Genomics and Cell Biology of the Apicomplexa
Transcript of Part 5: Designing and Mining Pathogen Genome Databases: From Genes to Drugs and Vaccines III
00:00:04.23 I hope you found our tour of 00:00:08.12 computational approaches for identifying candidate vaccine targets 00:00:12.27 of interest. 00:00:15.15 And needless to say, there are many other questions, 00:00:18.08 many other approaches, 00:00:20.09 many other strategies that one might want to explore 00:00:23.06 for addressing problems of interest in malaria biology -- 00:00:29.29 questions related to the cell biology of these organisms, 00:00:33.05 questions related to their biochemistry. 00:00:35.28 But in the applied realm, 00:00:38.07 certainly at least a co-equal 00:00:44.05 with the identification of vaccine targets 00:00:46.13 is the identification of drug targets as well, 00:00:49.02 and I'd like to take just a moment to put into context 00:00:52.11 the severe challenges and the reasons why 00:00:55.12 we're so concerned about the identification 00:00:57.15 of new drug targets. 00:00:59.19 If we step back a couple of generations, 00:01:01.22 we were fortunate in having several 00:01:07.13 inexpensive, effective antimalarials, 00:01:11.12 which, there was some hope, would be... 00:01:14.23 would enable us to eliminate 00:01:18.18 this devastating disease from Earth. 00:01:23.20 But unfortunately, the emergence and spread 00:01:27.18 of drug resistance to chloroquine 00:01:30.00 and to antifolates 00:01:32.11 has left us with far fewer effective drugs 00:01:36.16 than we once had. 00:01:38.28 Indeed, the first line course of treatment, now, 00:01:42.09 for malaria is primarily based 00:01:45.24 on a Chinese herbal remedy, artemisinin, 00:01:50.00 which is now... which is now extracted from plants 00:01:54.02 and can also be made synthetically. 00:01:56.19 And artemisinin common... combination chemotherapy 00:02:00.01 is the basis of treatment, 00:02:02.12 but also the source of some anxiety, 00:02:04.16 because history has shown 00:02:07.22 that time and again 00:02:10.23 overreliance on a single drug or a single treatment strategy 00:02:13.13 is likely to give rise to the emergence 00:02:16.04 of drug resistance. 00:02:17.21 And that has stimulated the development 00:02:22.12 of many efforts around the globe 00:02:24.19 to identify new antimalarials. 00:02:27.07 It's estimated that we will need 00:02:29.14 new antimalarials every 5-10 years 00:02:34.00 in order to keep pace with this devastating disease. 00:02:38.24 The Medicines for Malaria Venture 00:02:41.15 is a public-private partnership, 00:02:43.18 an interesting exploration in the development 00:02:46.07 of a sort of virtual pharmaceutical company driv... 00:02:48.23 pulling together the best of expertise in academics, 00:02:52.13 in the biotechnology sector and in the academic sector, 00:02:55.05 with government laboratories and private funding agencies 00:02:59.13 to try to expedite the development of drugs. 00:03:02.23 And I thought it might be informative 00:03:05.04 to look at the Medicines for Malaria Venture pipeline, 00:03:07.19 a pipeline... this sort of representation of drugs 00:03:13.24 is standard in the pharmaceutical industry, 00:03:16.14 describing the earliest stages of drug discovery, 00:03:18.23 from laboratory research through preclinical development, 00:03:23.21 and moving out into... into the clinic. 00:03:25.28 And the Medicines for Malaria Venture drug pipeline, 00:03:28.14 circa 2006, 00:03:32.01 included quite a full portfolio, 00:03:34.11 including a variety of artemisinin-based compounds, 00:03:38.13 indicated in red here, 00:03:41.03 in the late stages of development; 00:03:44.09 several candidate antimalarials 00:03:48.00 being developed using more traditional pharmaceutical chemistry approaches, 00:03:52.07 those indicated in orange; 00:03:54.10 dicationic molecules; 00:03:56.16 or some synthetic compounds that bear similarity to artemisinins; 00:03:59.11 some compounds that are based on natural products, 00:04:02.14 on herbal remedies; 00:04:04.25 manzamine alkalines, here, derived, 00:04:07.02 as it happens, from a sea sponge; 00:04:09.24 other compounds that that one might think of as new drugs from old... 00:04:13.16 based on old strategies, 00:04:15.25 compounds similar to chloroquine or to antifolates 00:04:19.00 that are indicated in pink. 00:04:20.22 But what's interesting is that all of the new compounds 00:04:22.24 entering into this pipeline 00:04:25.07 are based on genomic approaches, 00:04:27.27 and, just as we've discussed, 00:04:30.02 the kinds of strategies that one might want to consider 00:04:32.28 for mining genome databases 00:04:35.12 to identify candidate targets for vaccine development... 