all animals studied thus far display some form of sleep or a sleep / rest - like state . in many species including drosophila ,
chronic sleep loss severely impacts normal physiological functions to the extent that prolonged sleep loss can lead to death .
these simple observations , though not mechanistically insightful , do underscore the fundamental importance of sleep . since sleep is also a state in which an individual is in a position of increased vulnerability to predators , it has been argued that sleep must perform a vital biological function(s ) to have been so strongly conserved through evolution .
however , the identity of this function continues to be debated and remains inconclusively demonstrated .
typically , physiological and neurobiological correlates of sleep have been studied more readily in vertebrates , including humans .
to some extent this has been due to a more robust functional definition for sleep in these animals that has aided initial observational studies .
the advent of more sophisticated , non - invasive methods for measuring brain electrical activity has also led to consistently identifiable 
consequently , regions in the vertebrate brain that are involved in the regulation of sleep have been identified and studied for some time . by contrast , the molecular basis for sleep regulation is relatively poorly understood .
the highly complex and enigmatic nature of sleep and the absence of models that allow rapid multi - factorial genetic analysis have , in part , precluded a better molecular description of sleep . given that a number of human sleep disorders are likely to have genetic components ,
an appreciation for molecular mechanisms underlying sleep is not only vital to a better understanding of these disorders , but also to understanding the fundamental nature of sleep itself .
use of genetic methods to study sleep in the laboratory is a relatively modern phenomenon , though the significance of potential genetic determinants has been recognized for a long time .
the deployment of models systems , such as drosophila , that offer varied and powerful tools for genetic analysis , is an even more recent occurrence .
methodology and analytical tools of drosophila sleep research derive heavily from a sizeable body of work on circadian rhythms .
for example , the most widely used experimental setup to measure sleep in flies is the drosophila activity monitor or dam system .
this technique was developed to overcome laborious recordings of circadian phenomena in flies such as eclosion rates ( the rate at which adult flies emerge from their pupae ) . in this method
, the locomotor activity of individual flies could be monitored over a period of days by measuring the number of times each fly walked through an infrared beam in a single glass tube ( i.e. , a beam break ) .
periods of increased locomotor activity , such as during the day , result in a larger number of beam breaks , while decreased activity at night leads to fewer beam breaks .
recordings can be averaged over suitable bin sizes ( e.g. , 1 or 5 min ) and across a number of flies of the same age , sex and genotype , thereby increasing the power of this analysis .
periodicity of this rhythmic change in activity can be accurately measured using a number of statistical tools for the analysis of biological rhythm , and deviations from a circadian profile can be detected with ease .
thus , the dam system allowed automation , scaling , reproducibility and the ability to monitor locomotor rhythms in single flies over circadian time .
experiments using this method have given rise to a vast amount of data that has contributed richly to a very sophisticated understanding of the molecular clock that regulates circadian rhythms in flies , as well as in other animals .
more recently , this system has also been combined with natural variations in light and temperature to deduce the effect of seasonal variations on circadian rhythm . the first evidence that drosophila could be used to study sleep came from two reports published in early 2000 , both of which used the dam system .
together , these studies showed that periods of rest in flies correspond to a sleep - like state .
for instance , periods of rest in flies correspond to increased threshold for arousal by mechanical stimuli , and were responsive to stimulants and hypnotics , as well as to prior lack of rest / sleep .
thus , fly sleep showed a circadian pattern and was subject to strong homeostatic regulation .
once the correlates for sleep in drosophila had been defined , the stage was set for the discovery of sleep - related genetic pathways , the elucidation of neural correlates for sleep in flies , and perhaps even the modeling of human sleep disorders in this model system . in the last decade , several drosophila sleep mutants have been isolated through traditional forward genetic screens designed to isolate mutations with altered sleep patterns .
some of the genes implicated by these mutational studies lend additional support to known neurobiological pathways involved in sleep such as the dopaminergic system ( e.g. , fumin mutants are deficient in the drosophila dopamine transporter ) .
indeed , the dopaminergic system has been investigated extensively in the context of sleep in flies . yet
other mutations have led to the discovery of genes and pathways hitherto unstudied in the context of sleep .
for example , sleepless / quiver mutants are deficient in a small gpi - anchored protein that has been shown to influence potassium currents carried by shaker channels .
interestingly , previous mutational studies for short - sleeping mutants had already implicated shaker channels in the regulation of sleep in drosophila .
similarly , a recent study has reported the isolation of insomniac ( inc ) mutants that show disruptions in sleep consolidation .
