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Plenary discussion of the conceptual foundations of systems biology
Progress in Biophysics and Molecular Biology 111 (2013) 137e140
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Discussion
Plenary discussion of the conceptual foundations of systems biology Jonathan Bard*, Tom Melham, Eric Werner, Denis Noble Balliol Interdisciplinary Institute, Balliol College, Oxford, UK
The workshop from which all the articles in this focused issue of Progress in Biophysics and Molecular Biology are derived ended with a wide-ranging discussion of the nature of systems biology and its underlying principlesdtouching on complexity, the programming and database metaphors, dynamic modelling and underlying concepts. To give a flavour of the debate, we present here a lightly edited version of that discussion, which started with a brief commentary by Denis Noble on his ten Principles of Systems Biology (Noble, 2008).
1. Denis Noble’s 10 principles of systems biology 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.
Biological functionality is multi-level. Transmission of information is not one way. DNA is not the sole transmitter of inheritance. The theory of biological relativity: there is no privileged level of causality. Gene ontology will fail without higher-level insight. There is no genetic program. There are no programs at any other level. There are no programs in the brain. The self is not an object. There are many more to be discovered; a genuine ‘theory of biology’ does not yet exist.
Denis Noble: The article outlining these in 2008 was in response to an invitation by one of the scientific societies to give a lecture, which was published in a journal called Experimental Physiology (Noble, 2008). It is about 10 pages long, so each of these principles has a page or so. Notice that they are called ‘principles’, not ‘dogmas’, because all of them can probably be seriously criticized, replaced, conflated and so on. There is a sense in which they interrelate. I see number 4 e ‘There is no privileged level of causality’ e automatically leading to number 1, that there is multi-level functionality. That means the transmission of information cannot be one way; that DNA cannot be the sole transmitter of inheritance; and that you cannot identify the function of genes without having higher-level insight. I think those would probably be mostly non-controversial to most systems
* Corresponding author. E-mail address: [email protected] (J. Bard). 0079-6107/$ e see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.pbiomolbio.2012.11.002
biologists, because I cannot see how you can be a systems biologist if you are not looking at multi-level work. Then I come to the controversial ones. The next three e no genetic program, no programs at any other level and no programs in the brain e are essentially philosophical points. The sense in which I do not think there is a genetic program is that there is no way of parsing the sequences in DNA such that you could see the sequence in the way that Jacob and Monod were using the metaphor when they said it was a bit like the tape you put into old-fashioned computers: the complete program is on the tape, and you do not even need to know the computer in which you run it in order to know what that program can do. It would be the case that in any computer that runs like a Turing machine it should execute exactly what is on the tape. As we well know, you cannot put DNA from one species into another without there being problems. I think that is the justification for saying that, though I readily understand that other people have a different view of what a program could be.1 As to ‘no programs at any other level’ and ‘no programs in the brain’, I was taught physiology and anatomy by one of the greatest neuroscientists, JZ Young. He wrote a book called A model of the Brain. There is a very interesting comment in that book. Many people think that, having written a book with that title, he must have been one of those who believed that the program was in the brain. One very interesting quote e I cannot remember it in detail e is essentially that we should not think we will find in the brain code of the kind that a computer programmer would recognize.2 That was not what he was thinking at all, and he was well aware that there would be distribution. Another way to put it, in a more philosophical sense, is that, whatever we are doing e I am speaking, doing this with my hands and so on e neuronal activity is going on, but it is the process; it is not that there is another program forcing that to happen. Jonathan Bard: There are really two key ones are there not? One is about levels and one is about programs. Denis Noble: Exactly so, yes. Of course, I would stick very strongly by the multi-level one. As to the others, it is debatable; they are more philosophical than points about the nature of biology.
1 See (Werner, 2011) for a very different view. This paper was distributed to the participants before the workshop and influenced the discussion. 2 The quote is “it does not seem likely that the brain operates with a detailed programme of logical instructions”. JZ Young, A model of the Brain (Young, 1964), p. 27. Eds.
