1 00:00:02,550 --> 00:00:23,240 As. So start, Jim, by thanking you for coming here. 2 00:00:23,600 --> 00:00:32,930 And so I've asked the my colleagues and the students in the department to ask some questions, so they give me a list here. 3 00:00:33,650 --> 00:00:39,469 But before we get into this, this, too, just wanted to ask you, what attracted you to the academic world? 4 00:00:39,470 --> 00:00:43,820 Was it sort of a family history or when did you decide this is what you wanted to do? 5 00:00:44,690 --> 00:00:53,900 Well, it certainly wasn't family history, because if I hadn't had polio, I would have had to be a plumber and leave school at 14. 6 00:00:54,680 --> 00:01:01,220 Like all my family. And I went on to school, decided I wanted to go on to university. 7 00:01:01,790 --> 00:01:04,700 I never actually thought of academia to begin with. 8 00:01:04,970 --> 00:01:14,660 And at the end of the third year, I got a summer job, but an aircraft company called Fairy Aviation. 9 00:01:15,410 --> 00:01:27,500 And that was actually a major influence because I found out of a research group of I think it was about seven, six of them were unbelievable. 10 00:01:27,500 --> 00:01:32,180 I wouldn't have accepted them at university, never mind have them design aeroplanes. 11 00:01:32,750 --> 00:01:38,930 And one of them was I just thought I would never want to spend my life doing this sort of thing. 12 00:01:39,590 --> 00:01:46,250 So I went back and I just found academic life kind of attractive. 13 00:01:46,760 --> 00:01:51,590 And it was in St Andrew's, which is such a lovely place. 14 00:01:51,710 --> 00:02:05,510 This certainly was then. And so after doing this in mathematics with a kind of ordinary degree in physics or natural philosophy, 15 00:02:05,510 --> 00:02:11,330 in those days I decided to do a PhD, and that was it. 16 00:02:12,230 --> 00:02:17,180 When I finished the Ph.D., I managed to do this the work in a year. 17 00:02:17,480 --> 00:02:21,000 They let me go off and take a job for a year. 18 00:02:21,000 --> 00:02:26,690 So I got a lectureship in Durham University and King's College, Newcastle, 19 00:02:27,290 --> 00:02:34,910 and then I had always wanted to go off to America and that really took off from there. 20 00:02:35,150 --> 00:02:41,270 Okay. And you've sort of come back and forth between the UK and America. 21 00:02:41,270 --> 00:02:48,590 So why has that been? Well, I think one year I don't know how many times, 22 00:02:50,150 --> 00:03:01,430 but I went to Harvard for and then I saw my family pressure, I suppose, really brought us back in 1959. 23 00:03:02,240 --> 00:03:06,500 And I went to University College London, which was very nice. 24 00:03:08,090 --> 00:03:12,740 And then I applied for a job in Oxford and got a fellowship at Hertford. 25 00:03:13,550 --> 00:03:17,390 And the excessive teaching was just too much. 26 00:03:17,390 --> 00:03:27,410 I couldn't do any research, so I handed in my resignation after a year to the gross irritation of the of the principal, 27 00:03:27,830 --> 00:03:40,790 and then went back to America in 63 and was back there for University of Michigan and then at New York University. 28 00:03:41,330 --> 00:03:48,590 And then I decided one really missed Oxford and the job came up in one of the colleges I liked, 29 00:03:48,650 --> 00:03:53,870 namely Corpus Christi, and I applied for it and got it. 30 00:03:55,100 --> 00:04:05,420 It was very nice, very informal interviewing board, and they offered me the job before I could get to the lodge, which was nice. 31 00:04:06,740 --> 00:04:14,990 Yeah. And then of course, in those days you were working in fluid mechanics and then you went into mathematical biology. 32 00:04:14,990 --> 00:04:23,570 So what prompted that? Well, actually, I switched into biology in the early sixties, in effect. 33 00:04:24,020 --> 00:04:28,580 It's just that there's a fluid dynamics test I just found, you know, 34 00:04:30,110 --> 00:04:40,070 working out the third or fourth fashioned topic term and in flow past a semi infinite flat plate was just not very exciting. 35 00:04:40,850 --> 00:04:47,630 In fact, I didn't know the point of it. It was just mathematical. And when at the University of Michigan, 36 00:04:49,050 --> 00:05:00,530 a professor of biology came to see if I could help to make a model of how oxygen diffused into entropy nodules. 37 00:05:01,730 --> 00:05:12,140 And so I got involved in that. And but he I was in an engineer engineering department and as a professor of engineering mechanics. 38 00:05:12,980 --> 00:05:17,719 And he thought he thought I was a student. 39 00:05:17,720 --> 00:05:22,990 So he offered me five. And no, it was anyway, we, we started it. 40 00:05:23,000 --> 00:05:28,690 We worked together and. And that was really, I think, the start of it. 41 00:05:29,570 --> 00:05:42,310 And Michigan, then a professor of anatomy, was working on, uh, pilot ejection seats and back injuries. 42 00:05:42,430 --> 00:05:50,920 And he wondered if I could make a model of how the injuries occurred and what they could do about it. 43 00:05:51,550 --> 00:06:06,280 And so, along with a colleague, King Liu, we produced a model of really, if you have an accelerating seat, then it sends a wave up the spine. 44 00:06:06,760 --> 00:06:14,200 And because the spine is curved, we modelled it as a wave that kind of broke like a wave and uh, 45 00:06:14,440 --> 00:06:19,930 on the shore and that sort of took the vertebrae and just put them aside. 46 00:06:20,770 --> 00:06:27,100 But it was the sequel to that when he, he said they'll do some experiments. 47 00:06:27,610 --> 00:06:37,030 And I wondered how they could do an experiment with somebody being pushed up in the sphere on a chair and maybe have their back broken. 48 00:06:37,540 --> 00:06:45,699 And they said, well, they did it with they did it with cadavers from the down and out in Detroit and strapped them to 49 00:06:45,700 --> 00:06:52,590 the elevator chair and dropped them down the elevator shaft with strange gauges down the back. 50 00:06:53,080 --> 00:06:58,690 And he found that indeed it was some kind of wave. 51 00:06:58,990 --> 00:07:02,469 I mean, the work we did, it wasn't that quantitative. 52 00:07:02,470 --> 00:07:13,780 It was more qualitative. And then I had been involved before and fluid ization and the theory developing a theory of fluid ization, 53 00:07:13,780 --> 00:07:19,150 which was the last thing that I found interested in fluids. 