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First of all I am a physicist, so I apologise for my non-existent understanding of basic biology. However, I have what I think is an interesting question and this seems like a good place to ask it…
I've recently been thinking whether there is a way to measure time using biological processes. In other words some system, a characteristic of which can be correlated to elapsed time. One example I can come up with is counting the number of divisions of a cell. I guess this can be done, but will most likely be highly inaccurate…
So this is my question: Do you know of any papers even tangentially related to the idea of a biological system that can be used as a clock?
Any ideas will be greatly appreciated!
There are lots of biological clocks, or clocks made of biological components. The circadian clock is an important, though complicated, example. There are excellent engineered clocks that form some of the neatest examples of systems / synthetic biology. See for example Elowitz & Leibler's "repressilator" (link, link). The basic idea in all of these is to have gene regulatory circuits with feedback that lead to oscillation of the gene expression dynamics. Books on systems biology, or Philip Nelson's Physical Models of Living Systems (aimed at physicists), are good places to read more.
High School Biology : Understanding Biological Fitness
The biological fitness of an organism is dependent on its ability to survive and reproduce in a given environment. If different traits or alleles increase the fitness of an organism, those alleles will consequently increase in the gene pool, and that trait will increase in the population. This is how natural selection affects a population.
There is inherent trade-off in biological fitness. A trait that increases ability to survive, but makes an individual sterile, decreases fitness because the organism cannot produce offspring to carry on the trait. Similarly, if a trait increases the ability to reproduce, but makes it harder to the organism to survive, it may die before being able to produce offspring. Both survival and reproduction are essential to defining the fitness of an organism.
Example Question #1 : Understanding Biological Fitness
Which of the following best describes biological fitness?
Ability to compete against other organisms
Ability to grow to the largest size
Ability to reason and think logically
Ability to have superior physical strength
Biological fitness in the evolutionary sense is only related to fitness in terms to reproduction. Because the primary goal of all organisms is to reproduce, or to pass their DNA onto offspring, fitness is defined as the ability to reproduce and create viable offspring.
"Favorable" traits, such as intelligence, size, or strength, may increase the ability of an individual to survive and reproduce, thus increasing biological fitness, but cannot be used to directly define the fitness of the individual.
Example Question #3 : Understanding Biological Fitness
Darwinian fitness is a measure of __________ .
the ability of an organism to kill another organism
the ability of an organism to use tools
the ability of an organism to run for long periods of time
the ability of an organism to create offspring
the ability of an organism to protect its young
the ability of an organism to create offspring
The term "fitness" in evolutionary biology means the ability of an organism to pass on its genetic material to its offspring. Biological or "Darwinian" fitness is being able to live long enough to reproduce and keep the population or species alive. Most students confuse biological fitness with physical fitness because that is the context most often associated with the word.
Example Question #2 : Understanding Biological Fitness
In the study of evolution, sometimes it is useful to assess the biological fitness of an individual. What is the best criterion to use to measure the biological fitness of a certain large, strong iguana?
The number of the iguana's offspring who also survive to reproduce
The hunting ability of the iguana
The number of predators the iguana has in its environment
The number of the iguana's offspring who also survive to reproduce
Biological or Darwinian fitness is defined based on the specimen's ability to reproduce and generate viable offspring. Essentially, the fitness of the individual is based on its ability to pass genetic information on to the next generation, as opposed to any physical characteristic or trait.
Measuring the number of offspring who contribute to the gene pool is the best way to determine how genetically fit the iguana is. No matter how strong, large, old, or free of predation an animal is, if it cannot reproduce, it is not considered fit.
Example Question #2 : Understanding Biological Fitness
Which of the following is an example of an evolutionary advantage?
A white rabbit that lives in a snow covered environment
A bird with a beak that can crack nuts in an environment where nuts are the main food source
A cheetah that can run faster than the rest of his pack
A black moth that lives near an industrial site that produces a lot of soot
All of the examples given provide an evolutionary advantage. A white rabbit in a snow covered environment has camouflage, which protects it from its predators. The same is true with the black moth living in a in a soot-covered industrial area. A cheeta that can run fastest has the greatest chance of catching prey and feeding himself/herself and his/her offspring. The same is true for a bird that can crack nuts in an area where nuts are the main source of food.
Example Question #3 : Understanding Biological Fitness
A female cheetah in Africa has four litters of cubs over her lifetime. Her first litter has six cubs that grow to adulthood and is fathered by the most spotted male in the area. Her second litter has four cubs that grow to adulthood and is fathered by the fastest male in the area. Her third litter has two cubs that survive to adulthood and is fathered by the strongest male in the area. Her fourth litter has five cubs that survive to adulthood and is fathered by the smartest male in the area. Which male cheetah has the most biological fitness?
Can't tell from the given information
The term biological fitness refers to reproductive success and is different than physical fitness. Since the most spotted male fathered the most cubs that survived to adulthood to reproduce themselves, he would be considered the most biologically fit. It is also important to note the inclusion of the "survived to adulthood" aspect since reproductive success is dependent on an organism's offspring being able to reproduce and contribute to the gene pool as well. For example, if the most spotted male had fathered a litter that initially had nine cubs, but only one of them survived to adulthood to have cubs of its own, he would no longer be considered the most biologically fit.
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A biological system to measure time - Biology
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Martha Hotz Vitaterna, Ph.D., Joseph S. Takahashi, Ph.D., and Fred W. Turek, Ph.D.
MARTHA HOTZ VITATERNA, PH.D., is a senior research associate in the Center for Functional Genomics, Northwestern University, Evanston, Illinois.
JOSEPH S. TAKAHASHI, PH.D., is the director of the Center for Functional Genomics, the Walter and Mary E. Glass Professor in the Department of Neurobiology and Physiology, and an investigator at the Howard Hughes Medical Institute, Northwestern University, Evanston, Illinois.
FRED W. TUREK, PH.D., is the director of the Center for Sleep and Circadian Biology and is the Charles T. and Emma H. Morrison Professor in the Department of Neurobiology and Physiology, Northwestern University, Evanston, Illinois.
The daily light-dark cycle governs rhythmic changes in the behavior and/or physiology of most species. Studies have found that these changes are governed by a biological clock, which in mammals is located in two brain areas called the suprachiasmatic nuclei. The circadian cycles established by this clock occur throughout nature and have a period of approximately 24 hours. In addition, these circadian cycles can be synchronized to external time signals but also can persist in the absence of such signals. Studies have found that the internal clock consists of an array of genes and the protein products they encode, which regulate various physiological processes throughout the body. Disruptions of the biological rhythms can impair the health and well-being of the organism. KEY WORDS: circadian rhythm time of day biological regulation biological adaptation temperature light hypothalamus neural cell gene expression mutagenesis sleep disorder physiological AODE (effects of alcohol or other drug use, abuse, and dependence)
One of the most dramatic features of the world in which we live is the cycle of day and night. Correspondingly, almost all species exhibit daily changes in their behavior and/or physiology. These daily rhythms are not simply a response to the 24-hour changes in the physical environment imposed by the earth turning on its axis but, instead, arise from a timekeeping system within the organism. This timekeeping system, or biological clock, allows the organism to anticipate and prepare for the changes in the physical environment that are associated with day and night, thereby ensuring that the organism will do the right thing at the right time of the day. The biological clock also provides internal temporal organization and ensures that internal changes take place in coordination with one another.
The synchrony of an organism with both its external and internal environments is critical to the organisms well-being and survival a lack of synchrony between the organism and the external environment may lead to the individuals immediate demise. For example, if a nocturnal rodent were to venture from its burrow during broad daylight, the rodent would be exceptionally easy prey for other animals. Similarly, a lack of synchrony within the internal environment might lead to health problems in the individual, such as those associated with jet lag, shift work, and the accompanying sleep loss (e.g., impaired cognitive function, altered hormonal function, and gastrointestinal complaints).
