insight overview Computational systems biology Hiroaki Kitano Sony Computer Science Laboratories Inc. , 3-14-13 Higashi-gotanda , Shinagwa , Tokyo 141-0022 , ERATO Kitano Symbiotic Systems Project , Japan Science and Technology Corporation , and The Systems Biology Institute , Suite 6A , M31 , 6-31-15 Jingu-mae , Shibuya , Tokyo 150-0001 , School of Fundamental Science and Technology , Keio University , 3-14-1 Hiyoshi , Kohoku-ku , Yokohama , Kanagawa 223-8522 , Japan , and Control and Dynamical Systems , California Institute of Technology , Pasadena , California 91125 , USA -LRB- e-mail : kitano@csl.sony.co.jp -RRB- To understand complex biological systems requires the integration of experimental and computational research -- in other words a systems biology approach . Computational biology , through pragmatic modelling and theoretical exploration , provides a powerful foundation from which to address critical scientific questions head-on . The reviews in this Insight cover many different aspects of this energetic field , although all , in one way or another , illuminate the functioning of modular circuits , including their robustness , design and manipulation . Computational systems biology addresses questions fundamental to our understanding of life , yet progress here will lead to practical innovations in medicine , drug discovery and engineering . I t is often said that biological systems , such as cells , are ` complex systems ' . A popular notion of complex systems is of very large numbers of simple and identical elements interacting to produce ` complex ' behaviours . The reality of biological systems is somewhat different . Here large numbers of functionally diverse , and frequently multifunctional , sets of elements interact selectively and nonlinearly to produce coherent rather than complex behaviours . Unlike complex systems of simple elements , in which functions emerge from the properties of the networks they form rather than from any specific element , functions in biological systems rely on a combination of the network and the specific elements involved . For example , p53 -LRB- a 393-amino-acid protein sometimes called ` the guardian of genome ' -RRB- acts as tumour suppressor because of its position within a network of transcription factors . However , p53 is activated , inhibited and degraded by modifications such as phosphorylation , dephosphorylation and proteolytic degradation , while its targets are selected by the different modification patterns that exist ; these are properties that reflect the complexity of the element itself . Neither p53 nor the network functions as a tumour suppressor in isolation . In this way , biological systems might be better characterized as symbiotic systems . Molecular biology has uncovered a multitude of biological facts , such as genome sequences and protein properties , but this alone is not sufficient for interpreting biological systems . Cells , tissues , organs , organisms and ecological webs are systems of components whose specific interactions have been defined by evolution ; thus a system-level understanding should be the prime goal of biology . Although advances in accurate , quantitative experimental approaches will doubtless continue , insights into the functioning of biological systems will not result from purely intuitive assaults . This is because of the intrinsic complexity of biological systems . A combination of experimental and computational approaches is expected to resolve this problem . A two-pronged attack Computational biology has two distinct branches : knowledge discovery , or data-mining , which extracts the hidden patterns from huge quantities of experimental data , forming hypotheses as a result ; and simulation-based analysis , which tests hypotheses with in silico experiments , providing predictions to be tested by in vitro and in vivo studies . 206 Knowledge discovery is used extensively within bioinformatics for such tasks as the prediction of exon intron and protein structure from sequence1 , and the inference of gene regulatory networks from expression profile2 4 . These methods typically use predictions based on heuristics , on statistical discriminators that often involve sophisticated approaches -LRB- such as hidden Markov models -RRB- and on other linguistic-based algorithms -LRB- see review in this issue by Searls , pages 211 217 -RRB- . In contrast , simulation attempts to predict the dynamics of systems so that the validity of the underlying assumptions can be tested . Detailed behaviours of computer-executable models are first compared with experimental observation . Inconsistency at this stage means that the assumptions that represent our knowledge on the system under consideration are at best incomplete . Models that survive initial validation can then be used to make predictions to be tested by experiments , as well as to explore questions that are not amenable to experimental inquiry . Although traditional bioinformatics has been used widely for genome analysis , simulation-based approaches have received little mainstream