In addition to infectious diseases, MCA holds promise for identifying drug targets for treatment of multi-factorial diseases such as cancer, multiple sclerosis, diabetes type 2 and atherosclerosis. These diseases are complex by nature, hence difficult to understand. For cancer, for instance, many genes have been causally implicated in oncogenic transformation . Most of these genes function in signal transduction pathways governing cell proliferation, apoptosis, angiogenesis, metastasis or invasion [44, 45]. Complicated network organization (regulatory circuitry, crosstalk between pathways, etc.) and non-linear kinetics of biochemical reactions and the multitude of factors involved, complicate understanding of signaling. Furthermore, interactions between tumor cells and other cell types generate a complex supra-cellular communication network. Therefore, even though many molecular differences have been identified between cancer cells and their healthy counterparts, the emerging picture is overwhelmingly complex. It has therefore been argued by us and others that cancer should be studied from a systems biology perspective, complementary to the current molecular and cellular biology research strategies [46-52]. Upon integration of the many pieces of knowledge on the biology of cancer, MCA can become a valuable tool to determine which components (genes, pathways, etc.) are important for the functioning of the system as a whole .
More than for infective diseases, drug target selectivity is an enormous problem for cancer treatment, because tumor cells are so very similar to their normal counterparts. Conventional cancer treatment relies on radiotherapy and chemotherapy, which is based on the generally higher susceptibility of cancer cells to damage induced by irradiation or chemical compounds than their normal non-transformed counterparts . This therapeutic strategy, although successful to some extent, is rather nonspecific, leading to potentially severe side-effects and many cases where the disease becomes refractory to treatment. In addition, due to the increased mutation rates in many tumor cells, resistant cells often arise which, due to their selective advantage for growth during treatment, may out-compete their sensitive counterparts.
New therapies are currently emerging that aim to impair 'oncogenic' signal transduction by tyrosine kinase inhibitors or antibodies that block growth factor receptors [54-57]. The rationale behind this is that over-active signaling pathways, such as the mitogen-activated protein kinase (MAPK) pathway, are responsible for the transformed phenotype and that inhibition of those pathways should therefore reverse this. The question is however: which protein in a pathway would make the best drug target? Furthermore, the selectivity problem may remain, because some healthy cell types require the same enzymes and pathways for functional viability.
MCA may thus serve as a method to determine which reactions in a complex signaling network are actually controlling its behavior . The control on the amplitude and duration of signaling (i.e., the extent to which a pathway is activated and the period of time this activation lasts, respectively) was found to be distributed over multiple enzymes, but not uniformly . This means that inhibition of more than one enzyme might prove more effective than inhibition of a single enzyme. Recently, MCA was applied to the epidermal growth factor-induced MAPK pathway in order to calculate the extent to which the individual reactions and proteins control its output . This was done on the basis of an updated version of a detailed kinetic model of this system . An interesting observation was that most of the 148 studied reactions did not control the network at all (or to a very low extent). The activity of the Raf protein exerted the strongest control on the network, which may explain why mutated Raf confers a growth advantage to the affected cells and therefore why it is frequently reported to be mutated in cancer cells . In line with what was discussed for trypanosomiasis, above, optimal drug targets could then be identified by differential control analysis, i.e., by comparing what controls the output of the network between normal cells and cancer cells.
Besides aberrant signal transduction, cancer cells also display alterations in metabolism. The enhanced proliferation rate, induced by onco-genic mutations, requires high glucose turnover for the synthesis of nu-cleotides. The resulting sensitivity of transformed cells to nutrient shortage could be exploited for therapeutic purposes . As normal cells use glucose mainly for energy supply, it has been suggested to determine which enzymes in glucose metabolism strongly control nucleotide synthe sis, hereby identifying these proteins as potential drug targets . A good example would be transketolase, which was found to be enzyme exerting the most control in glucose metabolism over nucleotide synthesis .
Taken together, control analysis methods have a great potential in the discovery of targets for anti-cancer therapies, since controlling reactions in both signal transduction networks and metabolic pathways can be identified with MCA.
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