Download How do maggots and worms navigate temperature

Philippa Law
University of Kent, Canterbury
[email protected]
13/03/2013
How do maggots and worms navigate temperature
gradients?
Presenter: Aravi Samuel, Department of Physics, Stanford University
Recorded: 02/01/ 2010
http://media.physics.harvard.edu/video/?id=COLLOQ_SAMUEL_020110
Is understanding the worm brain a viable pathway to understanding the human brain? It is well
known that animals’ behaviour is determined by the function and the structure of its neural
networks. The difficulty in understanding animal behaviours is in understanding these hugely
complex neural networks. By understanding the way in which these neural networks govern
behaviours on a simplistic level, we are getting closer to working out complex human behaviour.
By linking patterns in behaviour to patterns in neural activity, we are beginning to understand
how neurons give rise to thought and action in a simple model organism. Not only is this
understanding used for animal behaviours, it is also utilized in creating artificial neural
networks used in computing. These systems mimic the properties of biological neurons for
solving artificial intelligence problems without creating a model of a real system1. Is this
knowledge beneficial to the United Kingdom and therefore a viable research topic to spend the
limited research council funds on?
This particular study carried out in the daVinci group, Harvard University uses nematode C.
elegans organisms. The nematode C. elegans are ideal for the study as they comprise of just 302
neurons, they are optically transparent and
about 1mm long therefore each neuron can be
seen with a light microscope. These animals
also learn temperatures and will travel down
temperature gradients to reach a preferred
temperature, normally the temperature they
are grown at. In a lab it is easy to regulate
temperature so cryogenic behaviour is easy to
control. This is a simple alternative to using
chemicals and other behavioural deterrents.
Furthermore, the simple biological structure of
the animals means it is possible to change
Image 1. Nematode C. elegan image hosted at
http://www.biologie.ens.fr/bcsgnce/
individual genes of the nematode C. elegans.
Samuel begins by explaining the most basic experiment undertaken within the group; looking at
nematode C. elegans grown at 15 degrees celcius Celsius. These are placed onto a petri dish with
temperature ranging from 22-18 degrees celcius from one side to the other. Using video
microscopy to observe the motion, cryogenic behaviours2 (cold loving behaviours) can clearly
1
J. J. Hopfield, Neural networks and physical systems with emergent collective computational abilities. Proc. NatL Acad.
Sci. USA Vol. 79, pp. 2554-2558, April 1982 Biophysics
2 Paul A. Garrity, Miriam B. Goodman,Aravinthan D. Samuel,and Piali Sengupta, Running hot and cold:
behavioralstrategies, neural circuits, and the molecular machinery for thermotaxis in C. elegans and Drosophila. Hosted
at: http://scholar.harvard.edu/files/aravisamuel/files/genes_dev_2010_garrity.pdf 2010
be seen. However, every animal has a different trajectory suggesting the animal’s incapability to
make decisions. The C. elegans motion begins in abrupt forward movement then a sudden
‘reorientation’ event occurs followed by more abrupt movement and so on. The animals do
however bias the random walk towards the cold direction. If it moves toward hot, life gets worst
over time shortening the time between the abrupt reorientation events. If it moves toward cold,
life gets better over time lengthening the time between the abrupt reorientation events. Simply
put, if the C. elegans sense warming they turn more, if they sense cooling they turn less. It must
be noted that these ‘turns’ are in random orientations and not directly toward the colder side.
This rule in biasing the walks is successful in reaching the preferred temperature. Even this
simple algorithm requires computation; the C. elegan needs to know if it is in the right direction.
Having a thermometer in the tail and the head would make sense to us however, the animals
only have one temperature sensor at the tip of its nose called the thermosensory
amphid neuron (AFD) sensor. As the animal moves around the spacial temperature gradient the
nose thermometer is moved around and change in the AFD signal is what the animal uses for
thermotaxis.
