Next Article in Journal
Perennial Forages as Second Generation Bioenergy Crops
Next Article in Special Issue
Remarks on Muscle Contraction Mechanism
Previous Article in Journal
Purification and Preliminary Crystallographic Analysis of a New Lys49-PLA2 from B. Jararacussu
Previous Article in Special Issue
Ballistic Protons and Microwave-induced Water Solitons in Bioenergetic Transformations
Article Menu

Export Article

Int. J. Mol. Sci. 2008, 9(5), 751-767; doi:10.3390/ijms9050751

Metabolic Compartmentation – A System Level Property of Muscle Cells
Valdur Saks 1,2,*, Nathalie Beraud 1 and Theo Wallimann 3
Laboratory of Bioenergetics, INSERM U884, Joseph Fourier University Grenoble, France
Laboratory of Bioenergetics, National Institute of Chemical and Biological Physics, Tallinn, Estonia
Institute of Cell Biology, ETH Zurich Hönggerberg, HPM D24, CH-8093 Zuerich, Switzerland
Author to whom correspondence should be addressed; Laboratory of Bioenergetics, Joseph Fourier University, 2280, Rue de la Piscine, BP53X – 38041 Grenoble Cedex 9, France; Tel.: 0033476635627; Fax: 0033476514218
Received: 15 April 2008; in revised form: 8 May 2008 / Accepted: 8 May 2008 / Published: 9 May 2008


Problems of quantitative investigation of intracellular diffusion and compartmentation of metabolites are analyzed. Principal controversies in recently published analyses of these problems for the living cells are discussed. It is shown that the formal theoretical analysis of diffusion of metabolites based on Fick's equation and using fixed diffusion coefficients for diluted homogenous aqueous solutions, but applied for biological systems in vivo without any comparison with experimental results, may lead to misleading conclusions, which are contradictory to most biological observations. However, if the same theoretical methods are used for analysis of actual experimental data, the apparent diffusion constants obtained are orders of magnitude lower than those in diluted aqueous solutions. Thus, it can be concluded that local restrictions of diffusion of metabolites in a cell are a system-level properties caused by complex structural organization of the cells, macromolecular crowding, cytoskeletal networks and organization of metabolic pathways into multienzyme complexes and metabolons. This results in microcompartmentation of metabolites, their channeling between enzymes and in modular organization of cellular metabolic networks. The perspectives of further studies of these complex intracellular interactions in the framework of Systems Biology are discussed.
diffusion; metabolic (micro)compartmentation; cell structure; cytoskeleton; multienzyme complexes; channeling; regulation; Systems Biology
“A new scientific theory does not triumph by convincing its opponents and making them see the light, but rather because its opponents eventually die and a new generation grows up that is familiar with it”
Max Planck Scientific autobiography and other papers, New York, 1949, pp 33–34


