2 infection of murine cells -LRB- 15 -RRB- and transgenic mice expressing human CD4 -LRB- 16 -RRB- and provides a rationale for transgenic approaches to developing animal models of HIV disease . REFERENCES AND NOTES 1 . F. Cocchi et al. , Science 270 , 1811 -LRB- 1995 -RRB- . 2 . Y. Feng , C. C. Broder , P. E. Kennedy , E. A. Berger , ibid . 272 , 872 -LRB- 1996 -RRB- . 3 . M. Samson , 0 . Labbe , C. Mollereau , G. Vassart , M. Parmentier , Biochemistry 35 , 3362 -LRB- 1996 -RRB- ; C. J. Raport , J. Gosling , V. L. Schweickart , P. W. Gray , I. F. Charo , J. Biol . Chem . 271 , 17161 -LRB- 1996 -RRB- . 4 . H. Choe et al. , Cell 85 , 1135 -LRB- 1996 -RRB- ; B. J. Doranz et al. , ibid. , p. 1 149 . 5 . T. Dragic et al. , Nature 381 , 667 -LRB- 1996 -RRB- ; H. Deng et al. , ibid. , p. 661 ; G. Alkhatib et al. , Science 272,1955 -LRB- 1996 -RRB- . 6 . S. Gartner et al. , Science 233 , 215 -LRB- 1986 -RRB- . 7 . R. Atchison et al. , unpublished observations . 8 . L. Boring et al. , J. Biol . Chem . 271 , 7551 -LRB- 1996 -RRB- . 9 . We cloned cDNAs encoding human or murine CCR5 into the expression vector pcDNA3 -LRB- Invitrogen -RRB- after engineering the FLAG epitope into the NH2-terminus as described -LRB- 13 -RRB- . Expression of each construct was determined by FACS with an antibody to FLAG -LRB- antiFLAG -RRB- -LRB- Boehringer Mannheim -RRB- , and relative expression for each -LRB- see below -RRB- was calculated as the percentage of cells expressing human CCR5 on the cell surface normalized to the expression of hCCR5 -LRB- defined as 100 % -RRB- , with standard errors of the mean . The mean fluorescence intensity of the positive cells from any single sample never varied from the average by more than 30 % in a single experiment . Therefore , neither the relative number of positive cells nor the absolute expression levels within transfected cells explains the differences in coreceptor activity . Chimeric receptors were prepared by the overlap polymerase chain reaction -LRB- PCR -RRB- method -LRB- 17 -RRB- . hCCR5 -LRB- HHHH -RRB- , human CCR5 -LRB- 1 00 % relative expression -RRB- ; mCCR5 -LRB- MMMM -RRB- , murine CCR5 -LRB- 126 + 49 % -RRB- ; HMMM , NH2-terminus of human CCR5 -LSB- amino acids -LRB- aa -RRB- 1 to 32 -RSB- fused to murine CCR5 -LRB- aa 35 to 354 -RRB- -LRB- 77 + 22 % -RRB- ; MHHH , NH2-terminus of murine CCR5 -LRB- aa 1 to 34 -RRB- fused to human CCR5 -LRB- aa 33 to 352 -RRB- -LRB- 73 + 17 % -RRB- ; MHMM , extracellular loop 1 and a portion of transmembrane domain 3 of human CCR5 -LRB- aa 86 to 118 -RRB- replacing the corresponding segment of the murine receptor -LRB- aa 88 to 120 -RRB- -LRB- 37 + 22 % -RRB- ; MMHM , extracellular loop 2 and adjacent portions of human CCR5 -LRB- aa 134 to 210 -RRB- replacing the corresponding region of the murine receptor -LRB- aa 136 to 212 -RRB- -LRB- 81 + 30 % -RRB- ; MMHH , NH2-terminal half of mCCR5 -LRB- aa 1 to 162 -RRB- fused to the COOH-terminal half of hCCR5 -LRB- aa 161 to 352 -RRB- -LRB- 80 + 39 % -RRB- . 10 . I. F. Charo et al. , Proc . Natl. Acad . Sci . U.S.A. 91 , 2752 -LRB- 1994 -RRB- . 11 . C. Franci , L. M. Wong , J. Van Damme , P. Proost , I. F. Charo , J. Immunol . 154 , 6511 -LRB- 1995 -RRB- . 12 . We cloned cDNAs encoding human CCR2B or chimeras into the expression vector pCMV4 -LRB- 18 -RRB- after engineering the FLAG epitope into the NH2-terminus as described -LRB- 13 -RRB- . Expression of each construct -LRB- see below -RRB- was determined as described earlier . Chimeric receptors were prepared by the overlap PCR method -LRB- 17 -RRB- . 5555 , human CCR5 -LRB- 100 % relative expression -RRB- ; 2222 , human CCR2B -LRB- 87 + 2 % -RRB- ; 5222 , NH2-terminus of CCR5 -LRB- aa 1 to 32 -RRB- fused to CCR2B -LRB- aa 45 to 360 -RRB- -LRB- 27 + 5 % -RRB- ; 2555 , NH2-terminus of CCR2B -LRB- aa 1 to 44 -RRB- fused to CCR5 -LRB- aa 33 to 352 -RRB- -LRB- 108 + 17 % -RRB- ; 2255 , CCR2B -LRB- aa 1 to 136 -RRB- fused to CCR5 -LRB- aa 124 to 352 -RRB- -LRB- 119 + 33 % -RRB- . 13 . F. S. Monteclaro and 1 . F. Charo , J. Biol . Chem . 271 , 19084 -LRB- 1996 -RRB- ; F. S. Monteclaro et al. , unpublished observations . 14 . J. Gosling et al. , unpublished observations . 15 . P. J. Maddon et al. , Cell 47 , 333 -LRB- 1986 -RRB- . 16 . P. Lores et al. , AIDS Res . Hum . Retroviruses 8 , 2063 -LRB- 1992 -RRB- . 17 . S. N. Ho , H. D. Hunt , R. M. Horton , J. K. Pullen , L. R. Pease , Gene 77 , 51 -LRB- 1989 -RRB- . 18 . S. Andersson , D. L. Davis , H. Dahlback , H. Jornvall , D. W. Russell , J. Biol . Chem . 264 , 8222 -LRB- 1989 -RRB- . 19 . M. A. Goldsmith , M. T. Warmerdam , R. E. Atchison , M. D. Miller , W. C. Greene , J. Virol . 69 , 4112 -LRB- 1995 -RRB- . 20 . COS-7 cells were transfected with 2 pLg of plasmid DNA per well in a six-well plate as described -LRB- 19 -RRB- . DNA samples consisted of appropriate combinations of 0.5 p.g of a human CD4 expression plasmid -LSB- pCD4Neo -LRB- 19 -RRB- -RSB- or plain vector , and 1.5 , g of a chemokine receptor-expressing plasmid or plain vector . About 30 hours after addition of DNA , the medium in each well was replaced with 1.0 ml of medium containing HIV-1 Ba-L -LRB- -100 to 170 ng of p24 per sample ; source : NIH AIDS Reagent Repository , passaged on primary human macrophages -RRB- . About 10 hours later , an additional 1.0 ml of medium was added to each well . After 30 hours , the cells were recovered from the dish as described -LRB- 19 -RRB- and analyzed with a FacScan -LRB- Becton Dickinson -RRB- . Staining for intracytoplasmic HIV-1 p24 was carried out with the Fix and Perm reagents -LRB- Caltag Laboratories -RRB- , with a monoclonal antibody to p24 -LRB- Coulter Immunology -RRB- and goat anti-mouse fluorescein isothiocyanate -LRB- FITC -RRB- - conjugated secondary antibody -LRB- Becton Dickinson -RRB- . Cells were further stained with phycoerythrin -LRB- PE -RRB- - conjugated antiCD4 -LRB- Becton Dickinson -RRB- . Appropriate controls indicated that the appearance of double-positive cells -LRB- FITC + PE -RRB- was dependent on cotransfection with both CD4 and human CCR5 expression plasmids and on the presence of HIV-1 Ba-L . 21 . H. Arai and 1 . F. Charo , J. Biol . Chem . 271 , 21814 -LRB- 1996 -RRB- . 22 . We acknowledge the advice of M. Warmerdam -LRB- transfection-infection assay -RRB- , E. Weider -LRB- FACS studies -RRB- , and L. Boring , H. Arai , and R. Speck -LRB- scientific interpretation -RRB- . We appreciate the assistance of J. Carroll and M. Ceniceros in the preparation of this manuscript . Supported in part by NIH grant HL52773 -LRB- I.F.C. -RRB- and by Pfizer -LRB- M.A.G. -RRB- . 