attention . This is now changing . Current experimental molecular biology is now producing the high-throughput quantitative data needed to support simulation-based research . Combined with rapid progress of genome and proteome projects , this is convincing increasing numbers of researchers of the importance of a system-level approach5 . At the same time , substantial advances in software and computational power have enabled the creation and analysis of reasonably realistic yet intricate biological models . There are still issues to be resolved , but computational modelling and analysis are now able to provide useful biological insights and predictions for well understood targets such as bifurcation analysis of the cell cycle6 ,7 , metabolic analysis8 ,9 or comparative studies of robustness of biological oscillation circuits10 . It is crucial that individual research groups are able to exchange their models and create commonly accepted repositories and software environments that are available to all . Systems Biology Markup Language -LRB- SBML ; http://www.sbml.org/ -RRB- , CellML -LRB- http://www.cellml.org/ -RRB- and the Systems Biology Workbench are examples of efforts that aim to form a de facto standard and open software platform for modelling and analysis11 ,12 . These significantly increase the value of the new generation of databases concerned with biological pathways , such as the Kyoto 2002 Nature Publishing Group NATURE | VOL 420 | 14 NOVEMBER 2002 | www.nature.com/nature insight overview Clinical , physiological and pharmacological knowledge and data Patient model -LRB- ADME/Tox , pharmacokinetics , disease development , etc. -RRB- Computational experiments to identify candidate lead compounds , treatment optimization and genetic circuits -LRB- virtual screening -RRB- Biological and physiological knowledge and data In vitro and in vivo experiments and clinical studies Drug discovery and treatment optimization ` Wet ' experiments to verify or reject hypotheses Hypothesis testing and knowledge discovery Models of gene regulation , biochemical networks , cells and organs -LRB- including physiome and virtual human -RRB- Computational ` dry'experiments and analysis to screen hypotheses Synthesis of lead compounds and genetic circuits Development of experimental techniques Experiment and treatment design to test hypotheses Experimental design to test hypotheses Figure 1 Linkage of a basic systems-biology research cycle with drug discovery and treatment cycles . Systems biology is an integrated process of computational modelling , system analysis , technology development for experiments , and quantitative experiments18 . With sufficient progress in basic systems biology , this cycle can be applied to drug discovery and the development of new treatments . In the future , in silico experiments and screening of lead candidates and multiple drug systems , as well as introduced genetic circuits , will have a key role in the ` upstream ' processes of the pharmaceutical industry , significantly reducing costs and increasing the success of product and service development . Encyclopedia of Genes and Genomes -LRB- KEGG -RRB- 13 , Alliance for Cellular Signaling -LRB- AfCS -RRB- 14 and Signal Transduction Knowledge Environment -LRB- STKE -RRB- 15 , by enabling them to develop machine-executable models , rather than mere human-readable forms . Such changes are fuelling a renewed interest in a system-level approach to biology , but we should not forget that this is an area with a long history16 ,17 , rooted as much as anywhere in classical physiology -LRB- see review in this issue by Buchman , pages 246 251 -RRB- . However , the close linkage between system-level understanding and molecularlevel knowledge was made possible only by the recent progress in genomics and proteomics . The approach attempts to understand biological systems as systems , specifically targeting the identification of their structures and dynamics , and the establishment of methods to control cellular behaviours by external stimuli and to design genetic circuits with desired properties . These aims will be achieved only by combining computation , system analysis , new technologies for comprehensive and quantitative measurements , and highthroughput quantitative experimental data18 ,19 . Multiple faces of robustness Among various scientific questions , one issue receiving considerable attention is how robustness is achieved and how it evolves within various aspects of biological systems . Robust systems maintain their state and functions against external and internal perturbations , and robustness is an essential feature of biological systems , having been studied since the earliest attempts at a system-oriented view -LRB- for example , Cannon 's homeostasis and Weiner 's cybernetics16 -RRB- . Biological systems have been found to be robust at a variety of levels from genetic switches to physiological reactions -LRB- see review in this issue by Buchman , pages 246 251 -RRB- . Robust systems are both relatively insensitive to alterations of their internal parameters and