Intensive studies of metabolic and signaling networks during more than three decades have resulted in a clear understanding of the importance of intracellularly restricted diffusion of metabolites, metabolite compartmentation and metabolite channeling for cellular metabolism, energetics, calcium signaling and signal transduction [13]. Indeed, numerous teams and laboratories have shown both experimentally and theoretically that the specific organization of the intracellular medium, often and wrongfully called “cytosol”, results in metabolic compartmentation, notably in muscle and brain cells [422]. Compartmentation, however, also means heterogeneity of intracellular diffusion [2327]. In spite of all these developments, there are still rather numerous groups of investigators who consider the content of a cell as a homogeneous diluted aqueous medium, and this is why the philosophical, somehow skeptical commentary of Max Planck given in the epigraph of this paper seems to represent a valid and proper description of the situation. Good examples of this controversy are two recent publications on the same topic in the Biophysical Journal, one in May 2007 [28] and another in June 2007 [29], concerned with spatial metabolite distributions and metabolite domains. By using similar theoretical methods, the two research teams come to totally opposite conclusions. This contradiction concerns one of the most important topics in modern cellular biochemistry, that is, the influence of intracellular organization on the regulation of metabolic processes. In the most recent article of the two mentioned above, the authors, Barros and Martinez [29], arrive at the remarkable, classical, general, firm and philosophical conclusion that “the notion of cytosolic compartmentation of metabolites needs reevaluation, as it seems to be in conflict with underlying physical chemistry”. The first paper by Selivanov et al., 2007 [28], however, shows exactly the opposite, that is, the real existence of metabolic compartments by modeling of spatial metabolite distribution in the cardiac sarcomere [28]. Both studies are based on the use of the same Fick's equation of diffusion [28, 29]. The principal difference is that one of articles (by Barros and Martinez) is purely theoretical and based entirely on the belief in simple diffusion in a homogenous medium [29], the second one, by Selivanov et al. [28] is based on a serious analysis of experimental data by quantitative methods and modeling.
Analysis of these two conflicting articles and their different historical and ideological backgrounds is most intriguing for the discussion of possible directions of development of strategies of metabolic research in the future. This is especially important because of the very rapid emergence of Systems Biology that is largely based on the application of mathematical modeling methods to systems with various complexities, in which compartmentation becomes one of the most important system – level properties, not predictable from the properties of isolated components only [27, 3034].

The need of experimental data for theoretical analysis

Barros and Martinez [29] consider the cell as a sphere of definite radius; inside these cells, a metabolite is produced by a single source and diffuses freely in a homogenous, isotropic medium, the “cytosol”. Thus, the cell is by definition considered as a “bag of enzymes”, these enzymes being the source of metabolites and the cell interior is a priori taken as a homogenous medium. This represents nothing less than the long - time advocated classical concept of cellular metabolism that is based entirely on mass action laws in homogenous diluted solutions, thus, on the physical chemistry of the glorious times of van't Hoff, Ostwald, Fick, Einstein – Smoluchowski and others, more than one hundred years ago. The analytical tools used by Barros and Martines [29] are restricted to the use of theory of Brownian diffusion and the turnover numbers of enzymes involved as either generators or sinks for the metabolites considered. The authors take the diffusion coefficients for metabolites determined for bulk water phase as the only realistic values, which for ATP would be equal to about 500 μm2/s [29]. The fact that these values for diffusion coefficients may be significantly different inside of cells with a protein concentration of approximately 200–300 mg/mL were not considered at all [29]. Thus, the possibility that physical barriers resulting from a dense cytoskeletal or mitochondrial network or from macromolecular crowding [127] were not taken into account at all. Taking this for granted, the authors conclude that “even under most favorable conditions that are compatible with the known physical constraints, it would be impossible that ATP pools could appear in the cytosol of a compact cell”, and then conclude that “unrealistic conditions were needed to form ATP domains” [29]. No experimental data were analyzed to verify the correctness of this model and the conclusions derived [29]. However, comparison of the same model with real experimental data was performed and published one month before in the very same Biophysical Journal with most remarkable results, e.g. what seemed most unrealistic to Barros and Martinez was, by analysis of experimental data by Selivanov et al. [28], found to be most realistic, since the experimental results showed that the apparent diffusion coefficients for ATP are locally at least three orders of magnitudes lower than those determined for the bulk water phase of the cells [28]. In the same volume of Biophysical Journal another article supported this conclusion: Iancu et al. [35] presented convincing evidence for compartmentation of cAMP in cardiac cells, with discussion of all available data on this subject, all explained by local restrictions of diffusion of cAMP and its compartmentation [3537]. These data are in good concord with the majority of developments of cellular biochemistry and particularly cellular bioenergetics during the last three decades, which have led to a clear understanding that a cell's interior, even in “compact cells” as they are called by Barros and Martinez [29], is clearly not a homogenous solution corresponding to a “bag of enzymes” but a highly organized medium with a well defined, structured dense cytoskeleton, where macromolecular crowding and protein-protein interactions etc., play important physiological roles, e.g. by giving rise to multienzyme complexes, metabolons and other organized systems with microcompartmentation and metabolic channeling as a main mechanisms for their proper and efficient functioning, as has been described in a large number of articles (references 1–27 are only some of them), none of which was referred to by the authors of reference [29].