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Jonathan Bard: Would you say that you could have a program that was multi-level? Denis Noble: You could have a distributed program, yes. I hand it over to the computer scientist to work out what that might be. You would have to say, ‘Okay. This if-then-else clause is down here, and “but under conditions in which it could be said” is up here.’ I should also say there is not a methodology point in any of these principles, so I would underline that. I would also add a point about ‘circular interaction’ because it is not in here, and without that you do not have the control. Tom Melham [Peter Kohl]: I wanted to communicate a message from Peter Kohl, who cannot be here today. He says: ‘First of all, I do believe that systems biology is an approach to bio research that applies the principles of systems research to biological entities. One may wish to add: it is characterised by the dynamic integration of both reductionist and integrationist concepts and tools.’ Peter’s second point is this: ‘As such’ e in other words, as an approach e ‘it is difficult to identify a “conceptual foundation” of an approach, but systems research is clearly a relevant contributor. System research combines five things: (i) identification and (ii) detailed description of parts; exploration of (iii) their interactions with each other; and (iv) characterisation of interactions with the environment, both of elements and inter-element interactions. The aim of all that is to develop a systematic understanding of the entity, and this must include (v) an appreciation of effects of the entity on the previous four things described (as the entity allows and restricts element properties and interactions). It is implicit in this that we can identify the entity that we wish to study at the outset, even if our understanding of it may, and hopefully will, change in the process.’ His third major point was: ‘In establishing the “conceptual foundations of anything” clear definitions and proper terminology matter. Imprecise use of language, whether intentional or accidental, does not help communication; in fact, with a field that brings together so many different streams of conceptual, intellectual and linguistic track records, we should all go the extra mile and try actively to pre-empt misunderstandings.’ 2. Biological systems Eric Werner: First, the complexity of development has to be reflected somehow in the complexity of the original egg and genome, and ultimately the network complexity has to reflect that complexity. These are hypotheses; they are not really about systems biology but are related in the sense that we do model and try to gain insight into biological systems by way of that. That does not mean these systems are not alive or flexible, etc., but there is control information, which does direct, influence and subsume lower levels of control. Ultimately, the formalism that we choose to express that control can vary. We can use attractors or a distributed program metaphor. Stig Omholt: First, could we qualify the database concept relative to genes and interpret it in dynamic terms? You could compare computer science thinking with dynamic system thinking. I would phrase it in terms of opening new possibilities for creation of form. That feeds back to what you have already been discussing. That is needed because you can look upon genes as the store for reparameterization and opening up gates that otherwise are locked. These gates are not within reach of open physical systems without that content. Another aspect is that we talk about principles and systems. These are more like deep principles of biology. This is deep theoretical biology and that takes us back 40 years to Waddington’s work on theoretical biology and his symposia on Towards a theoretical biology. Why can we not frame our meeting to make a link with Waddington’s work? How far have we come? Where are we now in terms of what the issues were at that time? Then, nobody
talked about systems biology but theoretical biology, and we are still on the way towards that. We are dealing with deep biology, and we should stress that. We are really trying to achieve more than just making a case for systems biology. To add to that, Waddington gave us the concept that you have stability through form change. As far as I can see, we still have no mathematics to deal with that. That is a tremendous challenge, because that is really development. How will that challenge our understanding of the attractor concept, because here we are talking about the system that redefines its own roots and survives while maintaining its abilities? When we talk about circular causality, that is one thing, but we have not distinguished between positive and negative feedback. We need to be very explicit about that, because without positive feedback you will not have much biological order. John Walmsley: I have two short points. First, if we talk about the trajectories of a system, we should really also talk about the large transitions between different attractors that depend on a perturbation to the system. Second, nowhere in the principles as they stand do we mention the word ‘model’, which is something that has come up time and time again over the past two days. Ought we to talk about models in the principles of systems biology, or is that better left to methodology, such as that described by Peter? Keith Baverstock: I also have two points. In biology and systems biology we are looking at organisation, and organisation has not been a strong point in the history of science; rather, it is ignored. Part of that is self-organisation and it is context-dependent. It is a process and it depends on other processes and structure in the cell and the environment of the cell. The second point is that we need to make up our minds whether we think we are dealing with any living system as a complex dynamic system or a machine, because the machine metaphor has also come through in this meeting to some extent. I think we need to make a decision on what we think it is. Jonathan Bard: I am glad that you mentioned processes; these are needed to direct and push change and are the multi-level outputs of networks that, in particular, drive embryonic development. When there is mutation that affects these outputs, novel phenotypes arise and, if these new phenotypes have a selective advantage, lead to evolutionary change. This is the natural link between the molecular underpinnings of life and the phenotype, or the living and evolving organism. Andy Gardner: Two points: first, if this is to be a programme of scientific research rather than just a philosophical enterprise, we need interplay between theory and empiricism. The second point is that, throughout this workshop, I have been struck again and again how life is social at every level, so right down to its core there are social interactions. The emphasis here has been on co-operation, but conflict is also of major importance in social evolution. Looking at conflicts can help clarify concepts and enable you to understand what is going on. Genes can come into conflict. Even within an apparently harmonious cell there can be a lot of conflicts and they should be considered. Eva Jablonka: I think that we have better theoretical understanding of learning e for example associative learning e than we have of embryological development. I think that what happens during learning is based on the principles that we were discussing e it is a process of finding a stable (reinforcing) attractor, and there are many ways of getting to this attractor, many possible trajectories can lead to it. Maybe we should start by looking at a very complex, but theoretically better understood system, like neural learning, and see how people are modelling learning in neuroscience and whether we can borrow such models for morphological development. Christian Matek: Thinking about systems biology, it has come up over and over again that in decomposition there is the concept of levels of abstraction that may correspond to different states of