able to adapt to changes in their environment . In highly robust systems , even damage to their very structure produces only minor alterations in their behaviour . Such properties are achieved through feedback , modularity , redundancy and structural stability . A variety of feedback and feed-forward control is observed throughout biology . For example , integral feedback is central to bacteria chemotaxis20 22 . And p53-based cell-cycle arrest displays what is NATURE | VOL 420 | 14 NOVEMBER 2002 | www.nature.com/nature known in the engineering field as ` bang-bang control ' , a subtype of feedback control . Damage to DNA is sensed by proteins such as ATM -LRB- for ataxia telangiectasia mutated , named after a disease in which this enzyme is mutated -RRB- and DNA-dependent protein kinase , which activate the p53 protein . Active p53 then transactivates p21 , which results in G1 arrest ; this state is released when DNA damage is repaired , thus forming a feedback loop . Cells themselves provide the most obvious form of biological modularity by physically partitioning off biochemical reactions . However , biochemical networks within cells also form modular compartments isolated by spatial localization23 , anchoring of proteins to plasma membranes and by dynamics . Cells also provide redundancy , with many autonomous units carrying out identical roles . But redundancy also appears at other levels by having multiple genes that encode similar proteins , or multiple networks with complementary functions . For example , Per1 , Per2 and Per3 genes encode proteins in the circadian oscillator , but knock-out of one or two of these produces no visible phenotype . The Cln gene family form redundant pairs for the cell cycle24 . The stringent response of Escherichia coli activates alternative metabolic dynamics depending upon the availability of lactose and glucose25 . Structurally stable network configurations increase insensitivity to parameter changes , noise and minor mutations . For example , elegant experiments on the archetypal genetic switch -- the lambda phage decision circuit -- have shown it to be robust against changes in binding affinity of promoters and repressors ; its stable switching action arises from the structure of its network , rather than the specific affinities of its binding site26 . Additionally , a number of networks for biological oscillations and transcriptional regulations have been shown to be tolerant against noise -LRB- ref . 27 ; and see review in this issue by Rao and colleagues , pages 231 237 -RRB- . But only computer simulation could have shown the degree to which the gene regulatory networks for segmentation during Drosophila embryogenesis remain robust over a large range of kinetic parameters28 ,29 . The robustness of a system is not always to an organism 's advantage . Cancer cells are extremely robust for their own growth and survival against various perturbations . They continue to proliferate , driven by the engine of the cell cycle , eliminating 207 2002 Nature Publishing Group insight overview communication with their external environment , thus making it insensitive against external perturbations . In addition , many anticancer drugs are rendered ineffective by the normal functioning of a patient 's body , including defence systems such as the metabolism of xenobiotics -LRB- most notably by cytochrome P450 -RRB- , the brain blood barrier , and the dynamics of gene regulatory circuits , which can adjust the concentration of drug targets through feedback mechanisms and redundancy . To establish treatments that move patients from a stable but diseased state to a healthy one will require an in-depth , system-level understanding of biological robustness . Although the general principles of robust systems are well established , there remain a number of unresolved issues concerning their evolution and execution in specific biological systems , and how they can be manipulated or designed . Control theory has been used to provide a theoretical underpinning of some robust systems , such as adaptation through negative feedback21 . However , this approach has limitations . For example , current control theory assumes that target values or statuses are provided initially for the systems designer , whereas in biology such targets are created and revised continuously by the system itself . Such self-determined evolution is beyond the scope of current control theory . from which they are assembled , such as genetic switches , flip-flops , logic gates , amplifiers and oscillators . Good examples come from the mechanisms of biochemical oscillations -LRB- see review in this issue by Goldbeter , pages 238 245 -RRB- , which have been the focus of numerous groups38 41 . These studies have facilitated their classification into several schemes , such as substrate-depletion oscillators , positive feedback loops , the Goodwin oscillator and time-delayed negative feedback oscillators41 . Similar attempts have also been made for other functional networks . Jordan and colleagues have identified various examples of multitasking