Critical analysis of application of diffusion theories in cellular systems

Diffusion of metabolites in organized intracellular media has been studied for several decades with very clear results showing its restriction due to many physical factors and a multitude of parameters that are characteristic for the intracellular milieu [127, 3840]. A most interesting and profound analysis has been performed by Wheatley and Agutter et al. [3840], who even arrived at the rather skeptical conclusion that the classical diffusion theory could not be directly applicable for the description of intracellular transport mechanisms and that its direct and non-critical use in biology would thus represent “a relic of mechanistic materialism” [38]. Classical diffusion theory has a macroscopic aspect, relating measurable movement rates of solute molecules to concentration gradients and physical characteristics of the bulk medium by Fick's law of diffusion (derived by Fick in 1855) [41], and a microscopic aspect which explains the macroscopic phenomena in molecular terms – the Einstein-Smoluchowski model of Brownian movement, deduced independently by Einstein in 1905 [42] and Smoluchowski in 1906 [43]. The latter works led to validation of the atomic theory [44]. Fick's first equation describes the linear dependence of the rate of diffusion across the unit surface, dc/dt (or flux J, J = dc/dt) on the concentration gradient dc/dx:
J = - D (dc/dx)
where the D is diffusion coefficient or diffusivity.
Both Einstein and Smoluchowski described diffusion at a microscopic level, describing its molecular mechanism – the Brownian movement [4247]. The Einstein – Smoluchowski equation gives the relationship between diffusion coefficient, D, distance of displacement, λ, and time, t, of this displacement t:
D = λ 2 / 2 t
This equation was found for the movement in one dimension [4247]. For two- and three-dimensional displacement of particles this equation will be D = λ2/4t and D = λ2/6t, respectively [4547]. These equations are valid for any kind of random movement of particles, known as random walk mechanism [47].
The diffusion coefficient or diffusivity D described by Einstein – Smoluchowski's equation is related to the particle radius r and the viscosity of the medium η by Stokes – Einstein equation [4447]:
D= kT/6πηr
All these classical theories have been developed for weakly interacting rigid particles at sufficiently low concentrations. Agutter et al. have found that at least eleven conditions assumed in derivation of Fick's law and the Einstein – Smoluchovski model are not entirely met in the intracellular milieu [3840]. The problem starts with cellular water, the intracellular mobility of which is significantly reduced, leading to partitioning of metabolites between different water phases and to changes in binding constants [3840]. Then there is low-affinity adsorbtion of metabolites, especially if charged as ATP, to intracellular surfaces increasing the viscosity. Due to this fact, the diffusion coefficient of metabolites is decreased by a factor of (1+C/Kd)−1 where C is concentration of binding sites and Kd is dissociation constant of solute from these complexes [40]. Furthermore, macromolecular crowding and cytoskeletal structures create barriers which increase the effective path-length of diffusion, and again, the diffusion coefficient is decreased by Δλ2 where Δλ is relative increase in path attributable to the barriers [3840]. Finally, the movements of individual molecules become co-ordinated and vectorially directed due to organization of enzymes into multi-enzyme complexes, and the randomness of molecular events may be lost [40, 4849]. Agutter et al. even recommend not to use “diffusivities” or “diffusion coefficients” for biological systems but to use some terms of the type of “empirical transport coefficient” when the Fick's equation is formally applied for intracellular processes [40]. In our analysis of local restrictions of the diffusion of ATP and ADP in the cells, we have used the term “apparent diffusion coefficient, Dapp “, Dapp = DFxD0, where Do is the diffusion coefficient in bulk water phase and DF is a diffusion factor accounting for all intracellular mechanisms locally restricting particles movement [23, 25, 26, 50]. The diffusion coefficients calculated by Selivanov from analysis of experimental data [28, 51, 52] have the same meaning of apparent constants. Both our and Selivanov's results show that DF value in some areas of cells, for example in myofibrils, close to sarcolemma and mitochondrial outer membrane may be in the range of 10−2–10−5 [23, 28, 5052].