J. Bard et al. / Progress in Biophysics and Molecular Biology 111 (2013) 137e140
the system or on very different scales. It can be very fruitful to refer to the language of statistical mechanics in physics because that is a relatively well-understood language to connect macroscopic/ microscopic properties of objects. That might lead to a point where we can have a firmer grounding on why we refer to high-level entities as being emergent. It is not just a question of it being in the eye of the beholder, but maybe it is a systematic way of connecting different levels, therefore allowing us to make contact from the point of view of physical chemistry. 3. Computational systems and levels Jim Shapiro: I just want to make a fundamental point, because we get hung up on Turing machines. In cells and even molecules in biological systems, the software and hardware are interchangeable; they are the same thing. I think we need to realise that is a very fundamental difference from the way we normally separate things out in computer science. I think that is a very basic and fundamental difference between a living system and a computer system. Jonathan Bard: Living systems are neither computer systems nor automata. Jim Shapiro: It is not to say that they are not computer systems, because I think that in many ways they are. They are not Turing machines, which is where there is a difference, even when it comes to the DNA, between the hardware and software, because the DNA is part of the hardware of many of the things that it does. Arthur Thomas: May I say that I think the distinction between software and hardware is a complete red herring, and also that it is important to remember that one of the essential properties of Turing machines is that they can write to as well as read from their tapes, and so in fact they are software. Turing machines support self-modifying programs, which I think is the essence of a lot of what has been discussed here over the past couple of days; that is, the idea that there is not a distinct level of an immutable program which gets translated in some environment into a functionality; rather, the program itself can be written dynamically on the fly by the environment in which it finds itself. I think that is a way of arriving at some sort of convergence that satisfies the constraints you are trying to identify. Jim Shapiro: I was trying to make what I think is a deeper comment than that, not that I claim to understand it. Not only is the system re-writable e I have tried to make that point with respect to DNA e but hardware and software operate together. When E. coli is distinguishing between glucose and lactose, it uses the glucose transport system to tell the enzyme that makes cAMP whether or not there is glucose around. That creates the symbol cAMP in the cell, which is then distributed and influences lots of other things. I do not know that computer science has yet a way of dealing with those situations. Eric Werner: I think that in the computer itself software is hardware as well. So, at the lowest level, when you translate higherlevel languages into microcode, they switch gates; everything is physical. There is no distinction between hardware and software at the lowest level, but it certainly helps conceptually. I think that in biology, too, if we look, say, at developmental networks it certainly helps to distinguish them and view them not just as hardware, because, if you look at it just as a molecule or DNA, it does not give you any conceptual insight. Even with the stuff that you do, Jim, it is fascinating precisely because it does the kinds of manipulations you can do on strings and things like that, which is a kind of software metaphor. It does not help understanding in the long run to say that everything is molecular. Arthur Thomas: Referring to Denis Noble’s comments, the levels being distributed and multi-levels are, I think, two quite
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different things. One of the interesting points that Tom Melham brought out in his talk was that abstraction is the most important concept in computer science, or in the notion of computation. Of course, it manifests itself in the form of functions, sub-routines or whatever you want to call them, in computing languages. I think the same thing holds in biological computational systems; they are multi-level in exactly the same sense as computer programs are multi-level, because they use the notion of abstraction to create modularity, which in turn creates multiple levels. Tom Melham: Can I kick in here with a plea for a little clarity in this space of levels versus scales versus hierarchical physical organisation of a system versus abstraction levels? These are all different things for us in computer science. They might or might not align. One of my standard jokes about downward causation is that I believe in it completely but it is not downward and it is not causation, because for me the up and down metaphors have to do with abstraction level, not the nesting depth of physical organisation; and scale has to do with the resolution of your measurements, not whether or not you are looking at an organ or cell. Jonathan Bard: When you say ‘scale’ to biologists they would agree with you, but ‘level’ would have a very clear meaning of going from the DNA to the proteins. That is the language of a biologist. Maria Tasaki: I am especially interested in biological relativity, because it expresses well modular activities. It seems to me that the concept of downward causation shares the same ideas with circular causality. Denis Noble: I totally agree with Tom there is a sense in which, if you talk about levels, you are talking about one thing; if you talk about scales, you are talking about another, and they are not to be confused. What does this mean? I think it relates to the point Maria is raising. When you think in terms of levels, of course you can ask questions like whether the integrated activity of, say, the cardiovascular system influences gene expression that enables the heart to function in the ordinary way. The answer is yes. You can set out the pathways by which all of that is achieved. It will look very multi-level, because on the one hand you will have a system measuring blood pressure, which is a high-level property of the system as a whole in the blood vessels; you have another part of it that secretes this particular chemical, which maybe noradrenaline; and in turn you will then have actions on various cells in the body. So, you are moving around between the levels. But in terms of scales I think the point you make, Tom, is absolutely right. You are really talking about resolution, because in the case of scales you can be molecular even at a very large scale. After all, the universe can be represented as a bunch of molecules, so there is a sense in which when talking about scale it is resolution. That was why I said at the very beginning of the meeting that that was the automatic way in which the supervenience principle is obeyed, even when you talk about downward causation because, provided you can make the system big enough, there will be a difference at the molecular level; there will be a difference in the level of strings too, for goodness sake. To come to Maria’s point, whether you talk in terms of levels or scales e I think it is absolutely essential to distinguish between the two e circularity, or circular causation, however you want to call it, is an absolutely necessary consequence of downward causation, whatever you want to call it. That is critical to the many pathways that enable regulation to be done, and so on. I cannot imagine a systems biology without that kind of interaction. Jonathan Bard: What I am really hearing is that causation is distributed. Denis Noble: Yes e and circular. Jim Shapiro: I just want to question the term ‘causation’, because I think that carries with it a lot of philosophical baggage. I
