Perspective on e-science, systems biology and drug discovery

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Video in TIB AV-Portal: Perspective on e-science, systems biology and drug discovery

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Perspective on e-science, systems biology and drug discovery
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CC Attribution - NoDerivatives 4.0 International:
You are free to use, copy, distribute and transmit the work or content in unchanged form for any legal purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
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2013
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English

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Abstract
Prof. Douglas Kell talks about systems biology, computational aspects of biochemistry, and drug discovery, as well as open-access publishing and the communication of research results. The questions were prepared and asked by Prof. Roy Goodacre.
Keywords systems biology biotechnology
Psychopharmakon
Tiermodell Regulatorgen Arzneimittelforschung CHARGE syndrome
Molecule Systemic therapy Ageing Tiermodell Arzneimittelforschung Chemistry Process (computing)
Tiermodell Recreational drug use Process (computing)
Organische Chemie Enzyme Singulettzustand Genotype
Slate Organische Chemie Recreational drug use
Systemic therapy Molecule Enzyme Tiermodell Sozialepidemiologie Risk factor Chemical structure Cholesterol Arzneimittelforschung Process (computing)
Kohlenstoff-14 Potenz <Homöopathie> Stickstoffatom Recreational drug use Pharmaceutical industry Carbon (fiber) Chemistry Molecule Hydrogen Silicon dioxide Chemical structure Cobaltoxide Process (computing)
Cheminformatics Arzneimittelforschung Process (computing)
Paste (rheology) Systemic therapy Proteomanalyse Functional group Concentrate Controller (control theory) Imidacloprid Microarray Herzfrequenzvariabilität
Waterfall Organische Chemie Recreational drug use Activity (UML) Cave Katalase Genotype Systemic therapy Molecule Enzyme Gesundheitsstörung Cupcake Tiermodell Bioverfahrenstechnik Metastasis
Fatty acid methyl ester Combine harvester Organische Chemie Tiermodell Cell (biology) Singulettzustand Diazepam Solution Genotype
Molecular biology Systemic therapy Enzyme Organische Chemie Tiermodell Recreational drug use Sea level
Systemic therapy Combine harvester Lactitol Biochemistry Wine tasting descriptors
Arzneimittelforschung Wursthülle
Recreational drug use Toll-like receptor
Zeta potential Systemic therapy Organische Chemie Recreational drug use Patent Functional group Protein Common land Base (chemistry) Screening (medicine) Arzneimittelforschung Process (computing)
Molecule Organische Chemie Recreational drug use Cell (biology) Transport Metabolism Active site Base (chemistry) Ash (analytical chemistry) Pharmacy
Cheminformatics Volumetric flow rate Area Recreational drug use Transport Transformation <Genetik> Arzneimittelforschung Columbia Records Chemistry Stuffing
Toxicity Pharmaceutical industry Active site
Area Recreational drug use Ageing Materials science Renewable resource Pharmaceutical drug Toll-like receptor Active site Genome Wursthülle
Ageing Gemstone Toll-like receptor
Kohlenhydratchemie Hydrocarbon Chemistry
Chemische Biologie Enzyme Organische Chemie Ageing Ocean Medical history Bioverfahrenstechnik Polyethylene Pharmacy Chemistry
Electrical mobility Process (computing)
Organische Chemie Base (chemistry) Abundance of the chemical elements Biomolecular structure
Orders of magnitude (radiation)
Stop codon Molecule Synthetic biology Biomolecular structure
Übergangszustand Lactitol Structural steel
Cheminformatics Mass spectrometry Chemistry
Volumetric flow rate Chemische Biologie Initiation (chemistry) Octane rating Nucleic acid sequence
it.
so.
hello on regulators on here talking to my colleague of those cow and we've got a few questions would like to ask you about some of the challenges will run charges that are coming up in the future and the first is with respect to drug discovery.
and some of the recent findings to do with computational wait you think the new computational modeling might help us in the future if so it's interesting facts that if an engineer wants to build a bridge and they don't make the model of the bridge that they're trying to design and the two things happen the bridge falls down and.