Structural basis for diffusion restrictions and compartimentation

The results of these local diffusion restrictions are microcompartmentation of metabolites and their channeling within organized multi-enzyme complexes which need to be accounted for to explain many biological phenomena. Indeed, none of the important observations in cellular bioenergetics could be explained by a paradigm describing a viable cell as a “mixed bag of enzymes” with homogenous metabolite distribution, since this simplistic theory excludes any possibility of metabolic regulation of cellular functions.

a) Macromolecular crowding, heterogeneity of diffusion, compartmentation and vectorial metabolism

The first phenomenon to be taken into account in all cells is macromolecular crowding: the high concentrations of macromolecules in the cells [49] decrease the volume available for free diffusion of substrates and accordingly make it difficult to use correctly those enzyme kinetics calculations and equations that were mostly worked out based on enzymatic studies carried out in dilute solutions with isolated enzymes [810]. Cytoplasmic protein concentration may be as high as 200 to 300 mg/mL, which corresponds to a volume fraction of about 20–30% of intracellular medium occupied by these proteins [4, 5]. In mitochondria, the high density of enzymes and other proteins constitutes more than 60% of the matrix volume [53]. Such a specific macromolecular architecture of the cells can be revealed and experimentally studied by cryoelectron tomography [54]. The effects of macromolecular crowding on diffusion in virtual cytoplasm were recently analyzed by Ridgway et al., who showed that only due to this important characteristics of intracellular medium, the apparent diffusion coefficients may be decreased by order of magnitude depending upon the size of the diffusing particles and occupied volume fraction [55].
At first sight, this macromolecular crowding should cause a real chaos by making intracellular communication by diffusion of reaction intermediates very difficult. This chaos and related problems are well described by Noble in his recent book [30]. In reality, however, macromolecular crowding gives rise to new mechanisms based on specific protein – protein interactions: microcompartmentation, metabolic channeling and functional coupling, and in this way to a fine organization and regulation of metabolism. The origin of this order, the program and source of these specific interactions and cellular organization are still a mystery, hidden in undiscovered genetic laws, including feedback control of gene expression, and in rules governing system level properties of cellular systems [3032]. Their elucidation will be the main challenge for cell sciences in the post–genomic era [3034].

b) Heterogeneity of intracellular diffusion, metabolic channeling

Many new experimental techniques have been developed to study the molecular networks formed by protein–protein interactions [56]. In the cells, the high protein density predominantly determines also the major characteristics of cellular environment such as diffusion in heterogeneous compartments [11, 13, 14, 57, 58]. There are distinct barriers to diffusion of solutes within the cells: binding and crowding. Whereas molecular crowding and sieving restrict the mobility of very large solutes, binding can severely restrict the mobility of even smaller solutes ([14] and references therein). This also explains the heterogeneity of the diffusion behavior of ATP in cells. Studies, using pulsed-gradient 31P-NMR, showed that the diffusions of ATP and phosphocreatine both are anisotropic in muscle cells [57, 58]. Recent mathematical modelling of the decreased affinity of mitochondria for exogenous ADP in situ in permeabilized cardiac cells also showed that ADP or ATP diffusion in cells is heterogeneous and that the apparent diffusion coefficient for ADP (and ATP) may be locally decreased (diffusion locally restricted) by an order, or even several orders of magnitude [50]. A similar limited diffusion of ATP in the subsarcolemmal area in cardiac cells was proposed by the Terzic and Dzeja group [51, 52]. In sea urchin sperm cells, it was shown that diffusion of ATP from the mitochondrion to the sperm tail and much more so of ADP from the sperm tail back to the mitochondrion were severely restricted and orders of magnitude slower than that of phosphocreatine and creatine, respectively [59]. There is also firm experimental evidence for compartmentalization of ATP in cardiomyocytes (see next section).
Due to molecular crowding and hindered diffusion cells need to compartmentalize metabolic pathways in order to overcome diffusive barriers. Biochemical reactions can successfully proceed and even be facilitated by metabolic channeling of intermediates due to structural organization of enzyme systems into organized multienzyme complexes. Metabolite channeling directly transfers the intermediate from one enzyme to an adjacent enzyme without the need of free aqueous-phase diffusion [13, 6, 13, 48, 49, 60]. This property was suggested to be a unique catalytic behavior of enzyme complexes due to their specific structural organization [17]. Gaertner reported that “physically associated multienzyme systems (enzyme clusters) have the potential of expressing unique catalytic properties in contrast to their non-associated counterparts” [17]. It is quite clear that the enhanced probability for intermediates to be transfered from one active site to the other by sequential enzymes requires stable or transient interactions between the relevant enzymes. Enzymes are able to associate physically in non-dissociable, static multienzyme complexes, which are not random associations but an assembly of sequentially related enzymes.