The mechanisms underlying the biological timekeeping systems and the potential consequences of their failure are among the issues addressed by researchers in the field of chronobiology. 1 ( 1 For a definition of this and other technical terms used in this article and throughout this issue of the journal, please see glossary, p. 92. ) In its broadest sense, chronobiology encompasses all research areas focusing on biological timing, including high-frequency cycles (e.g., hormone secretion occurring in distinct pulses throughout the day), daily cycles (e.g., activity and rest cycles), and monthly or annual cycles (e.g., reproductive cycles in some species). Among these interrelated areas of chronobiology, this article focuses on one frequency domain-the daily cycles known as circadian rhythms. (The term circadian derives from the Latin phrase circa diem, which means about a day.) Although virtually all life forms- including bacteria, fungi, plants, fruit flies, fish, mice, and humans-exhibit circadian rhythms, this review is primarily limited to the mammalian system. Other animals are discussed only in cases in which they have contributed to the understanding of the mammalian system, particularly in studies of the molecular genetic makeup of the time-keeping system. (For comparative discussions of other nonmammalian model systems that have contributed to the depth of understanding of circadian rhythmicity in mammals, the reader is referred to Wager-Smith and Kay 2000.) Overall, this article has the following major objectives: (1) to provide a highly selective historical overview of the field, (2) to review characteristic properties of circadian rhythms, (3) to define the structural components and the molecular genetic mechanisms comprising the biological clock, and (4) to explore the health effects of biological rhythms.
Historical Overview of Chronobiology
Researchers began studying biological rhythms approximately 50 years ago. Although no single experiment serves as the defining event from which to date the beginning of modern research in chronobiology, studies conducted in the 1950s on circadian rhythmicity in fruit flies by Colin Pittendrigh and in humans by Jurgen Aschoff can be considered its foundation. The area of sleep research, which also is subsumed under the field of chronobiology, evolved some-what independently, with the identification of various sleep stages by Nathaniel Kleitman around the same time (Dement 2000). The legacies of these pioneers continue today with the advancement of the fields they founded.
The roots of the study of biological rhythms, however, reach back even further, to the 1700s and the work of the French scientist de Mairan, who published a monograph describing the daily leaf movements of a plant. De Mairan observed that the daily raising and lowering of the leaves continued even when the plant was placed in an interior room and thus was not exposed to sunlight. This finding suggested that the movements represented something more than a simple response to the sun and were controlled by an internal clock.
Characteristic Properties of Circadian Rhythms
De Mairans apt observations illustrate one critical feature of circadian rhythms- their self-sustained nature. Thus, almost all diurnal rhythms that occur under natural conditions continue to cycle under laboratory conditions devoid of any external time-giving cues from the physical environment (e.g., under constant light or constant darkness). Circadian rhythms that are expressed in the absence of any 24-hour signals from the external environment are called free running. This means that the rhythm is not synchronized by any cyclic change in the physical environment. Strictly speaking, a diurnal rhythm should not be called circadian until it has been shown to persist under constant environmental conditions and thereby can be distinguished from those rhythms that are simply a response to 24-hour environmental changes. For practical purposes, however, there is little reason to distinguish between diurnal and circadian rhythms, because almost all diurnal rhythms are found to be circadian. Nor is a terminology distinction made among circadian rhythms based on the type of environmental stimulus that synchronizes the cycle.
The persistence of rhythms in the absence of a dark-light cycle or other exogenous time signal (i.e., a Zeitgeber) clearly seems to indicate the existence of some kind of internal timekeeping mechanism, or biological clock. However, some investigators have pointed out that the persistence of rhythmicity does not necessarily exclude the possibility that other, uncontrolled cycles generated by the Earths revolution on its axis might be driving the rhythm (see Aschoff 1960).
The hypothesis that such uncontrolled geomagnetic cues might play a role in the persistence of rhythmicity can be refuted by a second characteristic feature of circadian rhythms: These cycles persist with a period of close to, but not exactly, 24 hours. If the rhythms were exogenously driven, they should persist with a period of exactly 24 hours. The seeming imprecision is an important feature of rhythmicity, however. As Pittendrigh (1960) demonstrated, the deviation from a 24-hour cycle actually provides a means for the internal time-keeping system to be continuously aligned by and aligned to the light-dark environment. This continuous adjustment results in greater precision in controlling the timing, or phase, of the expressed rhythms, because little drift is allowed to occur before the rhythm is reset to the correct phase.
A third characteristic property of circadian rhythms is their ability to be synchronized, or entrained, by external time cues, such as the light-dark cycle. Thus, although circadian rhythms can persist in the absence of external time cues (meaning that they are not driven by the environment), normally such cues are present and the rhythms are aligned to them. Accordingly, if a shift in external cues occurs (e.g., following travel across time zones), the rhythms will be aligned to the new cues. This alignment is called entrainment.
Initially, it was unclear whether entrainment was achieved by modulating the rate of cycling (i.e., whether the cycle was shortened or lengthened until it was aligned to the new cues and then reverted to its original length) or whether entrainment was achieved by discrete resetting events. Experiments resulting from this debate led to fundamental discoveries. For example, researchers discovered that the organisms response to light (i.e., whether a cycle advances, is delayed, or remains unchanged) differs depending on the phase in the cycle at which it is presented (Pittendrigh 1960). Thus, exposure to light during the early part of the individuals normal dark period generally results in a phase delay, whereas exposure to light during the late part of the individuals normal dark period generally results in a phase advance. This difference in responses can be represented by a phase-response curve (see figure 1 for a schematic illustration of a circadian cycle as well as a phase-response curve). Such a curve can predict the manner in which an organism will entrain not only to shifts in the light-dark cycles but also to unusual light cycles, such as non-24-hour cycles or different light:dark ratios. The existence of a phase-response curve also implies that entrainment is achieved by discrete resetting events rather than changes in the rate of cycling.
In addition to the timing of the light exposure, the light intensity can modulate cycling periods when organisms are left in constant light. Thus, exposure to brighter light intensities can lengthen the period in some species and shorten it in other species. This phenomenon has been dubbed Aschoffs rule(Aschoff 1960). Ultimately, both mechanisms of entrainment appear to be aspects of the same thing, because the consequences of Aschoffs rule can be predicted or explained by the phase-response curves to light.
Although the light-dark cycle clearly is the major Zeitgeber for all organisms, other factors-such as social interactions, activity or exercise, and even temperature-also can modulate a cycles phase. The influence of temperature on circadian rhythms is particularly interesting in that a change in temperature can affect the phase of a cycle without substantially altering the rate of cycling. This means that the cycle may start at an earlier or later-than-normal time but still have the same length. On the one hand, this ability of the internal clocks pacemaker to compensate for changes in temperature is critical to its ability to predict and adapt to environmental changes, because a clock that speeds up and slows down as the temperature changes would not be useful. On the other hand, temperature compensation also is rather puzzling, because most kinds of biological processes (e.g., biochemical reactions in the body) are accelerated or slowed by temperature changes. Ultimately, this riddle has provided a clue to the nature of the internal clock- that is, the fact that circadian rhythms have a genetic basis. Such a program of gene expression would be more resistant to temperature alteration than, for example, a simple biochemical reaction.
Two final properties of circadian rhythms also provide important hints of the rhythms makeup. One of these properties is the rhythms ubiquity in nature: Circadian rhythms exist in a broad array of biological processes and organisms, with similar properties and even similar phase-response curves to light. The other property is that circadian rhythms appear to be generated at the cellular level, because the rhythms of unicellular organisms (e.g., algae or the dinoflagellate Gonyaulax) are much the same as rhythms of highly complex mammals. Both of these observations suggest that a cycle in the activation (i.e., expression) of certain genes might underlie the timekeeping mechanism.
Figure 1 Circadian rhythm responses to light.
A. Parameters of circadian rhythm
A representative circadian rhythm is depicted in which the level of a particular measure (e.g., blood hormone levels and activity levels) varies according to time. The difference in the level between peak and trough values is the amplitude of the rhythm. The timing of a reference point in the cycle (e.g., the peak) relative to a fixed event (e.g., beginning of the night phase) is the phase. The time interval between phase reference points (e.g., two peaks) is called the period. The rhythm shown persists even in continuous darkness (i.e., is free running).
B. Resetting the circadian rhythm
The effects of a rhythm-resetting signal, such as exposure to light by animals other-wise kept in continuous darkness, can shift the rhythm either back (upper panel) or ahead (lower panel), depending on when during the cycle the signal is presented. In the case of a phase delay, the peak levels are reached later than they would be had the rhythm not been shifted. In the case of a phase advance, the peak levels are reached earlier than they would be had the rhythm not been shifted. The black line shows how cycling would appear if the rhythm remained unchanged.
C. Changes in circadian rhythm in response to changes in light exposure
Virtually all species show similar phase-dependent-resetting responses to light, which can be expressed as a phase-response curve. Exposure to light during the early part of the animals night causes a phase delay, whereas exposure to light in the latter part of the animals night causes a phase advance. Light exposure during the animals usual daytime period produces little or no phase shift.