54 00:07:19,690 --> 00:07:29,649 And then by the roughly I suppose in the sixties I decided I didn't want to do fluids 55 00:07:29,650 --> 00:07:38,110 anymore and then started to get more and more involved in biological problems like oxygen, 56 00:07:38,110 --> 00:07:48,430 diffusion and muscles and why we didn't get cramps more often and why sea snakes didn't get the bends, just a variety of things. 57 00:07:48,820 --> 00:07:54,280 And when you moved into that area, you were already well known in Fluid Dynamics World. 58 00:07:54,280 --> 00:08:06,519 So what did those people think of you? Move. I'm not sure how well known I was, but certainly my first post-doc took me aside to lecture me on that. 59 00:08:06,520 --> 00:08:09,909 I was wasting my time and all this rubbish in any way. 60 00:08:09,910 --> 00:08:17,440 Biology is for girls, he says, which is so the know then. 61 00:08:17,620 --> 00:08:24,609 It did take a while before people started to think that mathematical biology or ecology 62 00:08:24,610 --> 00:08:30,910 or whatever one wants to call it was something worth doing for an applied mathematician, 63 00:08:31,290 --> 00:08:35,310 you know. And so one of the questions that actually her, Ms. 64 00:08:35,560 --> 00:08:38,680 Goodall, you asked was concerning that. 65 00:08:38,680 --> 00:08:44,390 And he he wondered, did you expect mathematical biology to grow in the way that it has done? 66 00:08:44,770 --> 00:08:56,290 Oh, yes. So I just thought the biology just was the subject in the general sense, including medicine and ecology and evolutionary biology. 67 00:08:56,770 --> 00:08:59,200 I mean, it just seemed to me there were so many problems. 68 00:08:59,830 --> 00:09:11,979 How could anyone not want to work in it if they are, you know, or stay with this queasy sort of non relative, 69 00:09:11,980 --> 00:09:18,910 uh, in the world point of view, mathematics to just produce mathematics just didn't appeal to me. 70 00:09:19,030 --> 00:09:30,640 Uh huh. And, and so one, one just, just being in Oxford at that time and certainly being in the universities I was at in America, 71 00:09:31,120 --> 00:09:34,510 there was no pressure to do anything. 72 00:09:34,840 --> 00:09:40,640 There was no necessity to. I mean, one applied for grants, but you didn't have to get them to. 73 00:09:41,440 --> 00:09:45,220 And the bureaucrats, the bureaucrats were there to help. 74 00:09:45,370 --> 00:09:53,469 Yeah. Unlike what seems to be the case now, and you mentioned that there was that scepticism when you moved into the area. 75 00:09:53,470 --> 00:09:59,260 And so her Mrs. asking you, do you think that there's still that scepticism or. 76 00:09:59,770 --> 00:10:04,299 Yeah, I, I think it's more it's more submerged. 77 00:10:04,300 --> 00:10:11,680 I don't think that they're quite as open about it, but there's a lot of people who still think so, particularly in biology. 78 00:10:12,160 --> 00:10:15,760 But the numbers, I think it's almost a question of age. 79 00:10:17,470 --> 00:10:24,400 The biologists who. Brought up in an era where they didn't have to do any mathematics, they didn't have to do any modelling. 80 00:10:25,330 --> 00:10:33,070 They did their experiments and that was it. Whereas now I find biologists, the really bright ones. 81 00:10:33,790 --> 00:10:38,710 I mean, they want it all to be interdisciplinary because they think one can help. 82 00:10:39,250 --> 00:10:44,830 Other people can help. And the evidence now, of course, is just overwhelming. 83 00:10:45,250 --> 00:10:48,520 But at the time, there were quite, 84 00:10:48,760 --> 00:10:59,660 quite a few biologists who were and I got lots of invitations to give talks to biological groups and the questions at the end, 85 00:10:59,980 --> 00:11:08,350 a lot of them were very really they really wanted to know they weren't aggressive, but they wanted to know why. 86 00:11:08,380 --> 00:11:13,090 Why should they bother? Yeah, in a sense, spending time with people like me. 87 00:11:13,960 --> 00:11:22,630 And so one of the senior postdocs and research group Alex Fletcher was wanting is hoping to continue in academia. 88 00:11:22,630 --> 00:11:26,560 And he asked the question of what sort of department do you see? 89 00:11:26,950 --> 00:11:34,000 So somebody from a mathematical background who was working in math biology, where will they be sitting in, say, 30 years time, would you think? 90 00:11:34,240 --> 00:11:37,690 Oh, I wish it would be me sitting there in 50 years time. 91 00:11:37,690 --> 00:11:49,569 But if that's not possible, let's I think I think I'd rather sit in the mathematics department and because although it's not so, 92 00:11:49,570 --> 00:11:59,229 it's not so crucial now with specialisation, but I would like to be able to work on whatever it was that I find exciting. 93 00:11:59,230 --> 00:12:10,810 So if I was in the biology department, one might be, in a sense at least more constrained to work on problems associated with that department. 94 00:12:10,820 --> 00:12:11,230 Yes. 95 00:12:11,830 --> 00:12:24,190 Whereas I've been able to work with people and work on problems that cover a whole spectrum of the biological sciences and then the medical sciences. 96 00:12:24,670 --> 00:12:30,550 So I am, I think the problem probably still in mathematics departments, 97 00:12:30,910 --> 00:12:37,750 but I think biology departments will certainly want people to be in that group. 98 00:12:38,230 --> 00:12:50,530 But I suspect they may be considered more a kind of there to help them rather than to for them to help 99 00:12:50,530 --> 00:12:57,670 the mathematicians to work on original research that might not be related to the work in the department. 100 00:12:57,720 --> 00:13:09,310 Yeah. Yeah. So I'm not sure what it will be, but certainly if I, if I was around at that time, I'd want to be in a mathematics department, 101 00:13:09,610 --> 00:13:17,650 but one that had real applied mathematicians and not which is a place like Oxford. 102 00:13:17,810 --> 00:13:21,800 Yeah. And there are not that many of them. Yeah. 103 00:13:22,600 --> 00:13:30,280 And so moving on then to Professor Helen Byrne asked a number of questions here. 104 00:13:31,300 --> 00:13:36,490 So from your start, your career, which piece of work you most proud of? 105 00:13:39,790 --> 00:13:44,439 I think that they're not quite sure. I I've enjoyed a lot. 106 00:13:44,440 --> 00:13:50,319 I mean, I liked working on animal patterns or how the leopard gets its spots. 107 00:13:50,320 --> 00:13:59,830 Which one? Uh, the early on, I found it interesting working on why we don't get camp. 108 00:13:59,920 --> 00:14:03,220 And this was with the biologist Jeffrey's women. 