24 September 1996 ; accepted 24 October 1996 Statistical Learning by 8-Month-Old Infants Learners rely on a combination of experience-independent and experience-dependent mechanisms to extract information from the environment . Language acquisition involves both types of mechanisms , but most theorists emphasize the relative importance of experience-independent mechanisms . The present study shows that a fundamental task of language acquisition , segmentation of words from fluent speech , can be accomplished by 8-month-old infants based solely on the statistical relationships between neighboring speech sounds . Moreover , this word segmentation was based on statistical learning from only 2 minutes of exposure , suggesting that infants have access to a powerful mechanism for the computation of statistical properties of the language input . Downloaded from www.sciencemag.org on May 7 , 2008 Jenny R. Saffran , Richard N. Aslin , Elissa L. Newport During early development , the speed and accuracy with which an organism extracts environmental information can be extremely important for its survival . Some species have evolved highly constrained neural mechanisms to ensure that environmental information is properly interpreted , even in the absence of experience with the environment -LRB- 1 -RRB- . Other species are dependent on a period of interaction with the environment that clarifies the information to which attention should be directed and the consequences of behaviors guided by that information -LRB- 2 -RRB- . Depending on the developmental status and the task facing a particular organism , both experience-independent and experience-dependent mechanisms may be involved in the extraction of information and the control of behavior . In the domain of language acquisition , two facts have supported the interpretation that experience-independent mechanisms are both necessary and dominant . First , highly complex forms of language production develop extremely rapidly -LRB- 3 -RRB- . Second , the language input available to the young child is both incomplete and sparsely repDepartment of Brain and Cognitive Sciences , University of Rochester , Rochester , NY 14627 , USA . resented compared to the child 's eventual linguistic abilities -LRB- 4 -RRB- . Thus , most theories of language acquisition have emphasized the critical role played by experience-independent internal structures over the role of experience-dependent factors -LRB- 5 -RRB- . It is undeniable that experience-dependent mechanisms are also required for the acquisition of language . Many aspects of a particular natural language must be acquired from listening experience . For example , acquiring the specific words and phonological structure of a language requires exposure to a significant corpus of language input . Moreover , long before infants begin to produce their native language , they acquire information about its sound properties -LRB- 6 -RRB- . Nevertheless , given the daunting task of acquiring linguistic information from listening experience during early development , few theorists have entertained the hypothesis that learning plays a primary role in the acquisition of more complicated aspects of language , favoring instead experience-independent mechanisms -LRB- 7 -RRB- . Young humans are generally viewed as poor learners , suggesting that innate factors are primarily responsible for the acquisition of language . Here we investigate the nature of the 1 926 SCIENCE * VOL . 274 * 13 DECEMBER 1996 experience-dependent factors involved in language acquisition . In particular , we ask whether infants are in fact better learners than has previously been assumed , thus potentially reducing the extent to which experience-independent structures must be posited . The results demonstrate that infants possess powerful mechanisms suited to learning the types of structures exemplified in linguistic systems . Experience may therefore play a more important role in the acquisition of language than existing theories suggest . One task faced by all language learners is the segmentation of fluent speech into words . This process is particularly difficult because word boundaries in fluent speech are marked inconsistently by discrete acoustic events such as pauses -LRB- 8 -RRB- . Although it has recently been demonstrated that 8-month-old infants can segment words from fluent speech and subsequently recognize them when presented in isolation -LRB- 9 -RRB- , it is not clear what information is used by infants to discover word boundaries . This problem is complicated by the variable acoustic structure of speech across different languages , suggesting that infants must discover which , if any , acoustic cues correlated with word boundaries are relevant to their native language -LRB- 10 -RRB- ; there is no invariant acoustic cue to word boundaries present in all languages . One important source of information that can , in principle , define word boundaries in any natural language is the statistical information contained in sequences of sounds . Over a corpus of speech there are measurable statistical regularities that distinguish recurring sound sequences that comprise words from the more accidental sound sequences that occur across word boundaries -LRB- 1 1 -RRB- . Within a language , the transitional probability from one sound to the next will generally be highest when the two sounds follow one another within a word , whereas transitional probabilities spanning a word boundary will be relatively low -LRB- 12 -RRB- . For example , given the sound sequence pretty #baby , the transitional probability from pre to ty is greater than the transitional probability from ty to ba . Previously , we showed that adults and children can use information about transitional probabilities to discover word boundaries in an artificial language corpus of nonsense words presented as continuous speech , with no acoustic cues to word boundaries -LRB- 13 -RRB- . We asked whether 8-month-old infants can extract information about word boundaries solely on the basis of the sequential statistics of concatenated speech . We used the familiarization-preference procedure developed by Jusczyk and Aslin -LRB- 9 -RRB- . In this procedure , infants are exposed to auditory material that serves as a potential learning experience . They are subsequently presented with two types of test stimuli : -LRB- i -RRB- items that were contained within the familiarization material and -LRB- ii -RRB- items that are highly similar but -LRB- by some critical criterion -RRB- were not contained within the familiarization material . During a series of test trials that immediately follows familiarization , infants control the duration of each test trial by their sustained visual fixation on a blinking light -LRB- 14 -RRB- . If infants have extracted the crucial information about the familiarization items , they may show differential durations of fixation -LRB- listening -RRB- during the two types of test trials -LRB- 15 -RRB- . We used this procedure to determine whether infants can acquire the statistical properties of sound sequences from brief exposures . In our first experiment , 24 8-month-old infants from an American-English language environment were familiarized with 2 min of a continuous speech stream consisting of four three-syllable nonsense words -LRB- hereafter , `` words '' -RRB- repeated in random order -LRB- 16 -RRB- . The speech stream was generated by a speech synthesizer in a monotone female voice at a rate of 270 syllables per minute -LRB- 180 words in total -RRB- . The synthesizer provided no acoustic information about word boundaries , resulting in a continuous stream of coarticulated consonant-vowel syllables , with no pauses , stress differences , or any other acoustic or prosodic cues to word boundaries . A sample of the speech stream is the orthographic string bidakupadotigolabubidaku ... . The only cues to word boundaries were the transitional probabilities between syllable pairs , which were higher within words -LRB- 1.0 in all cases , for example , bida -RRB- than between