Attractors and phase-space diagram
Regulation of cell functions can be thought of as physically and topologically structured molecular interactions and physical forces located at different hierarchical levels. In dissipative systems, such as living organisms, the overall system behaves according to a non-linear dynamics 31 . The dynamics of such a system is the concerted change in the levels of xi(t) [ the value of the node i ] for all the nodes i of the network (a metabolomic or genomic network) and can be represented by the N-dimensional state vector: S(t) = [ x1(t), x2(t),….xN(t)]. While the topology represents the network by a graph, for a network with N nodes, the dynamics can be better described in the Phase Space (with N-dimension or degree of freedom) in which S(t) changes its position along the time coordinate t as defined by its components [ x1(t), x2(t),….xN(t)]. The mathematical formalism of the phase space diagram for dissipative systems is described by the theory of dynamical non-equilibrium systems 32 . As proposed by Kauffman 33 , the Phase Space describes a landscape characterized by attractors (“valleys”) –surrounded by basins –separated by “hills”: the difference in the behavioral potential between cells lies in their position on the landscape and the associated accessibility to attractors ( Fig. 1 ). In this view, cell fate regulation is based on selection between pre-existing, limited, intrinsically robust fates. It must be stressed that the special state vector S*(t) is a stationary state in which there is no net driving force. The trajectories emanating from the neighbourhood are 𠇊ttracted” and converge to S*(t). This stable attractor state is robust to many perturbations: 𠇊ttractors” are, indeed, self-organizing structures and can pture” gene expression profiles associated with cell fates 34 .
A discrete finite number of attractor classes do exist. Generally, strange attractors (i.e., dynamics that do not follow a simple periodic trajectory) arise from non-linear dynamical systems. The phenotypic traits of the organism are embedded into the dynamic attractors of its underlying regulatory network. 35,36 Functional states depicted as attractors have been conceived as mainly specified by the gene-regulatory network 34 . However, “the stability of functional states clearly also depends on external cues” 37 . Thus, system s dynamics in the phase space cannot be “reduced” either to a genetic wiring diagram or, even to the integrated functioning of a genome-proteome-metabolome network. Additional influences –i.e., those of the intracellular topology that makes the chemical reactions of the “networks” possible, as well as those resulting from parenchyma-stroma interacting system –must be taken into consideration in order to give a more reliable definition of attractor. This revised conceptualisation of 𠇊ttractors” could fit numerous observations on tissue dynamics, the existence of different dynamical regimens, as well as the transitions between them 38 . Moreover, this hierarchical organization creates downward causation 39 complementing the better known upward causation, and thereby shaping the complex behaviour of the system 40 .
Proof of equation (1)
where ρSR(t0) is the system-reservoir state and U(τ) is the system-reservoir evolution operator for τ = t – t0. If ρSR(t0) is a classical state with no coherent components, then we have
where pn(t0) is the probability of measuring the system state n at time t0 for the classical mixture ρSR(t0), and R(t0) is the reservoir state at time t0 (which in principle depends on the measurement result Qn if the system and reservoir are classically correlated, i.e., are separable but in a mixture of product states). Then we have
where . The the reservoir state is assumed to be R(t0) = Σk prk |rk〉〈rk|. Hence the correlation 〈Qm(t)Qn(t0)〉Q for the system-reservoir classical mixture at the time t0 is
where is the propagator, i.e., the probability of finding the state m at the time t when the state at an earlier time t0 is initialized at n.
The Hierarchy model for FMO
The Hierarchy model was originally developed by Tanimura and Kubo 41 , and has been applied extensively to light-harvesting complexes 7 ,8 . We will not give a full description here, but will just summarize the main equation and parameters. It is always assumed that at t = 0 the system and bath are separable , and that the bath is in a thermal equilibrium state . The bath is assumed to have a Drude spectral density
where γj is the 𠇍rude decay constant” and each site j is assumed to have its own independent bath. In addition, λj is the reorganisation energy, and is proportional to the system-bath coupling strength. The correlation function for the bath is then given by,
where µj,0 = γj, and when m ≥ 1. The coefficients are
Under these assumptions, the Hierarchy equations of motion are given by,
The operator Qj = |j〉〈j| is the projector on the site j, and for FMO there are seven sites, thus N = 7. The Liouvillian L describes the Hamiltonian evolution of the FMO complex. The label n is a set of non-negative integers uniquely specifying each equation n = <n1, n2, n3, …, nN> = <<n10, n11, . n1K>, . <nN0, nN1, . nNK>>. The density matrix labelled by n = 0 = <<0, 0, 0.…>> refers to the system density matrix, and all others are non-physical density matrices, termed 𠇊uxiliary density matrices”. The density matrices in the equation labelled by indicate that that density matrix is the one defined by increasing or decreasing the integer in the label n, at the position defined by j and m, by 1.
The hierarchy equations must be truncated, which is typically done by truncating the largest total number of terms in a label . This value is termed the tier of the hierarchy. The choice of Nc should be determined by checking the convergence of the system dynamics. Here we also use the “Ishizaki-Tanimura boundary condition” 42
Journal of Biological Systems
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Uncovering universal behaviour in biological systems
Deciphering the patterns of nature is something that has occupied curious minds for countless generations, from swarms of bugs to the flight of birds to the movement of your bodies own cells.
Prof Andrea Cavagna of the Institute for Complex Systems leads a research group bringing together the worlds theoretical physics and experimental biology, building mathematical models of natural systems and uncovering the universal laws that underly the organisation structure of life.
This is an automatically generated transcript.
Hi, I’m Will. Welcome to researchpod.
Deciphering the patterns of nature is something that has occupied curious minds for countless generations of better understanding of the mathematical laws. Underlying those patterns is something that has only recently become available and have seen accelerated growth with new tools and technology.
Professor Andrea Cavagna of the Institute for Complex Systems leads a research group bringing together the worlds of theoretical physics and experimental biology, building mathematical models of natural systems, from bugs to birds to your body’s own cells, and uncovering the universal laws that underlie the organisational structures of life.
For my own sake and for everyone listening at home, could we start with hearing about what it is you’re currently working on?
I’m currently research director at the Institute for Complex Systems, which is part of the Italian National Research Council here in Rome.
I’m a physicist. I have a PhD in practical physics and I spent the first 10 years of my career between Rome, Oxford and Manchester and then back to Rome again working on problems in statistical physics and condensed matter.
And could you tell us a little bit more about what that is? Hopefully using some familiar terms from everyday life.
Broadly speaking, statistical physics studies system made of large number of particles, large number of units which interact with each other through some relatively simple physical laws.
They give rise to macroscopic emergent behaviour that cannot simply be guessed by the microscopic details of the particles.
One of these emergent behaviour is collective behaviour, so there are cases in which very many unit interacting with each other give rise to macroscopic patterns on large scales, and this will what we call collective behaviour. And I’ve been interested in collective behaviour basically for my life, even though in the first part of my career I focused on the collective behaviour or physical system.
And this is what convinced Martin studies rather than the collective behaviour of biological system, which is actually what I do right now.
When it comes to bringing together physics and biology like this, or do you say is the interest or the motivation that you have in the topic?
But you know, we turning from theoretical physics which what I was doing before 2 experimental biology was a very great change of course, and was a step that was also somewhat problematic for my career.
On the other hand, there was natural something natural in this change, because my research.
Which had been very much theoretical for many years, and at some point I probably felt that the direction I was going along with perhaps too abstract, kind of excessively detached from anything I could actually see or experience in my life.
But I really like the concept involved in collective behaviour and emergent phenomena in general, so I was probably longing for a change, but I change that did not bring me conceptually too far from this topic.
And then there were flocks so Starling flocks are really. Amazing abundant here in Roma. Extremely imposing during the whole winter months.
And their evolution patterns are truly beautiful.
Me and everybody in Rome is spending the best part of the sunset minutes by looking at them with your neck up to the Sky. So together with some other colleagues I was working with at the time, especially Georgia, Breezy and they’re injured.
We thought that perhaps we could use some of the knowledge or the tools that we had developed for the study of collective behaviour in physical system.
But to study biological assistance, in particular, this wonderful flocks that we had in front of our very eyes everyday at sunset. And this is what we did in 2005. We asked for some grant money to the European Commission to start the project for the experimental and theoretical study of the collective behaviour in Starling flocks.