00:04:38.12 we could develop similar approaches for drug development, 00:04:42.15 and I encourage you to think about ways of doing that 00:04:45.16 and explore it yourself online 00:04:48.06 using the Plasmodium genome database 00:04:50.20 or other resources. 00:04:52.27 But the challenge is really highlighted 00:04:55.17 by following not just the static view of the portfolio 00:04:58.23 of the Medicines for Malaria Venture, 00:05:00.26 but by considering what's happened in the interim, 00:05:03.03 in the past couple of years 00:05:05.08 since this portfolio was put together. 00:05:07.08 Fortunately, many of these compounds 00:05:10.04 are now emerging out into the clinic, 00:05:12.22 and we can see them passing on 00:05:15.01 into phase III trials. 00:05:18.16 Some compounds have encountered roadblocks 00:05:20.20 and have moved backwards 00:05:22.18 -- hopefully a temporary reverse 00:05:24.18 in which problems encountered 00:05:26.20 with a synthetic peroxide 00:05:28.25 may be solved by considering other related compounds, 00:05:31.10 and will reverse course and move back out into the clinic. 00:05:35.11 But many compounds -- very many compounds -- 00:05:37.10 have in fact fallen out of the pipeline altogether. 00:05:43.07 And that emphasizes the need 00:05:45.16 to identify new compounds to feed this pipeline 00:05:48.07 if we're going to have any compounds 00:05:50.14 coming out of... 00:05:52.05 out into the clinic in the years to come. 00:05:58.05 And the identification of drug targets 00:06:00.26 poses some severe challenges. 00:06:03.29 So, let's consider, for example, the identification... 00:06:07.13 one strategy for identifying targets, 00:06:09.05 what the information is that one might want to know. 00:06:12.29 Let's consider the various attributes 00:06:17.09 that would be of interest. 00:06:18.24 We can certainly imagine that we would be looking 00:06:21.00 for enzymes that will be annotated from the... 00:06:22.26 from the genome sequences for organisms of interest; 00:06:27.16 various features that we've looked at, for example, 00:06:29.23 whether proteins are secreted or how large they are 00:06:33.17 or whether they're predicted to be soluble. 00:06:36.07 We could take advantage of functional genomic approaches 00:06:38.24 to ask whether the candidate targets 00:06:41.24 for drug development 00:06:43.28 are expressed at the right time or in the right place, 00:06:46.19 and we might be interested in structural information 00:06:50.06 on which we could consider modeling 00:06:54.08 particular drugs that might fit into active site pockets, 00:06:58.00 a promising approach in the... 00:07:00.01 in the pharmaceutical sector. 00:07:02.04 Some of the most interesting information, 00:07:04.01 the most valuable information for identifying candidate targets 00:07:06.18 for drug development, 00:07:08.21 lie in the realm of identifying drugability 00:07:11.00 or essentiality criteria. 00:07:14.02 By drugability, we mean whether it's going to be possible 00:07:16.28 to develop a drug for a particular target, 00:07:20.04 whether it has the right attributes, 00:07:23.06 whether its active site is likely 00:07:26.01 to be amenable to inhibition 00:07:28.18 with small molecule antibiotics, 00:07:30.21 whether the target, when it's inhibited, 00:07:33.03 will in fact kill the organism. 00:07:36.16 And I'm sad to say much of this information on essentiality, 00:07:38.25 on drugability, on structural information 00:07:43.00 -- much of the information we would most like 00:07:45.02 to know about candidate targets for antimalarial development -- 00:07:47.08 is simply missing. 00:07:48.23 If we look, for example, for a particular enzyme in humans, 00:07:54.03 we have available information 00:07:57.06 from the annotation of the human genome, 00:08:00.02 we have information that we can calculate 00:08:02.02 automatically on the size of the... 00:08:04.29 of the protein, an enzyme in this case; 00:08:07.29 information on functional genomics data, 00:08:10.11 of whether it is expressed 00:08:12.11 and when it's expressed; 00:08:14.02 and even information on its drugability 00:08:16.13 or, in many cases, essentiality. 