in signal transduction42 ; Bhalla and Iyengar reported several circuits that may function as temporal information stores -LRB- that is , memory devices -RRB- 43 ; and Rao and colleagues have uncovered several circuits that mitigate the effect of noise and exploit it for specific functions -LRB- see review in this issue , pages 231 237 -RRB- . Although these functional networks have analogues in electronic and process engineering , they have been formed by evolution , which makes it unlikely that any kind of ` first principle ' underlies their design . However , a set of principles can be envisaged and identified through studying the structure and function of biological circuits , and their origin at the system level44 46 . What are their basic functional building blocks ? What are their dynamical properties and operating principles ? How has each module evolved ? And how can they be adapted or designed for alternative applications ? Recently , a systematic , high-throughput computational study was carried out by Shen-Orr and colleagues , which identified common motifs in the gene regulatory networks of E. coli using the RegulonDB database47 . They found that feed-forward loops , single-input modules and dense overlapping regulons appeared frequently . While this study only used a gene regulation database , this type of approach can be augmented to include protein protein and protein DNA interactions to systematically identify network design patterns from large-scale data . Such data , combined with function-driven identification of circuit patterns , will allow the creation of a large repository of functional biological networks , so enabling the systematic analysis of design patterns and their evolution . We already know of cases where the same circuit patterns and homologous genes produce similar system behaviours , but with unrelated physiological outcomes . We also know of cases where the same circuit patterns use different sets of genes to attain similar system behaviours , and where identical functions are achieved with degenerate paths involving different circuit patterns and different genes46 . More systematic surveys will be needed to determine how many evolutionary conserved circuits exist , in what functions and how they relate to the evolution of genes . It may be that functional circuits should be considered the units of evolution . No free lunch Although robustness is critical in assuring the survival of a biological system , it does not come without cost . Carlson and Doyle emphasize the `` robust , yet fragile '' nature of complex systems exhibiting highly optimized tolerance30 ,31 . Systems designed or evolved to be robust against common or known perturbations can often be fragile to new perturbations . Another view on the vulnerability of complex network comes from a statistical perspective32 34 . Comparative studies on robustness of large-scale networks show that scale-free networks -LRB- also known as ` small world ' or Erd s R nyi networks -RRB- are more robust than randomly connected networks against random failure of their components34 . However , scale-free networks are more vulnerable against malfunction of the few highly connected nodes that function as hubs . Scale-free networks can form by growth such that new nodes are connected preferentially to nodes that are already highly connected . Barabasi and colleagues claim that protein protein interaction networks , which constitute the protein universe -LRB- see review in this issue by Koonin and colleagues , pages 218 223 -RRB- , are scale-free32 ,35 and that mutations in highly connected proteins are more likely to be lethal than are mutations in less-connected nodes33 . Although they estimated connectivity from yeast two-hybrid data , which are notoriously noisy , this hypothesis is intuitively attractive . For example , the p53 protein is one of the most connected hubs in the protein universe , and its mutations cause serious damage to cellular functions , particularly in repair of DNA damage and tumour suppression36 . Nevertheless , some of the claims for scale-free networks are still controversial37 , and evidence for mechanisms leading to preferential attachment in biological systems remains equivocal . Furthermore , yeast two-hybrid assays produce many false-positive outcomes , and the current hand-crafted pathway maps may be heavily biased towards connection to functionally important genes simply because these have been popular targets for research . Even when these shortcomings are surpassed , such statisticsbased theories -- despite providing insights on macroscopic properties of the network -- will still have difficulty making predictions about specific interactions . It is analogous to telling a stock-market investor that `` one in 50 companies will go bankrupt '' , advice that is of little help if you are unable to identify which one . The challenge for statistical theories is to identify how they can be linked to specific behaviours and so make useful predictions . Systems drug and treatment discovery The systems biology approach , with its combination of computational , experimental and observational enquiry , is highly relevant to drug discovery and the optimization of medical treatment regimes for individual patients . Although the analysis of individual single nucleotide polymorphisms is expected to reveal individual genetic susceptibilities to all forms of pathological condition , it may be impossible to identify such relationships when complex interactions are involved . Consider a hypothetical example where variations of gene A induce a certain disease . Susceptibility relationships may not be apparent if circuits exist to compensate for the effects of the variability . Polymorphisms in gene A will be linked to disease susceptibility only if these compensatory circuits break down for some reason . A more mechanistic , systems-based analysis will be necessary to elucidate more complex relationships involving multiple genes that may create new opportunities for drug discovery and treatment optimization . Computer simulation and analysis , along with traditional bioinformatics approaches , have frequently been proposed to significantly increase the efficiency of drug discovery48 50 . At present , empirical ADME/Tox -LRB- absorption distribution metabolism excretion/toxicity -RRB- and pharmacokinetic predictions have been used with some success . Design patterns of functional modules Just as the principles behind robust networks can be classified into several types , so too can the various functional circuits or modules 208 2002 Nature Publishing Group NATURE | VOL 420 | 14 NOVEMBER 2002 | www.nature.com/nature insight overview For example , a human intestinal absorption model based on correlations between the passive permeation measurement of over 300 compounds and known structural features , such as hydrogen-bond donors , hydrogen-bond acceptors and molecular weight , has been used to predict the absorption of novel compounds by the human intestine51 . However , such models are not easily converted for use in other situations and they often require extensive data sets in order to address specific questions . What is needed are reliable , mechanism-based ADME/Tox and pharmacokinetic models52 56 , built on molecular-level models of cells , that are more easily transferable and accountable than are traditional , empirical , quantitative structure activity relations . system-oriented view may drastically change the way treatments are conducted . Two somewhat speculative scenarios illustrate these opportunities . Consider a feedback compensation circuit involving a drug target protein . Changes in the concentration of the protein resulting from drug administration may be neutralized by feedback control . High dosages of drugs will need to be administered to overcome this compensation mechanism , but this could produce serious side effects . Alternatively , small dosages of drugs could mitigate the feedback mechanism , so that the effect on the target protein will not be neutralized . Considering the p53 system , if there is abnormal overexpression of MDM2 -LRB- a protein that regulates p53 -RRB- , simply increasing p53 transcription may not restore the system to normal , as the excessive MDM2 protein will quickly ubiquitinate p53 , targeting it for destruction . Additionally , p53 itself transactivates MDM2 . MDM2 activity must be suspended or reduced to a normal level , at least temporarily , to make p53 stimulation effective in inducing cell-cycle arrest or apoptosis . The highly effective administration of multiple drug regimes can be accomplished only with a system-level analysis of the dynamics of gene regulatory circuits . A far more futuristic approach proposes the introduction of functional genetic circuits to control cellular dynamics in vivo -LRB- see review in this issue by Hasty and colleagues , pages 224 230 -RRB- . Already , a set of basic functional circuits , such as oscillators and toggle switches , has been constructed and its viability confirmed in E. coli -LRB- refs 69 71 ; and see review by Hasty and colleagues -RRB- . Computer simulation and comprehensive analysis will be needed to ensure that such circuits function as intended and do not result in significant side-effects . In the future , perhaps a genetic circuit can be devised to sense the level of p53 protein when DNA is damaged and switch on circuits to further increase transcription of p53 . The application of systems biology to medical practice is the future of medicine . Its realization will see drug discovery and the design of multiple drug therapies and therapeutic gene circuits being pursued just as occurs now with modern , complex engineering products -- through iterative cycles of hypothesis and simulation-driven processes -LRB- Fig. 1 -RRB- . Although the road ahead is long and winding , it leads to a future where biology and medicine are transformed into precision engineering . s doi : 10.1038 / nature01254 1 . Baldi , P. & Brunak , S. Bioinformatics : The Machine Learning Approach 2nd edn -LRB- MIT Press , Cambridge , MA , 2001 -RRB- . 