words -LRB- 0.33 in all cases , for example , kupa -RRB- . To assess learning , each infant was presented with repetitions of one of four threesyllable strings on each test trial . Two of these three-syllable strings were `` words '' from the artificial language presented during familiarization , and two were three-syllable `` nonwords '' that contained the same syllables heard during familiarization but not in the order in which they appeared as words -LRB- 17 -RRB- . The infants showed a significant testtrial discrimination between word and non - word stimuli -LRB- 18 -RRB- , with longer listening times for nonwords -LRB- Table 1 -RRB- . This novelty preference , or dishabituation effect , indicates that 8-month-olds recognized the difference between the novel and the familiar orderings of the three-syllable strings . Thus , 8-month-old infants are capable of extracting serial-order information after only 2 min of listening experience . Of course , simple serial-order information is an insufficient cue to word boundaries . The learner must also be able to extract the relative frequencies of co-occurrence of sound pairs , where relatively low transitional probabilities signal word boundaries . Our next experiment examined whether 8-month-olds could perform the more difficult statistical computations required to distinguish words -LRB- that is , recurrent syllable sequences -RRB- from syllable strings spanning word boundaries -LRB- that is , syllable sequences occurring more rarely -RRB- . To take an English example , pretty #baby , we wanted to see if infants can distinguish a wordinternal syllable pair like pretty from a wordexternal syllable pair like ty #ba . Another 24 8-month-old infants from an American-English language environment were familiarized with 2 min of a continuous speech stream consisting of three-syllable nonsense words similar in structure to the artificial language used in our first experiment -LRB- 19 -RRB- . This time , however , the test items for each infant consisted of two words and two `` part-words . '' The part-words were created by joining the final syllable of a word to the first two syllables of another word . Thus , the part-words contained three-syllable sequences that the infant had heard during familiarization but statistically , over the corpus , did not correspond to words -LRB- 20 -RRB- . These part-words could only be judged as novel if the infants had learned the words with sufficient specificity and completeness that sequences crossing a word boundary were relatively unfamiliar . Despite the difficulty of this word versus part-word discrimination , infants showed a significant test-trial discrimination between the word and part-word stimuli -LRB- 21 -RRB- , with longer listening times for part-words -LRB- Table 1 -RRB- . Thus , 2 min of exposure to concatenated speech organized into `` words '' was suffi - Table 1 . Mean time spent listening to the familiar and novel stimuli for experiment 1 -LRB- words versus nonwords -RRB- and experiment 2 -LRB- words versus part-words -RRB- and significance tests comparing the listening times . Mean listening times -LRB- s -RRB- Experiment Familiar items 1 2 Matched-pairs t test Novel items 8.85 -LRB- SE = 0.45 -RRB- 7.60 -LRB- SE = 0.42 -RRB- 7.97 -LRB- SE = 0.41 -RRB- 6.77 -LRB- SE = 0.44 -RRB- * t -LRB- 23 -RRB- t -LRB- 23 -RRB- = = 2.3 , P < 0.04 2.4 , P < 0.03 1 927 SCIENCE * VOL . 274 13 DECEMBER 1996 Downloaded from www.sciencemag.org on May 7 , 2008 cient for 8-month-old infants to extract information about the sequential statistics of syllables . Moreover , this novelty preference can not be attributed to a total lack of experience with the three-syllable sequences forming part-words , as was the case with the nonwords in the first experiment . Rather , infants succeeded in learning and remembering particular groupings of threesyllable strings-those strings containing higher transitional probabilities surrounded by lower transitional probabilities . The infants ' performance in these studies is particularly impressive given the impoverished nature of the familiarization speech stream , which contained no pauses , intonational patterns , or any other cues that , in normal speech , probabilistically supplement the sequential statistics inherent in the structure of words . Equally impressive is the fact that 8-month-old infants in both experiments were able to extract information about sequential statistics from only 2 min of listening experience . Although experience with speech in the real world is unlikely to be as concentrated as it was in these studies , infants in more natural settings presumably benefit from other types of cues correlated with statistical information . Our results raise the intriguing possibility that infants possess experience-dependent mechanisms that may be powerful enough to support not only word segmentation but also the acquisition of other aspects of language . It remains unclear whether the statistical learning we observed is indicative of a mechanism specific to language acquisition or of a general learning mechanism applicable to a broad range of distributional analyses of environmental input -LRB- 22 -RRB- . Regardless , the existence of computational abilities that extract structure so rapidly suggests that it is premature to assert a priori how much of the striking knowledge base of human infants is primarily a result of experience-independent mechanisms . In particular , some aspects of early development may turn out to be best characterized as resulting from innately biased statistical learning mechanisms rather than innate knowledge . If this is the case , then the massive amount of experience gathered by infants during the first postnatal year may play a far greater role in development than has previously been recognized . REFERENCES AND NOTES 1 . Certain species-specific skills develop without any experiential input , including bat echolocation -LSB- E. Gould , Dev . Psychobiol . 8 , 33 -LRB- 1975 -RRB- -RSB- and cricket song -LSB- R. Hoy , Am . Zool . 14,1067 -LRB- 1974 -RRB- -RSB- . 2 . Examples of behaviors mediated by early experience are imprinting -LSB- E. Hess , Imprinting -LRB- Van Nostrand , New York , 1973 -RRB- ; M. Leon , Physiol . Behav . 14 , 311 -LRB- 1975 -RRB- -RSB- and suckling responses in newborn rats -LSB- M. H. Teicher and E. M. Blass , Science 198 , 635 -LRB- 1977 -RRB- -RSB- . 3 . These milestones have been well-documented both in English -LSB- for example , R. Brown , A First Language -LRB- Harvard Univ. . Press , Cambridge , MA , 1973 -RRB- -RSB- and cross-linguistically -LSB- for example , E. Lenneberg , Biological Foundations of Language -LRB- Wiley , New York , 1967 -RRB- ; D. Slobin , Ed. , vols . 1 to 3 of The Crosslinguistic Study of Language Acquisition -LRB- Erlbaum , Hillsdale , NJ , 1985,1987,1992 -RRB- -RSB- . 4 . This `` argument from the poverty of the stimulus '' remains widely accepted -LSB- for example , N. Chomsky , Aspects of the Theory of Syntax -LRB- MIT Press , Cambridge , MA , 1965 -RRB- ; S. Crain , Behav . Brain Sci . 14 , 597 -LRB- 1991 -RRB- -RSB- . 5 . D. Bickerton , Behav . Brain Sci . 