The Foundation for Polish Science (FNP) celebrates its 25th anniversary this year. To mark the occasion, we have invited 25 beneficiaries of our programmes to tell us about how they “practise” science. What fascinates them? What is so exciting, compelling and important in their particular field that they have decided to devote a major part of their lives to it? How does one achieve success?
The interviewees are researchers representing many very different fields, at different stages of their scientific careers, with diverse experience. But they have one thing in common: they practise science of the highest world standard, they have impressive achievements to their credit and different kinds of FNP support in their extensive CVs. We are launching the publication of our cycle; successive interviews will appear regularly on the FNP website.
Let’s Create the Right Conditions
Prof. Tomasz Lipniacki, whose field is biomedical engineering, talks to Patrycja Dołowy.
Photo: Prof. Tomasz Lipniacki, photo by fot. Magdalena Wiśniewska-Krasińska
PATRYCJA DOŁOWY: What is your actual field? Is it biology? Mathematics?
TOMASZ LIPNIACKI: I use mathematics to describe biological systems. I’m interested in the mechanistic foundations of elementary biological processes involved in the transmission of signals in cells. A cell reacts to external stimuli or signals from other cells and does something in response to these stimuli. For example, it divides, undergoes apoptosis (cell death), differentiates, communicates with other cells by secreting specific signal proteins, e.g. cytokines. We want to understand the molecular foundations of response mechanisms. Earlier, I often asked myself how decisions were made in a cell, and what this even means. Is there any way of using simple models on the cellular level to present and verify processes that in a very complicated system lead to consciousness? What does it mean to make decisions? Should it be a stochastic (random) process that is organized spatially? A cell gets signals from outside and has to “make a decision”. Intuition tells us that signal processing and decision making is a process from the border of stochastics and determinism. Why? Because processing information deterministically is a defective strategy, both in games and in life. Let’s take, for example, innate immunity, the field we are involved in. If cells pursued a deterministic programme in response to pathogens, in the course of evolution a pathogen would quickly appear against which a given organism wouldn’t stand a chance. On the other hand, the programme cannot be random, because random responses usually don’t make sense. Therefore it seems the answer must lie in the stochastic-deterministic borderland.
But what does this mean? If I understand correctly, the point is that cells have many possibilities at signal input and a limited number, though not just one, at signal output, is that right?
Exactly – a lot of information is gathered at input. The output is not fully defined by the input, but it is produced during signal processing and at a later stage is transmitted by a greater number of molecules. This enables the decision to be sent and processed further deterministically. The number of possibilities does not really have to be high. The fact that the system gathers a lot of information and that it’s a good thing for it to gather a lot, does not at all mean that there are many possible responses. Alan Greenspan, head of the US Federal Reserve, and his people spent twenty years gathering enormous amounts of information about the state of finances in the US and world economy. In his responses Greenspan usually reached one of three decisions: leave the interest rate unchanged, raise it by a quarter of a percentage point, or reduce it by a quarter of a percentage point. Those were the three decisions coming from the central bank. It worked so well for twenty years that there was no crisis in the United States. Then a crisis came and Greenspan announced that unfortunately the adopted model had been wrong. As you can see, gathering lots of information doesn’t necessarily generate a large number of possible decisions. I think a cell, like the head of the Federal Reserve, also has quite well pre-defined choices. There’s not much it can do. In a way, it fits the signals it receives into the model. This method is called signal accommodation. It’s like you listening to me: you have a model of the words. You filter what I say, assigning the sounds you hear to the word models. That’s why we find it so hard to repeat a sentence in a completely foreign language – we cannot use a model.
How can this work in practice at the cellular level?
All kinds of pathogens exist. Responding to them, to everything that lies outside of an organism’s internal language, is the hardest task for cells. If a cell copes with it, things get easier. After that it communicates with further cells with the help of cytokines. This is a kind of cell language. Cells “know” what a given cytokine means and what, say, a combination of two cytokines means. Of course cells cannot be taught everything they might have to contend with. They don’t know all the pathogens. It’s really incredible how lymphocytes identify “alien” polypeptides. Every one of us has a few million of our own polypeptides, fragments of our own proteins, and the cells of our immunological system learn them all, in rather a brutal way. If a cell recognizes a native polypeptide, it is killed off. Only those survive which do not recognize them. When an alien peptide appears, one of the millions of lymphocytes will say: “you’re alien”. The chances that a given lymphocyte will recognize an alien viral polypeptide are slim, but since we have millions of different lymphocytes, the probability of detecting an alien protein is high. The mechanism is essentially quite obvious: it’s easier to teach the immunological system millions of native peptides than an infinite number of alien ones.