European Commission graciously granted us the fans, and in fact it has been funding our research on this topic ever since, so the first project gave us the resources to do this, and the project was quite successful. And in the course of the last 15 years, it expanded its boundaries quite a lot. So now we started a collective behaviour not only in Starling flocks, but also in insect swarms, and we are starting new experiments. Also on send colonies and others systems. But all of this is done with an approach which heavily uses the tools and the concept of statistical physics.
So in this sense, after all, I haven’t changed much my my topic of research.
Which leads us neatly to the RG BIO group today.
Yes. So when when we started this study to 15 years ago, What we had in mind was mostly to do models and theories of collective behaviour in by a system.
But we immediately realised two things.
The first thing was kind of disappointing. We realised that biologists, physicists and computer scientists.
They had already done a lot of work on modern collective behaviour, especially bird flocks and mammal heard, so we were definitely not the first one having this idea. For example, Craig Reynolds was a computer scientist doing computer animations or flock as early as 1986.
For 1985 I think, and some of these animation were actually very beautiful and they were even using the in the movie industry so.
Okay, we may have been smart, but certainly we were quite late in joining the field of modelling of computer model of collective behaviour. But the second thing we discovered was more encouraging.
Also, a bit weird because we discovered that there was a great variety of models and theories, but very, very little experimental data.
Especially in three dimension, where flocking actually course and that was you, of course. Today’s significant technical challenges of taking 3 dimensional data of large flocks of birds in the in the in the wild.
So there had been some pioneering studies in I think it’s late is back in the 70s, especially by Frank Heppner.
But they involved very small flocks, up to 810 pigeons. While a statistical physicists who were interested in large groups, there is a venerated article by Phil Anderson, was a Nobel Prize in condensed Matter Physics, which is famously titled more is different.
More is different, meaning that that emergent phenomena really require large number of particles or units or animals to be qualitatively different from their individual counterparts.
So what we decided then was to get out in the field and collected. Swear mental data on large flocks of Starling.
Choose what we did so in this way I turned from theoretical physics to experimental biology, sort of overnight.
And collecting this data on flocks of Starling took us very long time, even though we could use digital photography compared to people in the 70s, we still had some very big challenges ahead of us. The method we use is basically stereoscopy which is more or less what our eyes do to estimate the three dimensional position of objects in front of us. We take synchronised images of our targets.
In this case, the Birds within the flock from different point of view, so from different cameras we have a set of three Cameras and then we use these images to reconstruct the full 3 dimensional position of each bird. There velocities and whatever else we want to measure.
Well, this is easier said than done, and it requires us to develop some novel non trivial computer vision techniques. But it was worth it because.
The experimental data or what really made the difference in the first years of our studies and experiment was really what put us on the map in the field of collective behaviour. However, along the years we also started doing some new theories, building on our experimental day.
And so our theoretical approach expanded, and eventually two years ago I was awarded an advanced grant by the European Research Council.
For a project called RG Bio, in the context of which we apply while we try to apply one of the most fascinating tools of statistical physics, which is called the renormalization group to the study of collective biological systems.
So part physics, biology, part mathematics and part wildlife photography all coming together to make this free normalisation theory like where would this fit in in what other people might experience or understanding of research and academia?
To understand what the Renormalisation Group is , or RG, and so to understand what the RG is and how it can be used to uncover some of the important phenomenon collective.
Here one has first illustrate two crucial aspects to creature concept which are common to statistical thesis also which are Interaction and correlation.
So interaction correlation are really the two pillars of stat fees and condensed matter, and I also think that they are the Two basic pillars of collective behaviour in biological system.
So once we had the date that the experimental data, the first question we asked was how do Starling’s interact with each other within a flock? So clearly they do interact because their motion. This guy is wonderfully coordinated and very far from random. So how do they do that?
So by interaction here, I really mean what kind of rules do they use when keeping each other under control to get around so well together.
To previous models and theories had hypothesise that in fact there were some very simple rules at work. Simple rules that could be enough to grant their collective code initial absurd and the rules are most intuitive. Namely align your direction of motion to death of your neighbours so you don’t crash into the other guys. Keep a distance, but not too much of a distance, otherwise you get too far from the group, so these are very, very simple rules. Alignment and keeping a distance which is not too small and not too large.
So our data showed that these rules were basically sound very sound, but we also discover something quite new, Something that now we call topological interaction as opposed to metric interaction.
The fact is that all kind of interactions in nature and special in physics they decay with the distance particles or planets or whatever that are at a very large distance, interact more weekly, and if they are closer.
So this decay also happened to course in within a flock of Starling’s. The interaction is stronger the closer the neighbours are do each other.
But the crucial point is that while in physics this decay of the interaction depends on the actual physical distance metric distance between the particles, so whether they are one metre apart, 10 metres apart 100 metres apart.
What we discovered in Flock is that it is not like that If you imagine that you are one bird in the flock, you’re not interacting with all neighbours within given fixed distance.
But what you do is that you’re interacting with a fixed number of Neighbours.
And this number is between 7 and 10 on average. So this is something very peculiar which does not happen in physics.
So it’s like birds were measuring the distance between individuals, not in unit of metres, but in unit of other individuals And this is what we call topological distance . And so a topological interaction is based on this kind of distance.
Even though this is certainly not something that you find in a in a physical theory because it requires some neural activity by part of the the animal which is doing that because it’s keeping under control a fixed number of neighbours is something quite natural. When we think about the biological or even the social assistance.
For example, when we are in the subway, when we use the underground, we do not measure the distance we have to travel in unit of metres, but in unit of stop. So we count how many stops there are between this and this other place, not how many kilometres there are or if you consider a network there relevant distances, the number of nodes between two given notes.
So this concept of topological distance is quite natural when you go outside physics, and so it was funny that actually previous even biological models trying to describe this logical system were based.
On a strictly physical concept of interaction which was decaying with physical distance. While what our data showed is that it is not like that.
Sounds less like physics and more social networking.
Yes, exactly this seven neighbours with which they are interacting. I really the one nearest neighbour. The closest to the focal bird, so it’s not that they’re interacting with seven guys that could be wherever they want into the flock. Now they’re interacting with a fixed number of neighbours, which are the closest neighbour the nearest neighbours.
But their actual distance doesn’t matter. So if you have a flock which is expanding, mutual relative distances are increasing.
The strength of interaction remains exactly the same because it doesn’t depend on the actual distance between me and my neighbours because I’m only keeping under control. If it’s a number of guys.
First shell of neighbours around myself irrespective of our distance they are.
So in this sense, this interaction is not metric. Is topological, however, still very much local in space?
I know flocks can be huge formed up 200,000 birds, so it’s not that in a flock of 100,000 Starling, so they’re interacting everybody with everybody else, no, not at all.
Every bird is interacting only with a very relatively small number of neighbours, against something between 7 and 10.
So then our next question was how is this possible to achieve such long range coordination? Such huge and macroscopic coordination through such a short scale local interaction?
So this is where correlation comes into play, which is the 2nd crucial concept in our path.
So while interaction is a direct transfer of information between two or more individuals.
Correlation is an indirect transfer of information.
So two birds may be correlated to each other and therefore their behaviour may be similar to each other even though they have never interacted with each other.
A simply because there is a chain of interaction between those two birds which make them behave similarly even though they have never interacted with each.
And this is something which of course happens all the time. Also in social systems and as well as in physical system. So correlation is due to the indirect transfer of information through this chain of interaction.
A nice example of that is the phone game that children play, so each child whispers a message in the year of the nearby child, and that is a direct interaction. So each child is just interacting with one other child, and then the message travels travels through the whole chain of children. At some point it gets corrupted because there is noise in this channel of communication.
But the distance by which the messages travelled can be larger than the one child interaction range of community of direct communication. So this larger span of the travelling with information is what we call the correlation length. So the extra space extension of the correlation. Correlation is due to interaction is connected to interaction, but is something different.
So you can have very short range interaction, but a bit larger range correlation,
What we did with our data was to actually measure the correlation function in Real Flock of starlings. So we really measure how much they behaviour the direction of motion of 1 bird at some point is influenced by the change in the direction of motion of another birds 20 metres away.
We completed that and what we found was something quite surprising, so we found that this range of the correlation was as large as entire flock.
So this is what we call in physics scale free correlation, because mean that there is no actual correlation length and they only scale of the correlation is the size of the system itself, so it gets as large as it can. It cannot be larger than that because there is nothing larger than the system itself within the flock.