7 , 173 -LRB- 1984 -RRB- ; N. Chomsky , Rules and Representations -LRB- Columbia Univ. . Press , New York , 1981 -RRB- ; J. Fodor , Modularity of Mind -LRB- MIT Press , Cambridge , MA , 1983 -RRB- ; L. Gleitman and E. Newport , in Language : An Invitation to Cognitive Science , L. Gleitman and M. Liberman , Eds . -LRB- MIT Press , Cambridge , MA , 1995 -RRB- , pp. 1-24 . 6 . Examples include vowel structure -LSB- P. K. Kuhl , K. A. Williams , F. Lacerda , K. N. Stevens , B. Lindblom , Science 255 , 606 -LRB- 1992 -RRB- -RSB- , phonotactics -LSB- P. Jusczyk , A. Friederici , J. Wessels , V. Svenkerud , A. Jusczyk , J. Mem . Lang . 32 , 401 -LRB- 1993 -RRB- -RSB- , and prosodic structure -LSB- P. Jusczyk , A. Cutler , N. Redanz , Child Dev . 64 , 675 -LRB- 1993 -RRB- -RSB- . 7 . Exceptions include research on prenatal exposure to maternal speech -LSB- A. DeCasper , J.-P . Lecanuet , M.-C . Busnel , C. Granier-Deferre , R. Maugeais , Infant Behav . Dev . 17 , 159 -LRB- 1994 -RRB- -RSB- and early postnatal preferences -LSB- J. Mehler et al. , Cognition 29 , 149 -LRB- 1988 -RRB- -RSB- . 8 . R. Cole and J. Jakimik , in Perception and Production of Fluent Speech , R. Cole , Ed . -LRB- Erlbaum , Hillsdale , NJ , 1980 -RRB- , pp. 133-163 . 9 . P. Jusczyk and R. Aslin , Cognitive Psychol . 29 , 1 -LRB- 1995 -RRB- . 10 . A. Christophe , E. Dupoux , J. Bertoncini , J. Mehler , J. Acoust . Soc . Am . 95 , 1570 -LRB- 1994 -RRB- ; A. Cutler and D. Carter , Comput . Speech Lang . 2 , 133 -LRB- 1987 -RRB- . 11 . Z. Harris , Language 31 , 190 -LRB- 1955 -RRB- ; J. Hayes and H. Clark , in Cognition and the Development of Language , J. Hayes , Ed . -LRB- Wiley , New York , 1970 -RRB- . See M. Brent and T. Cartwright -LSB- Cognition 61 , 93 -LRB- 1996 -RRB- -RSB- for a discussion of related statistical cues to word boundaries . 12 . The transitional probability of Y , X frequency of XY frequency of X 13 . J. Saffran , E. Newport , R. Aslin , J. Mem . Lang . 35 , 606 -LRB- 1996 -RRB- ; R. Tunick , S. Barrueco , Psychol . Sci. , in press . 14 . Each infant was tested individually while seated on the parent 's lap in a sound-attenuated booth . Synthetic speech was generated off-line by the MacinTalk system and stored on disk at a sampling rate of 22 kHz for on-line playback through an Audiomedia board in an Apple Quadra 650 computer . An observer outside the testing booth monitored the infant 's looking behavior with the use of a color video system , using a buttonbox connected to the computer to initiate trials and score head-turn responses . Both the parent and the observer listened to masking music over headphones to eliminate bias . During the 2-min familiarization phase , the infant 's gaze was first directed to a blinking light located on the front wall of the testing booth , and then the sound sequence was presented from two loudspeakers located on the side walls . The infant 's gaze was directed to one of two blinking lights on these side walls during familiarization , but there was no relation between lights and sound . Immediately after familiarization , 12 test trials were presented -LRB- six words and six nonwords -RRB- . Each test trial began with the central blinking light . When the observer signaled with a button press that the infant had fixated on the central light , one of the two side blinking lights was turned on and the center light was extinguished . When the infant faced the side light -LRB- a head turn of at least 300 in the direction of the light -RRB- , the three-syllable test string was played and repeated until the infant looked away from the light for 2 s or until 15 s of looking had occurred . The observer simply recorded the direction of the infant 's head turn , and the computer measured looking times , determined when the 2-s lookaway criterion had been met , and controlled the randomization and presentation of stimuli . Cumulative looking time across each of the two types of test trials provided the measure of preference . 15 . The direction of the fixation preference depends on the degree of familiarity with the stimuli . If the infants have become highly familiar with the stimuli , they show dishabituation behavior , preferring the novel stimuli . 16 . Two counterbalanced stimulus conditions were generated . For each condition , 45 tokens of each of four trisyllabic nonsense words -LRB- condition A : tupiro , golabu , bidaku , and padoti ; condition B : dapiku , tilado , burobi , and pagotu -RRB- were spoken in random order to create a 2-min speech stream , with the stipulation that the same word never occurred twice in a row . 17 . Test stimuli : tupiro , golabu , dapiku , and tilado . In condition A , the first two strings were words and the last two strings were nonwords -LRB- the transitional probabilities between the syllables in the nonwords were all zero relative to the exposure corpus , as these syllable pairs had never occurred during familiarization -RRB- . In condition B , the first two strings were nonwords and the last two strings were words . This between-subjects counterbalanced design ensured that any observed preferences for words or nonwords across both conditions would not be artifacts of any general preferences for certain syllable strings . Each of the four test strings were presented -LRB- repeated with a 500-ms interval between test strings -RRB- on three different trials , resulting in a total of 12 test trials per infant . 18 . There were no significant differences between the infants in condition A and condition B : t -LRB- 22 -RRB- = 0.31 The data from the two groups were thus combined for the other analyses . 19 . Condition A words : pabiku , tibudo , golatu , and daropi ; condition B words : tudaro , pigola , bikuti , and budopa . 20 . Test stimuli : pabiku , tibudo , tudaro , and pigola . In condition A , the first two strings were words and the second two strings were part-words . For example , the part-word pigola spanned the word boundary between daropi #golatu and thus was heard during exposure . In condition B , the first two strings were part-words and the second two strings were words . The part-words were thus three-syllable sequences that the infants had heard during the course of the exposure period . The difficulty of this test discrimination can be seen by comparing the transitional probabilities between the syllables in the words -LRB- 1.0 between syllables 1 and 2 and between syllables 2 and 3 -RRB- to the transitional probabilities between the syllables in the part-words -LRB- 0.33 between syllables 1 and 2 and 1.0 between syllables 2 and 3 -RRB- . 21 . There were no significant differences between the infants in condition A and condition B : t -LRB- 22 -RRB- = 0.49 . The data from the two groups were thus combined for the other analyses . 22 . For example , this same general mechanism could be used to find an object , such as a human face , in the environment . 23 . We thank J. Gallipeau , J. Hooker , P. Jusczyk , A. Jusczyk , T. Mintz , K. Ruppert , and J. Sawusch for their help with various aspects of this research , and P. Jusczyk , S. Pollak , M. Spivey-Knowlton , and M. Tanenhaus for their helpful comments on a previous draft . Supported by an NSF predoctoral fellowship -LRB- J.R.S. -RRB- , NSF grant SBR9421064 -LRB- R.N.A. -RRB- , and NIH grant DC00167 -LRB- E.L.N. -RRB- . The parents of all participants gave informed consent . 10 May 1996 ; accepted 30 September 1996 1928 SCIENCE * VOL . 274 * 13 DECEMBER 1996 Downloaded from www.sciencemag.org on May 7 , 2008