transposon insertion induced inc mutants reduce expression of cg32810 , a btb - domain containing protein that is predicted to bind the e3 ubiquitin ligase , cullin-3 .
ubiquitination - dependent mechanisms have also been implicated in drosophila sleep regulation by two other studies , both of which used reverse genetic analysis to model genes linked to human disorders 
one to model angelman s syndrome and the other to test putative risk factors for restless legs syndrome / willis - ekbom disease in flies .
additionally , studies in drosophila are also beginning to shed light on why sleep is so fundamentally required in all animals .
perhaps the best explored of these is the notion that normal sleep is necessary for proper cognitive function and neuronal plasticity .
overall , both forward and reverse genetic approaches are increasingly being combined in drosophila to uncover molecular regulators of sleep .
sleep disorders and even specific sleep patterns tend to run in families , suggesting the presence of genetic factors that regulate sleep . in spite of this long - standing observation ,
narcolepsy with cataplexy is perhaps the best investigated among these . in general , genes identified from gwas and genetic typing have confirmed the idea that there might be an autoimmune etiology underlying this sleep disorder .
for instance , some hla alleles have been shown to confer protection , while others to increase relative risk .
another candidate , the gene p2ry11 encoding a purinergic receptor that is coupled to both camp and ip3 signaling pathways and expressed highly in the brain and white blood cells , has been postulated to function as a sensor for extracellular atp during immune regulation . similarly , t - cell receptor  or tcra , the major receptor for hla - peptide presentation has been strongly associated with narcolepsy with cataplexy in caucasians , asians and african americans . finally ,
though not linked to immune function , an additional polymorphism between two genes cpt1b and chkb has been associated with japanese and korean narcoleptics , but has not been replicated in european , african american or chinese populations .
restless legs syndrome ( rls , renamed willis - ekbom disease , we d ) is a disorder in which patients report an irresistible urge to move their limbs that worsens with rest and at night .
rls / wed patients display severe sleep fragmentation that can be managed clinically to some extent with dopaminergic agonists .
recent gwas have identified a set of risk alleles that point to the genes btbd9 , meis1 , map2k5/lbxcor1 , ptprd and tox3 .
while neuronal and/or developmental roles have been described for some of these genes , none of them immediately suggest molecular pathways that can be obviously connected to rls / wed or sleep .
however recent studies using model systems have begun to directly explore sleep - related functions for these genes in model systems ( see next section for details ) .
drosophila has been used extensively as a powerful and rapid means to model human diseases .
similar modeling of sleep disorders in flies has been initiated by the twin developments of  sleep genetics  in flies and high throughput whole - genome searches for sleep disease genes in patient populations .
though still in its infancy , we highlight here a few instances where drosophila has been used effectively to study disease genes in humans that also affect sleep .
two single - gene neurological disorders that are known to precipitate intellectual disability and also lead to sleep disturbances are angelman syndrome ( loss of function of ube3a ) and fragile x syndrome ( loss of fmr1 ) .
since drosophila possess a single homolog of both these genes , sleep studies have been performed with loss - of - function mutations in the fly homolog of ube3a ( dube3a ) and fmr1 ( dfmr1 ) .
loss of dube3a leads to strong defects in locomotion and circadian rhythm , though a specific defect in sleep has not been described . for dfmr1 ,
mean sleep duration is bi - directionally influenced by a loss or gain of fmr1 , such that dfmr1 amorphs are long sleepers , while increased dfmr1 leads to short sleepers .
both these proteins are expressed in the mushroom bodies , anatomical regions of the fly brain that are involved in sleep as well as higher cognitive functions such as learning and memory formation .
consistent with a potential relationship between brain plasticity and sleep , drosophila that express transgenic human disc1 ( a susceptibility factor in multiple mental illnesses including schizophrenia ) in the mushroom body also display significantly longer sleep bouts but normal bout frequency and circadian rhythms .
these studies exemplify rational sleep disorder modeling in flies and also strengthen the connection between sleep and cognitive function .
neurodegenerative disorders such as parkinson disease ( pd ) and huntington s disease ( hd ) are often accompanied by sleep disturbances , though these are not classified as primary sleep disorders .
for example in flies that express mutant forms of human huntingtin ( mhtt ) in the brain or in which the endogenous huntingtin protein ( dhtt ) is knocked down , displayed both sleep loss and sleep fragmentation .
similarly , expression of pre - fibrillar forms of human -synuclein  a protein implicated in pd  in dopaminergic and serotonergic neurons precipitated non - motor symptoms such as abnormal sleep and circadian rhythm .