And it works efficiently throughout the whole body!
Not always, unfortunately. One example is the Spanish flu pandemic which caused the death of several dozen million people, or the pandemic of diseases brought to South America by the conquistadors, which literally decimated the Native Americans. Unfortunately in these cases selection took place at the level of the body. The Native Americans, who had not had any contact with European diseases, did not have the necessary lymphocytes in their repertoire.
The mechanisms I want to understand concern the way in which cells integrate different kinds of signals in order to produce a response, what the transition is like between a stochastic decision-making process and the more deterministically regulated execution. A board meeting is a good analogy here. A company board gathers quite a lot of signals. This is a group of people that cannot be too big because they wouldn’t be able to communicate among themselves, and it cannot be too small because it wouldn’t be able to gather all that information. This group makes decisions; it is not a deterministic process, because if it were, the board could be replaced with an algorithm. Next, the objective is for the decision to be implemented in a deterministic way, meaning there shouldn’t be any interference on the line. Of course in a cell the decision-making stage is definitely not fully separate from the execution stage. However, particularly in a multicellular organism, it’s a good thing for the execution of a decision to take place without hesitation. It is better for a cell to follow the wrong path, but one that is part of the repertoire, than to go somewhere in between. If a healthy cell undergoes apoptosis, nothing special happens because we have a lot of cells. But if a cell initiates apoptosis but stops, breaks down and ultimately does not accomplish it, this could have dire consequences for a multicellular organism. So, a little like the military, it is better to carry out bad orders in a coordinated way than to do something in between.
Does this apply to all cell decisions?
It appears that a great majority of decisions in a cell have to be like that. It’s like with people: we invent something new once in a while, but most of what we do involves choosing between existing options. We have a repertoire of what we will do on a given day (e.g. in the morning: have coffee, tea or milk) and essentially choose one of those options. We very seldom do anything that’s not part of this repertoire.
Do cells ever do it?
Seldom, but the situations when they do are extremely important, and practically always pathological. That’s when, for example, a cell can become a cancer cell. Cells rarely become cancer cells, and manage to survive this even more rarely. On the other hand, such situations make evolution possible.
What is the most important result of your project?
We know that cells are extended in space and that this structuring is of key importance. Intuitively, we assume that in well mixed systems, the more molecules there are, the more stable are their states and the less frequent the transitions between them. It turns out, however, that if a system is spatially extended, this is not the case. The waiting time for a transition grows with the size of the system at first, but then starts to decrease. There can be a local transition of the system to a different state, and then this state travels like a propagating wave. It’s like setting fire to a forest. If we have a small particle, it’s very easy to set on fire. It is harder for a larger piece of wood, but if we have a whole forest it’s actually easier to set fire to because we have more potential places where you can start a fire. This is what we investigated, because it lies at the source of decision-making mechanisms. It seems that the number of molecules taking part in achieving a given state cannot be too large – so that stochastic leaps are possible. On the other hand, though, it has to be located in space in a way enabling the molecules to communicate.
This led to a very interesting collaboration with US researchers and a publication in Nature, didn’t it?
It actually started when we published a paper in 2007 in which we considered reinforcement mechanisms in stochastic decisions and concluded that it was possible for a cell to be responsible for individual cytokines. The cytokine in question was TNF, which has between a few hundred and several thousand receptors in the cell membrane. The earlier assumption had been that if there were many receptors, then many of them had to be activated for a cell response to occur. Biologists usually refer to concentrations when analysing experimental data, so I calculated the experimental concentrations of the TNF cytokine into the number of molecules. It turned out that the smallest concentration for which a response was observed corresponded to two tenths of a molecule per cell volume. It made us think. As we studied the responses at the level of individual cells, it seemed that some cells were activated sooner, others later, and others still – not at all. We proposed a mathematical model, published a theoretical paper. The paper was found three years later by a group of leading scientists from Stanford who proposed a collaboration. They had observed something like that in their lab. We performed an experiment in which we applied a very low concentration of cytokine so that only 10% of cells responded to the first signal. And it turned out that if you do that, 10% of cells respond only to the first cytokine impulse, 10% respond only to the second one, and 10% to both. This means that more sensitive cells exist and they are the ones responding to two impulses. If all cells were equally sensitive, there should be about 1 percent of cells responding to both impulses. But not only cell sensitivity decides about whether a cell responds to an impulse. The occurrence of a response remains random, but more sensitive cells, those with more receptors, have greater chances of responding.