2 . Onami , S. , Kyoda , K. , Morohashi , M. & Kitano , H. in Foundations of Systems Biology -LRB- ed . Kitano , H. -RRB- 59 75 -LRB- MIT Press , Cambridge , MA , 2001 -RRB- . 3 . Ideker , T. E. , Thorsson , V. & Karp , R. M. in Pac . Symp . Biocomput . -LRB- eds Altman , R. B. , Dunker , A. K. , Hunter , L. , Lauderdale , K. & Klein , T. E. -RRB- 305 316 -LRB- World Scientific , Singapore , 2000 -RRB- . 4 . Ideker , T. et al. . Discovering regulatory and signalling circuits in molecular interaction networks . Bioinformatics 18 -LRB- Suppl . 1 -RRB- , S233 S240 -LRB- 2002 -RRB- . 5 . Ideker , T. et al. . 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Robustness in bacterial chemotaxis . Nature 397 , 168 171 -LRB- 1999 -RRB- . Scaling up So far , most systems biology simulations have tended to target relatively small sub-networks within cells , such as the feedback circuit for bacteria chemotaxis20 ,21 , the circadian rhythm57 ,58 , parts of signal-transduction pathways43 ,59 , simplified models of the cell cycle7 ,60,61 and red blood cells62 64 . Notable larger simulations have attempted to model bacterial metabolic networks for analysis of metabolic control62 ,63 and flux balance8 ,65 , but these deal with steady-state rather than dynamic behaviour . Recently , research has begun on larger-scale simulations . At the level of the biochemical network , simulation of the epidermal growth factor -LRB- EGF -RRB- signal-transduction cascade has been carried out . The simulation involves over 100 equations and kinetic parameters and will be used to predict complex behaviours of the pathway , as well as to identify roles of external and internal EGF receptors59 . The physiome project is an ambitious attempt to create virtual organs that represent essential features of organs in silico66 ,67 . Simulation of the heart was one of the early attempts in this direction , integrating multiple scales of models from genetics to physiology68 . Even whole-patient models for specific disease , such as obesity and diabetes , are being developed for prediction of disease development and drug discovery . Building a full-scale patient model , or even a whole-cell or organ model , is a challenging enterprise . Multiple aspects of biological processes have to be integrated and the model predictions must be verified by biological and clinical data , which are at best sparse for this purpose . Integrating heterogeneous simulation models is a non-trivial research topic by itself , requiring integration of data of multiple scales , resolutions and modalities . Simulation often requires integration of multiple hierarchies of models that are orders of magnitude different in terms of scale and qualitative properties -LRB- for example , gene regulations , biochemical networks , intercellular communications , tissue , organ and patient -RRB- . Although some processes can be modelled by either stochastic computation or differential equations alone , many require a combination of both methods . But some biochemical processes take place within a millisecond whereas others can take hours or days . Additionally , biological processes often involve the interaction of different types of process , such as biochemical networks coupled to protein transport , chromosome dynamics , cell migration or morphological changes in tissues . Although biochemical networks may be reasonably modelled using differential equations and stochastic simulation , many cell biological phenomena require calculation of structural dynamics , deformation of elastic bodies , spring-mass models and other physical processes . Nevertheless , development of precision models and their applications to ADME/Tox models are expected to revolutionize the process of drug discovery by providing a capability for multipletarget identification and high-throughput virtual screening of compounds . Furthermore , target identification using cellular models may provide desirable structures for candidate compounds by applying multiple constraints to parallel virtual screening54 , rationalizing drug discovery into a more systematic process -LRB- Fig. 1 -RRB- . 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This research is , in part , supported by : the Rice Genome and Simulation Project -LRB- Ministry of Agriculture -RRB- , International Standard Development area of International Joint Research Grant -LRB- New Energy and Industrial Technology Development Organization -LRB- NEDO -RRB- / Japanese Ministry of Economy , Trade and Industry -LRB- METI -RRB- -RRB- , Exploratory Research for Advanced Technology -LRB- ERATO -RRB- and Institute for Bioinformatics Research and Development -LRB- BIRD -RRB- program -LRB- Japan Science and Technology Corporation -RRB- , and through the special coordination funds for promoting science and technology from the Japanese government 's Ministry of Education , Culture , Sports , Science , and Technology . 210 2002 Nature Publishing Group NATURE | VOL 420 | 14 NOVEMBER 2002 | www.nature.com/nature

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O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O