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think we can find a more neutral term like ‘regulation’ or ‘control’, which is what we deal with all the time. It gets rid of a number of definitional and epistemological problems that I think have muddied things. The other general point to make is that one of the exercises here is for the biologists to define the system as we understand it and for the computer scientists, who have thought about a lot of these definitional issues much more than we have, to inform us how they look at the way we parse things and how they would parse them, and where we may be missing something they have already worked out. I think that is an important part of the exercise. Eric Werner: I made that point before that, even though there are levels of abstraction, we have to distinguish those from the actual organisation of a system. I think that is real. One of the points that is good about Rosen is that you look at the reality of organisation itself; it is not just an abstraction, even though you can view it as such. That is why I think this kind of level, in the sense of organisation, can have causal efficacy. 4. Biological programs Anna Lewis: If we are using something like the word ‘program’, are we using it because in some deep sense we believe that what we are observing is a program, or are we just finding it a useful way to talk about things? Eva Jablonka: As an Aristotelian, I would start with execution. A program is an abstraction of an execution that is dynamically stable, and from this abstraction we can go back to the execution. Such back and forth change of perspective can be very useful, it is basically the back and forth movement between different levels of description. I want to say a few words about epigenetic ‘programs’. First, very often with epigenetic inheritance we see that thresholds of responses are altered. It is not easy to unravel how such changes in thresholds occur, because there are so many options. Even when thinking about simple models involving chromatin marks, there are many ways of changing thresholds. It is very context-dependent, and different cellular mechanisms can be used. Second, program and execution cannot be separated. If we consider self-sustaining loops, for example, where is the program? The program is the actual activity or inactivity of the loop. The realization of the program and the program itself are therefore one. I find it far more useful to think about attractor states than about programs. During development, when biological organisms develop, grow and reproduce, certain functionally important developmental states can be seen as attractor states, and it is heritable changes in how and when attractors are reached (changes in thresholds of reactivity), and in the stability and relationships among attractors that lead to evolutionary change. Eric Werner: In terms of programs again, I fully agree that there are programs where you cannot distinguish between them and the execution, but there are other ones that have a more global regulatory function and it is not just a matter of their execution, which I think it is good to separate. In terms of attractors, it might be very interesting to show some kind of mathematical relationships between the attractor formalism with program formalism. Maybe category theory is the way to do that. That would be an interesting method to explore in the future. There would be some rich ground there. Jim Shapiro: I too want to say couple of things on the genetic program. I think we need to be much more precise about how we talk about the genome and what it and DNA do. We have to avoid early 20th century notions, which obscure our thinking about this. We need to get more specific and realise that the DNA is organised and formatted in a variety of rather intricate ways, but that is part of
how the system works. We had the enhancer, a megabase away, within an intron and so forth. Those are terms we can define operationally. As to ‘no programs at any other level’, there are routines and networks that behave like programs, as Eva said. As Eric said, there is information there somewhere, which is what in some way influences the outcome of all of these extraordinarily complex processes and also gives them their capacity to be regulated. That is a key goal: what does that mean, and where is that information? I think we can agree that it is not just in the DNA; it is distributed in a different way. I think that is the key thing. Arthur Thomas: I have two interrelated points. Given that the notion of program seems to be controversial, or fuzzy at the very least, I am wondering whether it might be worthwhile to replace it with the notion of algorithm, which Tom talked about in his presentation on Monday. That is what computer scientists think about, not so much programs. I think that has some major advantages, one of which is that you can talk about algorithms in metatheoretic terms; you can prove properties of algorithms, such as whether they are correct or whether they will finish. You can talk about their complexity or simplicity. There are very well developed theories of algorithmic complexity and so forth. I think that leads into the kind of idea that Eric in particular and Eva to some extent talked about, which is that in the things we have talked about in the past couple of days we have not grasped the nettle of organisation. Of course, when computer scientists think about organisation they use the word ‘architecture’, because it draws out the idea that it is not just pieces but the way the pieces are put together at multiple levels according to some organisational or architectural style. As Eric mentioned, one of the ways in which people are now beginning to think about that kind of compositionality in computing is to use category theory, which is an explicit theory of how you put things together and make mappings between different kinds of things. Therefore, I would plead for that kind of algorithmic thinking to be taken seriously by this group. Tom Melham: The perspective of seeing biological systems as machines has run through a great deal of work in which computer scientists were excited about getting into systems biology and thought they had something applicable. For us in computer science, abstraction in the sense I explained it, and in some other senses too, is a very rich theory, which we view as absolutely essential for making sense of the complexity of our designs as engineers and for thinking about our designs. It is a fundamental mental tool that has precise meaning. For me, the fascinating question is whether that conceptual underpinning is applicable to biology, or whether we can achieve some abstraction relations between models in systems biology. It is not clear to me that we can do it. I would like to think we could. Perhaps the inherent modularity of biological systems will give us purchase on the problem. My talk was more a plea to be very open-minded about how we would structure those models in order to give ourselves the best chance of achieving compositional abstraction systems where we can control the conceptual complexity of what we are dealing with. That is why I am suggesting we think about decompositions that do not respect the organisational structure; we should feel free to pick levels of abstraction that work for us, and we should be very flexible about what constitutes a state, for example, which hopefully will give us the best scope for doing something.
References Noble, Denis, 2008. Claude Bernard, the first systems biologist, and the future of physiology. Experimental Physiology 91 (1), 16e26. Young, J.Z., 1964. A Model of the Brain. Clarendon Press, Oxford. Werner, E., 2011. On Programs and Genomes. arXiv:1110.5265v1 [q-bio.OT].
