How the zebrafish got its stripes
Zoom into the zebrafish's alternating pattern and the stripes of colour превращаются individual pigment cells like a pointillist painting.
Animal patterns are a source of endless fascination, and now researchers at the University Bath have worked out how zebrafish develop their stripes.
Animal patterns - the stripes, spots and rosettes seen in the wild - are a source of endless fascination, and now researchers at the University Bath have developed a robust mathematical model to explain how one important species, the zebrafish, develops its stripes.
In the animal kingdom, the arrangement of skin pigment cells starts during the embryonic stage of development, making pattern formation an area of keen interest not only for a непрофессиональной аудитории but also for scientists - in particular, developmental biologists and mathematicians.
Zebrafish are бесценны for studying human disease. These скромные, незаметные freshwater мелкие рыбёшки may seem to have little in common with mammals but in fact they show many genetic similarities to our species and boast a similar list of physical characteristics (including most major organs).
Zebrafish also provide fundamental insights into the complex, and often невиданных, удивительных processes that лежат в основе biology. Studying their striking appearance may, in time, be relevant to medicine, since pattern formation is an important general feature of organ development. therefore, a better understanding of pigment pattern formation might give us insights into diseases caused by нарушением to cell arrangements within organs.
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Zoom into the zebrafish's alternating pattern and the stripes of colour превращаются individual pigment cells like a pointillist painting.
Animal patterns are a source of endless fascination, and now researchers at the University Bath have worked out how zebrafish develop their stripes.
Animal patterns - the stripes, spots and rosettes seen in the wild - are a source of endless fascination, and now researchers at the University Bath have developed a robust mathematical model to explain how one important species, the zebrafish, develops its stripes.
In the animal kingdom, the arrangement of skin pigment cells starts during the embryonic stage of development, making pattern formation an area of keen interest not only for a непрофессиональной аудитории but also for scientists - in particular, developmental biologists and mathematicians.
Zebrafish are бесценны for studying human disease. These скромные, незаметные freshwater мелкие рыбёшки may seem to have little in common with mammals but in fact they show many genetic similarities to our species and boast a similar list of physical characteristics (including most major organs).
Zebrafish also provide fundamental insights into the complex, and often невиданных, удивительных processes that лежат в основе biology. Studying their striking appearance may, in time, be relevant to medicine, since pattern formation is an important general feature of organ development. therefore, a better understanding of pigment pattern formation might give us insights into diseases caused by нарушением to cell arrangements within organs.
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113.To resolve into - превращать (во что-то)
114.Lay audience - не специалисты, непрофессиональная аудитория
115.Invaluable - бесценный, неоценимый
116.Humble - скромный, простой, незаметный
117.Minnow - мелкая рыбёшка
118.Wondrous - невиданный, удивительный
119.To underpin - поддерживать, лежать в основе
120.Disruption - нарушение, подрыв, срыв (чего-то)
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113.To resolve into - превращать (во что-то)
114.Lay audience - не специалисты, непрофессиональная аудитория
115.Invaluable - бесценный, неоценимый
116.Humble - скромный, простой, незаметный
117.Minnow - мелкая рыбёшка
118.Wondrous - невиданный, удивительный
119.To underpin - поддерживать, лежать в основе
120.Disruption - нарушение, подрыв, срыв (чего-то)
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The new mathematical model разработанная in Bath прокладывает the way for further explorations into pigment patterning systems, and their similarity across different species. Pigmentation in zebrafish is an example of an emergent phenomenon - one in which individuals (cells in this case), all acting according to their own local rules, can self-organise to form an ordered pattern в масштабе much larger than one might expect.
Other examples of emergent phenomena in biology include the собирание в стаи of скворцов and the synchronised swimming seen in стаях рыб.
Dr Kit Yates, the mathematician from Bath who led the study, said: "It's fascinating to think that these different pigment cells, all acting without coordinated centralised control, can reliably produce the striped patterns we see in zebrafish. Our modelling highlights the local rules that these cells use to interact with each other in order to generate these patterns robustly."
"Why is it important for us to find a correct mathematical model to explain the stripes on zebrafish?" asks Professor Robert Kelsh, co-author of the study.
"Partly, because pigment patterns are interesting and beautiful in their own right. But also because these stripes are an example of a key developmental process. If we can understand what's going on in the pattern development of a fish эмбриона, we may be able to gain deeper insight into the complex choreography of cells within embryos more generally."
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Other examples of emergent phenomena in biology include the собирание в стаи of скворцов and the synchronised swimming seen in стаях рыб.
Dr Kit Yates, the mathematician from Bath who led the study, said: "It's fascinating to think that these different pigment cells, all acting without coordinated centralised control, can reliably produce the striped patterns we see in zebrafish. Our modelling highlights the local rules that these cells use to interact with each other in order to generate these patterns robustly."