00:08:18.26 If we turn to neglected diseases, 00:08:22.09 such as malaria or African sleeping sickness, 00:08:25.22 caused by the parasite Trypanosoma brucei, 00:08:28.26 well, we are now fortunate in having 00:08:32.05 the complete genome sequence available 00:08:34.09 for trypanosomes, 00:08:37.01 and therefore can use the annotation 00:08:39.05 to identify the putative farnesyl pyrophosphate synthase 00:08:45.08 for this... for this organism. 00:08:47.08 And we can calculate information, 00:08:49.21 such as the predicted molecular weight. 00:08:52.17 We don't necessarily have the functional genomics data 00:08:56.29 that would tell us whether this enzyme 00:08:59.01 is expressed at the right time 00:09:01.00 and in the... in the right place, 00:09:03.17 and neither do we have structural information, 00:09:05.26 or information on drugability or essentiality. 00:09:09.24 We can, however, consider taking advantage 00:09:12.04 of the evolutionary relatedness 00:09:14.25 between these proteins in inferring orthology. 00:09:19.11 And by orthology, 00:09:21.28 what we mean is the evolutionary relatedness of proteins, 00:09:25.07 proteins that have a shared evolutionary history 00:09:29.06 and a shared function. 00:09:31.02 And if we could define a true ortholog 00:09:36.28 -- a protein with likely similar function -- 00:09:40.14 then we could infer these various attributes of interest, 00:09:44.28 by identifying an orthologous group 00:09:47.17 and inferring information, for example, on drugability, 00:09:50.29 by analogy with known drug ability criteria in humans 00:09:56.04 or in yeast or in other organisms of interest. 00:10:01.13 And so, my laboratory group and many others 00:10:06.07 have invested a great deal of effort 00:10:10.10 in attempts to try to define orthologs across species boundaries, 00:10:13.23 and I'd like to briefly introduce you to another database, 00:10:17.08 and we will do this simply by virtue of screen dumps 00:10:20.06 rather than taking the time to walk through this live, 00:10:22.25 but I encourage all of you 00:10:25.23 to take a look at the OrthoMCL database 00:10:28.01 at orthomcl.org, 00:10:30.29 accessible via the Plasmodium genome database 00:10:35.17 or by... as a direct URL, here. 00:10:39.09 This database is designed to facilitate queries 00:10:42.24 across all of life, focusing particularly on the eukaryo... 00:10:47.01 eukaryotic species, 00:10:49.16 on all eukaryotic species for which complete genome sequence 00:10:52.04 is available, 00:10:53.29 and a representative selection of bacteria 00:10:56.24 and archaebacterial taxa. 00:10:59.02 So, imagine, for example, 00:11:01.15 that we were interested in ask... 00:11:03.22 in formulating an evolutionary question 00:11:06.10 related to the origin of the apicoplast, 00:11:08.04 that organelle that we described earlier. 00:11:11.00 We might in this case be interested in asking 00:11:15.21 about proteins that are derived 00:11:19.08 from a plant or algal ancestor, 00:11:23.24 present in apicomplexan parasites, 00:11:26.15 including Plasmodium parasites and other haemosporida, 00:11:31.14 Toxoplasma parasites, Theileria parasites, 00:11:35.07 but absent from Cryptosporidium, 00:11:37.12 which interestingly has lost this organelle, 00:11:41.28 but also proteins which are lacking... 00:11:45.05 have been... that are absent from any mammalian 00:11:51.12 or animal or fungal taxa 00:11:53.20 where the apicoplast is not present. 00:11:57.01 And when we run a query like that, 00:11:59.15 we come up with a list of groups of proteins, 00:12:01.19 proteins indicated here... 00:12:03.27 a few dozen protein groups 00:12:07.07 represented by the phylogenetic spectrum 00:12:09.26 across various organisms the... 00:12:12.26 from the bacteria and archaebacteria, 00:12:16.03 metazoan and protozoan organisms. 00:12:18.09 This gives us a list of several genes 00:12:21.21 including targets of fluoroquinolone antibiotics; 00:12:27.00 enzymes that are known as... 00:12:28.25 such as this DNA gyrase, two subunits indicated here; 00:12:32.18 proteins that are known to be associated with the... 00:12:35.15 with the apicoplast, 00:12:37.15 this LytB protein, for example, 00:12:39.07 a rapid means of identifying, 00:12:42.05 purely on the basis of evolutionary conservation, 00:12:44.22 the proteins that we can find 00:12:49.07 and that might be of interest, for example, 00:12:51.00 in defining metabolic pathways 00:12:53.25 associated with the apicoplast. 