Contents lists available at SciVerse ScienceDirect
Progress in Biophysics and Molecular Biology journal homepage: www.elsevier.com/locate/pbiomolbio
Discussion
Plenary discussion of the conceptual foundations of systems biology Jonathan Bard*, Tom Melham, Eric Werner, Denis Noble Balliol Interdisciplinary Institute, Balliol College, Oxford, UK
The workshop from which all the articles in this focused issue of Progress in Biophysics and Molecular Biology are derived ended with a wide-ranging discussion of the nature of systems biology and its underlying principlesdtouching on complexity, the programming and database metaphors, dynamic modelling and underlying concepts. To give a flavour of the debate, we present here a lightly edited version of that discussion, which started with a brief commentary by Denis Noble on his ten Principles of Systems Biology (Noble, 2008).
1. Denis Noble’s 10 principles of systems biology 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.
Biological functionality is multi-level. Transmission of information is not one way. DNA is not the sole transmitter of inheritance. The theory of biological relativity: there is no privileged level of causality. Gene ontology will fail without higher-level insight. There is no genetic program. There are no programs at any other level. There are no programs in the brain. The self is not an object. There are many more to be discovered; a genuine ‘theory of biology’ does not yet exist.
Denis Noble: The article outlining these in 2008 was in response to an invitation by one of the scientific societies to give a lecture, which was published in a journal called Experimental Physiology (Noble, 2008). It is about 10 pages long, so each of these principles has a page or so. Notice that they are called ‘principles’, not ‘dogmas’, because all of them can probably be seriously criticized, replaced, conflated and so on. There is a sense in which they interrelate. I see number 4 e ‘There is no privileged level of causality’ e automatically leading to number 1, that there is multi-level functionality. That means the transmission of information cannot be one way; that DNA cannot be the sole transmitter of inheritance; and that you cannot identify the function of genes without having higher-level insight. I think those would probably be mostly non-controversial to most systems
* Corresponding author. E-mail address: [email protected] (J. Bard). 0079-6107/$ e see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.pbiomolbio.2012.11.002
biologists, because I cannot see how you can be a systems biologist if you are not looking at multi-level work. Then I come to the controversial ones. The next three e no genetic program, no programs at any other level and no programs in the brain e are essentially philosophical points. The sense in which I do not think there is a genetic program is that there is no way of parsing the sequences in DNA such that you could see the sequence in the way that Jacob and Monod were using the metaphor when they said it was a bit like the tape you put into old-fashioned computers: the complete program is on the tape, and you do not even need to know the computer in which you run it in order to know what that program can do. It would be the case that in any computer that runs like a Turing machine it should execute exactly what is on the tape. As we well know, you cannot put DNA from one species into another without there being problems. I think that is the justification for saying that, though I readily understand that other people have a different view of what a program could be.1 As to ‘no programs at any other level’ and ‘no programs in the brain’, I was taught physiology and anatomy by one of the greatest neuroscientists, JZ Young. He wrote a book called A model of the Brain. There is a very interesting comment in that book. Many people think that, having written a book with that title, he must have been one of those who believed that the program was in the brain. One very interesting quote e I cannot remember it in detail e is essentially that we should not think we will find in the brain code of the kind that a computer programmer would recognize.2 That was not what he was thinking at all, and he was well aware that there would be distribution. Another way to put it, in a more philosophical sense, is that, whatever we are doing e I am speaking, doing this with my hands and so on e neuronal activity is going on, but it is the process; it is not that there is another program forcing that to happen. Jonathan Bard: There are really two key ones are there not? One is about levels and one is about programs. Denis Noble: Exactly so, yes. Of course, I would stick very strongly by the multi-level one. As to the others, it is debatable; they are more philosophical than points about the nature of biology.
1 See (Werner, 2011) for a very different view. This paper was distributed to the participants before the workshop and influenced the discussion. 2 The quote is “it does not seem likely that the brain operates with a detailed programme of logical instructions”. JZ Young, A model of the Brain (Young, 1964), p. 27. Eds.