Droplet assay is a better method of analysing the motion of the animal. By placing a worm put
inside a droplet it can swim freely but because the droplets are so small it is easy to manipulate
the animal’s motion. In this situation the temperature of the water droplet is oscillated around
23.1 degrees celcius Celsius as a sin wave function. The turning rate is observed as a function of
temperature.
Image 2. Results of turning rate as the temperature is oscillated in a sin wave both plotted against time
As seen in image 2 the turning rate was shifted by 90 degrees celcius to the input temperature
signal showing the motion output is the first derivative of the sensory input dT/dt. It would
have been nice to be able to see the effect over a longer time period. Other arbitrary input
functions such as a saw wave were also used to test the response. These followed the pattern
but the results were unfortunately not shown in the presentation.
The next step is to make connections between the neuron behaviour and overall behaviour to
find how behaviour is encoded. For this, recordings of neural activity are needed, normally you
would use electrodes to measure neural activity but inconveniently the C. elegan literally
explodes using this method! Fortunately, C. elegans are transparent and with the use of
florescent proteins, which have been developed during the last 5-10years, it is possible to
monitor neural activity. Cyan florescent protein (CFP) is linked to Calcium-Modulated protein
(calmodulin), M13 peptide and a yellow florescent protein (YFP) this called chameleon. Calcium
ions are released with increased neural activity and when chameleon reacts with calcium it
deforms and gives of yellow Florence rather than cyan. This measures how many calcium ions
are present thus showing the neural activity.
They then immobilised animals and measured the AFD activity as a function of temperature as
the temperature was oscillated in small steps from approximatly 16 degrees celcius to 19
degrees celcius. It can be seen in image 3 the neuron is ‘woken up’ at 17 degrees celcius, the
temp the animal was grown at.
Image 3. The ratio between Cyan florescent protein (CFP) and yellow florescent protein (YFP) in the
AFD sensor as the temperature is oscillated in small steps from around 16 degrees Celsius to 19
degrees Celsius.
This method gave a high signal to noise ratio of 20% which degrades the reliability of the results.
As a solution to this problem a better chameleon was discovered that gave a much lower signal
to noise ratio, in the order of 100% meaning it was less sensitive to motion artefacts. These
repeated results were much more definitive.
Image 4. The response of the old chameleon YC2.12 and the new chameleon YC3.60 to the neural
activity stimulated by increasing temperature.
Optogenetics, is a neuromodulation technique utilized in behavioral neuroscience that uses a
combination of techniques from optics and genetics to control the activity of individual neurons
in living tissue even within freely-moving animals3. This study uses Optogenetics to genetically
engineer molecules that react to light, opening and closing neuron channels in the C. elegans.
Using green light to hyper polarise and demobilise the animal’s muscle cells, halorhodopsin4, it
was observed that when the light was turned off thus mobilising the animal again the whole
length of the animal began undulating at the same time, it didn’t start moving at the tip and
progressing to the back end. Consequently, illustrating how the whole animal turns on at the
same time therefore the undulation motion must be encoded into the individual cells. This
unexplained behaviour triggered research focusing on shining light onto single neurons like a
spot light. This required a digital mirror device with hundreds of mirrors to target single pixels.
It also needed the development of a feedback loop fast enough to react to the movement of the
animal. It was observed that when you highlight the whole animal it reverses its motion,
Deisseroth, K; G Feng, A K Majewska, G Miesenböck, A Ting, M J Schnitzer. "Next-generation optical technologies for
illuminating genetically targeted brain circuits". J Neuroscience. PMID 10380-6.
4 Atsushi Kuhara, Noriyuki Ohnishi,Tomoyasu Shimowada & Ikue Mori. Neural coding in a single sensory neuron
controlling opposite seeking behaviours in Caenorhabditis elegans, 2011, doi:10.1038/ncomms1352
3
highlight the head only and it also reverses its motion. However, highlighting the back end of the
animal has no effect on the direction of motion. It is inferred that the back neurons are not
connected to the nerve sensors. This was carried out with a 30Hz camera but recent
advancements have meant 200Hz are available for this procedure. This advancement in
technology makes it more likely to be able to locate single neurons in the future.