pan - neuronal expression of human -synuclein also resulted in aberrant short - term memory formation when these flies were sleep deprived , once again highlighting the interconnectedness of sleep and cognitive function . while examples mentioned above demonstrate the utility of flies in exploring sleep phenotypes in a variety of neurological disorders , genetic regulators of human sleep are also beginning to be explored directly in flies .
the gene abcc9  encoding the protein sur2 a pore - forming subunit of an atp - sensitive potassium channel  was identified through genome - wide association studies as a factor that might explain a fraction of the natural variation in sleep duration in human populations . independently , this channel had also been identified to play a role in the etiology of cardiovascular disease and endocrine disorders that are associated with abnormalities in sleep durations .
rna interference mediated knock down of the drosophila homolog , dsur , in the brain preferentially affected night - time sleep while leaving both day - time sleep and overall circadian rhythm intact . in another study , a gene btbd9 , first identified as a risk factor for restless legs syndrome ( willis - ekbom disease , rls / wed ) , was knocked out in flies .
dbtbd9 mutants showed appreciable sleep fragmentation and increased motor activity , suggesting a physiological role for btbd9 in the regulation of sleep architecture and rls / wed pathophysiology .
taken together , these studies clearly demonstrate that the power and elegance of drosophila genetics has the potential to shed light on conserved features of sleep and human sleep disorders .
we predict an increasing application of drosophila ( and other genetic models ) to the study of human sleep disorders , drug target discovery and the development of pharmacological screening platforms .
given the relentless wave of information in the post - genomic era , sleep research in drosophila will have to overcome significant challenges if it is to continue to be a potent platform for interrogating the genetic basis for sleep and sleep disorders . here , we explore two criteria that will help in discovering functional networks of genes that regulate sleep using drosophila as a model system . as more genes / proteins get implicated in the regulation of sleep , we will have to ask whether these genes belong to discrete signaling pathways and genetic networks that function in concert to control various aspects of sleep .
identification of these networks through computational approaches has become much more accessible and powerful in recent times .
algorithms can now draw from a number of databases that report genetic and physical interactions and collate this information with functional and structural information on proteins of interest .
for instance , the program genemania , maintained by the university of toronto and available either as a stand - alone web application or as a plugin in cytoscape ( an open source freeware for complex network analysis ) , is capable of generating gene networks based on user supplied criteria from a list of relevant genes .
an example of this is shown in figure  1 , which depicts a network created from all genes that have identified sleep regulatory roles in flies .
one can see immediately from figure  1a that the majority of these genes fall within a network that is connected by either reported genetic or protein - protein interactions .
closer scrutiny of this interaction map reveals that map kinase pathways are over - represented ( fig .  1b ,
see figure legend for details ) . therefore , such analysis , while confirming previous studies , also helps prioritize future experiments . in this specific instance
, we would predict that other components of the map kinase cascade that have not been explicitly tested for sleep - related functions are likely to play a role in the regulation of sleep .
such predictions can be tested for parity with gene ontology enrichment using additional resources such as defog , another web - based application that prioritizes go analysis on top of genemania network outputs . with the discovery of additional genes , such networks are likely to become more enriched and lead to increased predictive power .
components from such networks will then have to be analyzed , most likely in a combinatorial fashion , using functional studies that are capable of identifying the unique ways in which these genes influence sleep architecture .
( a ) a gene interaction network built based on gene ontology weighting using genemania from genes implicated in sleep regulation in drosophila .
dark blue circles represent genes that are part of a network , while light blue circles are genes that are not part of a network based on current experimental evidence .
blue lines denote reported physical interactions between protein products , while green lines represent known genetic interactions from previous studies .
( b ) gene ontology ( go ) analysis in genemania reveals a high degree of enrichment for signaling and map kinase pathway members .
the network can be easily rearranged to highlight these components ( green circles ) and obtain an idea of the number and identity of these genes .
genes that are part of this network include the three map kinase members ( known as basket ( jnk ) , rolled ( erk ) and p38b ( p38 mapk ) in flies ) , upstream kinases such as mekk1 and target transcription factors such as fos ( known as kayak in flies ) and jun ( known as jra in flies ) .
such analysis suggests that future studies should focus on members of this network as potential sleep regulatory elements .
the rest of the network shows the original sleep - related genes in gray and other members of the overall network in white .
since the first time drosophila was shown to be a viable organism for examining sleep , the readily available dam system has been crucial in providing a simple and scalable platform for rapid screening of molecular components that underlie the behavior .