00:12:55.24 Turning to the question of drug target discovery, 00:12:58.14 we can use this information 00:13:01.07 -- inference based on orthology -- 00:13:04.09 to fill in many of the gaps in our knowledge, 00:13:08.08 and this has explicitly driven yet another database 00:13:13.08 that I'd like to tell you about today, 00:13:16.11 the last of the databases we'll be discussing today, 00:13:18.29 the TDR Targets database 00:13:22.02 available at TDRtargets.org, 00:13:24.29 which differs from the databases that we've described thus far 00:13:28.24 in considering a much wider range of species, 00:13:30.21 organisms of particular interest 00:13:35.21 to the Tropical Disease Research unit 00:13:37.25 of the World Health Organization, 00:13:40.02 and where we can infer absent information based on... 00:13:44.19 based on orthology. 00:13:46.18 This is a project that has involved 00:13:48.13 a collaboration between many different groups 00:13:50.05 and many different funding agencies, 00:13:51.28 and I'd like to briefly take you through 00:13:54.03 a tour of some of the kinds of questions 00:13:56.21 that we can ask. 00:13:58.10 If we were to turn to the OrthoMCL... 00:14:00.21 if you were to turn to the TDR Targets database, 00:14:03.05 you would be presented 00:14:06.22 with a series of quite simple forums 00:14:09.04 focused explicitly on drug target identification. 00:14:12.17 You might, for example, 00:14:14.15 want to define targets based on predicted enzymes -- 00:14:18.21 whether they have an EC number, 00:14:20.20 whether they've been annotated as being... 00:14:22.19 as having catalytic activity. 00:14:24.15 You might look for small and soluble molecules, 00:14:26.14 and I have formulated this query 00:14:28.20 to simply ask for proteins under 00:14:31.10 a predicted molecular weight of 100,000 00:14:33.03 and with no transmembrane domains, 00:14:35.02 for which a crystal structure is available, 00:14:36.29 or at least a structural model, 00:14:39.28 which are shared across related organisms, 00:14:44.11 nd in this... and in this question, 00:14:46.29 for targets in the Trypanosoma brucei parasites 00:14:53.04 responsible for sleeping sickness, 00:14:56.16 I've asked for proteins that are found in related parasites, 00:15:00.27 Leishmania major, responsible for kala-azar, 00:15:02.25 Trypanosoma cruzi, responsible for Chagas disease, 00:15:06.03 as well as Trypanosoma brucei, 00:15:08.28 but absent from humans, 00:15:10.14 and one might imagine absent from some of these other organisms 00:15:13.09 as well... 00:15:15.17 looking for proteins for which we have, 00:15:18.06 if not essentiality data in trypanosomes, 00:15:22.09 where we have very limited essentiality data, 00:15:24.21 any evidence of essentiality in any other organism 00:15:28.10 -- in yeast, in E. coli, in other bacteria, in mice -- 00:15:36.15 evidence of essentiality 00:15:39.13 based on a variety of tools 00:15:42.05 developed in the pharmaceutical industry 00:15:45.14 for drugability or for the nature of the chemical compounds 00:15:49.01 which target, in this case, 00:15:51.06 orthologs of these proteins, 00:15:53.05 where there is curated information 00:15:55.16 validating the phenotype that's observed when this... 00:16:04.19 when this enzyme is targeted, 00:16:06.18 whether we have literature references or assays, 00:16:10.00 for example, that are available for evaluating performance. 00:16:14.11 And if we turn back to the page 00:16:16.14 and ask this long series of questions... 00:16:19.04 we can once again look at the history of the questions 00:16:22.02 that we've asked, 00:16:23.19 just as we did in considering the Plasmodium genome database... 00:16:26.18 only in this case, in this list of queries 00:16:29.17 we've also... we've also introduced the ability 00:16:33.11 to weight those queries. 00:16:36.06 So, in this particular query, 00:16:39.07 I've insisted on proteins being enzymes -- 00:16:42.02 I've given that a very high weight. 