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Jonathan Bard: Would you say that you could have a program that was multi-level? Denis Noble: You could have a distributed program, yes. I hand it over to the computer scientist to work out what that might be. You would have to say, ‘Okay. This if-then-else clause is down here, and “but under conditions in which it could be said” is up here.’ I should also say there is not a methodology point in any of these principles, so I would underline that. I would also add a point about ‘circular interaction’ because it is not in here, and without that you do not have the control. Tom Melham [Peter Kohl]: I wanted to communicate a message from Peter Kohl, who cannot be here today. He says: ‘First of all, I do believe that systems biology is an approach to bio research that applies the principles of systems research to biological entities. One may wish to add: it is characterised by the dynamic integration of both reductionist and integrationist concepts and tools.’ Peter’s second point is this: ‘As such’ e in other words, as an approach e ‘it is difficult to identify a “conceptual foundation” of an approach, but systems research is clearly a relevant contributor. System research combines five things: (i) identification and (ii) detailed description of parts; exploration of (iii) their interactions with each other; and (iv) characterisation of interactions with the environment, both of elements and inter-element interactions. The aim of all that is to develop a systematic understanding of the entity, and this must include (v) an appreciation of effects of the entity on the previous four things described (as the entity allows and restricts element properties and interactions). It is implicit in this that we can identify the entity that we wish to study at the outset, even if our understanding of it may, and hopefully will, change in the process.’ His third major point was: ‘In establishing the “conceptual foundations of anything” clear definitions and proper terminology matter. Imprecise use of language, whether intentional or accidental, does not help communication; in fact, with a field that brings together so many different streams of conceptual, intellectual and linguistic track records, we should all go the extra mile and try actively to pre-empt misunderstandings.’ 2. Biological systems Eric Werner: First, the complexity of development has to be reflected somehow in the complexity of the original egg and genome, and ultimately the network complexity has to reflect that complexity. These are hypotheses; they are not really about systems biology but are related in the sense that we do model and try to gain insight into biological systems by way of that. That does not mean these systems are not alive or flexible, etc., but there is control information, which does direct, influence and subsume lower levels of control. Ultimately, the formalism that we choose to express that control can vary. We can use attractors or a distributed program metaphor. Stig Omholt: First, could we qualify the database concept relative to genes and interpret it in dynamic terms? You could compare computer science thinking with dynamic system thinking. I would phrase it in terms of opening new possibilities for creation of form. That feeds back to what you have already been discussing. That is needed because you can look upon genes as the store for reparameterization and opening up gates that otherwise are locked. These gates are not within reach of open physical systems without that content. Another aspect is that we talk about principles and systems. These are more like deep principles of biology. This is deep theoretical biology and that takes us back 40 years to Waddington’s work on theoretical biology and his symposia on Towards a theoretical biology. Why can we not frame our meeting to make a link with Waddington’s work? How far have we come? Where are we now in terms of what the issues were at that time? Then, nobody
talked about systems biology but theoretical biology, and we are still on the way towards that. We are dealing with deep biology, and we should stress that. We are really trying to achieve more than just making a case for systems biology. To add to that, Waddington gave us the concept that you have stability through form change. As far as I can see, we still have no mathematics to deal with that. That is a tremendous challenge, because that is really development. How will that challenge our understanding of the attractor concept, because here we are talking about the system that redefines its own roots and survives while maintaining its abilities? When we talk about circular causality, that is one thing, but we have not distinguished between positive and negative feedback. We need to be very explicit about that, because without positive feedback you will not have much biological order. John Walmsley: I have two short points. First, if we talk about the trajectories of a system, we should really also talk about the large transitions between different attractors that depend on a perturbation to the system. Second, nowhere in the principles as they stand do we mention the word ‘model’, which is something that has come up time and time again over the past two days. Ought we to talk about models in the principles of systems biology, or is that better left to methodology, such as that described by Peter? Keith Baverstock: I also have two points. In biology and systems biology we are looking at organisation, and organisation has not been a strong point in the history of science; rather, it is ignored. Part of that is self-organisation and it is context-dependent. It is a process and it depends on other processes and structure in the cell and the environment of the cell. The second point is that we need to make up our minds whether we think we are dealing with any living system as a complex dynamic system or a machine, because the machine metaphor has also come through in this meeting to some extent. I think we need to make a decision on what we think it is. Jonathan Bard: I am glad that you mentioned processes; these are needed to direct and push change and are the multi-level outputs of networks that, in particular, drive embryonic development. When there is mutation that affects these outputs, novel phenotypes arise and, if these new phenotypes have a selective advantage, lead to evolutionary change. This is the natural link between the molecular underpinnings of life and the phenotype, or the living and evolving organism. Andy Gardner: Two points: first, if this is to be a programme of scientific research rather than just a philosophical enterprise, we need interplay between theory and empiricism. The second point is that, throughout this workshop, I have been struck again and again how life is social at every level, so right down to its core there are social interactions. The emphasis here has been on co-operation, but conflict is also of major importance in social evolution. Looking at conflicts can help clarify concepts and enable you to understand what is going on. Genes can come into conflict. Even within an apparently harmonious cell there can be a lot of conflicts and they should be considered. Eva Jablonka: I think that we have better theoretical understanding of learning e for example associative learning e than we have of embryological development. I think that what happens during learning is based on the principles that we were discussing e it is a process of finding a stable (reinforcing) attractor, and there are many ways of getting to this attractor, many possible trajectories can lead to it. Maybe we should start by looking at a very complex, but theoretically better understood system, like neural learning, and see how people are modelling learning in neuroscience and whether we can borrow such models for morphological development. Christian Matek: Thinking about systems biology, it has come up over and over again that in decomposition there is the concept of levels of abstraction that may correspond to different states of