however , the traditional dam detects fly locomotion by relying on the signal of a single infrared ( ir ) beam .
consequently , the technology has limited temporal resolution , almost no spatial resolution and in many cases vastly over - estimates sleep amount .
in addition , the software tools that are currently available interpret the dam data at a gross level , only yielding population - wide average quantities such as daily sleep and bout length .
powerful theoretical methods from time - series analysis and statistical theory , though applicable to fly activity recordings , have not seen wide applications in drosophila sleep studies , thus leaving the information contained in the data relatively underutilized .
drawbacks of existing tools have meant that only severe variations in fly behavioral phenotypes , several standard deviations from the mean , can be reported with statistical confidence .
various subtle , yet physiologically interesting , changes in sleep pattern are likely being overlooked due to a lack of appropriate experimental and theoretical methods .
below , we examine a few improvements to methods presently employed in drosophila sleep studies .
a natural extension of the single beam hardware is the multi - beam activity monitor ( mam ) , also from trikinetics , inc . which manufactures the commonly used dam .
in contrast to the dam , the mam is equipped with 17 ir sources per tube and can therefore detect fly movement approximately every 3 mm , an order - of - magnitude improvement in spatial resolution over current detection capability .
the higher sensitivity of the mam system should significantly reduce measurement errors inherent in the dam .
also , its similarity to the previous single - beam technology implies it can be seamlessly integrated with existing equipment .
the higher fidelity data , in conjunction with simple incorporation into ongoing experimental design , makes upgrading to the mam system an attractive prospect .
although a considerably different design from the dam system , video - tracking of movement is perhaps the most accurate method to - date for studying drosophila sleep .
a basic system for video monitoring of flies can be set up with only an ir - sensitive digital camera , source of ir illumination , and a laptop computer .
flies can be monitored either in glass tubes that are standard with the dam system or in a makeshift arena designed such that movement is confined to a fixed focal plane .
image acquisition and subsequent analysis can be performed with one of several freely available software packages such as pysolo , tracker and ctrx - flytrax . unlike the commercial dam and mam packages , setting up a video tracking system demands an initial investment of time for assembling and testing the individually procured components .
in addition , video - tracking requires visual access to the flies at all times while the dam and mam systems can be operated with the individual monitors stacked on top of each other . in a large - scale study where the priority is to rapidly screen tens of thousands of flies ,
however , the advantages gained from the new methodology far surpass the additional effort and space requirements .
first , with just off - the - shelf components the researcher can achieve sub - millimeter and sub - second spatiotemporal resolution of locomotion .
the higher precision measurement directly translates into a more accurate estimate of the standard fly sleep parameters .
second , the video images contain detailed information on a fly s physical status and can be mined to investigate association between sleep and other behavioral attributes such as place preference , grooming , and food proximity .
third , it allows the researcher to design and execute experiments in arbitrary environments , with the only restriction being visibility to the camera .
finally , from an economic standpoint , a comparative study reported that the cost of tracking 30 flies using video - recording is at least one - fifth of that required for an equivalent ir detection system .
thus , the benefits of studying sleep through video - recording are many and should make the technique appealing to the fly community .
several drosophila sleep laboratories have already adopted this new approach and we anticipate more will follow suit in the near future .
in addition to incorporating better measurement techniques , fly sleep studies can also benefit from taking advantage of more rigorous mathematical tools .
analysis of ion channel recordings with hidden markov models , estimation of brain eeg parameters with time - series methods , and statistical modeling of animal movement patterns are examples where advanced mathematics has been successfully applied to address questions related to animal behavior .
so far , most drosophila sleep studies have relied on a handful of metrics that describe behavior averaged over many flies and several days . while these metrics have been useful in the initial characterization of novel mutants , they have limited ability to provide detailed insights into neuronal firing patterns modulating sleep or construct a systems - level link among the various regulators of the behavior . as a consequence of the former ,
the model of fly sleep still remains rudimentary while description of mammalian sleep is well - developed with the classification of several different stages of sleep and wake states .
access to real - time fly neuronal activity , such as eeg , would permit enhancement of the fly model but since such direct read - out remains technically challenging , an alternative approach may be to more carefully examine available locomotor data with the aim of establishing connections to brain activity .
we sketch here a simple analytic strategy that may offer insights into the type of neuronal processes that underlie sleep - wake transitions . instead of averaging over individual bouts ,
a running list of sleep bouts of all flies of one genotype can be pooled and the frequency of each distinct bout length can be determined to produce a distribution of the type shown in figure  2a ( purple squares ) .