Samuel’s team has also begun investigating how electric fields affect the C. elegans. Preliminary
results suggest an increase of the electric field experienced by the animal made the animal move
directly in the negative direction whereas, with a decreasing field it moved diagonally. This is a
particularly interesting adaptation for parasitic nematodes who aim to travel towards the brain
or muscle which both give off small electrical signals.
The future for this research is vast. This study is looking at animals with just 302 neurons, it is
predicted that the human body has as many as 1,000 billion neurons5, to put that in perspective
the human body has more neurons than the Milky Way has stars6. Is the real question whether it
is possible to fully understand human neural networks? The next phase has started by working
on the maggot which has a central brain similar to human nervous systems. Maggots act
differently in thermotaxis to C. elegans as they make decisions at the turning points. The
decision making is encoded into small neural circuits. A step in the right direction to
understanding the complex human input to output neural systems. Samuel believes it is
reasonable to expect huge advancements in understanding to occur in the next few decades
with the use and innovation of the methods to manipulate and control neural activity. It is
hoped to fully understand the full process from input to output.
Questions
How much chameleon protein is in a C. elegans cell?
- Its negligible amount therefore it is assumed not to affect the cells behaviour.
What are the memory capabilities of a C.elegan?
-It’s short term memory last about 20seconds. This is inferred from the time delay in abrupt turns.
The C. elegans compare the last 10 seconds with the current 10 seconds. The long term memory of
the C. elegan lasts for about 4 hours, seen when the animals acclimatise to different temperatures if
you keep them at that temperature for 4 hours.
Unfortunately, the other questions were inaudible from the recording.
5 http://www.humanillnesses.com/Behavioral-Health-A-Br/The-Brain-and-Nervous-System.html
6 Nicholos Wethington, How Many Stars are in the Milky Way? 2008, hosted at:
http://www.universetoday.com/22380/how-many-stars-are-in-the-milky-way/#ixzz2NvSAHEjY accessed
18/03/2013
Should neural network research receive funding support from the United Kingdom
research council?
The research presented by the DaVinci group of Harvard University can deduce clear
behavioural output linked to an input variable. The results seem sufficient and reliable enough
to draw conclusions about the ‘algoritms’ the animals use to navigate their environment. It was
comforting to see the error analysis in the AFD activity experiment considering noise. This kind
of consideration builds confidence in the team’s experimental practice. The success experienced
here gives the funding bodies extra secuirity over future investments in the field. As neural
network computation runs over many subject areas it also is relevent to many of the UK
research council’s sub devisions: Medical Research Council; Engineering and Physical Sciences
Research Council; and Biotechnology and Biological Sciences Research Council. In a sense this
could multiply the possible funding available to the subject. Futhermore, this research area has
grown extremely fast suggesting many important discoveries are still to be made. An example
of this is the Artificical Intelligence created by making artificial neural networks. Artificial
intelligence in recent years has taken huge leaps from its introduction. It is now helping you
drive a car7 and has beaten some of the best human stock traders in simulated financial trading8.
The monetary benefit of these systems is easy to realise. The investment should pay for itself.
However, the research into the C. elegans motion using optogenetics highlights the lack of
knowledge into how these ‘algorithms’ are encoded into the animals nervous system. A lack of
knowledge at the most basic level suggests that there needs to be more consideration of
behaviour encoding before the area can progress sufficiently onto more complex neural
networks and thus reducing the risk of wasting limited resources.
Overall, I would recommend investment from the UK research council into neural network
research. The potential profit to the UK economy is uncapped. Numerous applications of
knowledge in neural networks and potential advancements could be an invaluable resource to
the currently stagnant economy.