c) Compartmentation phenomenon and vectorial metabolism

Thus, the principal ways and mechanisms of organization of cell metabolism are macro- and microcompartmentation, metabolic channeling and functional coupling. By definition, the term compartmentation is usually related to the existence of intracellular macrocompartments – subcellular regions which are large relative to the molecular dimension, and microcompartments which are of the order of the size of metabolites. Compartment means “subcellular region of biochemical reactions kinetically isolated from the rest of cellular processes” [61, 62]. Macrocomparments are easy to understand, and they can be visualized by electron or confocal microscopy. They are compartments inside organelles such as mitochondria or lysosomes, and are insulated from the cytoplasm by membrane envelopes. The concept of microcompartmentation is more mysterious, since these microcompartments are not usually visible by electron microscopy. However, now it is becoming clear that microcompartments related to multienzyme complexes and metabolic channeling, as described above, are the principal basis of organization and compartmentation of cellular metabolism. They are formed by specific protein-protein interactions within multienzyme complexes due to the macromolecular crowding. For example, anchoring of the glycolytic [6367] and other enzymes to the cytoskeleton or to membrane channels and transporters [68] has been demonstrated. Investigations carried out in Clegg's and Deutscher's laboratories have shown that mammalian cells behave as highly organized macromolecular assemblies dependent on the cytoskeleton [20, 21]. Multienzyme complexes may be of different size and even include whole metabolic pathways – then they are called metabolons, according to the terminology introduced by Srere in 1985 [22]. Thus, there are glycolytic [6467], Krebs cycle [69] and many other metabolons [70, 71]. New techniques such as FRET have been developed to study and visualize such micro-compartments, of which a good example is the study of microcompartmentation of cyclic AMP [36, 37]. Microcompartmentation is sometimes taken to be synonymous of metabolic channeling. However, metabolic channeling of the reaction intermediate between two enzymes (or a transporter and an enzyme) may occur via microcompartments or by direct transfer [48, 49, 60]. In both cases, it results in functional coupling (see below). Importantly, microcompartmentation may be of dynamic nature, and this may finally result in co-existence of a whole set of organized metabolic networks [62].
Interestingly enough, there is an exciting hypothesis that these phenomena, in particular metabolic channeling, are even older than life itself and related to its origin. Edwards and others (see [72] for a review) have put forward the hypothesis according to which on the prebiotic earth, when no enzyme or metabolic complexes were initially present, archetypal catalytic complexes were formed at the mineral surface (for example, iron sulfide minerals) and biomolecules and catalysts would have been formed at specific sites relative to these complexes [72]. The evolution of metabolic pathways in this case would have been dictated by the relative positions of substrates and catalysts, where only closely juxtaposed species would have been allowed to interact [72]. Thus, the cell as a “bag of enzymes” probably never existed. It is now becoming clear that the living cells are much more complicated, and much better organized than thought before [15, 16].
In the case of metabolic channeling of species via microcompartments with only a small number of molecules, the validity of introducing chemical potentials is questionable [49], whereas for macrocompartments and larger microdomains this classical theory remains useful. In the first case, application of thermodynamic activities instead of concentrations [79], and stochastic kinetics based on probabilities of different states of enzymes will be essential [49, 73].
An important consequence of the organization of the enzymes into multienzyme complexes is vectorial metabolism and ligand conduction, a general principle proposed by Peter Mitchell after extensive enzymological studies and detailed characterization of mitochondrial proteins. One important example is the chemiosmotic coupling of energetic processes through the “protonic current” [74, 75]. It brought together “transport and metabolism into one and the same chemiosmotic molecular level - biochemical process catalyzed by group-conducting or conformationally mobile group-translocating enzyme system” [74]. In his latest reviews, Mitchell encouraged a wider use of the chemiosmotic principle and the biochemical concept of specific ligand conduction in explaining organization and operation of metabolic and transport processes within the cell [74]. Today this idea receives increased attention and is certainly another important insight of Mitchell to the understanding of cellular energy conversion processes.