"Why is it important for us to find a correct mathematical model to explain the stripes on zebrafish?" asks Professor Robert Kelsh, co-author of the study.
"Partly, because pigment patterns are interesting and beautiful in their own right. But also because these stripes are an example of a key developmental process. If we can understand what's going on in the pattern development of a fish эмбриона, we may be able to gain deeper insight into the complex choreography of cells within embryos more generally."
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121.To devise - разрабатывать
122.To pave - мостить, прокладывать путь
123.At a scale - в масштабе
124.Flock - стая (птиц)
125.Starling - скворец
126.School of fish - стая (рыб)
127.Embryo - [ˈembrɪəʊ] - эмбрион, зародыш
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121.To devise - разрабатывать
122.To pave - мостить, прокладывать путь
123.At a scale - в масштабе
124.Flock - стая (птиц)
125.Starling - скворец
126.School of fish - стая (рыб)
127.Embryo - [ˈembrɪəʊ] - эмбрион, зародыш
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📌В статье была использована фраза flocking of starlings.
Её точный перевод - мурмурация скворцов.
Мурмурация - это явление скоординированного полёта огромных стай птиц (чаще всего скворцов), создающих зрелищные сжимающиеся и разжимающиеся облака с чётко очерченными контурами.
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Её точный перевод - мурмурация скворцов.
Мурмурация - это явление скоординированного полёта огромных стай птиц (чаще всего скворцов), создающих зрелищные сжимающиеся и разжимающиеся облака с чётко очерченными контурами.
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The stripes of an adult 'wild type' zebrafish are formed from pigment-containing клетки called chromatophores. There are three different types of chromatophore in the fish, and as the animal develops, these pigment cells shift around on the animal's surface, interacting with one other and self-organising into the полосатый pattern for which the fish are named. Occasionally, mutations appear, changing how the cells interact with each other during pattern development resulting in spotty, leopard-skin or лабиринт-like запутанных markings.
Scientists know a lot about the biological interactions needed for the self-organisation of a zebrafish's pigment cells, but there has been some uncertainty over whether these interactions offer a исчерпывающее explanation for how these patterns form. To test the biological theories, the Bath team developed a mathematical model that объединило, соединило the three cell types and all their known interactions. The model has proven successful, predicting the pattern development of both wild type and mutant fish.
Mathematicians have been trying to explain how zebrafish stripes form for many years, however many previous modelling attempts have been unable to account for the broad range of observed fish mutant patterns. Jennifer Owen, the scientist responsible for building and running the model, said "One of the benefits of our model is that, due to its complexity, it can help to predict the developmental defects of some less understood mutants. For example, our model can help to predict the cell-cell interactions that are defective in mutants such as leopard, which displays spots."
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Scientists know a lot about the biological interactions needed for the self-organisation of a zebrafish's pigment cells, but there has been some uncertainty over whether these interactions offer a исчерпывающее explanation for how these patterns form. To test the biological theories, the Bath team developed a mathematical model that объединило, соединило the three cell types and all their known interactions. The model has proven successful, predicting the pattern development of both wild type and mutant fish.
Mathematicians have been trying to explain how zebrafish stripes form for many years, however many previous modelling attempts have been unable to account for the broad range of observed fish mutant patterns. Jennifer Owen, the scientist responsible for building and running the model, said "One of the benefits of our model is that, due to its complexity, it can help to predict the developmental defects of some less understood mutants. For example, our model can help to predict the cell-cell interactions that are defective in mutants such as leopard, which displays spots."
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128.Cell - клетка
129.Stripy - полосатый
130.Maze - лабиринт
131.Labyrinthine - [læbəˈrɪnθaɪn] - подобный лабиринту, запутанный
132.Comprehensive - исчерпывающий, всеобъемлющий
133.To incorporate - объединять, соединять
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128.Cell - клетка
129.Stripy - полосатый
130.Maze - лабиринт
131.Labyrinthine - [læbəˈrɪnθaɪn] - подобный лабиринту, запутанный
132.Comprehensive - исчерпывающий, всеобъемлющий
133.To incorporate - объединять, соединять
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Mathematical model shows how brain remains stable amid varying disturbances
Whether you are playing Go in a park amid чирикающих birds, a gentle breeze and kids playing catch nearby or you are playing in a den with a ticking clock on a bookcase and a мурлыкающим cat on the sofa, if the game situation is identical and clear, your next move likely would be, too, regardless of those different conditions. You'll still play the same next move despite a wide range of внутренних feelings or even if a few neurons here and there are just being a little неустойчивы.
How does the brain overcome unpredictable and varying disturbances to produce reliable and stable computations? A new study by MIT neuroscientists provides a mathematical model showing how such stability от природы arises from several known biological mechanisms.