the engineer gets probably put in prison for professional misconduct so part of the difficulty of the drug discovery process has been the failures have a decent model of the system that people trying to put these molecules into maybe the human by chemical networks so a lot of the heavy lifting in the future as we move into the age of the science of even for such.
from the like it's going to be making models of the things that we think we study and see what happens when we manipulate multiple parts of them i say multiple parts of them because part of whether drugs covering process went on his in believing that a single target is likely to be a good thing for a drug to hit it will have a big if.
act and that's simply not true and it's not true for multiple reasons first reason is that evolution selects for robustness if you're an enzyme and one mutation cause you not to work that organism and the organism dies out organism is selected out pretty quickly so early in evolution evolution selects for robustness so in order to.
a big effect on an organism you actually have to hit multiple places at the same time if you look back a successful drugs you find that despite the fact that some of them were isolated from the base of make the top it's actually that's what they do with stats is a classic example they are mostly i slate on the basis of their ability to innovate h m g i read up.
so is an enzyme of cholesterol boss insist but actually the reason they put years on your life epidemiologic lee nothing to do with cholesterol which is actually not a risk factor in the normal range but because of that and inflammatory another effects so understanding the network the system and modeling which possible ways it's going which parts the network is going to it.
it's going to be important to the drug discovery process of looking individual molecules and how we might seek to isolate them our knowledge of the structures of individual target molecules improving all the time of calls in principle we can seek to dock candid target structures with those molecules problem in part is the no.
most number of structures that could be estimates to process we tend to the powers seventy small molecules of interest in pharmacology even the number of all possible molecules containing carbon hydrogen oxygen nitrogen and we have thirteen carbon atoms which would be very small drug is about least several hundred billion is so. nobody has got close to making those kinds of numbers of molecules if we made a fractional so a big part of the chemical universe remains on taps and in silica by computer nation building is going to be possible way to address it so there isn't any doubts that while many pharmaceutical companies have lost him and for mass.
its troops now the whole of computational motoring is going to extremely important to the drug discovery process and elsewhere as well.
but so how do you think you're going to convince follow up to go down this particular route since in the past they work that had a love affair with proteomics didn't deliver much before that they have a love affair with microarray technology so is this just in the another new own makes all you know thing on.
the street that the following ago know this is just academic and we're not interested in the side of the difference from the air makes the overuse of the system variables you do something and they vary their full that's why you measure the owns very is a function of different things and variables do not actually control system only promises control a system the premises of the system all concentrated.
patients of enzymes activities of enzymes the cake cats the caves of the mets would you put it together and the way in which they hooked up and it's those that change and disease as those of the drugs got to manipulate back changing a variable cannot affect the system a tall so the only still do something about the state of the system that doesn't necessarily tell you what you've got to do about that you need.
promises and they for you the time the models and that's very much the way forwards falls like i could also say the same fall when you want to use organisms to make a molecule so to be to overproduce molecule industrial biotechnology and here again in order to do it one mutation is usually enough because of.
solution selected of course selected for the organism off its ability to produce things but in order to make organisms extreme lots of buy products you have to make multiple mutations of manipulations of the time and then you have a common as tory a problem so if there's a mobile with the thousand and signs i want to manipulate just three of them to get a. the effect but i don't know which three number of ways of commuting three out of thousand is actually hundred sixty six million was ways of putting for outs of. thousand is in the billions and so on the cell so doing its the west way is almost impossible because there's just too many combinations whereas having the model in to start with allows you to do that sort of famous paper in two thousand and seven sign up the showed that he wants to make the climate valium.
and he made his mobile and he said finally which three enzymes i need to change to make this organism spew out of a lean and it said you only need to do these streets to start off with a week molecular biology later he's done the street and it makes point four per gram of fighting per gram because at it and that's the way we have is do things in the future because of the communist world problem. you cannot sold that any other way it's a fundamental problem as a computer can navigate these big spaces and tell you where to look for the right on so it's very much the way the future so that's in mind the the models also have to be funny how when you think we are going to be in a state where we may have them.
i have system modeled correctly where we're going to know exactly let's say how a drug may interacts with that system so at one level the perfect is the enemy of the good off were asked this year we can't pull the system because we don't know what it's like and that's exactly the reason why the precise to do model system because you don't know what it's like and that's when and.