Compartmentation of ATP and ADP in cardiac and brain cells – a system level property

Cellular bioenergetics of cardiac cells provides plenty of evidence for compartmentation of metabolites, most clearly – of adenine nucleotides -, some of them are described below. It was discovered by Neely et al. [76, 77] and Gudbjrnason et al. [78] some thirty years ago that under ischemic or hypoxic conditions, cardiac contraction stops in the presence of about 80–90% of original ATP, but when most of the phosphocreatine is exhausted. Recent studies by the Neubauer group showed that in patients with dilated cardiomyopathy, the high mortality rate is related to a decreased PCr/ATP ratio below 1.6 meaning again that even if global ATP content in the cells stays high, the level of phosphocreatine is critically important, not only for maintaining global ATP, but much more so local ATP concentrations [79]. If there were a homogenous intracellular medium with unrestricted diffusion, why then would this homogenous pool of ATP not be used to support the cellular functions [7680]? Some time ago, it was experimentally shown that anomalies in diffusion behavior and equilibration of ATP and other metabolites, contrary to such as expected from Brownian diffusion, can indeed be directly measured in vivo [81, 82]. These data explain both the reasons for realistic diffusion restrictions inside the cells and metabolic compartmentation, within a combined framework of experimental and theoretical research. An overwhelming body of experimental evidence shows the existence of distinct ATP pools (compartments) in mitochondria, as well as at the sites of ATP utilization that are connected by phosphotransfer networks, notably via PCr-creatine kinase pathway (reviewed in [2427, 59, 8390]). These networks function to bypassing and overcoming of the local restrictions of ATP or ADP diffusion and thus perform the important tasks of both energy supply and metabolic feedback regulation of respiration. When PCr decreases in the cell, local ATP pools are rapidly exhausted by ATPases with all pathogenetic consequences (cessation of contraction) [24, 25, 27, 77, 89, 90]. Paul's laboratory in Cincinnati has demonstrated the compartmentation of oxidative and glycolytic metabolic systems, as well as PCr-CK pathway, even in smooth muscle cells that are rather small compared to skeletal muscle cells [91]. As another example, excitation – contraction coupling which controls cardiac contraction and bioenergetics is strictly dependent on the action potential duration and plasma membrane (sarcolemmal) repolarisation via K-ATP channels [92]. These channels were discovered by Noma, who found a very high affinity of these channels for ATP, the apparent Km (ATP) being about 0.1 mM [93]. ATP inhibits the opening of this channel. Nevetherless, in cells in vivo, these channels are open after a significant decrease of the PCr content in the presence of bulk ATP that is still high in millimolar concentration [51, 52]. Again, combined experimental and theoretical studies of the opening of the K-ATP channels by Abraham et al. [51] showed that in the subsarcolemmal area, the diffusion of ATP is strongly restricted and the apparent diffusion coefficient for this nucleotide calculated from Fick's equation may be decreased in vivo in this cellular microcompartment by five orders! of magnitude, meaning that the K-ATP channel is not seeing bulk ATP but rather a very local ATP compartment that can be quite different in concentration from bulk ATP. And again “unrealistic” becomes realistic, if theoretical calculations are carefully checked by experiments! Numerous experimental and theoretical studies concerning the regulation of respiration in permeabilized cardiomyocytes by exogenous ADP have revealed the local restrictions of ADP diffusion in cells and again support the concept of compartmentation of adenine nucleotides between multiple subcellular pools, most probably microcompartments [3, 2327, 34, 79, 84].