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O B O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O B O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O B O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O B I O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

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Systems

Biology

A

Brief

Overview

Hiroaki

Kitano

et

al.

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SYSTEMS

BIOLOGY

THE

GENOME

LEGOME

AND

BEYOND

Systems

Biology

A

Brief

Overview

Hiroaki

Kitano

To

understand

biology

at

the

system

level

we

must

examine

the

structure

and

dynamics

of

cellular

and

organismal

function

rather

than

the

characteristics

of

isolated

parts

of

a

cell

or

organism

Properties

of

systems

such

as

robustness

emerge

as

central

issues

and

understanding

these

properties

may

have

an

impact

on

the

future

of

medicine

However

many

breakthroughs

in

experimental

devices

advanced

software

and

analytical

methods

are

required

before

the

achievements

of

systems

biology

can

live

up

to

their

much-touted

potential

Since

the

days

of

Norbert

Weiner

system-level

understanding

has

been

a

recurrent

theme

in

biological

science

-LRB-

-RRB-

The

major

reason

it

is

gaining

renewed

interest

today

is

that

progress

in

molecular

biology

particularly

in

genome

sequencing

and

high-throughput

measurements

enables

us

to

collect

comprehensive

data

sets

on

system

performance

and

gain

information

on

the

underlying

molecules

This

was

not

possible

in

the

days

of

Weiner

when

molecular

biology

was

still

an

emerging

discipline

There

is

now

a

golden

opportunity

for

system-level

analysis

to

be

grounded

in

molecular-level

understanding

resulting

in

a

continuous

spectrum

of

knowledge

System-level

understanding

the

approach

advocated

in

systems

biology

-LRB-

-RRB-

requires

a

shift

in

our

notion

of

what

to

look

for

in

biology

While

an

understanding

of

genes

and

proteins

continues

to

be

important

the

focus

is

on

understanding

a

system

's

structure

and

dynamics

Because

a

system

is

not

just

an

assembly

of

genes

and

proteins

its

properties

can

not

be

fully

understood

merely

by

drawing

diagrams

of

their

interconnections

Although

such

a

diagram

represents

an

important

first

step

it

is

analogous

to

a

static

roadmap

whereas

what

we

really

seek

to

know

are

the

traffic

patterns

why

such

traffic

patterns

emerge

and

how

we

can

control

them

Identifying

all

the

genes

and

proteins

in

an

organism

is

like

listing

all

the

parts

of

an

airplane

While

such

a

list

provides

a

catalog

of

the

individual

components

by

itself

it

is

not

sufficient

to

understand

the

complexity

underlying

the

engineered

object

We

need

to

know

how

these

parts

are

assembled

to

form

the

structure

of

the

airplane

This

is

analogous

to

drawing

an

exhaustive

diagram

of

gene-regulatory

networks

and

their

biochemical

interactions

Such

diagrams

provide

limited

knowledge

of

how

changes

to

one

part

of

a

system

may

affect

other

parts

but

to

understand

how

a

particular

system

functions

we

Sony

Computer

Science

Laboratories

Inc.