No upper limits or boundaries, just as far as the reach extents.
Exactly, so this is something which can happen also in physical system, but it’s something quite rare.
You have to normally tune many parameters in your system in order to have a correlation length, which is as large as the entire system.
So that were the two great pieces of experimental phenomenology we got from our data, very short range interaction but very long range correlation.
On one hand, these kind of explained how they can get this fantastic coordination of the microscopic scale. Anyway, it’s a it’s a technical definition of what do we mean by saying that the whole is more than the sum of its parts, which is something.
The nice catchphrase that you listen a lot when you study collective behaviour, but it’s never quite clear what it means.
Well, given what we discover, it really means that because the correlation is as large as entire system, so parts of the system which are very far from each other are still correlated to each other. If you now divide the system in smaller sub part, if you really take them apart from each other, there are different because you’re killing that correlation.
So in that respect, is the long range scale for correlation, which makes the system the whole system more than the mere some of its part.
On the other hand, this is cover the short range interaction accompained by long range. Correlation was really what justified us to make the next step, which is what this current project of ours is about. It is to go something towards something more ambitious. Is the scaling hypothesis there re normalisation group and eventually to universal.
And when you say universality is that a universal set of laws are universal modelling of behavior.
By universality, mean that you can have cases in which different microscopic set of rules can end up causing the same macroscopic behaviour of the system.
And this is something very profound that was discovered by the physics of the last century, which is really what we would like to export to biological system. The scenario described before, in which short range interactions give rise to long range correlation they prefer.
At that, we discovered that in flock of stylings was also great paradigm within statistical physics. In the last sent.
It was really the pillar of condensed matter physics.
And some of the great scientist that time were prompted by this scenario to formulate very very bold hypothesis very bowl for those years, which is normally called descaling ipadis. So this is part of this states that whenever the conditioning systems are such that long range correlations emerge from short range interaction then Microscopic details, Teeny tiny details do not matter anymore.
And this was something truly revolutionary at the time, because after all, microscopic details are what actually define an differentiate the system with respect to one another. So how can they not matter anymore when correlations are so long range? And so when the correlation spanned inter sets?
It’s like the system emancipate from its microscopic constituents and its behaviour then only depends on a handful of very general things like the dimensional space, the symmetries of the system, the conservation loads, but not the actual small details defining the dynamics.
The important point is this. Killing hypothesis is not only something very beautiful from a theoretical point of view.
But it’s also incredibly useful and practical from the experimental point of view, because If the microscopic details don’t matter anymore.
Then systems apparently different from each other. They actually end up having the same macroscopic, the same large scale behaviour.
And this is what really happens in physical systems and is what is called universality.
And the RG their realisation group Was the way in which this hypothesis was actually proved was mathematical and physical proved to be correct?
Well, with reference to the telephone game that you mentioned, is there a chance for erosion or decay in the message that the signal going in might become corrupted by the time it reaches the stage of manifesting in behaviour? I’m thinking of what people listening at home might think of as chaos theory that even under ideally reproduced circumstances, there might be some subtle or undetected shift or change in circumstances that leads to a different end, but.
I. wouldn’t say that this is that the chaos paradigm is relevant in in this particular system, actually quite the opposite. I would say that they one of the great features of collective theological system is their great robustness.
So system there is noise. There are allsorts of noise that is noise due to the external environment. There is noise because no biological being can be able to perfectly implement some set of behaviour rules. So there all sort of noise.
However, the overall microscopic behaviour of the system is very stable against this noise, so I would say that probably evolution worked towards making this kind of system very stable against chaos.
So even though there are definitely, there is definitely room for no linearity is and non linear physics in these systems. I would not say that chaos is the right paradigma.
So how do we get from these Sometimes microscopic details out to a universal law?
The central idea of the RG is that, of course, graining integrating out erasing. Anyway, information about a system at the short scales.
And see how this integration modifies the behaviour of the system at the largest case.
It is this operation of changing scales iteratively, going from shorter to increasingly larger landscaped, increasing the largest distances.
That is regulated by the by their normalisation group. So the idea is that at the beginning when you are still at the level of the small microscopic details, really they short scale interaction rules.
Then they re scaling procedure, changes the behaviour of the system and a lot. Once this course training reaches the macroscopic level, the behaviour does not change anymore. So in this sense they system is at what we call a fixed point of the randomization group.
And the crucial thing is that through this process, the physical system you’re studying, which is a sort of basing of attraction To which other system belongs so that season that had very different microscopic details that were starting in this?
Crosses from very different, less a position. The parameter space. All this system in fact flow towards the same basing of attraction, so they float towards the same physical behaviour through their normalisation group.
The fact that many different microscopic system, which were apparently very different from each other.
Flow to the same microscopic physics to the same microscopic behaviour is what we call universality, which was hugely powerful concept. This was a tool.
Thanks to each people understood why in the in the 22nd why system as different as the liquid vapour transition or actually described by exactly the same physical laws that was describing Ferromagnets.
There was no connection whatsoever at the microscopic system between, you know the the liquid vapour transition. So the world of condensed matter and liquid, and so one. And the physics of ferromagnets, but people were obtaining experimentally the same answers over Andover again.
And finally, the RG explained what it was going on because those systems when you start integrating out short scale details By re scaling the system that is watching the system at larger and larger scale.
You end up in the same basing of attraction with the same exact physical behaviour at the largest case. The RG is also not only constable is also a practical tool by which you can compute physical quantities characterising assistant.
And it is clear then why it could be very useful. So in biological system We found this phenomenology of having large scale correlations. Not all in flux.
I don’t see an apparently very different system like swarms, swarms of insects in that case, to you have a short range interaction but long range correlation and other people. Other groups I’ve found a similar behaviour is similar phenomenology. In other system bacterial clusters. And many different systems in biology.
So if one could apply the same tools of universality and their normalisation group in biology, that would be extremely useful, because the diversity of physics is really nothing compared to the incredible diversity of biology, so the role of small details it’s really are.
Really arresting it biology, but if something like the rumour is Action Group.
Can be used also in biology. Then perhaps we can just hand a little bit all these microscopic different details and just classify biological systems into perhaps few universality classes with the same behaviour.
For more about this research in action. I spoke with Dr Giulia Pisegna from the argie bio group about a paper from the physical review letters looking at the renormalization group approach to swarm behaviour in insects and they started off by asking Julia what it was that she got from the work in the research that she does.
Basically, what excites me to do our line of research is that of course. You you start with a problem and the problem here is an evidence that comes from experimental data.
And then you try to make the best, but also the simplest choice from a theoretical point of view to see if the theory can relate to the experimental data.
But anyway, at the beginning is just a tentative, so you have some kind of intuitions, but you don’t know if that intuition will be correct.
So the result that now we explained we are explaining in this paper starting from the fact that a Andrea and also the other seniors tried to apply that specific model to the problem as worms, but they were not really convinced that that model would have worked to really give a value consistent with experimental one.
At the beginning I did some numerical simulations of this model, so some investigation about that model and I found a value that was more or less a surprise for me, but also for all the other people that were participating to the research.
And it was really exciting because that result was confirming that the intuition at the beginning was.
Wrecked and hold so that we were on the right path to really hear. I’ve tried radical explanation of living phenomena that seems very far from Henny mathematical and physical description, so I was feeling a very good sensation to say, okay, I’m doing something correct and something that really can explain something that was so unclear to everyone and so.
Full of mystery, because it’s a phenomenon of life, basically.
One of the most exciting things of this field is that now physicists are trying to transfer their knowledge and methodology to the study of living and biological systems.
On the one hand, we are all used to think about the physics of inanimate matter has, for instance elementary particles or new states of matter.
On the other, it is a bit unusual to think about the physics of insects, worms, or flocks of birds. However, their research of our group.
Revealed that this natural living system, that one can see everyday flying in the Sky or swarming in the parks, and that seems also very far from any kind of physical and mathematical description.
In fact can be studied using the same mathematical and physical tools of classical statistical physics.
An what like of this kind of research is the general approach of the new and modern biophysics, which is trying to say that as there exists, come on mathematical laws to describe phenomena, has the motion of planets or electromagnetic waves. There could exist also an underlining mathematical structure.
Yeah, in living natural phenomena that has yet to be discovered Insect swarms are one of the biological system on which we focus our research.
Our group had been able to collect many experimental data in the past years for these systems that is warm in their natural environment of the packs of room.
By using the same technology to record the trajectory’s of each individual in the group already applied to flexibles.