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the system or on very different scales. It can be very fruitful to refer to the language of statistical mechanics in physics because that is a relatively well-understood language to connect macroscopic/ microscopic properties of objects. That might lead to a point where we can have a firmer grounding on why we refer to high-level entities as being emergent. It is not just a question of it being in the eye of the beholder, but maybe it is a systematic way of connecting different levels, therefore allowing us to make contact from the point of view of physical chemistry. 3. Computational systems and levels Jim Shapiro: I just want to make a fundamental point, because we get hung up on Turing machines. In cells and even molecules in biological systems, the software and hardware are interchangeable; they are the same thing. I think we need to realise that is a very fundamental difference from the way we normally separate things out in computer science. I think that is a very basic and fundamental difference between a living system and a computer system. Jonathan Bard: Living systems are neither computer systems nor automata. Jim Shapiro: It is not to say that they are not computer systems, because I think that in many ways they are. They are not Turing machines, which is where there is a difference, even when it comes to the DNA, between the hardware and software, because the DNA is part of the hardware of many of the things that it does. Arthur Thomas: May I say that I think the distinction between software and hardware is a complete red herring, and also that it is important to remember that one of the essential properties of Turing machines is that they can write to as well as read from their tapes, and so in fact they are software. Turing machines support self-modifying programs, which I think is the essence of a lot of what has been discussed here over the past couple of days; that is, the idea that there is not a distinct level of an immutable program which gets translated in some environment into a functionality; rather, the program itself can be written dynamically on the fly by the environment in which it finds itself. I think that is a way of arriving at some sort of convergence that satisfies the constraints you are trying to identify. Jim Shapiro: I was trying to make what I think is a deeper comment than that, not that I claim to understand it. Not only is the system re-writable e I have tried to make that point with respect to DNA e but hardware and software operate together. When E. coli is distinguishing between glucose and lactose, it uses the glucose transport system to tell the enzyme that makes cAMP whether or not there is glucose around. That creates the symbol cAMP in the cell, which is then distributed and influences lots of other things. I do not know that computer science has yet a way of dealing with those situations. Eric Werner: I think that in the computer itself software is hardware as well. So, at the lowest level, when you translate higherlevel languages into microcode, they switch gates; everything is physical. There is no distinction between hardware and software at the lowest level, but it certainly helps conceptually. I think that in biology, too, if we look, say, at developmental networks it certainly helps to distinguish them and view them not just as hardware, because, if you look at it just as a molecule or DNA, it does not give you any conceptual insight. Even with the stuff that you do, Jim, it is fascinating precisely because it does the kinds of manipulations you can do on strings and things like that, which is a kind of software metaphor. It does not help understanding in the long run to say that everything is molecular. Arthur Thomas: Referring to Denis Noble’s comments, the levels being distributed and multi-levels are, I think, two quite
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different things. One of the interesting points that Tom Melham brought out in his talk was that abstraction is the most important concept in computer science, or in the notion of computation. Of course, it manifests itself in the form of functions, sub-routines or whatever you want to call them, in computing languages. I think the same thing holds in biological computational systems; they are multi-level in exactly the same sense as computer programs are multi-level, because they use the notion of abstraction to create modularity, which in turn creates multiple levels. Tom Melham: Can I kick in here with a plea for a little clarity in this space of levels versus scales versus hierarchical physical organisation of a system versus abstraction levels? These are all different things for us in computer science. They might or might not align. One of my standard jokes about downward causation is that I believe in it completely but it is not downward and it is not causation, because for me the up and down metaphors have to do with abstraction level, not the nesting depth of physical organisation; and scale has to do with the resolution of your measurements, not whether or not you are looking at an organ or cell. Jonathan Bard: When you say ‘scale’ to biologists they would agree with you, but ‘level’ would have a very clear meaning of going from the DNA to the proteins. That is the language of a biologist. Maria Tasaki: I am especially interested in biological relativity, because it expresses well modular activities. It seems to me that the concept of downward causation shares the same ideas with circular causality. Denis Noble: I totally agree with Tom there is a sense in which, if you talk about levels, you are talking about one thing; if you talk about scales, you are talking about another, and they are not to be confused. What does this mean? I think it relates to the point Maria is raising. When you think in terms of levels, of course you can ask questions like whether the integrated activity of, say, the cardiovascular system influences gene expression that enables the heart to function in the ordinary way. The answer is yes. You can set out the pathways by which all of that is achieved. It will look very multi-level, because on the one hand you will have a system measuring blood pressure, which is a high-level property of the system as a whole in the blood vessels; you have another part of it that secretes this particular chemical, which maybe noradrenaline; and in turn you will then have actions on various cells in the body. So, you are moving around between the levels. But in terms of scales I think the point you make, Tom, is absolutely right. You are really talking about resolution, because in the case of scales you can be molecular even at a very large scale. After all, the universe can be represented as a bunch of molecules, so there is a sense in which when talking about scale it is resolution. That was why I said at the very beginning of the meeting that that was the automatic way in which the supervenience principle is obeyed, even when you talk about downward causation because, provided you can make the system big enough, there will be a difference at the molecular level; there will be a difference in the level of strings too, for goodness sake. To come to Maria’s point, whether you talk in terms of levels or scales e I think it is absolutely essential to distinguish between the two e circularity, or circular causation, however you want to call it, is an absolutely necessary consequence of downward causation, whatever you want to call it. That is critical to the many pathways that enable regulation to be done, and so on. I cannot imagine a systems biology without that kind of interaction. Jonathan Bard: What I am really hearing is that causation is distributed. Denis Noble: Yes e and circular. Jim Shapiro: I just want to question the term ‘causation’, because I think that carries with it a lot of philosophical baggage. I
