoptosis and cell cycle arrest.¹ In head and neck carcinoma cells, EGCG also increased the percentage of cells at phase G1, the initial growth cycle of the cell, and induced apoptosis.¹ Similar results were found by adding the extracted water-soluble fraction from green tea to mouse epidermal cells JB6, which both inhibited carcinogenesis and induced apoptosis.⁵

The extensive evidence presented here illustrates the cancer-preventive and inhibitory effects of green tea. However, we must consider that most of the data were collected through in vitro and in vivo experiments. Clinical trials with human beings have yet to confirm the preventive effects of tea polyphenol against cancer.⁵ Current research does not present significant evidence to determine the true effects of tea. On the other hand, a negative correlation has been observed between green tea consumption and cancer mortality along with general mortality rate in Japanese populations.⁵ In general, increasing the amount of green tea consumed per day indicated a reduced chance of cancer. These results suggest that tea, even with its vast number of health benefits, is not a cureall. In conjunction with regular exercise and vegetables with each meal, however, many diseases can be prevented. By drinking tea, one can partake in a tradition passed down for centuries while keeping the body healthy.

References

1. Jain, N. K. et al. Protective Effects of Tea on Human Health; CAB International: Cambridge, 2006.
2. Borgwardt, S. et al. Eur. J. Clin. Nutr. 2012, 66, 1187-1192.
3. Zhang, Q. et al. Nutr. J. 2013, 12, 84.
4. Dulloo, A. G. et al. Am. J. Clin. Nutr. 1999, 70, 1040-1045.
5. Kuroda, Y. et al. Health Effects of Tea and Its Catechins; Kluwer Academic/Plenum Publishers: New York, 2004.
6. Yammamoto, T. et al. Chemistry and Applications of Green Tea; CRC Press LLC: New York, 1997.