00:16:45.03 I've also placed very high value on targets 00:16:50.19 that have been manually curated 00:16:53.14 and those for which we have crystal structure information -- 00:16:57.13 structural models are potentially valuable, 00:16:59.19 although perhaps not as useful as a crystal structure -- 00:17:02.15 and a whole series of other weights. 00:17:05.07 And the net result of this is the ability 00:17:07.15 to produce a long list of prioritized targets 00:17:12.23 with weights that are applied from the most... 00:17:16.26 from the most promising 00:17:20.00 down to the least promising. 00:17:21.13 And we can consider this long list of targets 00:17:27.00 as a ranked and weighted list, 00:17:28.18 returning as desired to the... 00:17:31.24 to the weights that we've applied, 00:17:33.13 modifying those, modifying our queries. 00:17:35.17 And finally, we can take these lists 00:17:37.12 and post them for others 00:17:39.24 who may want to explore the list 00:17:42.18 or modify the various current of criteria 00:17:45.20 that one would be interested in looking at. 00:17:48.09 And in fact, there are many posted lists of candidate targets 00:17:51.11 that you may wish to explore 00:17:53.07 if you're interested in targets for trypanosomes, 00:17:56.09 for malaria, 00:17:57.28 or for various other organisms. 00:18:00.10 Now, I'd like to close with an interesting story that highlighted, 00:18:06.03 at least for me, the potential of these sorts of approaches. 00:18:10.02 Recently I was giving a presentation 00:18:12.27 on this TDR Targets database, 00:18:15.18 and I was discussing candidate targets for... 00:18:20.01 candidate targets for drug development 00:18:22.13 and formulated a query a little bit more extensive 00:18:25.04 than the queries we've looked at before. 00:18:27.15 This is a query with 14 parameters 00:18:30.12 and a variety of associated weight criteria, 00:18:34.18 giving rise to a long list of candidate targets 00:18:39.14 for trypan... antitrypanosome drug development, 00:18:43.26 including many targets that are the subject of intense exploration 00:18:51.29 as potential new drug targets for African sleeping sickness. 00:18:55.24 And a hand went up in the audience 00:18:57.19 from a colleague who said, you know, 00:18:59.05 this looks very nice, but to be honest, 00:19:02.15 most of us in the audience are... 00:19:06.19 work on other organisms, 00:19:08.09 on malaria or on tuberculosis. 00:19:12.00 What would happen if... 00:19:14.24 how would we go about exploring potential targets there? 00:19:18.05 And without knowing the answer to that question, 00:19:21.15 I said, let's take the same set of criteria 00:19:25.16 about crystallographic information 00:19:28.24 and annotation and solubility and drugability 00:19:32.29 and simply -- blind -- apply the same criteria 00:19:36.00 to Plasmodium parasites 00:19:37.22 and to the bacteria responsible for tuberculosis. 00:19:42.24 And it was certainly gratifying 00:19:45.18 to find that top on the list of candidate mycobacterial targets 00:19:48.20 was INHA, the target for isoniazid. 00:19:52.12 Second on that list 00:19:54.21 was dihydrofolate reductase. 00:19:55.28 Third on that list was one of the enzymes 00:19:58.25 associated with lipid metabolism. 00:20:01.06 In fact, it's the same pathway 00:20:03.17 associated with the apicoplast in Plasmodium 00:20:05.25 and a promising target for drug development. 00:20:07.24 So, this served to nicely validate 00:20:10.13 the promise of these kinds of strategies, 00:20:13.15 and I hope that each of you will consider 00:20:16.08 exploring genome databases 00:20:19.16 as ways of carrying out computational questions, 00:20:28.25 computational experiments, 00:20:31.12 to formulate hypotheses that can be tested further 00:20:34.14 in the laboratory 00:20:36.02 and bringing to bear the wide range of techniques 00:20:38.11 that we have available. 00:20:40.10 Now, needless to say, these resources, 00:20:46.21 and we've talked about... 00:20:48.28 we have talked about several different databases 00:20:51.17 over the course of... 00:20:53.11 over the course of this lecture... 00:20:54.28 the PlasmoDB database that's a component of the apicomplexan database 00:20:58.05 and the EuPathDB database, 00:21:00.27 the ortholog database 00:21:04.14 that's used for interrogating across all of life 00:21:07.13 and the TDR Targets database 00:21:09.12 focused on drug target development... 00:21:12.08 all of these resources 00:21:16.20 rely on input from many investigators in my laboratory 00:21:18.29 and elsewhere. 