similarly , an equivalent plot can be generated for the wake states ( fig .  2a , gray circles ) .
without any further quantification , data in figure  2a makes a number of refinements to our current knowledge : ( i ) sleep durations are dominated by short bouts , < 50 min , underscoring the need for temporal resolution higher than 1 min ; ( ii ) since the distribution is heavily skewed with the bouts spanning several orders of magnitude , quoting only the mean bout size is misleading ; ( iii ) comparing overall shapes of sleep and wake distributions can provide clues as to whether similar or different processes regulate the two states .
well - established modeling and inference methods can be used for robust quantification of such data ( fig . 
for instance , if the data are best mimicked by a power - law model , it would suggest that sleep events are likely modulated by scale invariant neuronal dynamics . if instead the analysis favors an exponential or inverse gaussian model , the evidence would point toward a process which has a characteristic timescale .
these model dynamical processes have been investigated extensively in mammalian brains and therefore , establishing links between them and fly sleep - wake events could facilitate the gradual construction of a quantitative model of fly sleep ( syed and kidd , in preparation ) .
( a ) frequency plot of wild - type drosophila sleep and wake events show distributions with long tails . schematic on the right indicates the data are pooled from n fly recordings . here
, n = 20 flies , each measured for 5 d in 1 min interval using the dam system .
starting with arbitrary long - tailed distributions , maximum likelihood theory is first used to calculate parameters ( ci , 95% confidence intervals ) for each model , given the data .
next , the log - likelihood ( ll ) ratio test computes statistical evidence in favor of the model with the highest akaike weight . if evidence is significant ( according to p - value of ll test ) , the favored model subsequently undergoes a 
g- or chisq - test , finally becoming the selected model with probability given by the test p - value .
( c ) interrogating the data from ( a ) for correlation between sleep and wake bouts .
all n sleep events of duration i minutes are located in each of the n = 20 recordings ( see schematic below data ) and average size of the subsequent wake bouts is computed , ( w1+w2+  +wn)/n .
the analysis shows on the whole a weak negative correlation between sleep bouts and average duration of the following wake bouts ( solid line , smoothed data ) .
( d ) parameters yielded by the types of analyses in ( a - c ) could be used to relate different genotypes . in a hypothetical scenario ,
fly strains with differentially modulated levels of arousal - promoting dopamine and octopomine may appear as two distinct  classes .
 a future unknown strain can then be placed on such a graph to uncover its functional proximity to a known class of flies , thereby generating a more integrated view of various sleep models .
a different method of analysis can reveal time - dependent features of fly sleep - wake architecture by examining temporal correlations among bouts .
for example , is a long sleep bout , on average , followed by a long wake bout ?
such a question can be answered by first determining the distinct sleep epochs of a cohort and then computing the average wake event that immediately follows each distinct sleep bout .
any correlation among the opposing sleep - wake states can be deduced from the pooled data ( fig . 
the group of flies in figure  2c does not exhibit a strong coupling between the duration of the two states , as evidenced by the line with an overall small negative slope , suggesting two partially independent mechanisms are likely generating sleep and wake events .
in contrast , if strongly coupled processes underlay the two states , we would likely find significant positive or negative correlation in the data .
thus , this analytic strategy can potentially reveal temporal organization of the neuronal processes that signal sleep and wake events .
the two general analytic approaches sketched here introduce novel parameters that , since they quantify individual events rather than population - averaged behavior , should allow detection of previously elusive changes in sleep - wake patterns .
such subtle changes may be expected in response to novel stimuli such as new drugs and complex environmental variables like sinusoidal light modulation .
these candidate effectors could , for instance , alter the temporal distribution of sleep bouts without changing the overall daily sleep amount .
moreover , the mathematical parameters could serve as diagnostic markers in the study of poorly understood sleep disorders in drosophila . in the study of epilepsy , for example , rigorous analysis of eeg patterns has led to the development of powerful computational tools for human patient diagnosis .
additionally , the new strategies could help classify genetically distinct mutants into groups ( fig . 