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think we can find a more neutral term like ‘regulation’ or ‘control’, which is what we deal with all the time. It gets rid of a number of definitional and epistemological problems that I think have muddied things. The other general point to make is that one of the exercises here is for the biologists to define the system as we understand it and for the computer scientists, who have thought about a lot of these definitional issues much more than we have, to inform us how they look at the way we parse things and how they would parse them, and where we may be missing something they have already worked out. I think that is an important part of the exercise. Eric Werner: I made that point before that, even though there are levels of abstraction, we have to distinguish those from the actual organisation of a system. I think that is real. One of the points that is good about Rosen is that you look at the reality of organisation itself; it is not just an abstraction, even though you can view it as such. That is why I think this kind of level, in the sense of organisation, can have causal efficacy. 4. Biological programs Anna Lewis: If we are using something like the word ‘program’, are we using it because in some deep sense we believe that what we are observing is a program, or are we just finding it a useful way to talk about things? Eva Jablonka: As an Aristotelian, I would start with execution. A program is an abstraction of an execution that is dynamically stable, and from this abstraction we can go back to the execution. Such back and forth change of perspective can be very useful, it is basically the back and forth movement between different levels of description. I want to say a few words about epigenetic ‘programs’. First, very often with epigenetic inheritance we see that thresholds of responses are altered. It is not easy to unravel how such changes in thresholds occur, because there are so many options. Even when thinking about simple models involving chromatin marks, there are many ways of changing thresholds. It is very context-dependent, and different cellular mechanisms can be used. Second, program and execution cannot be separated. If we consider self-sustaining loops, for example, where is the program? The program is the actual activity or inactivity of the loop. The realization of the program and the program itself are therefore one. I find it far more useful to think about attractor states than about programs. During development, when biological organisms develop, grow and reproduce, certain functionally important developmental states can be seen as attractor states, and it is heritable changes in how and when attractors are reached (changes in thresholds of reactivity), and in the stability and relationships among attractors that lead to evolutionary change. Eric Werner: In terms of programs again, I fully agree that there are programs where you cannot distinguish between them and the execution, but there are other ones that have a more global regulatory function and it is not just a matter of their execution, which I think it is good to separate. In terms of attractors, it might be very interesting to show some kind of mathematical relationships between the attractor formalism with program formalism. Maybe category theory is the way to do that. That would be an interesting method to explore in the future. There would be some rich ground there. Jim Shapiro: I too want to say couple of things on the genetic program. I think we need to be much more precise about how we talk about the genome and what it and DNA do. We have to avoid early 20th century notions, which obscure our thinking about this. We need to get more specific and realise that the DNA is organised and formatted in a variety of rather intricate ways, but that is part of
how the system works. We had the enhancer, a megabase away, within an intron and so forth. Those are terms we can define operationally. As to ‘no programs at any other level’, there are routines and networks that behave like programs, as Eva said. As Eric said, there is information there somewhere, which is what in some way influences the outcome of all of these extraordinarily complex processes and also gives them their capacity to be regulated. That is a key goal: what does that mean, and where is that information? I think we can agree that it is not just in the DNA; it is distributed in a different way. I think that is the key thing. Arthur Thomas: I have two interrelated points. Given that the notion of program seems to be controversial, or fuzzy at the very least, I am wondering whether it might be worthwhile to replace it with the notion of algorithm, which Tom talked about in his presentation on Monday. That is what computer scientists think about, not so much programs. I think that has some major advantages, one of which is that you can talk about algorithms in metatheoretic terms; you can prove properties of algorithms, such as whether they are correct or whether they will finish. You can talk about their complexity or simplicity. There are very well developed theories of algorithmic complexity and so forth. I think that leads into the kind of idea that Eric in particular and Eva to some extent talked about, which is that in the things we have talked about in the past couple of days we have not grasped the nettle of organisation. Of course, when computer scientists think about organisation they use the word ‘architecture’, because it draws out the idea that it is not just pieces but the way the pieces are put together at multiple levels according to some organisational or architectural style. As Eric mentioned, one of the ways in which people are now beginning to think about that kind of compositionality in computing is to use category theory, which is an explicit theory of how you put things together and make mappings between different kinds of things. Therefore, I would plead for that kind of algorithmic thinking to be taken seriously by this group. Tom Melham: The perspective of seeing biological systems as machines has run through a great deal of work in which computer scientists were excited about getting into systems biology and thought they had something applicable. For us in computer science, abstraction in the sense I explained it, and in some other senses too, is a very rich theory, which we view as absolutely essential for making sense of the complexity of our designs as engineers and for thinking about our designs. It is a fundamental mental tool that has precise meaning. For me, the fascinating question is whether that conceptual underpinning is applicable to biology, or whether we can achieve some abstraction relations between models in systems biology. It is not clear to me that we can do it. I would like to think we could. Perhaps the inherent modularity of biological systems will give us purchase on the problem. My talk was more a plea to be very open-minded about how we would structure those models in order to give ourselves the best chance of achieving compositional abstraction systems where we can control the conceptual complexity of what we are dealing with. That is why I am suggesting we think about decompositions that do not respect the organisational structure; we should feel free to pick levels of abstraction that work for us, and we should be very flexible about what constitutes a state, for example, which hopefully will give us the best scope for doing something.
References Noble, Denis, 2008. Claude Bernard, the first systems biologist, and the future of physiology. Experimental Physiology 91 (1), 16e26. Young, J.Z., 1964. A Model of the Brain. Clarendon Press, Oxford. Werner, E., 2011. On Programs and Genomes. arXiv:1110.5265v1 [q-bio.OT].