Higashi-Gotanda

Shinagawa

Tokyo

Japan

and

Kitano

Symbiotic

Systems

Project

ERATO

JST

and

the

Systems

Biology

Institute

Suite

Jingumae

Shibuya

Tokyo

Japan

E-mail

kitano@csl.sony.co.jp

REVIEW

must

first

examine

how

the

individual

components

dynamically

interact

during

operation

We

must

seek

answers

to

questions

such

as

What

is

the

voltage

on

each

signal

line

How

are

the

signals

encoded

How

can

we

stabilize

the

voltage

against

noise

and

external

fluctuations

And

how

do

the

circuits

react

when

a

malfunction

occurs

in

the

system

What

are

the

design

principles

and

possible

circuit

patterns

and

how

can

we

modify

them

to

improve

system

performance

A

system-level

understanding

of

a

biological

system

can

be

derived

from

insight

into

four

key

properties

-RRB-

System

structures

These

include

the

network

of

gene

interactions

and

biochemical

pathways

as

well

as

the

mechanisms

by

which

such

interactions

modulate

the

physical

properties

of

intracellular

and

multicellular

structures

-RRB-

System

dynamics

How

a

system

behaves

over

time

under

various

conditions

can

be

understood

through

metabolic

analysis

sensitivity

analysis

dynamic

analysis

methods

such

as

phase

portrait

and

bifurcation

analysis

and

by

identifying

essential

mechanisms

underlying

specific

behaviors

Bifurcation

analysis

traces

time-varying

change

-LRB-

s

-RRB-

in

the

state

of

the

system

in

a

multidimensional

space

where

each

dimension

represents

a

particular

concentration

of

the

biochemical

factor

involved

-RRB-

The

control

method

Mechanisms

that

systematically

control

the

state

of

the

cell

can

be

modulated

to

minimize

malfunctions

and

provide

potential

therapeutic

targets

for

treatment

of

disease

-RRB-

The

design

method

Strategies

to

modify

and

construct

biological

systems

having

desired

properties

can

be

devised

based

on

definite

design

principles

and

simulations

instead

of

blind

trial-and-error

Progress

in

any

of

the

above

areas

requires

breakthroughs

in

our

understanding

of

computational

sciences

genomics

and

measurement

technologies

and

integration

of

such

discoveries

with

existing

knowledge

Identification

of

gene-regulatory

logic

-LRB-

-RRB-

and

biochemical

networks

is

a

major

challenge

The

conventional

methods

for

creating

a

network

model

include

performing

a

series

of

ex

periments

to

identify

specific

interactions

and

conducting

extensive

literature

surveys

Several

attempts

are

under

way

to

create

a

large-scale

comprehensive

database

on

gene-regulatory

and

biochemical

networks

-LRB-

-RRB-

Although

such

databases

are

useful

sources

of

knowledge

many

network

structures

remain

to

be

identified

Substantial

research

has

been

done

on

expression

profiling

in

which

clustering

analysis

is

used

to

identify

genes

that

are

coexpressed

with

genes

of

known

function

-LRB-

-RRB-

Although

clustering

analysis

provides

insight

into

the

correlation

among

genes

and

biological

phenomena

it

does

not

reveal

the

causality

of

regulatory

relationships

Several

methods

have

been

proposed

to

automatically

discover

regulatory

relationships

solely

on

the

basis

of

microarray

data

-LRB-

-RRB-

At

present

such

methods

use

information

derived

from

mRNA

abundance

so

there

is

limited

scope

to

infer

causality

based

on

transcriptional

regulation

Posttranscriptional

and

posttranslational

mechanisms

of

regulation

must

be

incorporated

as

large-scale

data

become

available

but

many

properties

have

yet

to

be

measured

with

sufficient

accuracy

or

in

high

throughput

Although

it

is

not

possible

to

incorporate

all

the

desired

data

into

the

automated

discovery

system

analysis

of

transcriptional

regulation

may

provide

very

useful

information

because

of

the

possible

hypotheses

it

generates

to

allow

us

to

infer

the

network

structure

In

general

when

multiple

hypotheses

are

generated

by

automated

discovery

analysis

it

reflects

a

lack

of

information

This

type

of

analysis

can

be

combined

with

entropy-based

decision-making

algorithms

to

theoretically

suggest

an

experiment

that

most

reduces

the

number

of

ambiguous

network

hypotheses

Although

such

algorithms

have

yet

to

reach

a

level

of

practical

application

they

may

prove

useful

for

determining

the

optimal

order

of

experiments

needed

to

resolve

ambiguous

hypotheses

-LRB-

-RRB-

Progress

in

this

area

would

lead

to

an

increased

emphasis

on

hypothesisdriven

research

in

biology

-LRB-

Fig.

-RRB-

Once

we

have

attained

an

understanding

of

network

structure

we

will

be

able

to

investigate

network

dynamics

In

reality

analysis

of

dynamics

and

structure

on

the

basis

of

network

dynamics

are

overlapping

processes

because

dynamic

analysis

may

yield

useful

predictions

of

unknown

interactions

For

dynamic

analysis

of

a

cellular

system

we

need

to

create

a

model

But

first

it

is

important

to

carefully

consider

the

purpose

of

model

building

Whether

it

is

to

obtain

an

in-depth

understanding

of

system

behavior

or

to

predict

complex

behaviors

in

response

to

complex

stimuli

we

must

first

define

the

scope

and

abstraction

level

of

the

model

MARCH

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The

choice

of

analytical

method

used

depends

on

the

availability

of

biological

knowledge

to

incorporate

into

the

model

A

steadystate

analysis

can

be

done

using

only

the

network

structure

without

knowing

the

rate

constants

for

a

particular

reaction

For

example

flux

balance

analysis

-LRB-

FBA

-RRB-

was

used

to

predict

switching

of

the

metabolic

pathway

in

Escherichia

coli

under

different

nutritional

conditions

based

on

knowledge

of

only

the

metabolic

network

structure

this

was

experimentally

confirmed

-LRB-

-RRB-

With

some

knowledge

of

steadystate

rate

constants

traditional

stability

analysis

and

sensitivity

analysis

provide

insights

into

how

systems

behavior

changes

when

stimuli

and

rate

constants

are

modified

to

reflect

dynamic

behavior

Bifurcation

analysis

in

which

a

dynamic

simulator

is

coupled

with

analysis

tools

can

provide

a

detailed

illustration

of

dynamic

behavior

-LRB-

-RRB-

This

type

of

analysis

has

become

conventional

in

dynamic

systems

and

is

already

used

in

many

studies

on

biological

simulation

Once

both

the

network

structure

and

its

functional

properties

are

understood

for

a

large

number

of

regulatory

circuits

studies

on

classifications

and

comparison

of

circuits

will

provide

further

insights

into

the

richness

of

design

patterns

used

and

how

design

patterns

of

regulatory

circuits

have

been

modified

or

conserved

through

evolution

The

hope

is

that

intensive

investigation

will

reveal

a

possible

evolutionary

family

of

circuits

as

well

as

a

periodic

table

for

functional

regulatory

circuits

Robustness

is

an

essential

property

of

biological

systems

-LRB-

-RRB-

Understanding

the

mechanisms

and

principles

underlying

biological

robustness

is

necessary

for

an

in-depth

understanding

of

biology

at

the

system

level

The

phenomenological

properties

exhibited

by

robust

systems

can

be

classified

into

three

areas

-LRB-

i

-RRB-

adaptation

which

denotes

the

ability

to

cope

with

environmental

changes

-LRB-

ii

-RRB-

parameter

insensitivity

which

indicates

a

system

's

relative

insensitivity

to

specific

kinetic

parameters

and

-LRB-

iii

-RRB-

graceful

degradation

which

reflects

the

characteristic

slow

degradation

of

a

system

's

functions

after

damage

rather

than

catastrophic

failure

In

engineering

systems

robustness

is

attained

by

using

-LRB-

i

-RRB-

a

form

of

system

control

such

as

negative-feedback

and

feed-forward

control

-LRB-

ii

-RRB-

redundancy

whereby

multiple

components

with

equivalent

functions

are

introduced

for

backup

-LRB-

iii

-RRB-

structural

stability

where

intrinsic

mechanisms

are

built

to

promote

stability

and

-LRB-

iv

-RRB-

modularity

where

subsystems

are

physically

or

functionally

insulated

so

that

failure

in

one

module

does

not

spread

to

other

parts

and

lead

to

system-wide

catastrophe

Not

surprisingly

these

approaches

used

in

engineering

systems

are

also

found

in

biological

systems

Bacterial

chemotaxis

is

an

example

of

negative

feedback

that

attains

all

three

aspects

of

robustness

-LRB-

-RRB-

Redundancy

is

seen

at

the

gene

level

where

it

functions

in

control

of

the

cell

cycle

and

circadian

rhythms

and

at

the

circuit

level

where

it

operates

in

alternative

metabolic

pathways

in

E.