2d ) or complement findings of virtual sleep gene networks such as those by genemania .
finally , the strategies seem capable of connecting behavioral output to certain aspects of activity of the fly sleep- and wake - promoting neurons .
ultimately , relevant findings from drosophila studies will have to be explained in the context of human sleep and having a quantitative fly sleep model involving neuronal input and behavioral output will greatly facilitate such comparisons .
as more genes / proteins get implicated in the regulation of sleep , we will have to ask whether these genes belong to discrete signaling pathways and genetic networks that function in concert to control various aspects of sleep .
identification of these networks through computational approaches has become much more accessible and powerful in recent times .
algorithms can now draw from a number of databases that report genetic and physical interactions and collate this information with functional and structural information on proteins of interest .
for instance , the program genemania , maintained by the university of toronto and available either as a stand - alone web application or as a plugin in cytoscape ( an open source freeware for complex network analysis ) , is capable of generating gene networks based on user supplied criteria from a list of relevant genes .
an example of this is shown in figure  1 , which depicts a network created from all genes that have identified sleep regulatory roles in flies .
one can see immediately from figure  1a that the majority of these genes fall within a network that is connected by either reported genetic or protein - protein interactions .
closer scrutiny of this interaction map reveals that map kinase pathways are over - represented ( fig .  1b ,
therefore , such analysis , while confirming previous studies , also helps prioritize future experiments . in this specific instance
, we would predict that other components of the map kinase cascade that have not been explicitly tested for sleep - related functions are likely to play a role in the regulation of sleep .
such predictions can be tested for parity with gene ontology enrichment using additional resources such as defog , another web - based application that prioritizes go analysis on top of genemania network outputs . with the discovery of additional genes , such networks are likely to become more enriched and lead to increased predictive power .
components from such networks will then have to be analyzed , most likely in a combinatorial fashion , using functional studies that are capable of identifying the unique ways in which these genes influence sleep architecture .
( a ) a gene interaction network built based on gene ontology weighting using genemania from genes implicated in sleep regulation in drosophila .
dark blue circles represent genes that are part of a network , while light blue circles are genes that are not part of a network based on current experimental evidence .
blue lines denote reported physical interactions between protein products , while green lines represent known genetic interactions from previous studies .
( b ) gene ontology ( go ) analysis in genemania reveals a high degree of enrichment for signaling and map kinase pathway members .
the network can be easily rearranged to highlight these components ( green circles ) and obtain an idea of the number and identity of these genes .
genes that are part of this network include the three map kinase members ( known as basket ( jnk ) , rolled ( erk ) and p38b ( p38 mapk ) in flies ) , upstream kinases such as mekk1 and target transcription factors such as fos ( known as kayak in flies ) and jun ( known as jra in flies ) .
such analysis suggests that future studies should focus on members of this network as potential sleep regulatory elements .
the rest of the network shows the original sleep - related genes in gray and other members of the overall network in white .
since the first time drosophila was shown to be a viable organism for examining sleep , the readily available dam system has been crucial in providing a simple and scalable platform for rapid screening of molecular components that underlie the behavior . however , the traditional dam detects fly locomotion by relying on the signal of a single infrared ( ir ) beam .
consequently , the technology has limited temporal resolution , almost no spatial resolution and in many cases vastly over - estimates sleep amount .
in addition , the software tools that are currently available interpret the dam data at a gross level , only yielding population - wide average quantities such as daily sleep and bout length .
powerful theoretical methods from time - series analysis and statistical theory , though applicable to fly activity recordings , have not seen wide applications in drosophila sleep studies , thus leaving the information contained in the data relatively underutilized .
drawbacks of existing tools have meant that only severe variations in fly behavioral phenotypes , several standard deviations from the mean , can be reported with statistical confidence .
various subtle , yet physiologically interesting , changes in sleep pattern are likely being overlooked due to a lack of appropriate experimental and theoretical methods .
below , we examine a few improvements to methods presently employed in drosophila sleep studies .
a natural extension of the single beam hardware is the multi - beam activity monitor ( mam ) , also from trikinetics , inc . which manufactures the commonly used dam .
in contrast to the dam , the mam is equipped with 17 ir sources per tube and can therefore detect fly movement approximately every 3 mm , an order - of - magnitude improvement in spatial resolution over current detection capability .
the higher sensitivity of the mam system should significantly reduce measurement errors inherent in the dam . also , its similarity to the previous single - beam technology implies it can be seamlessly integrated with existing equipment .
the higher fidelity data , in conjunction with simple incorporation into ongoing experimental design , makes upgrading to the mam system an attractive prospect .
although a considerably different design from the dam system , video - tracking of movement is perhaps the most accurate method to - date for studying drosophila sleep . a basic system for video monitoring of flies can be set up with only an ir - sensitive digital camera , source of ir illumination , and a laptop computer .
flies can be monitored either in glass tubes that are standard with the dam system or in a makeshift arena designed such that movement is confined to a fixed focal plane .