00:21:21.24 and I certainly won't take the time to read 00:21:23.12 the long list of names that's involved. 00:21:25.24 But most importantly, I'd like to acknowledge, 00:21:27.12 from the standpoint of Plasmodium, 00:21:29.18 the Plasmodium genome consortium 00:21:33.29 and, from the standpoint of all of these databases for all organisms, 00:21:37.12 that the many researchers worldwide 00:21:41.08 who've been responsible for generating and making available 00:21:44.01 the data on which these... 00:21:46.17 on which these tools are based. 00:21:51.10 And so, as we come to the end of this third lecture 00:21:54.21 on apicomplexan parasites 00:21:56.29 -- their biology, their evolution, 00:22:00.22 and the exciting new opportunities 00:22:04.01 for research in parasitology 00:22:07.10 and many other... in many other areas -- 00:22:11.04 I'd like to end on a final note 00:22:14.04 that is particularly relevant to researchers 00:22:17.07 in countries that suffer from these parasitic diseases, 00:22:22.22 pointing out that particularly with respect 00:22:25.08 to the genome database project we've discussed, 00:22:30.21 there are tremendous opportunities 00:22:32.29 for working anywhere in the world, 00:22:35.05 even in environments that may be... 00:22:36.29 with... have limited infrastructure. 00:22:40.11 The computational approaches that we've described, 00:22:43.20 I hope you will agree, 00:22:45.21 offer fantastic opportunities for biological 00:22:49.14 and therapeutic perspectives 00:22:51.07 on all organisms, pathogen or not, 00:22:54.20 and capitalize on computational tools 00:23:02.03 that are universally available throughout the world. 00:23:06.25 Unlike some of the sophisticated molecular genetics 00:23:11.14 or cell biological approaches 00:23:13.08 that we've discussed 00:23:15.09 that rely on very expensive equipment 00:23:16.25 that's of limited availability, 00:23:19.04 computers are inexpensive and are ubiquitous, 00:23:22.03 and can be used even in areas 00:23:26.07 that have no reliable power 00:23:29.20 and only occasional low-speed internet access. 00:23:35.01 Moreover, as a relatively new discipline, 00:23:37.11 the area of computational biology 00:23:40.04 is certainly ripe for exploitation, 00:23:43.01 and the demand for researchers 00:23:45.07 conducting such work is certainly likely to remain high. 00:23:50.09 And because most of the communication 00:23:53.26 in this area is net-based, 00:23:55.26 it diminishes somewhat 00:23:59.02 the critical mass issues 00:24:01.22 that pose a challenge for researchers 00:24:04.19 trying to work in environments 00:24:07.04 without a rich and diverse research infrastructure. 00:24:11.29 As an area that overall is capital intens... 00:24:15.01 is intellect intensive rather than capital intensive 00:24:17.22 or technology intensive, 00:24:19.09 I think computational biology 00:24:21.26 is particularly exciting for work 00:24:24.06 wherever you may happen to live, 00:24:26.00 in areas where malaria or trypanosomes 00:24:29.00 are endemic, 00:24:31.08 in areas... as well as in areas 00:24:34.27 with a rich source of technological and academic infrastructure. 00:24:40.26 So, I hope you've enjoyed this series of lectures 00:24:43.03 as part of the iBio seminar series, 00:24:45.28 and that you will explore other iBio lectures as well 00:24:50.14 and consider ways in which the kinds of approaches 00:24:53.07 that we've talked about today 00:24:55.11 might be applied to the experimental systems 00:25:00.11 that you find of interest, 00:25:02.19 and perhaps consider ways in which 00:25:05.18 the approaches that you have taken yourself, 00:25:07.19 in your own work, 00:25:09.07 or that you've learned about in other iBio lectures or elsewhere 00:25:13.18 can be applied to the various systems that I've talked about today. 00:25:15.16 And I look forward to hearing more about the work 00:25:17.26 that you do and to following the develop... 00:25:23.16 the continued development of this area in a time 00:25:27.25 where the rapidly advancing technology 00:25:31.07 in cell biology, in molecular biology, 00:25:33.15 in computational biology 00:25:35.14 has offered many opportunities for all of us. 00:25:37.26 Thanks very much for your attention, 00:25:39.14 and best wishes with your further studies and research.