More fundamental than the умышленное использование of cognitive control over attention, the model the team developed describes an склонность toward robust stability that is built in to neural circuits by virtue of the connections, or "synapses" that neurons make with each other.
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Whether you are playing Go in a park amid чирикающих birds, a gentle breeze and kids playing catch nearby or you are playing in a den with a ticking clock on a bookcase and a мурлыкающим cat on the sofa, if the game situation is identical and clear, your next move likely would be, too, regardless of those different conditions. You'll still play the same next move despite a wide range of внутренних feelings or even if a few neurons here and there are just being a little неустойчивы.
How does the brain overcome unpredictable and varying disturbances to produce reliable and stable computations? A new study by MIT neuroscientists provides a mathematical model showing how such stability от природы arises from several known biological mechanisms.
More fundamental than the умышленное использование of cognitive control over attention, the model the team developed describes an склонность toward robust stability that is built in to neural circuits by virtue of the connections, or "synapses" that neurons make with each other.
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134.To chirp - [ʧɜːp] - чирикать, щебетать
135.To purr - мурлыкать, урчать
136.Internal - внутренний
137.Erratic - неустойчивый, колеблющийся
138.Inherently - от природы, по своему существу, в своей основе
139.Wilful - умышленный, преднамеренный
140.Exertion - использование, применение
141.Inclination - склонность (к чему-либо)
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134.To chirp - [ʧɜːp] - чирикать, щебетать
135.To purr - мурлыкать, урчать
136.Internal - внутренний
137.Erratic - неустойчивый, колеблющийся
138.Inherently - от природы, по своему существу, в своей основе
139.Wilful - умышленный, преднамеренный
140.Exertion - использование, применение
141.Inclination - склонность (к чему-либо)
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The уравнения they вывели and published in PLOS Computational Biology show that networks of neurons involved in the same computation will repeatedly сходиться toward the same patterns of electrical activity, or "firing rates," even if they are sometimes произвольно perturbed by the natural noisiness of individual neurons or arbitrary sensory stimuli the world can produce.
To find out, Miller's lab, which studies how neural networks represent information, joined forces with BCS colleague and mechanical engineering Professor Jean-Jacques Slotine, who leads the Nonlinear Systems Laboratory at MIT.
Slotine brought the mathematical method of "analysis сжатия," a concept developed in control theory, to the problem along with tools his lab developed to apply the method. Contracting networks exhibit the property of trajectories that start from разрозненных points ultimately converging into one trajectory, like притоки in a watershed.
They do so even when the inputs vary with time. They are robust to noise and disturbance, and they allow for many other contracting networks to be combined together without a loss of overall stability - much like brain typically integrates information from many specialized regions.
"In a system like the brain where you have [hundreds of billions] of connections the questions of what will preserve stability and what kinds of constraints that imposes on the system's architecture become very important," Slotine said.
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To find out, Miller's lab, which studies how neural networks represent information, joined forces with BCS colleague and mechanical engineering Professor Jean-Jacques Slotine, who leads the Nonlinear Systems Laboratory at MIT.
Slotine brought the mathematical method of "analysis сжатия," a concept developed in control theory, to the problem along with tools his lab developed to apply the method. Contracting networks exhibit the property of trajectories that start from разрозненных points ultimately converging into one trajectory, like притоки in a watershed.
They do so even when the inputs vary with time. They are robust to noise and disturbance, and they allow for many other contracting networks to be combined together without a loss of overall stability - much like brain typically integrates information from many specialized regions.
"In a system like the brain where you have [hundreds of billions] of connections the questions of what will preserve stability and what kinds of constraints that imposes on the system's architecture become very important," Slotine said.
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142.Equation - уравнение
143.To derive - выводиить, получать (формулу, знания, новый вид растений и т.д.)
144.To converge - сводить воедино, сходиться в одной точке
145.Arbitrarily - произвольно
146.Contraction - сжатие, сокращение
147.Disparate - разрозненный, отличающийся в корне
148.Tributary - приток (реки)
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142.Equation - уравнение
143.To derive - выводиить, получать (формулу, знания, новый вид растений и т.д.)
144.To converge - сводить воедино, сходиться в одной точке
145.Arbitrarily - произвольно
146.Contraction - сжатие, сокращение
147.Disparate - разрозненный, отличающийся в корне
148.Tributary - приток (реки)
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Math reflects natural mechanisms
Leo Kozachkov, a graduate student in both Miller's and Slotine's labs, led the study by applying contraction analysis to the problem of the stability of вычисления in the brain.
What he found is that the variables and terms in the resulting equations that enforce stability directly mirror properties and processes of synapses: inhibitory схема, цепь connections can get stronger, excitatory circuit connections can get weaker, both kinds of connections are typically tightly balanced relative to each other, and neurons make far fewer connections than they could (each neuron, on average, could make roughly 10 million more connections than it does).