image acquisition and subsequent analysis can be performed with one of several freely available software packages such as pysolo , tracker and ctrx - flytrax . unlike the commercial dam and mam packages , setting up a video tracking system demands an initial investment of time for assembling and testing the individually procured components .
in addition , video - tracking requires visual access to the flies at all times while the dam and mam systems can be operated with the individual monitors stacked on top of each other . in a large - scale study where the priority is to rapidly screen tens of thousands of flies ,
however , the advantages gained from the new methodology far surpass the additional effort and space requirements .
first , with just off - the - shelf components the researcher can achieve sub - millimeter and sub - second spatiotemporal resolution of locomotion .
the higher precision measurement directly translates into a more accurate estimate of the standard fly sleep parameters .
second , the video images contain detailed information on a fly s physical status and can be mined to investigate association between sleep and other behavioral attributes such as place preference , grooming , and food proximity .
third , it allows the researcher to design and execute experiments in arbitrary environments , with the only restriction being visibility to the camera .
finally , from an economic standpoint , a comparative study reported that the cost of tracking 30 flies using video - recording is at least one - fifth of that required for an equivalent ir detection system .
thus , the benefits of studying sleep through video - recording are many and should make the technique appealing to the fly community .
several drosophila sleep laboratories have already adopted this new approach and we anticipate more will follow suit in the near future .
in addition to incorporating better measurement techniques , fly sleep studies can also benefit from taking advantage of more rigorous mathematical tools .
analysis of ion channel recordings with hidden markov models , estimation of brain eeg parameters with time - series methods , and statistical modeling of animal movement patterns are examples where advanced mathematics has been successfully applied to address questions related to animal behavior .
so far , most drosophila sleep studies have relied on a handful of metrics that describe behavior averaged over many flies and several days . while these metrics have been useful in the initial characterization of novel mutants , they have limited ability to provide detailed insights into neuronal firing patterns modulating sleep or construct a systems - level link among the various regulators of the behavior . as a consequence of the former ,
the model of fly sleep still remains rudimentary while description of mammalian sleep is well - developed with the classification of several different stages of sleep and wake states .
access to real - time fly neuronal activity , such as eeg , would permit enhancement of the fly model but since such direct read - out remains technically challenging , an alternative approach may be to more carefully examine available locomotor data with the aim of establishing connections to brain activity .
we sketch here a simple analytic strategy that may offer insights into the type of neuronal processes that underlie sleep - wake transitions . instead of averaging over individual bouts ,
a running list of sleep bouts of all flies of one genotype can be pooled and the frequency of each distinct bout length can be determined to produce a distribution of the type shown in figure  2a ( purple squares ) .
similarly , an equivalent plot can be generated for the wake states ( fig .  2a , gray circles ) .
without any further quantification , data in figure  2a makes a number of refinements to our current knowledge : ( i ) sleep durations are dominated by short bouts , < 50 min , underscoring the need for temporal resolution higher than 1 min ; ( ii ) since the distribution is heavily skewed with the bouts spanning several orders of magnitude , quoting only the mean bout size is misleading ; ( iii ) comparing overall shapes of sleep and wake distributions can provide clues as to whether similar or different processes regulate the two states .
well - established modeling and inference methods can be used for robust quantification of such data ( fig . 
for instance , if the data are best mimicked by a power - law model , it would suggest that sleep events are likely modulated by scale invariant neuronal dynamics .
if instead the analysis favors an exponential or inverse gaussian model , the evidence would point toward a process which has a characteristic timescale .
these model dynamical processes have been investigated extensively in mammalian brains and therefore , establishing links between them and fly sleep - wake events could facilitate the gradual construction of a quantitative model of fly sleep ( syed and kidd , in preparation ) .
( a ) frequency plot of wild - type drosophila sleep and wake events show distributions with long tails .
, n = 20 flies , each measured for 5 d in 1 min interval using the dam system .
starting with arbitrary long - tailed distributions , maximum likelihood theory is first used to calculate parameters ( ci , 95% confidence intervals ) for each model , given the data .
next , the log - likelihood ( ll ) ratio test computes statistical evidence in favor of the model with the highest akaike weight .
if evidence is significant ( according to p - value of ll test ) , the favored model subsequently undergoes a  goodness - of - fit  g- or chisq - test , finally becoming the selected model with probability given by the test p - value .
( c ) interrogating the data from ( a ) for correlation between sleep and wake bouts .
all n sleep events of duration i minutes are located in each of the n = 20 recordings ( see schematic below data ) and average size of the subsequent wake bouts is computed , ( w1+w2+  +wn)/n .
the analysis shows on the whole a weak negative correlation between sleep bouts and average duration of the following wake bouts ( solid line , smoothed data ) .