7
8
Computational Intelligence in Vehicles and Vehicular Systems IEEE 2009 DOI:10.1109/CIVVS.2009.4938716
Robots Beat Humans in Trading Battle. BBC.com (August 8th, 2001)
Peer Review
Strengths
Aravi Samuel comes across as having an in-depth knowledge and genuine enthusiasm for the
DaVinci group’s area of research. The talk is well-pitched for scientists who do not have
previous knowledge in neural science. The pace of the talk was perfect, it felt relaxed yet still
presented sufficient information to keep the listeners engaged. I particularly enjoyed the
explanations of the attained experimental data and data analysis. Samuel took time to carefully
explain what was seen in each graph which helped the listeners to visualise and clarify the
information given verbally. Furthermore, when asked on the spot questions toward the end of
the talk, Samuel gave clear and concise answers to questions displaying his confidence in the
subject.
Weaknesses
As the research group is a combination of the Department of Physics, the Division of
Engineering and Applied Sciences and the Department of Molecular and Cellular Biology the
colloquium lacked in-depth knowledge in any area. Although it would have been nice to include
firm physics, it is easy to appreciate the difficulty in doing this within the time constraints. The
Colloquium further goes on to talk about the results of the various experiments, however, it
would have been nice to see more of these. For example, when Samuel mentions how the
arbitrary function used to oscillate the temperature was changed and the results of this still
followed as predicted, there is no visual or verbal proof of this during the colloquium. The real
life applications for neural network research are unfortunately missed out of the colloquium
which could be a seamless continuation of the future developments section. It would also be my
suggestion to look into the work of the HHMI Janelia Farm Research Group9 as this work seems
very similar to the DaVinci group’s and in recent years has seen some large breakthroughs.
Overall, Samuel’s colloquium left me wanting to learn more about neural networks (enough for
me to look into PhD study on the topic!). The talk, which lasted for just over an hour, went very
fast and left me with a keen interest to learn more.
9
http://www.janelia.org/search/apachesolr_search/neural%20biology%20research
References
Images 2, 3, 4
Taken from the presentation slides. 02/01/ 2010 Hosted at
http://media.physics.harvard.edu/video/?id=COLLOQ_SAMUEL_020110
[1]
J. J. Hopfield, Neural networks and physical systems with emergent collective computational abilities. Proc.
NatL Acad. Sci. USA Vol. 79, pp. 2554-2558, April 1982 Biophysics
[2]
Paul A. Garrity, Miriam B. Goodman,Aravinthan D. Samuel,and Piali Sengupta, Running hot and cold:
behavioralstrategies, neural circuits, and the molecular machinery for thermotaxis in C. elegans and
Drosophila. Hosted at: http://scholar.harvard.edu/files/aravisamuel/files/genes_dev_2010_garrity.pdf 2010
[3]
Deisseroth, K; G Feng, A K Majewska, G Miesenböck, A Ting, M J Schnitzer. "Next-generation optical
technologies for illuminating genetically targeted brain circuits". J Neuroscience. PMID 10380-6.
[4]
Atsushi Kuhara, Noriyuki Ohnishi,Tomoyasu Shimowada & Ikue Mori. Neural coding in a single sensory
neuron controlling opposite seeking behaviours in Caenorhabditis elegans, 2011, doi:10.1038/ncomms1352
[5]
The Brain and Nervous System http://www.humanillnesses.com/Behavioral-Health-A-Br/The-Brain-andNervous-System.html
[6]
Nicholos Wethington, How Many Stars are in the Milky Way? 2008, hosted at:
http://www.universetoday.com/22380/how-many-stars-are-in-the-milky-way/#ixzz2NvSAHEjY accessed
18/03/2013
[7]
Computational Intelligence in Vehicles and Vehicular Systems IEEE 2009 DOI:10.1109/CIVVS.2009.4938716
[8]
Robots Beat Humans in Trading Battle. BBC.com (August 8th, 2001)
[9]
Janelia research group, http://www.janelia.org/search/apachesolr_search/neural%20biology%20research