So you started from modelling and later were able to confirm the models experimentally?
The experiment was not performed here, we didn’t have such capacity at the time. It was carried out at Stanford. The experiments we perform today are related to early, nonspecific immunological responses to viral infections. Activating cells with polyriboinosinic polyribocytidylic acid, or poly(I:C), a mimic of viral RNA, we observe that some cells are more sensitive while others are less so. The response starts from the sensitive cells. We have developed a model of such a response and are trying to verify it experimentally. The most sensitive cells act as guards, they are the first to be activated by a virus, they have a chance of reaching an antiviral state, produce interferon and undergo apoptosis. But all this takes a long time – long enough for the virus to be able to multiply. However, once the first cells make their decision, they send interferon to neighbouring cells which, if they receive the signal transmitted by interferon sooner, can shorten the decision-making stage of responding to the virus because they already have all the data processed for them. And these cells are able to undergo apoptosis before the virus multiplies in them. That halts the infection. We analyse the regulation of the cell population by observing the activation of key proteins (fluorescently marked) in individual cells watched under a confocal microscope in real time.
Thanks to a grant from the FNP, you developed an experimental group here in Warsaw.
The TEAM project enabled me to build a team, whereas thanks to funding from a Centre for Preclinical Research and Technology project and a restructuring grant received by our Institute I was able to outfit the lab with very good equipment for observing processes at the level of individual cells. We have a very good confocal microscope, a very good TIRF microscope for observing processes on cell membranes. We can look much deeper. We have an atomic force microscope. This means we can peek at what goes on in cells on many levels. There are about ten people working in the experimental part of the lab. In particular, there are three guys who completed their PhDs at the Nencki Institute of the Polish Academy of Sciences. Theoreticians make up the other part of the team: mathematicians, physicists, a computer scientist. Meanwhile, we’re short of people who’d be able to combine laboratory work with modelling. This is our bottleneck. I’d like to form a team in which at least some of the researchers would be able to deal with the experimental part and theoretical modelling at the same time.
Would that move the work forward faster?
Not necessarily faster. But I think it’s easier to discover something this way. We have theoretical predictions based on the literature and intuition. We try to perform experiments that will confirm them. Meanwhile, discoveries are often accidental. You wander around. To notice something in that wandering, you have to look at experimental results and procedures from an integrative perspective. Also, having one person involved in both experiments and theory allows mistakes to be avoided. A simple example: cells secrete interferon. In the well of a cell culture dish, due to diffusion, the interferon activates neighbouring cells. But experiments are often performed in culture dishes with eight or sixteen wells. To fix the cells at specific moments, the dish is taken out of the incubator, the cells in one well are fixed, and the dish is put back in the incubator. And what happens? Of course the taking out and putting back means that it’s not diffusion that is the dominating process for spreading the interferon, but mixing! To be aware of this, you have to be “on the inside” – in the lab. If an experimenter works separately from a theoretician, the latter receiving fixed specimens has insufficient information to draw the right conclusions, interpret the data.
But how do you train such scientists?
You probably have to start early. One good way is inter-faculty studies, such as the Inter-Faculty Individual Studies in Mathematics and Natural Sciences at the University of Warsaw, where young people can acquire extensive knowledge. It’s better than taking courses such as biotechnology for physicists or physics for biologists. There, young people learn real biology from real biologists who have ambitions to give them the knowledge, and they learn mathematics from mathematicians and with mathematicians. The mathematicians know they are talking to partners and not biology students who need to be taught statistics in order to process their own data. I think such curricula are valuable, and they enable students to obtain knowledge by themselves.
Is there a lack of systemic solutions in Poland?