some point you compare the predictions of the mall the with the experiment measurements you see what you've got too small to make it better and here again the evolutionary robust this thing comes to our rescue because actually because of this relative independence of individual parameters for most bits of the mobile doesn't actually matter what bio it only.
prices for some small important combinations of things of the hubs of the the center of big networks and some of the things to the main point is to find which of the very important ones because trade or energies on them knowing exactly for all of that promise of the mobile what they are doesn't matter at all because change them has no effect on the behavior of the system mass again whether by.
logical robust this comes to our rescue the problem is much less hard it might first appear once you know the topology of the network the new begins probably tries off to that and so we talked predominately about exciting things some positive things over to science is full of no exciting all may be negative.
the results so if a drug discovery this is quite an important aspect has cut really quite quiet what will your views on this if so it certainly the case that from an intellectual academic points of view and eight as a result i did this struggle he didn't do anything much is all even worse he killed the rat.
it is something that is not either going to lead to good drug all necessary publication one of the top journals but that kind of knowledge is still extremely useful and especially near of open access it is going to be incumbent on people much more to make available knowledge of experiments that i didn't work as expected it didn't work a toll had drugs had a bad effect.
and often you will find that. if everyone does it sort of the the benefits of the commons if everyone does it contributes there are almost certain each country we see new things and gaining a gear in from the contributions of everyone else the likelihood because of the possible huge number of drugs that you could have tried to everyone's tried the same ones is precisely zero in the patent system which rules out.
anyway so having knowledgeable the drugs that failed all the way through the the drug discovery process is going to be important there and of course it interesting question as to why drugs five zetas to the drug discovery process part of the reason of course is that drugs are not intractable parts of the system that you would like they have often been nice place. on the basis of a specified target protein and yet it is the system as a whole it has to respond we moved in the early days from a system of phenotypic screen where we looked at the organism function first to see if the drug had an effect then moved back to see people the molecular bases of it might be that we change to an era.
which we decide what the molecular bases all to be at the site purposes and then look to see if it works in the organs most the time it did with a substantial reason for that is what i regard as the misapprehension that drugs just float into cells called into their life list that's a very widely held view and it's actually complete the norm is now up.
huge amount of evidence which are some rise will refuse to the effects that drugs gets into cells by hitchhiking on the back of transport is the to normally that for getting more kills into an ounce of cells but those molecules of not the molecules designed by pharmaceutical chemist and the molecules used by nature norm. metabolism and ashes lot of evidence for the is that drugs get into an ounce of cell solely by hitchhiking on transport as we can begin to use that knowledge to our benefit from what we already know the human rights public network fully one third of the most acute was involved in that network are actually trying.
and sports for moving stuff around so it's incredibly poll their cars cinderella subject got forgotten politic as people just believe the drugs flow to date in which they don't and partly because actually that they their site invisible because they don't do chemical transformations pond last they actually just move stuff around as we now know both in human.
and the societies and drugs movie stuff around is an increased extremely important part what you do so looking in new drug transports said yang to be a big area and again making this kind of knowledge available through the internet through the literature. having computers to read the two papers the minutes this of being published is going to be possible in the open accessing possible new way we do science so can possibly on certain alone something you discussed earlier about stats and having off target of facts live for look at via groom credibly successful programs now being mocked.
for all sorts of other effects of it may have to think of drugs companies need to rethink their strategies because if you have a hierarchy way you kill products because they're toxic but only took particular proportion of society and yet the you know you may be able to go back to sort of that site.
the cupboard who look to see what else could be used for so drug repurchasing is extremely important as a lawsuit was in she knows about that and the flip side of the change from the blockbuster age to the fact that we know that drugs have differential successful effects on different people and indeed in some cases will kill them it's.
seems to me very old that you would ban a drug completely because it kills a small number of people while having a very great benefit putting five years on their life sites hundreds of thousands of if the same applied to most because we wouldn't have an automobile industry so our risk reward ratio is calibrates different. the for motor cars and drugs in society and i think we will soon begin to find out through pharmacogenomics another means exactly which populations will benefit most and that will indeed rescue some drugs might have either been taken off the market will knock on their toll when they would have great therapeutic benefit to specific subset. it's the population which we can detect by genome sequencing alone as well as by phenotypic measurements so that's very much going to be part of the future it's it has some bad effects on drug development on one side but rescues of the drugs on the other side so the individualized nation personalized medicine is usually called is going to be very important part of the future. ok so in in the not so distant future we're running out of high value products as a starting materials for drugs and there's a sort of away even the in the area now those thinking that renewable sources could be used let's say to end up with new synthetic rooms on what to.