( d ) parameters yielded by the types of analyses in ( a - c ) could be used to relate different genotypes . in a hypothetical scenario ,
fly strains with differentially modulated levels of arousal - promoting dopamine and octopomine may appear as two distinct  classes .  a future unknown strain can then be placed on such a graph to uncover its functional proximity to a known class of flies , thereby generating a more integrated view of various sleep models . a different method of analysis
can reveal time - dependent features of fly sleep - wake architecture by examining temporal correlations among bouts .
for example , is a long sleep bout , on average , followed by a long wake bout ?
such a question can be answered by first determining the distinct sleep epochs of a cohort and then computing the average wake event that immediately follows each distinct sleep bout .
any correlation among the opposing sleep - wake states can be deduced from the pooled data ( fig . 
the group of flies in figure  2c does not exhibit a strong coupling between the duration of the two states , as evidenced by the line with an overall small negative slope , suggesting two partially independent mechanisms are likely generating sleep and wake events .
in contrast , if strongly coupled processes underlay the two states , we would likely find significant positive or negative correlation in the data .
thus , this analytic strategy can potentially reveal temporal organization of the neuronal processes that signal sleep and wake events .
the two general analytic approaches sketched here introduce novel parameters that , since they quantify individual events rather than population - averaged behavior , should allow detection of previously elusive changes in sleep - wake patterns .
such subtle changes may be expected in response to novel stimuli such as new drugs and complex environmental variables like sinusoidal light modulation .
these candidate effectors could , for instance , alter the temporal distribution of sleep bouts without changing the overall daily sleep amount .
moreover , the mathematical parameters could serve as diagnostic markers in the study of poorly understood sleep disorders in drosophila . in the study of epilepsy , for example , rigorous analysis of eeg patterns has led to the development of powerful computational tools for human patient diagnosis . additionally , the new strategies could help classify genetically distinct mutants into groups ( fig . 
2d ) or complement findings of virtual sleep gene networks such as those by genemania .
finally , the strategies seem capable of connecting behavioral output to certain aspects of activity of the fly sleep- and wake - promoting neurons .
ultimately , relevant findings from drosophila studies will have to be explained in the context of human sleep and having a quantitative fly sleep model involving neuronal input and behavioral output will greatly facilitate such comparisons .
although the application of drosophila as a model system to the study of sleep has made serious inroads into this fascinating problem , future research must take into consideration the obvious complexity of biological systems . specifically , we are now in a position to consider the role not just of individual genes , but rather interconnected gene networks functioning together as an ensemble
however , harnessing the full power of this model system will also require the integration of higher resolution observational tools and more sophisticated analytical approaches . with the application of these strategies
, these tiny flies will continue to make vast contributions to our understanding of sleep and sleep disorders .
 sleep research in drosophila is not only here to stay , but is making impressive strides towards helping us understand the biological basis for and the purpose of sleep  perhaps one of the most complex and enigmatic of behaviors . 
 thanks to over a decade of sleep - related studies in flies , more molecular methods are being applied than ever before towards understanding the genetic basis of sleep disorders . 
 the advent of high - throughput technologies that can rapidly interrogate whole genomes , epigenomes and proteomes , has also revolutionized our ability to detect genetic variants that might be causal for a number of sleep disorders . in the coming years 
 , mutational studies in model organisms such as drosophila will need to be functionally connected to information being generated from these whole - genome approaches in humans . 
 this will necessitate the development of appropriate methods for interpolating data and increased analytical power to synthesize useful network(s ) of sleep regulatory pathways  including appropriate discriminatory and predictive capabilities . 
 ultimately , such networks will also need to be interpreted in the context of fundamental neurobiological substrates for sleep in any given species . in this review , 
 we highlight some emerging approaches , such as network analysis and mathematical modeling of sleep distributions , which can be applied to contemporary sleep research as a first step to achieving these aims . 
 these methodologies should favorably impact not only a mechanistic understanding of sleep , but also future pharmacological intervention strategies to manage and treat sleep disorders in humans . 
Sleep research in 
The Genetic Basis for Sleep Disorders in Humans
Modeling Human Sleep Disorders in Drosophila
Multi-Factorial Analysis of Sleep: Future Challenges
A. Analysis of sleep-regulatory networks
B. High-resolution recordings of sleep
C. New computational methods to analyze sleep data
Summary