What we discovered, starting from this data is that the motion of the system is not purely random or DIS organised. Indeed, insect swarms show the same fundamental ingredients of collective behaviour that we found also in flux, namely short range interaction of alignment of their direction of motion. And also are strong long range correlation that spans all the linear size of these groups.
This basically means that even for neutral swarms, the behaviour of an element of the group can be influenced by the behaviour of another element that can be hold so very far from it.
And with this system is quite different from the one a flexible.
First, because we are Speaking of different classes of animals, but also because their phenomenology is quite different.
In the sense that swarms don’t show a global coordinated motion as birds do.
But a weather disorder one around a specific point of this space.
Anyway, despite this difference, the two systems seem to display the same characteristics that define collective behaviour.
And we think that this fact could really suggest the existence of a possible common theory able to describe both of them.
So how are recent research started from the experimental evidence?
That natural swarms follow our fundamental law of classical physics systems that we call the dynamic scaling. So this property says that if we look at devolution in time of are strongly correlated system.
Microscopic details of the system itself don’t count allot. Also, in determining the dynamical macroscopic behaviour of this system.
The fact that these systems display collective behaviour, it means in a sense that we can forget about the tiny details of these animals and of the particles that compose this system. For instance, if we are looking at images or.
Or if you are looking at birds, we can neglect the fact that they are particular shape or that they fly in a particular way.
We can neglect hold this kind of also let me say biological but also important very important details to simplify as much as we can Howard description and so try to use the same model for systems that are apparently very far from each other. So we are just interested in how these elements move in this space, but not how they biologically.
Do this so how they fly or how they see the space around and so on.
What happens is that the huge extent of correlation that one can measure in space with the correlation length.
Reflection also in a logical relation in time, described by a quantity that we call the characteristic time scale.
The strong correlated physical systems are characterised by the fact that these two quantities, so the correlation length and their characteristic time scale and linked by a power law and bionic spoon, and that describes this relation.
As a result, it means that basically you need only the information about the value of this exponent to fully characterise the large scale dynamics of your system.
And one can prove that different physical systems with different microscopic details if they satisfy this expertise.
They share the same value of the exponent and in this case, we say that they belong to the same universality class so surprisingly. They date about nature as worms showed that this system really verifies this property, but with a non trivial value of the Exponent.
That we think could really identify knew universality class for this kind of strong correlated biological system.
And in our recent paper, we therefore propose a study of a theory in order to reproduce the value of the exponent and the dynamical properties of the natural system of worms.
So we studied the model that had been previously introduced for flocks of birds, because these two systems, again apparently very far from each other, seem to share another important feature that is the mechanism of information propagation in the group.
That we are able to describe without specific set of dynamical laws. Properly defining a model.
And to understand if this was really useful for her biological system, we performed an analytic calculation on this model using the realisation with technique.
In order to relate the value of the exponent of this theory to the natural one.
An we obtain a very compelling and promising result that anyway needs lots of additional work to be a perfected but heavy hand of our analysis. We found a valuable exponent, almost consistent with the experiments.
Which confirms that our intuitions about the dynamical loss describing the system were correct. And also this calculation made has also to understand that this system of the size of a Metro system displaying collective behaviour is not just an arbitrary feature of this system.
Animal groups have to be large to have collected properties, but small enough to ensure the most efficient information proposition and to show the right physical quantities as the value of the exponent.
We also think that with this work we can say that there is no Magician Group is a theory that can be expanded and successfully use also for Heather application to biology.
The fact that we use the same model and mathematical equations for flocks of birds and images worms. And they also seem to work because they are really fitting experimental data.
Could be very interesting from the point of view to find a common explanation and description to the great variety of logical system. And so we really like this and we will like to go on along this line of research. In this concept lies the concept of universality. Also all the models that we use.
Must have an inspiration in the condensed matter physics in which one represents the items, for instance, of a particular element of matter, has just heralds or just spin that can align to other spins over other hatem’s for interaction. So basically we are trying to translate this simplification of theory. Also to the biological systems.
That in each individual you can recognise level of complexity because of course they are living systems, but if you look at the system far enough in a sense you can again described this. Particles has heroes that move in space and so trying to use the same models that you can use for condensed matter physics.
And back now with Professor Cavagna I can already see that there might be some risk of this research being either misinterpreted or misrepresented as having a formula for feet up crystal ball, or prediction through renormalization group research that will have an answer for everything, and I can imagine this is something that you might have concerns about it.
Yes, of course, because on one hand there is the hope of using and applying exporting these fantastic tools. Statistical physics to biology, on the other hand, you have to be very careful.
So one hand you want to be able to forget about some of the details that make biology so **** ** the other hand, you have to remember that you must remember that.
A certain extent, biology is all about the details, so yes, it is not a miracle thing, so we need to be careful.
For example, we made experiments about Starling’s and we got some results.
Now Starling’s are just one species and Moreover. We did experiment just in one location here in Rome. So imagine that we now go for to another species of birds doing flocks like downlinks for example, there are beautiful flocks and balance.
Do we expect the collective behaviour of this system to be totally different?
Well, first of all. One should do the experiment and we don’t have the data on many different species of birds. We only have Starling’s, but.
My expectation, as a physicist would be, well, probably the collective behaviour that really the large scale behaviour is not that different. Of course, this is by now, simply hope both from despair, mental from the theoretical point of view.
But that in a way, is the very reason why I do this kind of studies. If I told that whatever I discover at the collective level in a certain system, a changes dramatically in a system be just because I’m no longer studying the stylings in Rome, but studying the starling’s in Milan.
Well, first of all, that is a motivation with me down a little bit . But also because when I do the experiment an I study the theories tying together this experiment, I really see that the microscopic details lose some of their relevance when I integrate up to larger scale.
I’m not very optimistic to be able to classify biological system in so few classes as we do for physical systems. So certainly the great complexity and diversity of life Would reflect on stronger limitations of the applicability of the real meditation group in biology, okay?
And Moreover, one has never just trust that theoretical mechanisms that we develop, or one should always cheque this. So we have anyway to perform more experiment to compare our.
Theoretical prediction with the with the heart fruit.
And apart from the diversity in the huge complexity of life, there is another issue, which is that the physics of the randomization group was developed mainly in what we call equilibrium systems in physics, while biology is the quintessential out of equilibrium science. So there is a constant injection and dissipation of energy. So biological system are all.
All while the out of equilibrium.
So even though there had been some step forward in the physics of out of equilibrium systems, we can safely say that is much less under control then the physics of equilibrium systems.
So in this respect, I believe that not only we have to push forward the boundaries of the biology, but of physics set.
So all these challenges from the biology of collective system is actually stimulating a physicist to broaden the scope and capabilities of theoretical physics.
We are not simply applying the good old methods of statistical physics to biology, not at all, because they are not sophisticated enough. What we’re doing is that we are trying to apply the same concept.
But for doing that we have to expand the tools that we’ve been using up to now in in physics.
Now, to just briefly come back to something that you mentioned about Swarm behaviours earlier, I’ve just remembered seeing videos of Micro Copter display in place of fireworks oversold using little drone copters to fly around in shine messages out into the Sky. I was wondering if you had any thoughts on the application of renormalization group findings about swarm behaviour in engineering or an industrial ways like this or possibly coming at it from a different angle to take the population behaviour findings and coming up with the kind of social media software way of understanding or maybe even influencing behaviour.
I truly believe that there should be room. There should be funds and there should be encouragement for science, which is not driven by applications but just…. Driven by curiosity.
And I think that this is actually good also for the technological advancement of humanity, because I think that most, if not of the machinery, is that we are using today for doing this interview. A computer microphone lies every.
Thing was originally developed by building on ideas of people who were totally completely uninterested in the applications of their ideas.
All of these, I mean you. You simply can think about the determination principle by then Eisenberger that was something incredibly abstract and theoretical, which ended up funding the entire quantum mechanics and all day digital devices that were using today.
So yes, I can answer about that is not something. However, that motivates might research, however this said.
There is in fact a huge technological interest for the for the research on collective behaviour in biology and the interest is quite obvious. Is that of distributed behaviour so.
Yes, there are artificial networks of everything of drones of devices of all swords, and they will increasingly more in the future.
The problem is how to control these swarms. Yes, you can call them artificial Swans now. The classic way of controlling that is a kind of top down control in which you have some central control that’s control all the peripheral arms of the system.