Remarkably, in the heart the intracellular energy transfer networks are structurally organized in the intracellular medium where macromolecules and organelles, surrounding a regular mitochondrial lattice, are involved in multiple structural and functional interactions [3, 2527, 34]. These complexes were called ‘intracellular energetic units”, ICEUs, and taken to represent the basic pattern of organization of muscle energy metabolism [25, 26]. There are no physical barriers between ICEUs, each mitochondria (or several adjacent mitochondria) can be taken to be in the centre of its own ICEU. This concept describes the organized functional connections of mitochondria with their neighbours. ICEUs are analogous to calcium release units, (CRUs), structurally organized sites of Ca2+ microdomains (Ca2+ sparks) which form a discrete, stochastic system of intracellular calcium signaling in cardiac cells [94]. The structural organization of ICEUs results in local confinement of adenine nucleotides and Cr/PCr couple in discrete dynamic energetic circuits between actomyosin and membrane ATPases and mitochondrial ATPsynthase [2427, 34, 8487]. These phosphotransfer networks are necessary for effective energy transfer from mitochondria to all sites of energy utilization under conditions when the diffusion of ATP and ADP may be locally restricted due to structural organization of the cardiac cells [2328, 5052, 8390]. Important studies of mitochondrial membrane topology by Mannella's and Perkins’ laboratories [95, 96] using electron tomography have revealed narrow tubular junctions between intracristal compartments and intermembrane space which represent first diffusion barriers for adenine nucleotides. Next are the restrictions of the permeability of the outer mitochondrial membrane due to interactions of voltage-dependent anion channels (VDAC) of this membrane with cytoskeletal components and diffusion restrictions within ICEUs [23, 61]. To overcome all these restrictions, mitochondrial ATP is transported across the inner membrane by adenine nucleotide translocase (ANT) directly to mitochondrial creatine kinase (MtCK) in exchange to ADP produced by MtCK [3, 2427]. Due to effective functional coupling between ANT and MtCK, ATP and ADP are continuously recycling within mitochondria, energy is carried out into cytoplasm by phosphocreatine molecules and respiration is controlled to significant extent both by the MtCK reaction and inorganic phosphate fluxes between ATPases and mitochondria [2328, 5052, 8390]. This sequence of events has been experimentally confirmed with application of new methodologies of investigations of in vivo kinetics of the energy transfer by which high-energy phosphoryl fluxes through creatine kinase, adenylate kinase and glycolytic phosphotransfer, captured with 18O-assisted 31P-NMR, were shown to tightly correlate with the performance of the myocardium under various conditions of load [34, 97, 98], implicating phosphotransfer reactions as indispensable routes that direct flow of high energy phosphoryls between cellular ATPases and the ATP production machinery in mitochondria [34]. Very similar results were obtained by 31P-NMR saturation or inversion transfer methods [99, 100]. Most remarkably, these phosphotransfer networks ensure effective metabolic feedback signalling to mitochondria under physiological conditions of metabolic stability when heart function and respiration is governed by the classical Frank-Starling law of the heart [34]. The most recent publications show that the phosphocreatine (PCr) – creatine kinase (CK) system is equally important in skeletal muscle function and development: it is the PCr-CK system which sustains localized ATP-dependent reactions during actin polymerization in myoplast fusion to form myotubes during myogenesis [101]. All these results add new insight into the functioning of the PCr-CK system in muscle cells, showing its new role in energy supply for cytoskeletal remodelling. By controlling the rate of regeneration of ATP in local microcompartments, the PCr-CK system helps to shape the muscle cells, to keep them healthy and alive [101]. Similar discrete microdomains in cardiomyocytes have been shown for cAMP in the range of approximately 1 μm exhibiting high local concentrations [3537]. This concept is supported by recent work published by Weiss et al. [102]. These authors presented a holistic view of cardiovascular metabolism, considering it from the perspective of physical network, in which various metabolic modules are spatially distributed