109 00:14:03,940 --> 00:14:18,460 Um, maybe. Maybe because of Alan Turing and this reaction diffusion theory that was purported to really explain biological development. 110 00:14:19,060 --> 00:14:27,970 And since it's been 60 years since his paper came out, it was only two years ago that someone actually found a chemical. 111 00:14:28,120 --> 00:14:31,719 Yeah. And I just think of that as deferred gratification. 112 00:14:31,720 --> 00:14:36,910 So it was it was good to sort of, in a sense, play games, to get patterns. 113 00:14:37,540 --> 00:14:45,910 And so when George Foster came to spend six months in the Centre for Mathematical Biology, um, 114 00:14:46,510 --> 00:14:56,830 we thought we'd try and develop a practical, experimentally verifiable theory of, uh, biological development. 115 00:14:57,250 --> 00:15:07,120 And so we produced the, we proposed the mechanical theory of, um, development of morphogenesis, 116 00:15:07,690 --> 00:15:15,670 and it was based entirely on quantifiable, experimentally determined, uh, 117 00:15:15,700 --> 00:15:25,870 quantities like cells having traction, being able to deform the matrix within it, and then how to, 118 00:15:26,170 --> 00:15:31,840 how they formed aggregations which eventually became bones and the developing limb. 119 00:15:32,680 --> 00:15:42,130 And then we were able to do hypothetical experiments and we had a colleague, uh, Peer Albert, 120 00:15:43,090 --> 00:15:54,970 who did experiments and they confirmed some of the predictions such that you could have a condensation, 121 00:15:55,330 --> 00:15:59,080 it could move out and you could have something long. 122 00:15:59,080 --> 00:16:04,420 It would be like that. We actually considered limbs like the humerus, and then it would bifurcate. 123 00:16:04,900 --> 00:16:12,940 And what we show is that you can't get a tri fixation on the which was the reason for the title of the lecture. 124 00:16:13,360 --> 00:16:22,830 You know, why are there No. Three headed monsters? Then it was applied to a large number of problems by you fill up of course with uh, 125 00:16:23,440 --> 00:16:32,800 as the major figure in the, in the eighties and some of, uh, some of my other graduate students. 126 00:16:33,670 --> 00:16:38,050 And we just were able to show that it really had a relevance in biology. 127 00:16:38,350 --> 00:16:50,260 So I think it's a long way around to say. I think that's one of the things that, uh, I think, I really think has survived, I think will increase. 128 00:16:50,620 --> 00:17:04,270 And I suppose when I wrote the book on mathematical biology in the late eighties, which is now into its third edition. 129 00:17:04,590 --> 00:17:09,670 Yes. And still seems to be accepted as a as a textbook. 130 00:17:10,630 --> 00:17:21,610 And and it covers a lot of the problems that I worked on, like sex determination and alligators, 131 00:17:21,880 --> 00:17:28,020 marital interaction and divorce prediction, but a host of different things. 132 00:17:28,180 --> 00:17:33,340 Yes. And certainly anytime I go anywhere, that that book is always on the shelf. 133 00:17:33,520 --> 00:17:43,030 Oh, that's nice to know that. Yes. And so then the another question repellant, which you may not want to answer, is the other flip side of the coin. 134 00:17:43,150 --> 00:17:47,740 Is there any work that you're not particularly bright? Oh, yes. 135 00:17:48,090 --> 00:17:58,450 Yeah. Yes, it was when I at Michigan, I wanted to be back in the mathematics department. 136 00:17:58,900 --> 00:18:06,879 So I got a I got a job at a New York University and New York University. 137 00:18:06,880 --> 00:18:16,450 Uh, I, as you know, has the current institute where a lot of the people from Germany came and set it up for Quant, for example. 138 00:18:17,110 --> 00:18:29,800 And there was a colleague there, Don Ludvig, who became a close friend, and he was on the pure side of applied mathematics. 139 00:18:30,370 --> 00:18:36,370 And at lunch one day I said, You know, Don, you've never done anything practical in your life. 140 00:18:37,060 --> 00:18:40,500 And he said, You've never. Prove the theorem in your life. 141 00:18:41,280 --> 00:18:48,720 And so I thought, I'm going to show you. So I worked on a problem and wrote a paper. 142 00:18:49,230 --> 00:18:53,310 And in it, there is a theorem. And I proved the theorem. 143 00:18:54,000 --> 00:18:57,960 And it really had the papers. 144 00:18:58,860 --> 00:19:03,660 I mean, I forgotten even the title, and I hope everybody else has a. 145 00:19:04,260 --> 00:19:10,170 But the effect on Don Ludwig was he thought about it actually seriously. 146 00:19:10,860 --> 00:19:18,090 And within a year, he had resigned from New York University, gone off to the University of British Columbia. 147 00:19:18,540 --> 00:19:23,400 And he became the major one of the major world figures in marine ecology. 148 00:19:25,050 --> 00:19:30,600 And so that paper. Maybe maybe I shouldn't be ashamed of it, 149 00:19:31,170 --> 00:19:39,510 because it had the effect of really making Don move into a field that he found exciting for the rest of his career. 150 00:19:40,220 --> 00:19:44,050 Yeah, there's other things, probably. But I've probably forgotten. 151 00:19:44,100 --> 00:19:50,250 Yeah, I actually think this actually relates to one of the other questions that Helen asked, which says, 152 00:19:50,520 --> 00:19:54,470 Do you think it's important to be able to prove existence and uniqueness in mathematical biology? 153 00:19:54,480 --> 00:20:04,680 So I guess which also means like how important or sort of underpinning theory of mathematical of mathematics and mathematical biology. 154 00:20:05,160 --> 00:20:08,309 Well, I mean, maybe I'm out of it. 155 00:20:08,310 --> 00:20:13,320 I think it's got no relevance whatsoever. I mean, I I've never needed it. 156 00:20:14,370 --> 00:20:17,160 Maybe the models should have had it. 157 00:20:17,880 --> 00:20:31,830 But I found the models that I've worked on are based on intuition of what's going on and the process that I'm trying to model. 158 00:20:32,490 --> 00:20:38,130 And so if the equations can capture that, then I think there's probably something there. 159 00:20:38,640 --> 00:20:52,260 But whether or not the solution is unique. I mean, one would like it to be unique, but certainly in the case of digit development, for example, 160 00:20:52,260 --> 00:21:00,990 or the growth of the humerus radius and owners and so on, small variations are there all the time. 161 00:21:01,440 --> 00:21:09,990 Yeah. And so the solution is very difficult for the solution might be able to be proved, proved to be unique. 