This kind of control is kind of easy to implement, but has a problem to be quite fragile because if you go and you damage in some way the central control you have a problem with the whole system.
So This is why people are increasingly turning to nature to get inspiration, because in collective biological system typically you have a distributed control, so sort of bottom up control.
In the system we study in Starling, flocks swarms and so on. You do not have any head of the system any.
Leader, you may have leaders in other kind of behaviour, like when birds are migrating from one location to another, but in the incredible display done by Starling’s in the evening during winter with absolutely no central leader coordinating them. So that is a sort of distributed.
Control, which is clearly much more robust because no matter how many units get damaged, you still get very, very stronger coordination of the SIS.
Now the possible application of these are many from the nice and desirable to the less desirable of military nature.
Oh yes, there is a great technological interested in that I’m not sure how much the RG the realisation group will contribute to that technological advancement. My concern is only to try and find the most general lowest possible to quantitatively describe collected biological systems.
Complexity and emergent properties
Many of the most-critical aspects of how a cell works result from the collective behaviour of many molecular parts, all acting together. Those collective properties—often called “ emergent properties”—are critical attributes of biological systems, as understanding the individual parts alone is insufficient to understand or predict system behaviour. Thus, emergent properties necessarily come from the interactions of the parts of the larger system. As an example, a memory that is stored in the human brain is an emergent property because it cannot be understood as a property of a single neuron or even many neurons considered one at a time. Rather, it is a collective property of a large number of neurons acting together.
One of the most-important aspects of the individual molecular parts and the complex things they constitute is the information that the parts contain and transmit. In biology information in molecular structures—the chemical properties of molecules that enable them to recognize and bind to one another—is central to the function of all processes. Such information provides a framework for understanding biological systems, the significance of which was captured insightfully by American theoretical physical chemist Linus Pauling and French biologist Emil Zuckerkandl, who stated in a joint paper, “Life is a relationship among molecules and not a property of any one molecule.” In other words, life is defined in terms of interactions, relationships, and collective properties of many molecular systems and their parts.
The central argument concerning information in biology can be seen by considering the heredity of information, or the passing on of information from one generation to the next. For a given species, the information in its genome must persist through reproduction in order to guarantee the species’ survival. DNA is passed on faithfully, enabling a species’ genetic information to endure and, over time, to be acted on by evolutionary forces. The information that exists in living things today has accumulated and has been shaped over the course of more than 3.4 billion years. As a result, focusing on the molecular information in biological systems provides a useful vantage point for understanding how living systems work.
That the emergent properties derived from the collective function of many parts are the key properties of biological systems has been known since at least the first half of the 20th century. They have been considered extensively in cell biology, physiology, developmental biology, and ecology. In ecology, for example, debate regarding the importance of complexity in ecological systems and the relationship between complexity and ecological stability began in the 1950s. Since then, scientists have realized that complexity is a general property of biology, and technologies and methods to understand parts and their interactive behaviours at the molecular level have been developed. Quantitative change in biology, based on biological data and experimental methods, has precipitated profound qualitative change in how biological systems are viewed, analyzed, and understood. The repercussions of that change have been immense, resulting in shifts in how research is carried out and in how biology is understood.
A comparison with systems engineering can provide useful insight into the nature of systems biology. When engineers design systems, they explore known components that can be put together in such a way as to create a system that behaves in a prescribed fashion, according to the design specifications. When biologists look at a system, on the other hand, their initial tasks are to identify the components and to understand the properties of individual components. They then attempt to identify how interactions between the components ultimately create the system’s observable biological behaviours. The process is more closely aligned with the notion of “systems reverse engineering” than it is with systems design engineering.
The Human Genome Project contributed broadly to that revolution in biology in at least three different ways: (1) by acquiring the genetics “parts list” of all genes in the human genome (2) by catalyzing the development of high-throughput technology platforms for generating large data sets for DNA, RNA, and proteins and (3) by inspiring and contributing to the development of the computational and mathematical tools needed for analyzing and understanding large data sets. The project, it could be argued, was the final catalyst that brought about the shift to the systems point of view in biology.
Systems Biology as Defined by NIH
It used to be as simple as “the knee bone connected to the thigh bone.” Now scientists use systems biology approaches to understand the big picture of how all the pieces interact in an organism. The above illustration depicts an interactome, the whole set of molecular interactions in cells. The interactome is considered an essential systems biology resource.
Ask five different astrophysicists to define a black hole, the saying goes, and you’ll get five different answers. But ask five biomedical researchers to define systems biology, and you’ll get 10 different answers . . . or maybe more.
Systems biology is an approach in biomedical research to understanding the larger picture—be it at the level of the organism, tissue, or cell—by putting its pieces together. It’s in stark contrast to decades of reductionist biology, which involves taking the pieces apart.
Yet with its complicated flow charts that can (in the words of T.S. Eliot) “follow like a tedious argument of insidious intent,” systems biology has scared away more than a few researchers. Still others fail to fully appreciate its usefulness because it lacks a concise, unified definition.
“There [are] an endless number of definitions,” said Ron Germain, chief of NIAID’s new Laboratory of Systems Biology, NIH’s first organized foray into systems biology, which has been nearly a decade in the making. “It’s even worse than the elephant,” that infamous elephant that stymies the attempts of blind men to describe it because each feels just one part.
“Some people think of it as bioinformatics, taking an enormous amount of information and processing it,” Germain said. “The other school of thought thinks of it as computational biology, computing on how the systems work. You need both of these parts.”
The new NIAID lab reflects an intellectual journey that Germain and some of his NIH colleagues embarked upon as the Human Genome Project was nearing completion.
If the NIAID Systems Biology Laboratory were a symphony orchestra, lab chief Ron Germain would be its conductor-musician. As conductor, Germain provides the necessary structure for tempo and harmony. As musician, he provides the immunology base, essentially the study of macrophages. He has recruited "orchestra" members for the lab who have the skills to work on their own but are also able to work together in the name of systems biology.
At that time, circa 2001, biology was rich in genomic data proteomics had come of age and immunologists had identified many cellular and even molecular components of the immune system. Yet predicting immune system behavior remained as elusive as ever.
Whether a systems biology lab could tease out answers was far from clear. But despite the risk, NIAID Director Anthony Fauci and Scientific Director Kathy Zoon committed a steady stream of resources. Together with Germain, they hoped for, and threw their energy into, a new approach to understanding the immune system that would better embrace experimental and computational techniques to explore connections in all their intricate glory.
The new lab, formed in early 2011 from the Program in Systems Immunology and Infectious Disease Modeling (PSIIM), comprises Martin Meier-Schellersheim, head of the Computational Biology Unit Iain Fraser, head of the Signaling Systems Unit Aleksandra Nita-Lazar, head of the Cellular Networks Proteomics Unit John Tsang, head of the Systems Genomics and Bioinformatics Unit and Germain, chief of NIAID’s Lymphocyte Biology Section, providing the immunology base to this operation.
Independently, the unit heads interact with labs at NIH and beyond to establish and incorporate systems biology methods. In true team spirit, they work together to attack the most basic elements of immunology such as a response to an infection or vaccination.
Ironically, to best understand this new lab, we should take a reductionist approach to defining its parts. The system, it seems, is more than the sum of its parts.
Start with Computational Modeling
Sophisticated computational models and simulations represent integral parts of systems biology. In immunology, they are needed to understand the complex biochemical networks that regulate the interactions among the immune system’s cells and between these cells and infectious organisms.
Enter Martin Meier-Schellersheim, a physicist by training. He was the first to join NIAID’s venture in 2001, even before the launch of PSIIM. He has been most successful in empowering non–computational biologists to do computational biology. Indeed, he has helped foster the very team concept that underlies the new lab his software brings advanced computational capacity to a broad range of biologists.
This willing involvement of biologists is paramount because models need solid experimental data as input and as a reference to ensure reality checks. Otherwise the biological models are likely to be oversimplified either for lack of data or because their development suffers from poor communication between experimentalists and theorists.
Meier-Schellersheim’s primary software tool, called Simmune, facilitates the construction and simulation of realistic multiscale biological processes. He is also involved in the ongoing development of a systems biology markup language, SBML3, that can encode advanced models of cellular signaling pathways.
Add Some Cell Biology
Iain Fraser, a biochemist and molecular biologist interested in the mechanisms of cell signaling, arrived at NIH in 2008. As the lead high-throughput member of the lab, he has several powerful tools on hand to generate key data sets. These data sets ultimately feed into Meier-Schellersheim’s software to produce quantitative models.