coli

Structural

stability

provides

insensitivity

to

parameter

changes

in

the

network

responsible

for

segment

formation

in

Drosophila

-LRB-

-RRB-

And

modularity

is

exploited

at

various

scales

from

the

cell

itself

to

compartmentalized

yet

interacting

signal-transduction

cascades

-LRB-

-RRB-

To

conduct

a

systems-level

analysis

a

comprehensive

set

of

quantitative

data

is

required

Projects

already

under

way

such

as

the

Alliance

for

Cellular

Signaling

-LRB-

AfCS

-RRB-

-LRB-

-RRB-

are

making

large-scale

measurements

with

the

ultimate

goal

of

creating

an

in-depth

simulation

model

of

cells

Exploratory

studies

on

modeling

should

be

done

at

the

earliest

stage

of

such

a

project

to

identify

where

measurement

bottlenecks

exist

in

building

the

final

model

and

to

avoid

acquiring

data

with

little

value

for

model

building

such

as

measurements

of

insufficient

coverage

and

accuracy

Comprehensiveness

in

measurements

requires

consideration

of

three

aspects

-LRB-

i

-RRB-

factor

comprehensiveness

which

reflects

the

numbers

of

mRNA

transcripts

and

proteins

that

can

be

measured

at

once

-LRB-

ii

-RRB-

time-line

comprehensiveness

which

represents

the

time

frame

within

which

measurements

are

made

and

-LRB-

iii

-RRB-

item

comprehensiveness

which

refers

to

the

simultaneous

measurement

of

multiple

items

such

as

mRNA

and

protein

concentrations

phosphorylation

localization

and

so

forth

Model-based

experiment

planning

dictates

where

accuracy

is

critical

and

where

it

is

not

so

that

resources

can

be

optimally

allocated

Complete

system-level

analysis

of

biological

regulation

requires

high

throughput

and

accurate

measurements

goals

that

are

perhaps

beyond

the

scope

of

current

experimental

practices

Technical

innovations

in

experimental

devices

single-molecule

measurements

femto-lasers

that

permit

visualization

of

molecular

interactions

and

nano-technologies

are

critical

aspects

of

systems

biology

research

For

example

microfluidic

systems

also

known

as

micro-TAS

-LRB-

total

analysis

system

-RRB-

enable

minute

quantities

-LRB-

picoliters

-RRB-

of

samples

to

be

measured

more

rapidly

and

more

precisely

Various

prototypes

for

polymerase

chain

reaction

and

electrophoresis

have

been

developed

-LRB-

-RRB-

Such

methods

not

only

speed

up

measurements

but

also

encourage

automation

Software

infrastructure

is

another

critical

component

of

systems

biology

research

Although

attempts

have

been

made

to

build

simulation

software

and

to

make

use

of

the

many

analysis

and

computing

packages

originally

designed

for

general

engineering

purposes

there

is

no

common

infrastructure

or

standard

to

enable

integration

of

these

resources

The

Systems

Biology

Mark-up

Language

-LRB-

SBML

-RRB-

along

with

CellML

represent

attempts

to

define

a

standard

for

an

XML-based

computer

Fig.

Hypothesis-driven

research

in

systems

biology

A

cycle

of

research

begins

with

the

selection

of

contradictory

issues

of

biological

significance

and

the

creation

of

a

model

representing

the

phenomenon

Models

can

be

created

either

automatically

or

manually

The

model

represents

a

computable

set

of

assumptions

and

hypotheses

that

need

to

be

tested

or

supported

experimentally

Computational

dry

experiments

such

as

simulation

on

models

reveal

computational

adequacy

of

the

assumptions

and

hypotheses

embedded

in

each

model

Inadequate

models

would

expose

inconsistencies

with

established

experimental

facts

and

thus

need

to

be

rejected

or

modified

Models

that

pass

this

test

become

subjects

of

a

thorough

system

analysis

where

a

number

of

predictions

may

be

made

A

set

of

predictions

that

can

distinguish

a

correct

model

among

competing

models

is

selected

for

wet

experiments

Successful

experiments

are

those

that

eliminate

inadequate

models

Models

that

survive

this

cycle

are

deemed

to

be

consistent

with

existing

experimental

evidence

While

this

is

an

idealized

process

of

systems

biology

research

the

hope

is

that

advancement

of

research

in

computational

science

analytical

methods

technologies

for

measurements

and

genomics

will

gradually

transform

biological

research

to

fit

this

cycle

for

a

more

systematic

and

hypothesis-driven

science

SCIENCE

VOL

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from

on

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SYSTEMS

BIOLOGY

THE

GENOME

LEGOME

AND

BEYOND

readable

model

definition

that

enables

models

to

be

exchanged

between

software

tools

Systems

Biology

Workbench

-LRB-

SBW

-RRB-

is

built

on

SBML

and

provides

a

framework

of

modular

open-source

software

for

systems

biology

research

Both

SBML

and

SBW

are

collective

efforts

of

a

number

of

research

institutions

sharing

the

same

vision

-LRB-

-RRB-

How

does

the

idea

of

systems

biology

impact

pharmaceutical

industries

and

medical

practice

The

most

feasible

application

of

systems

biology

research

is

to

create

a

detailed

model

of

cell

regulation

focused

on

particular

signal-transduction

cascades

and

molecules

to

provide

system-level

insights

into

mechanismbased

drug

discovery

-LRB-

-RRB-

Such

models

may

help

to

identify

feedback

mechanisms

that

offset

the

effects

of

drugs

and

predict

systemic

side

effects

It

may

even

be

possible

to

use

a

multiple

drug

system

to

guide

the

state

of

malfunctioning

cells

to

the

desired

state

with

minimal

side

effects

Such

a

systemic

response

can

not

be

rationally

predicted

without

a

model

of

intracellular

biochemical

and

genetic

interactions

It

is

not

inconceivable

that

the

U.S.

Food

and

Drug

Administration

may

one

day

mandate

simulation-based

screening

of

therapeutic

agents

just

as

plans

for

all

highrise

building

are

required

to

undergo

structural

dynamics

analysis

to

confirm

earthquake

resistance

Although

systems

biology

is

in

its

infancy

its

potential

benefits

are

enormous

in

both

scientific

and

practical

terms

A

transition

is

occurring

in

biology

from

the

molecular

level

to

the

system

level

that

promises

to

revolutionize

our

understanding

of

complex

biological

regulatory

systems

and

to

provide

major

new

opportunities

for

practical

application

of

such

knowledge

N.

Weiner

Cybernetics

or

Control

and

Communication

in

the

Animal

and

the

Machine

-LRB-

MIT

Press

Cambridge

MA

-RRB-

H.

Kitano

Foundations

of

Systems

Biology

-LRB-

MIT

Press

Cambridge

MA

-RRB-

E.