162 00:21:10,440 --> 00:21:19,530 But if you start solving it numerically, there's always a there's always some minor variation. 163 00:21:19,530 --> 00:21:24,900 And the initial conditions could be in whatever it is, a fifth place or whatever. 164 00:21:25,680 --> 00:21:27,810 And that is going to change the solution. 165 00:21:28,200 --> 00:21:42,480 So I think uniqueness well, existence is, is certainly a thing, but the proof of it has never really got me involved or got me interested. 166 00:21:42,840 --> 00:21:54,300 I'd rather work on simpler models, which is what I've tried to do, that it be as simple as possible enough to capture the phenomena. 167 00:21:54,330 --> 00:21:59,370 Yeah. And then view that. How do you view the presently? 168 00:21:59,370 --> 00:22:08,190 There seems to be emphasis being put on very complicated multi-scale models, obviously comprehensive models. 169 00:22:08,370 --> 00:22:11,490 Oh well no. I think they're I think they're important. 170 00:22:12,630 --> 00:22:20,790 And I think it's probably what leads to much more specialisation within the people working in biology and medicine. 171 00:22:21,430 --> 00:22:30,209 And I was just fortunate to get in kind of early on and where the models could be quite simple. 172 00:22:30,210 --> 00:22:45,180 But Multiscale models, I think are incredibly important, but much more complicated mathematically, numerically, and they are certainly needed. 173 00:22:45,180 --> 00:22:49,589 But I think in the case of some of the stuff I did, 174 00:22:49,590 --> 00:23:01,350 like the diffusion of oxygen into muscles that combined with haemoglobin or myoglobin, the, the modelling was really very, very, 175 00:23:01,800 --> 00:23:10,290 very simple and one could use some of the, the analytical work that I had worked on for quite a while, 176 00:23:10,290 --> 00:23:19,620 mainly asymptotic theory and really getting approximations to the solutions where there is a small parameter. 177 00:23:19,710 --> 00:23:27,360 Yes. And so I think I think that actually is a natural development that is important. 178 00:23:27,600 --> 00:23:36,780 Yeah. Yeah. And did you have a role model when you were young, academic, academically or in biology? 179 00:23:37,620 --> 00:23:42,410 Well, I guess it doesn't. She just doesn't specify that as a role model. 180 00:23:42,440 --> 00:23:47,990 Role model, academic or non-academic. I mean, also. 181 00:23:48,290 --> 00:23:58,130 Well, first, when I went to Harvard, George Carey at Harvard, uh, was one of the cleverest people I've known. 182 00:23:59,300 --> 00:24:04,220 And he was interested in anything. 183 00:24:05,000 --> 00:24:14,900 And he, he applied mathematical modelling to a huge variety of problems, not biological or medical. 184 00:24:15,710 --> 00:24:23,240 And he believed in real simplicity as well as simple as you could possibly make it. 185 00:24:23,810 --> 00:24:28,550 And I suppose that, George, I never thought of him as a role model. 186 00:24:29,510 --> 00:24:39,050 He was a good friend. Yes. Yes. But certainly in as a student, I didn't really have a role model. 187 00:24:39,470 --> 00:24:45,740 And that I mean, I liked my PhD supervisor, who was also a professional football player. 188 00:24:45,920 --> 00:24:49,420 You know, in the syllabus, Mitchell said. 189 00:24:51,290 --> 00:24:58,190 But he wanted me to be a numerical analyst, and I managed to figure out by getting an analytical so we could. 190 00:24:58,970 --> 00:25:09,020 Yes. And I bet a lot of people have influenced me and I've been fortunate and meeting a lot of people who were very bright and. 191 00:25:10,400 --> 00:25:14,180 But I think George Carey as is the one that kind of stands out. 192 00:25:14,510 --> 00:25:20,810 Yes. And it's a rather gerben who's Royal Society Research fellow in the department 193 00:25:21,620 --> 00:25:24,590 asked a question which also actually links in with one of Helen's questions, 194 00:25:24,950 --> 00:25:32,060 which said, as you know, in mathematics of Fermat's Last Theorem, you have the Hilbert problems, etc., etc. 195 00:25:32,900 --> 00:25:35,980 These are sort of holy grails of people. Yes, yes, sure. 196 00:25:36,080 --> 00:25:41,240 Yes. Lives trying to solve. Do you think there's any equivalent in in biology, mathematical biology? 197 00:25:41,690 --> 00:25:45,140 Oh, I think there's a lot like that. 198 00:25:45,800 --> 00:25:56,090 I mean, I think we have I mean, the the whole business of genes, for example, I mean, unbelievably important. 199 00:25:56,660 --> 00:26:03,080 But remember that my friend Lewis Wolpert, a developmental biologist who you knew. 200 00:26:03,080 --> 00:26:10,160 Of course. Yeah. He said that when genetics is a subject finished, we won't know how to build a chicken. 201 00:26:10,820 --> 00:26:14,170 And in a sense, in a sense, he's right. 202 00:26:14,180 --> 00:26:21,950 The genes control the processes, but they don't actually do through the actual mechanisms, don't they? 203 00:26:21,950 --> 00:26:25,070 Don't put the cells together and form cartilage. 204 00:26:25,100 --> 00:26:35,089 Yes. And and so I, I really am not sure that's a kind of controversial sort of thing. 205 00:26:35,090 --> 00:26:41,960 But it said that in 1900, David Hilbert presented his famous list of 23 unsolved problems in mathematics. 206 00:26:42,320 --> 00:26:47,660 Would it be possible to formulate a similar list of inferential, unsolved problems in mathematical biology, 207 00:26:48,170 --> 00:26:56,270 which is very mention one which is the the the mechanisms that lead to patterns within those. 208 00:26:56,270 --> 00:26:58,940 There are a number of that type of questions. 209 00:26:59,180 --> 00:27:11,550 Yes, I think I think there's almost fundamental questions that, like, could we create life without in some artificial way? 210 00:27:11,570 --> 00:27:22,700 Yes. That really I mean, even if it was just an amoeba that we could actually make based on some modelling process, we haven't got there yet. 211 00:27:22,780 --> 00:27:26,750 No, that's just right. And maybe eventually. 212 00:27:27,200 --> 00:27:37,250 I mean, there is there's so many exciting new things going on in biology that we might we might stumble on it. 213 00:27:37,400 --> 00:27:37,840 Yes. 214 00:27:37,850 --> 00:27:49,550 But I think a lot of the research people are just because it's becoming so complex, all we can do is focus down the narrow tube to see their problem. 