Definitely, but I don’t think that’s the core of the problem. It seems to me that a large part of progress, discoveries, to put it grandiloquently, is the effect of doggedness, of people’s individual creativity, which should be stimulated but which cannot be decreed or enacted. Let’s take how Facebook emerged, for example. Today it is a company generating annual net income in excess of three billion dollars (much more than KGHM or ORLEN), and it was first created as the antics of students who set up a ranking of girls’ photos at a university. It’s impossible to imagine that this kind of project could emerge from plans made by a government, by some kind of official bodies saying, “Ok, boys, you have some fun now and we’ll see what happens”. It seems a lot of high-tech companies set up in recent years go against whatever authorities of different levels would like to induce. It’s simply that almost every authority is concentrated on the status quo and on upholding the interests of existing groups. Take Poland, for example. We have miners, so we should take care of Polish coal. It’s unrealistic to expect that the authorities will stimulate fun in the hope that it could lead to a profit-generating company being set up. Facebook’s founder Mark Zuckerberg, who broke into the university network, was almost expelled from Harvard for “violation of privacy”.
So you don’t think it’s worth supporting science in a systemic way?
I didn’t say that. But I think the dominant way of thinking in Poland is that we lack the conditions. However, you can also put it differently: we don’t create the conditions. This attitude probably depends on age and on how much someone is actually able to do. In research, a key role is played by research schools, individual communication between a professor and the students and postdocs. The students of an active professor have much bigger chances of success and the students of a really good professor stand a chance of becoming better than him or her. It is common in education to complain about the previous stage of the teaching process. Universities accuse secondary schools of giving them graduates who are no good at studying and have to be shown how to do it. Secondary schools complain about middle schools, middle schools complain about primary schools etc. Teachers will never say: we are incompetent, teach poorly and then universities have issues with our pupils. If well-educated young people, or at least those who underline their good education, say they can’t find a job worthy of their qualifications in Poland today, then we should ask them why they don’t create their own jobs. Why don’t you set up a high-tech business that will employ you, your friends? I don’t know what the reason is. Serfdom, years of partitions by foreign powers, dependence on Russia convincing us that someone has organized our world badly?
You don’t complain. The project completed under the FNP’s TEAM programme, which you initiated, enabled the careers of many young researchers to get a kick start.
The most valuable thing to me in the TEAM project was the requirement of setting up a sizable team. To obtain a grant from the FNP and carry out a project from it, you have to jump a little higher, sail into deeper waters, build a new group or significantly increase your team. In my case it truly did initiate enormous development. When I took over the lab it comprised four senior researchers and a PhD student of mine. Now there are twenty-seven people, including three senior researchers. The rest are postdoctoral fellows, PhD students and graduate students. We are the youngest lab at the Institute. This was also important support for master’s students. People in Poland are still not very mobile. The best students want to stay on at university. The possibility of drawing students to your lab, financing a stipend for them, is very valuable, especially for Polish Academy of Sciences institutes. And for those young people of course. In connection with carrying out the project under the TEAM programme I oversaw four master’s theses; each of these students published a paper as the first author in a decent journal. This is usually impossible at a university because teachers have too many graduate students and cannot devote that much time to them. I think it’s important to start scientific research early. Not at the cost of your university studies, but parallel to them. Talented young people in fields that are developing quickly are able – if they are given a well-defined problem – to get results. Last year a graduate of the Staszic Secondary School with whom I started collaborating just after he left school published a paper in a decent journal a year before his bachelor’s degree.
What do you look for when you hire future researchers?
I look with complete humility at various complicated life stories, untypical cases, realizing that if I had functioned within a rigid system I would never have become a scientist. Going to school (the Gottwald Secondary School, which today is again the Staszic Secondary School), I slipped through from year to year, with half-year fail marks. I never had more than a C minus in biology, my present field of research. I felt perverse satisfaction when our paper in Nature came second on the Faculty1000Biology ranking list. After leaving school I studied for a long time, I spent six months in New York painting walls to earn money to continue my studies. I spent a long time pursuing a doctoral degree in a completely different field than I’m working in today. A master’s in cosmology, a PhD in quantum turbulence in superfluid helium. Now I’m working in cellular biology. I try not to look at young people through grades and ranking lists.
PROF. TOMASZ LIPNIACKI, (born 1965) heads the Division of Modelling in Biology and Medicine at the Institute of Fundamental Technological Research of the Polish Academy of Sciences. A beneficiary of the FNP’s TEAM programme (2009).