Complexity in Biological Information Processing

دسته بندی: زیست شناسی
ویرایش:  
نویسندگان: Bock G.,  Goode J.  
سری:  
 
ناشر:  
سال نشر:  
تعداد صفحات: 258 
زبان: English 
فرمت فایل : PDF (درصورت درخواست کاربر به PDF، EPUB یا AZW3 تبدیل می شود) 
حجم فایل: 1 مگابایت 

قیمت کتاب (تومان) : 51,000

Издательство John Wiley, 2001, -258 pp. I am looking forward, over the next two days, to exploring in depth this exciting and emerging area of biological complexity. It was Dobzhansky who once said that nothing in biology makes sense except in the light of evolution, and this is certainly true of biological complexity. In some ways, complexity is something that many biologists try to avoid. After all, it is a lot easier to study a simple subject than a complex one. But by being good reductionists - taking apart complex creatures and mechanisms into their component parts - we are left at the end with the problem of putting them back together. This is something I learned as a child when I took apart my alarm clock, discovering it didn't go back together nearly as easily as it came apart. What is emerging, and what has given us the opportunity for this meeting, is the fact that over the last few years there has been a confluence of advances in many different areas of biology and computer science which make this a unique moment in hi story. It is the first time that we have had the tools to actually put back together the many pieces that we have very laboriously and expensively discovered. In a sense, we are at the very beginning of this process of integrating knowledge that is spread out over many different fields. And each participant here is a carefully selected representative of a particular sub-area of biology.
In real estate there is a well known saying that there are three important criteria in valuing a property: location, location and location. In attempting to identify a theme to integrate the different papers we will be hearing in this symposium, it occurred to me that, likewise, there are three important threads: networks, networks and networks. We will be hearing about gene networks, cell signalling networks and neural networks. In each of these cases there is a d ynamical system with many interacting parts and many different timescales. The problem is coming to grips with the complexity that emerges from those dynamics. These are not separate networks: I don't want to give the impression that we are dealing with compartmentalized systems, because all these networks ultimately are going to be integrated together.
One other constraint we must keep in mind is that ultimately it is behaviour that is being selected for by evolution. Although we are going to be focusing on these details and mechanisms, we hope to gain an understanding of the behaviour of whole organisms. I tow is it, for example, that the fly is able to survive autonomously in an uncertain world, where the conditions under which food can be found or under which mating can take place are highly variable? And how has the fly done so well at this with such a modest set of around 100000 neurons in the fly brain? We will hear from Simon Laughlin that one of the important constraints is energetics.
I have a list of questions that can serve as themes for our discussion. I want to keep these in the background and perhaps return to them at the end in our final discussion session. First, are there any general principles that will cut across all the different areas we are addressing? These principles might be conceptual, mathematical or evolutionary. Second, what constraints are there? Evolution occurred for many of these creatures under conditions that we do not fully understand. We don't know what prebiotic conditions were like on the surface of the earth, and this is partly why this is such a difficult subject to study experimentally. The only fossil traces of the early creatures are a few forms preserved in rock. What we would really like to know is the history, and there is apparently an opportunity in studying the DNA of many creatures to look at the past in terms of the historical record that has been left behind, preserved in stretched of DNA. But the real quest ion in my mind concerns the constraints that arc imposed on any living entity by energy consumption, Information processing and speed of processing. In each of our areas, if we come up with a list of the constraints that we know arc important, we may find some commonality.
The third question is, how do we make progress from here? In particular, what new techniques do we need in order to get the information necessary for progress? I am a firm believer in the idea that major progress in biology requires the development of new techniques and also the speeding-up of existing techniques. This is true in all areas of science, but is especially relevant in biology, where the impact of techniques for sequencing DNA, for example, has been immense. It was recently announced that the sequence of the human genome is now virtually complete. This will be an amazingly powerful tool that we will have over the next 10 years. As we ask a particular quest ion we will be able to go to a database and come up with answers based on homology and similarities across species. Who would have guessed even 10 years ago that all of the segmented creatures and vertebrates have a common body plan based around the I-lox family of genes? This is something that most of the developmental biologists missed. They didn't appreciate how similar these mechanisms were in different organisms, until it was made obvious by genetic techniques. Another technique that will provide us with the ability both to do experiments and collect massive amounts of data is the use of gene microarrays. It is now possible to test for tens of thousands of genes in parallel. We can take advantage of the fact that over the last 50 years, the performance of computers, both in terms of memory and processing power, has been rising exponentially. In 1950 computers based on vacuum tubes could do about 1000 operations per second; modern parallel supercomputers are capable of around 10¹³ operations per second. This is going to be of enormous help to us, both in terms of keeping track of information and in performing mathematical models. Imaging techniques are also extremely powerful. Using various dyes, it is possible to get a dynamic picture of cell signalling within cells. These are very powerful techniques for understanding the actual signals, where they occur and how fast they occur. Please keep in mind over the next few days that we need new techniques and new ways of probing cells. We need to have new ways of taking advantage of older techniques for manipulating cells and the ability to take into account the complexity of all the interactions within the cell, to develop a language for understanding the significance of all these interactions.
I very much look forward to the papers and discussions that are to follow. Although it win be a real challenge for us to understand each other, each of us coming from our own particular field, it will be well worth the effort. Introduction
Functional modules in biological signalling networks
Design of immune-based interventions in autoimmunity and viral infections - the need for predictive models that integrate time, dose and classes of immune responses
Controlling the immune system: diffuse feedback via a diffuse informational network
General discussion I
The versatility and complexity of calcium signalling
Multiple pathways of ERK activation by G protein-coupled receptors
Heterogeneity of second messenger levels in living cells
Humoral coding and decoding
From genes to whole organs: connecting biochemistry to physiology
Development of high-throughput tools to unravel the complexity of gene expression patterns in the mammalian brain
General discussion II Understanding complex systems: top-down, bottom-up or middle-out?
Regulation of gene expression by action potentials: dependence on complexity in cellular information processing
Laughlin Efficiency and complexity in neural coding
Neural dynamics in cortical networks - precision of joint-spiking events
Predictive learning of temporal sequences in recurrent neocortical circuits
Final discussion