H.

Davidson

et

al.

Science

-LRB-

-RRB-

Examples

of

such

databases

are

Signal

Transduction

Knowledge

Environment

-LRB-

STKE

-RRB-

KEGG

-LRB-

-RRB-

EcoCyc

-LRB-

http

ecocyc.org

-RRB-

M.

Eisen

et

al.

Proc

Natl.

Acad

Sci

U.S.A.

-LRB-

-RRB-

S.

Chu

et

al.

Science

-LRB-

-RRB-

S.

Onami

et

al.

in

Foundations

of

Systems

Biology

H.

Kitano

Ed

-LRB-

MIT

Press

Cambridge

MA

-RRB-

pp.

S.

Imoto

et

al.

Pacific

Symposium

on

Biocomputing

-LRB-

World

Scientific

Singapore

-RRB-

pp.

C.

Yoo

et

al.

Pacific

Symposium

on

Biocomputing

-LRB-

World

Scientific

Singapore

-RRB-

pp.

T.

Ideker

et

al.

Pacific

Symposium

on

Biocomputing

-LRB-

World

Scientific

Singapore

-RRB-

pp.

J.

Edwards

et

al.

Nature

Biotechnol

-LRB-

-RRB-

M.

Borisuk

et

al.

J.

Theor

Biol

-LRB-

-RRB-

K.

Chen

et

al.

Mol

Biol

Cell

-LRB-

-RRB-

M.

E.

Csete

J.

C.

Doyle

Science

-LRB-

-RRB-

N.

Barkai

et

al.

Nature

-LRB-

-RRB-

U.

Alon

et

al.

Nature

-LRB-

-RRB-

T.-M

Yi

et

al.

Proc

Natl.

Acad

Sci

U.S.A.

-LRB-

-RRB-

V.

Dassaw

et

al.

Nature

-LRB-

-RRB-

G.

Weng

et

al.

Science

-LRB-

-RRB-

Alliance

for

Cellular

Signaling

-LRB-

-RRB-

M.

Burns

et

al.

Science

-LRB-

-RRB-

R.

Anderson

et

al.

Nucleic

Acids

Res

-LRB-

-RRB-

P.

Simpson

et

al.

Proc

Natl.

Acad

Sci

U.S.A.

-LRB-

-RRB-

P.

Gilles

et

al.

Nature

Biotechnol

-LRB-

-RRB-

Additional

information

can

be

obtained

at

http

or

J.

Gibbs

Science

-LRB-

-RRB-

C.

Sander

Science

-LRB-

-RRB-

D.

Noble

Science

-LRB-

-RRB-

I

thank

J.

Doyle

M.

Simon

and

members

of

ERATO

Kitano

project

for

fruitful

discussions

Supported

by

the

ERATO

and

BIRD

program

of

the

Japan

Science

and

Technology

Corporation

and

the

Rice

Genome

and

Simulation

Project

of

the

Ministry

of

Agriculture

Japan

References

and

Notes

REVIEW

Reverse

Engineering

of

Biological

Complexity

Marie

E.

and

John

C.

Advanced

technologies

and

biology

have

extremely

different

physical

implementations

but

they

are

far

more

alike

in

systems-level

organization

than

is

widely

appreciated

Convergent

evolution

in

both

domains

produces

modular

architectures

that

are

composed

of

elaborate

hierarchies

of

protocols

and

layers

of

feedback

regulation

are

driven

by

demand

for

robustness

to

uncertain

environments

and

use

often

imprecise

components

This

complexity

may

be

largely

hidden

in

idealized

laboratory

settings

and

in

normal

operation

becoming

conspicuous

only

when

contributing

to

rare

cascading

failures

These

puzzling

and

paradoxical

features

are

neither

accidental

nor

artificial

but

derive

from

a

deep

and

necessary

interplay

between

complexity

and

robustness

modularity

feedback

and

fragility

This

review

describes

insights

from

engineering

theory

and

practice

that

can

shed

some

light

on

biological

complexity

The

theory

and

practice

of

complex

engineering

systems

have

progressed

so

radically

that

they

often

embody

Arthur

C.

Clarke

's

dictum

Any

sufficiently

advanced

technology

is

indistinguishable

from

magic

Systems-level

Departments

of

Anesthesiology

and

Cell

and

Developmental

Biology

University

of

Michigan

Medical

School

Ann

Arbor

MI

USA

and

Dynamical

Systems

Electrical

Engineering

and

Bioengineering

California

Institute

of

Technology

Pasadena

CA

USA

To

whom

correspondence

should

be

addressed

Email

doyle@cds.caltech.edu

approaches

in

biology

have

a

long

history

-LRB-

-RRB-

but

are

just

now

receiving

renewed

mainstream

attention

-LRB-

-RRB-

whereas

systems-level

design

has

consistently

been

at

the

core

of

modern

engineering

motivating

its

most

sophisticated

theories

in

controls

information

and

computation

The

hidden

nature

of

complexity

-LRB-

magic

-RRB-

and

discipline

fragmentation

within

engineering

have

been

barriers

to

a

dialog

with

biology

A

key

starting

point

in

developing

a

conceptual

and

theoretical

bridge

to

biology

is

robustness

the

preservation

of

particular

characteristics

despite

uncertain

ty

in

components

or

the

environment

-LRB-

-RRB-

Biologists

and

biophysicists

new

to

studying

complex

networks

often

express

surprise

at

a

biological

network

's

apparent

robustness

-LRB-

-RRB-

They

find

that

perfect

adaptation

and

homeostatic

regulation

are

robust

properties

of

networks

-LRB-

-RRB-

despite

exploratory

mechanisms

that

can

seem

gratuitously

uncertain

-LRB-

-RRB-

Some

even

conclude

that

these

mechanisms

and

their

resulting

features

seem

absent

in

engineering

-LRB-

-RRB-

However

ironically

it

is

in

the

nature

of

their

robustness

and

complexity

that

biology

and

advanced

engineering

are

most

alike

-LRB-

-RRB-

Good

design

in

both

cases

-LRB-

e.g.

cells

and

bodies

cars

and

airplanes

-RRB-

means

that

users

are

largely

unaware

of

hidden

complexities

except

through

system

failures

Furthermore

the

robustness

and

fragility

features

of

complex

systems

are

both

shared

and

necessary

Although

the

need

for

universal

principles

of

complexity

and

corresponding

mathematical

tools

is

widely

recognized

-LRB-

-RRB-

sharp

differences

arise

as

to

what

is

fundamental

about

complexity

and

what

mathematics

is

needed

-LRB-

-RRB-

This

article

sketches

one

possible

view

using

experience

and

theoretical

insights

from

engineering

complexity

that

are

relevant

to

biology

MARCH

VOL

SCIENCE

Downloaded

from

on

May

O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O