215 00:27:50,600 --> 00:28:07,040 And I think the the fact that getting grant support is becoming so, frankly, irrational, I think that is detrimental to science in a major way. 216 00:28:07,100 --> 00:28:14,450 Mm hmm. And when the Centre for Math Biology was started, I applied for again. 217 00:28:14,660 --> 00:28:18,470 It was quite it wasn't a large application. 218 00:28:18,860 --> 00:28:24,560 There were none of these ridiculous questions that one has now got to answer. 219 00:28:25,340 --> 00:28:31,190 And they gave me the money and that was it. Yeah, I could do whatever I wanted with it. 220 00:28:31,940 --> 00:28:38,570 I would meet somebody at a conference who I thought was doing interesting thing and I could see. 221 00:28:38,910 --> 00:28:45,270 Why don't you come and spend a month or two months in the centre and we can cover all expenses 222 00:28:46,290 --> 00:28:53,099 and they would come covered all the expenses and all I had to do was at the end of three years, 223 00:28:53,100 --> 00:28:57,150 send in a report of what one had done with the grant. 224 00:28:57,810 --> 00:29:05,160 And that that to me was I mean, the effect was unbelievably productive. 225 00:29:05,310 --> 00:29:09,120 Yeah. All these visitors or masses of them, 226 00:29:09,120 --> 00:29:19,920 brilliant people from around the world who just felt all that was essential was to do research and interact with other people. 227 00:29:20,460 --> 00:29:30,000 And they didn't have to worry about this this ridiculous situation that the administrators have now introduced. 228 00:29:30,050 --> 00:29:37,710 Yeah. And the government, of course. So I, I just feel that it's got to go back to that. 229 00:29:38,490 --> 00:29:47,910 I think one's going to lose brilliant academics who will feel that it's not free in the universities anymore. 230 00:29:48,630 --> 00:29:56,340 And the they will go into the outside world, which it's not necessarily been easy either. 231 00:29:57,060 --> 00:30:02,580 But academia is not. Nothing like as attractive as it was when I started. 232 00:30:02,880 --> 00:30:03,350 Yeah. 233 00:30:03,930 --> 00:30:13,890 And I think that where, like, the Hilbert's problems are a sign of where a field is, well, mature, and you know what the fundamental problems are. 234 00:30:14,220 --> 00:30:17,580 One of the exciting things about mathematical biology is that we still haven't 235 00:30:17,580 --> 00:30:23,700 reached that level of knowing precisely what the problems are in a new way. 236 00:30:23,790 --> 00:30:32,670 Oh, absolutely. That's what makes it very exciting to work in. But it's also, as you mentioned, one gets hamstrung by the grant system. 237 00:30:32,940 --> 00:30:42,239 That is it. I think. I think an indication of the system is in, in America. 238 00:30:42,240 --> 00:30:45,150 Many of the committees, uh, 239 00:30:46,130 --> 00:30:55,230 the chairman of the committee or the chair of the committee quite often tend to be people who didn't make it research wise. 240 00:30:55,500 --> 00:31:01,200 And this is a way that they feel that somehow that they're somehow moving in the big leagues. 241 00:31:01,390 --> 00:31:10,250 Yeah. Yeah. And I found some of the people I met in that thing they one wouldn't want, wouldn't give them a job in the department. 242 00:31:10,260 --> 00:31:16,140 Yes. And so I think their effect has actually been quite negative, 243 00:31:17,100 --> 00:31:28,260 whereas certainly certainly in the earlier the people who ran these committees, uh, such as in the, 244 00:31:28,560 --> 00:31:36,120 the National Science Foundation really where world class scientists applied 245 00:31:36,120 --> 00:31:43,260 mathematicians who agreed to do it for three years because they felt it was important. 246 00:31:43,530 --> 00:31:45,900 Yes. About the research giving. Yeah. 247 00:31:45,930 --> 00:31:56,910 Whereas I think it just changed dramatically now and I think it's had a negative very negative effect in academia of academic research. 248 00:31:57,150 --> 00:32:08,460 Yes. And in fact, your discussion about the early funding of the centre leads nicely into some questions that Peter Grindrod, whom I asked. 249 00:32:08,760 --> 00:32:14,400 So his first question was what were the biggest hurdles in establishing a centre for mathematical biology, 250 00:32:14,530 --> 00:32:17,910 research, funding, fellow mathematicians, outreach? 251 00:32:19,170 --> 00:32:27,899 I guess you've really answered the funding question that you were able to get this funding that allowed you a freedom to do what you wanted to do. 252 00:32:27,900 --> 00:32:34,800 Yeah, but I think getting there, getting the Centre for Mathematical Biology started in Oxford, 253 00:32:35,550 --> 00:32:43,500 that it had the support of one or two people in, in, in the Mathematical Institute. 254 00:32:43,500 --> 00:32:48,420 But a major figure was Dick Southwood or Sir Richard Southwood. 255 00:32:48,810 --> 00:32:57,930 And at that time he was a Vice-Chancellor and he was incredibly supportive and helped to push it through. 256 00:32:57,930 --> 00:33:11,850 Yeah. And then it was formally recognised or set up by the university and with the expectation that one would be able to get funding. 257 00:33:12,240 --> 00:33:13,620 But that wasn't the condition. 258 00:33:14,250 --> 00:33:26,610 It was genuinely wanting to help to set up a centre that was interdisciplinary and Dick Southwood thought this was very important. 259 00:33:26,970 --> 00:33:37,180 And being, being a biologist himself, he, he approved that it was with both biology and the Royal Society. 260 00:33:38,100 --> 00:33:50,750 Well. Few people in the Royal Society were supportive, but the society is still a touch focussed on discipline rather than interdisciplinary work. 261 00:33:51,050 --> 00:33:58,310 And so it got started and it attracted then got some of the major people in the world 262 00:33:58,790 --> 00:34:03,230 well who wanted to come and draw more people than the centre could accommodate. 263 00:34:03,510 --> 00:34:14,840 Oh yes, yes. Yeah. And the students of course flocked to the course in maths biology because if you took mathematical biology they, 264 00:34:15,230 --> 00:34:24,020 they saw how their mathematics could be used in the real world as and so the average class specialised. 265 00:34:24,020 --> 00:34:35,569 Finally, if class size was around 9100 compared with some of the pure topics which had the size maybe ten in the class. 266 00:34:35,570 --> 00:34:40,100 Yeah. And because these students they weren't all going to be academics. 