"These are all things that neuroscientists have found, but they haven't linked them to this stability property," Kozachkov said. "In a sense, we're synthesizing some disparate findings in the field to explain this common phenomenon."
The new study, which also involved Miller lab postdoc Mikael Lundqvist, was hardly the first пытавшийся решить (проблему) stability in the brain, but the authors argue it has produced a more advanced model by учитывая the dynamics of synapses and by allowing for wide variations in starting conditions. It also offers mathematical proofs of stability, Kozachkov added.
Though focused on the factors that ensure stability, the authors noted, their model does not go so far as to обречь the brain to inflexibility or determinism. The brain's ability to change - to learn and remember - is just as fundamental to its function as its ability to consistently reason and formulate stable behaviors.
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Leo Kozachkov, a graduate student in both Miller's and Slotine's labs, led the study by applying contraction analysis to the problem of the stability of вычисления in the brain.
What he found is that the variables and terms in the resulting equations that enforce stability directly mirror properties and processes of synapses: inhibitory схема, цепь connections can get stronger, excitatory circuit connections can get weaker, both kinds of connections are typically tightly balanced relative to each other, and neurons make far fewer connections than they could (each neuron, on average, could make roughly 10 million more connections than it does).
"These are all things that neuroscientists have found, but they haven't linked them to this stability property," Kozachkov said. "In a sense, we're synthesizing some disparate findings in the field to explain this common phenomenon."
The new study, which also involved Miller lab postdoc Mikael Lundqvist, was hardly the first пытавшийся решить (проблему) stability in the brain, but the authors argue it has produced a more advanced model by учитывая the dynamics of synapses and by allowing for wide variations in starting conditions. It also offers mathematical proofs of stability, Kozachkov added.
Though focused on the factors that ensure stability, the authors noted, their model does not go so far as to обречь the brain to inflexibility or determinism. The brain's ability to change - to learn and remember - is just as fundamental to its function as its ability to consistently reason and formulate stable behaviors.
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149.Computation - вычисление
150.Circuit - [ˈsɜːkɪt] - схема, цепь, система
151.To grapple with - пытаться разрешить или преодолеть (задачу, проблему, вопрос и т.д.)
152.To account for - учитывать
153.To doom - обрекать, предназначать
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149.Computation - вычисление
150.Circuit - [ˈsɜːkɪt] - схема, цепь, система
151.To grapple with - пытаться разрешить или преодолеть (задачу, проблему, вопрос и т.д.)
152.To account for - учитывать
153.To doom - обрекать, предназначать
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"We're not asking how the brain changes," Miller said. "We're asking how the brain keeps from changing too much."
Still, the team plans to keep iterating on the model, for instance by включая, охватывая a richer accounting for how neurons produce individual всплески of electrical activity, not just rates of that activity.
They are also working to compare the model's predictions with data from experiments in which animals repeatedly performed tasks in which they needed to perform the same neural computations, despite experiencing неизбежный internal neural noise and at least small sensory input differences.
Finally, the team is considering how the models may inform understanding of different disease states of the brain. Отклонения (от нормы) in the delicate balance of excitatory and inhibitory neural activity in the brain is considered crucial in epilepsy, Kozachkov notes.
A symptom of Parkinson's disease, as well, влечёт за собой, вызывает a neurally-rooted loss of motor stability. Miller adds that some patients with autism spectrum disorders struggle to stably repeat actions (e.g. brushing teeth) when external conditions vary (e.g. brushing in a different room).
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Still, the team plans to keep iterating on the model, for instance by включая, охватывая a richer accounting for how neurons produce individual всплески of electrical activity, not just rates of that activity.
They are also working to compare the model's predictions with data from experiments in which animals repeatedly performed tasks in which they needed to perform the same neural computations, despite experiencing неизбежный internal neural noise and at least small sensory input differences.
Finally, the team is considering how the models may inform understanding of different disease states of the brain. Отклонения (от нормы) in the delicate balance of excitatory and inhibitory neural activity in the brain is considered crucial in epilepsy, Kozachkov notes.
A symptom of Parkinson's disease, as well, влечёт за собой, вызывает a neurally-rooted loss of motor stability. Miller adds that some patients with autism spectrum disorders struggle to stably repeat actions (e.g. brushing teeth) when external conditions vary (e.g. brushing in a different room).
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154.To encompass - охватывать, включать
155.Spike - всплеск (активности, напряжённости и т.д.)
156.Inevitable - неизбежный
157.Aberration - отклонение (от нормы), уклонение (от правильного пути и т.д.)
158.To entail - влечь за собой, вызывать
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154.To encompass - охватывать, включать
155.Spike - всплеск (активности, напряжённости и т.д.)
156.Inevitable - неизбежный
157.Aberration - отклонение (от нормы), уклонение (от правильного пути и т.д.)
158.To entail - влечь за собой, вызывать
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