Fraser’s tools include in-house genome-wide RNAi screens to characterize signaling network relationships in hematopoietic cells. Such screens are beginning to identify key components in innate immune pathogen-sensing networks. He interacts closely with the NIH-wide RNAi screening group at the NIH Chemical Genomics Center and also with the RNAi Global consortium.
Fraser said immune-system signaling networks can be unraveled by using proper systematic approaches to interpret complex data sets. He offers the example of Toll-like receptors (TLRs), which trigger an intricate cellular response that activates multiple intracellular signaling pathways.
Excessive activation can lead to chronic inflammatory disorders insufficient activation can render the host susceptible to infection. Unbiased screening approaches can help identify the components that allow the immune system to maintain a homeostatic balance in the face of microbial challenges.
One of Fraser’s early successes, using a systems biology approach, was demonstrating how a single protein kinase can mediate the anti-inflammatory effects of cyclic adenosine monophosphate in its crosstalk with TLR4.
Fraser sums up his time at NIH as establishing “the screening infrastructure for dissecting the response of the macrophage to a broad range of pathogenic stimuli.”
The “orchestra” members for NIAID’s new Systems Biology Laboratory include (clockwise from top left) Martin Meier-Schellersheim, head of the Computational Biology Unit Iain Fraser, head of the Signaling Systems Unit Aleksandra Nita-Lazar, head of the Cellular Networks Proteomics Unit and John Tsang, head of the Systems Genomics and Bioinformatics Unit.
One Generous Serving of Proteomics
Aleksandra Nita-Lazar is developing new methods to obtain quantitative data that improve our understanding of cell biology and also funnel key information into model building. Her domain is the system-wide analysis of the proteome, which has fallen behind DNA analysis partly for want of the necessary tools.
The difference stems from the accommodating nature of DNA. DNA is easily recognized, replicable, and relatively stable, whereas the folded structure of proteins can’t be amplified. Yet protein studies are essential in developing useful models for many reasons, Nita-Lazar said. Such studies can reveal the molecular constituents of a cell provide information about the biochemical state of the proteins and determine catalytic rates and the association and disassociation rates for molecular pairs.
Nita-Lazar uses mass spectrometry to investigate protein phosphorylation, the process of binding with a phosphate group, one of the most common modes of protein-function regulation. She can use the same protocols that Fraser helped develop, and the same cell types, to determine which proteins are phosphorylated in response to a particular stimulus, when they are phosphorylated, and how those data fit into what is known about the transcriptional response.
Nita-Lazar’s group, with Fraser’s group, has been harvesting from these screens the key components required for the signal to flow through a pathway and also for the induction of the inflammatory cytokine messenger RNAs that arise. “This kind of approach used to be dismissed as a fishing expedition,” said Nita-Lazar. The goal is not to catch that one big tuna, however nice that would be, but rather to see the whole school of fish, the entire ecosystem.
Mix Well with Genomics
The enormous amount of data being collected requires processing and analysis—computational tools plus genetics and genomics “to build things from the top down,” Germain said.
Enter John Tsang, the most recent member of Germain’s lab and the element that transformed the PSIIM into a full-fledged systems biology lab.
On the genomics side, Tsang collects and analyzes data on gene expression, miRNAs, epigenetic modifications, and commensal microbes, and he conducts experiments to connect signaling to gene expression. On the bioinformatics side, he develops and applies statistical tools for large and diverse data sets, such as data from microarrays and high-throughput screenings, with an eye toward network models that involve genes, proteins, miRNAs, and epigenetic states.
Tsang also heads bioinformatics at the trans-NIH Center for Human Immunology (CHI), using similar integrative genomics approaches to study the human immune system, such as immune reactions to the flu vaccine in patients.
A core theme for building network models is capitalizing on systematic perturbations and -omics technologies to measure genome-wide responses. From the TLR stimulations that Fraser studies to vaccinations and natural genetic variations in humans, “all are valuable perturbations to help us figure out the wiring and function of the underlying system,” said Tsang.
Oh, Right, Immunology Too
“So, what am I doing in all of this aside from raising money and pontificating?” Germain joked.
If the NIAID Systems Biology Laboratory could be considered a symphony orchestra, Germain would be its conductor-musician. As conductor, he provides the necessary structure for tempo and harmony. As the musician, he provides the immunology base, essentially the study of macrophages.
Germain has seen systems biology labs in which collaborations are more opportunistic than routine, the shortsighted result of building a building, adding smart people, and hoping it all works out. His strategy instead has been to recruit individuals with the necessary skill sets to work on their own but also to work together in the name of systems biology.
“We all have slightly different interests, but there is enough overlap between those interests for us to develop those core projects and for us to be invested in them,” said Fraser.
Don W. Fawcett, Emma Shelton
Macrophages and lymphocytes, the two types of immune cells pictured above, interact with their surroundings in complicated ways. NIH researchers are using systems biology approaches to understand the totality of such interactions.
All Together Now
To understand the response to infection or vaccination at an integrated level, the lab is studying the intersection of innate and adaptive receptor-dependent pathways and their control of gene networks. The researchers have bottom-up projects to understand and model the signaling within specific cell types at a fine-grained level.
And they have a top-down approach that uses inferences from perturbation analyses to probe the large-scale structure of the interactions not only at the cellular level, but also at the tissue and even the organism level.
To accomplish this grand goal, Germain said, the lab works in digestible chunks, focusing on pathogen sensing in key innate cells, such as macrophages, and the intersection of signaling by antigen receptors, cytokines, and TLRs in determining whether B cells become memory cells or long-lived plasma cells.
This process is critical for vaccine development. At the top-down level, the lab uses host genetics and microbiota variation to explore how the immune system’s set point is determined for responses to infections and vaccines.
This early into the chase, the lab has not yet published results on these pursuits, although a paper is pending on the lab’s work with CHI and the flu.
Towards a Trans-NIH Approach
Germain hopes the Laboratory of Systems Biology will serve “as an intellectual resource for people who are thinking in the systems mode and have their hands on these technologies [and want] to see how they could be applied to their work.”
He named the lab the Laboratory of Systems Biology with no mention of immunology or host-pathogen interaction to designate its raison d’être. Inspired by NIAID’s efforts, NCI and NHLBI are actively recruiting researchers to establish systems biology programs. NHLBI has just named Keji Zhao, senior investigator, as director of its new Systems Biology Center.
Meanwhile, the trans-NIH effort for a Center for Systems Biology is not dead. A search for a very senior systems biologist to develop and lead the center came up dry, and now the budgetary stresses have put the search on hold. But most NIH researchers understand that purely reductionist approaches to biology are no longer enough to solve complex biological problems and that integrated approaches are needed. David Levens (NCI), Dan Camerini (NIDDK), and Alan Michelson (NHLBI), along with Germain, continue to lead efforts for this trans-NIH initiative.
NIAID’s Laboratory of Systems Biology is “a smaller model of what the larger enterprise could be,” Germain said. The new lab “is very good for the NIH. We are getting applicants from top universities who want to come to the lab as fellows.”
And the NIH intramural research program is well suited for systems biology, with a long-term perspective and a retrospective review process that doesn’t require grant writing.
Germain helped change the NIH tenure process, too, to be sure that team science, and not necessarily a steady stream of published papers, is recognized and rewarded.
“Nothing happens if you don’t put work into it,” he added.
Ron Germain does have his own definition of systems biology that he’s sticking to: a scientific approach that combines the principles of engineering, mathematics, physics, and computer science with extensive experimental data to develop a quantitative as well as a deep conceptual understanding of biological phenomena, permitting prediction and accurate simulation of complex (emergent) biological behaviors.
A biological system to measure time - Biology
The Systems Biology Institute (SBI) is a non-profit private research institution established in 2000 with the aim of promoting systems biology research and its application to medicine and global sustainability. SBI focuses on rapid translation of basic research to practical outcomes for both business and clinical applications.
Systems biology is an academic discipline that aims at system-level understanding of biological systems. Desire to understand living organisms as systems is not new. It can be traced back over a half century when Norbert Weiner pursued Cybernetics and Walter Canon proposed a concept of homeostasis. However, most discussions at that time have been phenomenological as molecular biology was only about to emerge. With breathtaking progress of molecular biology, computer science, control theory, precision manufacturing and measurement technologies, it is now feasible to challenge in-depth understanding of living organisms at system-level while grounding firmly to the molecular basis.
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