267 00:34:40,370 --> 00:34:51,569 Yes. They became they became really very interested in it and they remember saying, well, 268 00:34:51,570 --> 00:35:00,500 we're in a we're going to develop a model for the spread of sexual disease in one country or another. 269 00:35:00,500 --> 00:35:04,030 You've at that age, you've got their attention so vicious. 270 00:35:05,360 --> 00:35:09,860 And so you mentioned about lots of visitors coming to the centre and this actually, John, 271 00:35:09,860 --> 00:35:14,360 to Peter's next question, which was what do you think makes a good visiting scientist? 272 00:35:15,170 --> 00:35:28,219 Oh, yes, I think it's somebody who well, if they're nice and friendly and not arrogant, of which they were during my time, 273 00:35:28,220 --> 00:35:35,630 there were none like that in this in the centre but who are really genuinely interested in interdisciplinary work. 274 00:35:36,320 --> 00:35:38,960 And it was one of the nice things about the centre, 275 00:35:39,890 --> 00:35:50,510 the original building in the centre that you remember of course was that you went down into the comment room to have coffee with it. 276 00:35:50,540 --> 00:35:53,599 There was always somebody there and there were the board, 277 00:35:53,600 --> 00:36:00,469 the tables to write on and every time you went down they were talking about something and the 278 00:36:00,470 --> 00:36:08,660 biologists then got known in the university and biologists would get in touch with me and say, 279 00:36:08,990 --> 00:36:16,580 I'd like to come and talk about a problem, because I think it might be one might be able to model, model it. 280 00:36:17,000 --> 00:36:22,400 And so a lot of them came and gave informal seminars to the group. 281 00:36:23,150 --> 00:36:29,690 And then anybody in the group, visitors, students, post whatever, would then say, no, that's interesting. 282 00:36:29,690 --> 00:36:34,940 And they would get together and then they would work. They produce a model and they'd produce papers. 283 00:36:36,260 --> 00:36:39,290 And sometimes they were a little complicated. 284 00:36:40,070 --> 00:36:47,000 I mean, one came about how if you mean they said if you were a medic, he said, 285 00:36:47,000 --> 00:36:52,430 if you break your arm and your elbow, you know, how do you make a model how it all goes together? 286 00:36:52,430 --> 00:37:06,200 Again, we weren't quite at that level. And I remember one pure mathematician came who I mustn't see who it is because he's changed dramatically. 287 00:37:07,070 --> 00:37:13,370 He came. He wanted to said to me, Could he come and talk to the group in the centre? 288 00:37:14,090 --> 00:37:16,250 I don't know if you were at that meeting. 289 00:37:17,120 --> 00:37:29,820 Anyway, he came and he was a pure, relatively relative, pure mathematician and he wanted to hear about problems because then he'd be able to help him. 290 00:37:30,230 --> 00:37:42,110 And it just happened that there were a bunch of experimental biologists visiting at the time and I've never seen anyone get such a hard time. 291 00:37:42,110 --> 00:37:47,929 They they said to him, So, so if I describe what I'm working on, which is, you know, 292 00:37:47,930 --> 00:37:53,720 animals see animals near the shore who can jump, what can you do about it? 293 00:37:54,620 --> 00:37:58,340 And there was a humming and hawing and say, well, of course, that's very complicated. 294 00:37:58,790 --> 00:38:08,660 And so he went off effectively with his tail between his legs and but interesting, he's now a significant figure in America. 295 00:38:11,120 --> 00:38:16,100 Encouraging real mathematical biology was just another convert. 296 00:38:16,350 --> 00:38:27,350 Yes. Yes. And and that actually leads onto the question that Peter had to some extent was that, um, nowadays, math, biology, 297 00:38:27,410 --> 00:38:31,310 in those days you mentioned that this was one of the very few places it did mathematical biology, 298 00:38:31,730 --> 00:38:37,790 but now it's sort of established in quite a few departments and quite a few universities are doing it. 299 00:38:38,120 --> 00:38:43,050 You. Still think that it needs something like a special treatment or nurturing. 300 00:38:43,070 --> 00:38:49,220 Or should we just say, well, it's actually no mainstream mathematics, let's just get on with it? 301 00:38:50,060 --> 00:38:53,480 I think there's still a lot of prejudice right around. 302 00:38:54,140 --> 00:39:03,540 I mean, like international prizes for mathematical sciences that is translated into pure mathematics. 303 00:39:03,560 --> 00:39:08,660 Yeah. And so I still think there's always been, I think, 304 00:39:08,960 --> 00:39:15,830 a feeling among pure mathematicians that applied mathematics and mathematical biology 305 00:39:15,830 --> 00:39:21,860 is just grouped with it is somehow inferior because they don't prove theorems. 306 00:39:21,890 --> 00:39:33,350 Yeah. And I think in development the, the conditions for rigorous theorems, they just don't exist in biology. 307 00:39:33,470 --> 00:39:39,590 Yeah. I mean we if you weigh less than me or whatever, you know, 308 00:39:39,590 --> 00:39:52,940 I mean there's just so many variations in everything biological that one just can't, one can't relate it in the same way to pure mathematics. 309 00:39:53,090 --> 00:40:00,260 Yeah, I don't mean it's, it can't be useful, but I know very little that is which is. 310 00:40:00,260 --> 00:40:03,260 But then that's probably an old fashioned view. 311 00:40:04,700 --> 00:40:10,150 Other fields in math biology that you think should have happened sooner or, you know, or. 312 00:40:10,910 --> 00:40:14,629 Well, I think medicine would have been the. 313 00:40:14,630 --> 00:40:17,880 And the medical world just never, ever thought about it. 314 00:40:17,900 --> 00:40:29,510 Yeah. And but that's why I got involved in the medical side from the medical people approaching me, which I thought were very forward looking. 315 00:40:29,810 --> 00:40:33,140 And then in Oxford, because of the high table. 316 00:40:33,500 --> 00:40:42,980 Yeah. System here, which to me is one of the most invaluable things in Oxford for promoting interdisciplinary things. 317 00:40:43,640 --> 00:40:53,540 It's how I got involved in things like quantifying medication for obese patients based on body weight, 318 00:40:53,630 --> 00:40:56,930 which you couldn't take, the obese weight you had to take. 319 00:40:56,930 --> 00:41:11,040 Really. How do you how do you determine what the the kind of the real body is inside an obese person or things like prostate specific antigen? 