you think your what are your thoughts in this particular our side so the opening statement is that the stone age did not end because we rouse of stand stone age ended because we found something better and the fossil fuel age will have to end not because rafa fossil fuel because we can't afford to put the toll in the atmosphere c o two because week.
thanks replace it with something else something else call sculpt the moment for sinn sisamouth which typically is going to make carbohydrates robin hydrocarbons so all the kinds of chemicals both bulk and find out we make are probably going to start off in substantial measure with different starting points this is good because you don't make different products.
seven making polyethylene bags that accumulates the pacific ocean you make biodegradable bags that did not and all this is going to be good so there will now be a move towards true sustainability the fossil fuel age will be seen as a little blip in the history of evolution and humanity.
same for nuclear age. and the to do list is to recognize that biological organisms have long ago learns to transform chemicals rather more effectively and chemists spawn knowledge and that in the future we're going to want to do this by the so-called directed evolution of enzymes by manipulating organisms so that they. change their pockets to make products cost much as the way that that we've been making all his might lysine antibiotics insightful for many is and so the whole chemical biology agenda is going to be substantially moving into industrial biotechnology what in europe is called a knowledge base by economy which is already well.
the two trillion euros and twenty two million jobs so it's got a good starting point many countries in europe are getting that together the uk among them. so don't you say he we're going to be able to do this in the future but what kind of mobile technologies ago and we made it so you there's a famous saying from sydney brenner this i know you like as well which is that science advances by new techniques new developments a new idea is probably in that old and new techniques.
we've got in now in abundance that a real game changes fall sequencing is a total change change so. knowing what's the sequence of an organism is and learning which different sequences are going to be good for making things mall is extremely important it took fred sang at a year and a half ninety seventy seven to sequence the five thousand bases of five one seven full time it takes to do that now on a modern instrument this size is one.
a second ten orders of magnitude quake.
so cheap sequencing is an extraordinary pulled technology of course what is it we're going to sequence on some hour days d.n.a. that we have made ourselves synthetic biology in which we create including whole organisms but for all present purposes maybe just important possible isms that make the soles molecules we want to be able to make. is going to be incredibly important so synthetic biology is a complete game changer.
noble sequencing methods that are going to be even foster currently we use methods actually the main ones that events to cambridge by policy remain tenement now the new ones based on not of polls that are coming through next year invented in oxford were developed in oxford and taking to be extremely important and this point stop.
the general importance of our school methods generally. most of what has been happy with analysis is foster small a cheaper so in a transition is a complete game changer because it takes a huge amounts reagent costs and asked say that used to doing three hundred michael he says a night six well plate takes thirty might creates is the three engines sixty to eighty four will plate.
and a few my creates is to fifteen thirty six well plate and in a microfluidic device way down to manage its people he says so the number of things again it comes to the community moral problem almost all of science and i've written or you'll miss science as a community hall was a shoe problem is around.
being able to measure lots and lots of different things both simultaneously in seriously so all the alice cool methods including non-invasive ones chemical imaging is going to be important here in for enrollment clearly life peaceful huge changes in mass spectrometry that have driven the projects agenda that you mention and now again the way he microarrays law.
the disappear because we will do deep sequencing including of our night so out of school methods are absolutely the forefront of what we want to do so you do think that can overtake computation in terms of the you know the bottleneck so do we have to do so they impressive took over morsel.
well actually we are beating moore's law hands down both in the rates of genomic the acquisition and have nots the busy all the data still to keep up with this kind of thing flow from the point of view of analytical methods were way ahead of some analysts who matters were way ahead of moles little genetic sequencing rates have increased a far greater.
to write them that so what we're going to need that is the wet and dry to go forward in parallel and initiative cycle in fact if i have to end up with the strapline chemical biology the future is going to be moist by the wet no dry but both.
i.
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