320 00:41:11,580 --> 00:41:21,640 Uh, food. Lots of people never heard about it, but it is a, it can be an incredibly good indicator of prostate cancer and the, 321 00:41:21,700 --> 00:41:32,450 the number of medical problems, like many that you've been involved in, really benefit enormously from mathematical modelling. 322 00:41:32,480 --> 00:41:43,130 Yes. And I just feel, you know, mathematics should recognise I don't necessarily mean mathematical biology, 323 00:41:43,400 --> 00:41:52,280 but that this is an incredibly important area in the field of mathematics and that 324 00:41:52,760 --> 00:41:57,950 I think its usefulness along with lots of other areas in applied mathematics. 325 00:41:58,490 --> 00:42:02,420 Um, I think it would help if it was ranking, if it were recognised, 326 00:42:02,930 --> 00:42:09,050 I think it would help to attract bright people into the field rather than just having them leave academia. 327 00:42:09,080 --> 00:42:17,780 Yeah. Yeah. And so what advice would you have for a graduate student who's wishing to have a career in mathematical biology? 328 00:42:19,400 --> 00:42:22,550 Well, I suppose I am a little prejudiced. 329 00:42:22,850 --> 00:42:31,190 I think that you'd come to Oxford and be associated with the associated with the Centre for Mathematical Biology. 330 00:42:31,870 --> 00:42:35,779 Um, but I think there is still more freedom in America. 331 00:42:35,780 --> 00:42:39,109 There are just few places that you can actually do math, 332 00:42:39,110 --> 00:42:45,410 biology and but certainly I still think it should be a mathematics department 333 00:42:45,770 --> 00:42:52,010 because that could end up being like higher technician in biology department. 334 00:42:52,040 --> 00:42:55,550 Yeah. And. And they would have less freedom. 335 00:42:56,730 --> 00:43:02,480 Yeah. And I still think it's mathematics. Mathematics department, but forward looking ones. 336 00:43:03,380 --> 00:43:09,560 And I think Oxford has always had applied and it's still got quite a ways to go, but. 337 00:43:10,490 --> 00:43:15,680 So talking to a graduate students of Fred Hoffmann, who's a mathematical biology graduate student, 338 00:43:16,790 --> 00:43:22,100 asked what you thought were the most exciting advances and experimentation from 339 00:43:22,430 --> 00:43:26,300 the point of view of mathematical biology that might impact mathematical biology? 340 00:43:28,790 --> 00:43:40,970 Well, I don't know. I think I think pattern formation and development is one, uh, the, I think in ecology and evolutionary biology, 341 00:43:41,360 --> 00:43:48,200 it's a huge field and that there is very exciting work going on there. 342 00:43:48,650 --> 00:43:53,600 And there's a lot going on in Princeton, where I am now, 343 00:43:54,020 --> 00:44:08,509 where the graduate students are incredibly enthusiastic and they do their modelling and then in the summer they go off to Kenya, I mean Panama, 344 00:44:08,510 --> 00:44:15,590 South America, they do their own experimentation, they gather data, 345 00:44:16,190 --> 00:44:27,620 and the work that they produce is in the department is genuinely interdisciplinary except it's all done in the department. 346 00:44:27,950 --> 00:44:33,560 Yes, the and they just have a very, very bright group of people. 347 00:44:34,280 --> 00:44:42,769 And what's interesting is people who want to be graduate students in the department have to come to the department 348 00:44:42,770 --> 00:44:51,320 for a week and they interact with the faculty and the graduate students and the postdocs and everybody has to see, 349 00:44:52,040 --> 00:44:53,560 which is amazing. Yeah. 350 00:44:54,170 --> 00:45:05,840 And if the student said, you know, this guy, you know, I'm not sure he would fit in, his chances of being accepted are extremely small. 351 00:45:05,870 --> 00:45:12,049 Yeah. And which is why the atmosphere is so, so cohesive. 352 00:45:12,050 --> 00:45:20,030 Everybody talks to each other and that's quite difficult to achieve in a in a department, 353 00:45:20,030 --> 00:45:23,960 a mathematics department, such as in Oxford, because it's large. 354 00:45:24,650 --> 00:45:34,670 And, uh, some people in the mathematics department, what they work on is more foreign than most biologists to me. 355 00:45:35,480 --> 00:45:38,630 So I think it's, I think that is a problem. 356 00:45:39,110 --> 00:45:44,090 And I guess with the things that you mentioned about data that you've been able to like get data on, 357 00:45:44,160 --> 00:45:48,530 on tracking things or imaging which are spatial temporal data. 358 00:45:48,770 --> 00:45:53,599 Yes, what these mathematical models are designed to try and predict. 359 00:45:53,600 --> 00:46:03,350 So that's yes, I think that is incredibly important in the case of the work on obesity that I did here. 360 00:46:04,070 --> 00:46:08,660 I mean, we talked about it as we tucked into high table food that drank this great line. 361 00:46:09,830 --> 00:46:17,570 But the very simple model and the professor of clinical medicine at the time, 362 00:46:18,410 --> 00:46:23,630 I said, well, it'd be good if we could have some data from real patients. 363 00:46:24,530 --> 00:46:36,560 So he got ten, uh, obese patients who had a thing called hyper lactate tibia where which is quite dangerous, 364 00:46:36,950 --> 00:46:42,740 and they wanted to quantify the level of medication, and so we had to determine the weight. 365 00:46:43,280 --> 00:46:50,090 And so we came up with the idea of injecting lactate here for 10 minutes and measuring it out of this item. 366 00:46:50,960 --> 00:46:58,790 And so and then the model really required only two parameters for the differential equations. 367 00:47:00,320 --> 00:47:09,139 And from that data we could evaluate the parameters. And then we saw the effect of obesity or hyper lac, 368 00:47:09,140 --> 00:47:20,540 the tibia and the curves that came out and ten patients where I said that, that's the real inner body doing this. 369 00:47:21,320 --> 00:47:30,050 And so to really confirm that, um, we came up with the idea that how could we get people to lose weight quickly? 370 00:47:30,530 --> 00:47:41,840 And so ten patients, obese patients, agreed to take diuretics and they lost on average ten kilos in five days. 371 00:47:42,590 --> 00:47:46,400 We re did the experiment and the parameters were the same. 372 00:47:47,180 --> 00:48:02,360 And then he wanted to get normal people and he went into the lab and, you know, old graduates, pre-med students, etc., the medical students there. 373 00:48:02,660 --> 00:48:11,480 And he said, I need 20 volunteers. So I looked around all the students kind of raising their head and, you know, again. 374 00:48:11,930 --> 00:48:15,260 And so they came and did the tests. 375 00:48:15,800 --> 00:48:24,680 And we found that people with obese people with hyper lactate tibia had totally different parameters. 376 00:48:24,680 --> 00:48:33,580 And we answered the key question that. They don't metabolise lactate as well as normal people rather than produce more. 377 00:48:33,720 --> 00:48:39,530 Oh, and there were other problems that came from dinner conversations. 378 00:48:39,630 --> 00:48:49,210 Yeah, and it did. It's just a fabulous system to effect interdisciplinary research. 379 00:48:50,110 --> 00:49:01,510 Yeah, it is sort of funny that like the funding bodies and everybody else is trying to find ways of promoting interdisciplinary research. 380 00:49:01,660 --> 00:49:04,660 And yet Oxford has had it there all the time. 381 00:49:04,900 --> 00:49:08,790 So they should give and they should give money for kidneys. And why should you? 382 00:49:08,860 --> 00:49:15,150 It is it should be. And so David Gavigan from. 383 00:49:15,340 --> 00:49:23,290 Yeah. Yes, of course. Asks, What is the biggest challenge that we face in developing a quantitative and predictive biology? 384 00:49:26,080 --> 00:49:35,920 Well, other than other than money to help to get poor, I, I think that's I think that's quite difficult. 385 00:49:35,920 --> 00:49:50,590 I think biologists of biomedical scientists in the general sense who feel that the research interdisciplinary search has been effective, 386 00:49:50,590 --> 00:50:03,310 has been successful, and that that can result in exciting results that they wouldn't have thought about had they not worked with other people. 387 00:50:03,970 --> 00:50:14,440 And on the the sort of model side, it's that they have to be interested in a whole new world that they know nothing about. 388 00:50:14,470 --> 00:50:25,300 Yes. Yeah. And that the biologist has to be sufficiently, uh, interested of patient to in effect teach him enough about it. 389 00:50:25,660 --> 00:50:35,410 So one has to go to labs and all one has people like Lewis Wolpert who distinguished uh, 390 00:50:36,010 --> 00:50:43,330 uh, by all the developmental biologist who I, we never, ever wrote a paper together, 391 00:50:43,750 --> 00:50:48,910 but we spent an incredible amount of time, days over, 392 00:50:49,900 --> 00:51:00,850 over many years where he patiently would talk about these experiments and tell me about this theory that I never quite believed. 393 00:51:01,720 --> 00:51:09,340 And when we when we produced that, you remember the mechanical theory of pattern formation. 394 00:51:09,850 --> 00:51:14,230 Lewis wanted to do experiments to show it was wrong and didn't exist. 395 00:51:14,950 --> 00:51:21,060 He didn't. But as a result he discovered other things in the development of the limb. 396 00:51:21,070 --> 00:51:27,000 Yeah. And so that sort of interaction it I thought was invaluable. 397 00:51:27,700 --> 00:51:36,580 But of course it needed to have someone who was as clever as Lewis has such a warm personality. 398 00:51:37,150 --> 00:51:41,770 Um, and so that has to be, I think, really to work. 399 00:51:42,460 --> 00:51:46,600 I think there has to be this feeling that you really like the person you know. 400 00:51:46,630 --> 00:51:56,500 Yes. Rather than somebody who is using you like a like some neutral computer or something. 401 00:51:56,850 --> 00:52:04,270 Yeah. And I think you stressed also there the idea that you really need to go into the lab and learn a new subject, 402 00:52:04,270 --> 00:52:09,450 or the biologist really need to have the patience to teach the the mathematicians. 403 00:52:09,790 --> 00:52:15,939 And then this is a real issue in the present state of academia where it's one size fits all. 404 00:52:15,940 --> 00:52:21,280 So if you're doing a doctorate, it doesn't matter what subject you do in the doctor, then you have to complete within four years. 405 00:52:21,670 --> 00:52:27,940 If you get a grant as opposed to a three year grant, it doesn't allow for the flexibility of the. 406 00:52:27,970 --> 00:52:41,050 I think that that is a problem. And I mean, in America, five years is a typical time for a PhD and that's where the grant money goes for five years. 407 00:52:41,740 --> 00:52:46,510 And they feel certainly students feel much more relaxed. 408 00:52:46,840 --> 00:52:53,170 I know. Yeah. And when I talked to them, they said, well, this is just my third year or something. 409 00:52:53,480 --> 00:52:58,300 He was here, you know, almost a minutes of thinking by. 410 00:52:58,310 --> 00:53:08,110 Yes, yeah. There. And, uh, and I think with postdocs it's the same their limited time and uh, 411 00:53:09,430 --> 00:53:15,490 but certainly more and more universities around the world are looking for postdocs. 412 00:53:15,720 --> 00:53:26,950 Mm. Yes. And I, I get emails perhaps once every two weeks saying, do I know anybody who would be interested in a postdoc? 413 00:53:27,430 --> 00:53:31,629 Depends where. Yes. Yes. And so last question. 414 00:53:31,630 --> 00:53:40,050 And if if you were a graduate student at the moment, would you work in academia to. 415 00:53:42,950 --> 00:53:52,880 If I knew all that I know now and I've seen in the past few years, I would think very carefully about it. 416 00:53:53,900 --> 00:54:02,780 Postdocs aren't able to see usually don't see the bigger picture, but with all the bureaucratic interference, 417 00:54:02,780 --> 00:54:10,700 the difficulty of getting grants based on real science, the the system in universities, that is different. 418 00:54:11,150 --> 00:54:15,440 Uh, I should have serious second thoughts. 419 00:54:15,480 --> 00:54:25,760 Yeah, I suspect. I mean, I suspect I would go still go into academia, but one should be aware. 420 00:54:25,760 --> 00:54:30,490 But it's not all just teaching bright students. 421 00:54:30,510 --> 00:54:42,260 So being enthusiastic about research, it's dealing with the main ridiculous menu tie, such as a foreign postdoc, 422 00:54:42,260 --> 00:54:49,760 somebody who's not part of the EU has to see where they're going almost every minute of the day. 423 00:54:49,790 --> 00:55:01,130 Yes. And if they go off to London for a day and they don't tell you, you know, you and they are breaking the rules. 424 00:55:01,190 --> 00:55:07,780 Yeah. And it could have serious consequences. Jim, thank you for taking time to feel a bit about this. 425 00:55:07,840 --> 00:55:08,270 Thank you.