throughout the interior of the cell to optimize ATP delivery to specific ATPases [102]. In addition to mitochondrial module (which is represented ICEUs), the authors considered also a module consisting of glycolytic enzyme complexes serving for energy channeling to molecular complexes in sarcolemma and sarcoplasmic reticulum, and modules of calcium cycling [102]. These modules were further analyzed from the abstract perspective of fundamental concepts in network theory [103, 104] and dynamic perspective of interactions between modules [102]. The authors emphasized that understanding the nature of these interactions within hiercharchical modular structures is a main challenge of research of cardiac metabolism to gain deeper understanding of possible mechanisms of cardioprotection [102].
Some 10 years ago, based on measurements, utilizing a new technology that made it possible to directly measure by saturation transfer 31P-NMR global creatine kinase flux in muscle in vivo, some laboratories [105, 106] came to conclusions similar to those in [29] saying that “the CK chemical reaction kinetics in the intact cell is adequately described in terms of a well-mixed solution of enzyme, substrate and products”, as also published in Biophysical Journal [105], and that “the thermodynamic characteristics of the cytosol can be predicted as if these metabolites were freely mixing in solution” [106]. This simplistic view has been refuted, among others mostly by arguments based on the specific subcellular localization of creatine kinase isoenzymes at distinc compartments which cannot be resolved by global bulk NMR methods [107] and on anomalous 31P-NMR flux behaviour of the creatine kinase system in creatine kinase knock-out mice or such with graded expression of creatine kinase [107, 108], as discussed in detail [109]. Now, it seems that similar views are resurrected again by some members of the research community involved in metabolic modelling approaches [29].
However, modern biophysical chemistry can no longer be restricted to classical theories of diluted homogenous solutions, as misleadingly taken by Barros and Martinez [29]. The second important conclusion of these considerations is that purely theoretical constructs have a very limited (mostly only virtual, as some good computer games) value if not checked, verified and corrected by inputs from actual experimental results.
What modern biophysical chemistry needs is to critically reevaluate of use of the principles of classical physical chemistry that have been worked out for diluted aqueous solutions and to adapt these concepts to the in vivo situation for cells that possess an extensive myofilament lattice, as muscle cells, or a dense cytoskeletal an mitochondrial network and other diffuson restrictions inferred by intracellular organization or macromolecular crowding. Moreover, biological sciences witness now a radical change of paradigms. Reductionism that used to be the philosophical basis of biochemistry and molecular biology, when everything from genes to proteins and organelles was studied in their isolated state, are superseded by Systems Biology, a holistic view that favours the study of integrated systems at all levels: cellular, organ, organism, and population, accepting that the physiological whole is greater than the sum of its parts [3, 3034]. Reductionism was justified in the initial stages of biological research giving a wealth of information on system components. The aim of Systems Biology is to put them together and analyze them in interaction, to understand the principles of functioning of the whole. At the cellular level, it is becoming clear that most of biological characteristics arise from complex interactions between the cell's numerous constituents, and based on protein-protein interactions, cellular metabolism is likely to be carried out in a highly modular manner within hierarchically organized networks [3, 102]. The situation is definitely contrary to that described by Barros and Martinez [29], who contend that “the cytosol behaves as a well-mixed, homogeneous compartment”. The in vivo situation is much more realistically described and well illustrated by Selivanov et al. [28]. Clearly, his model is also a simplified one, a first approximation to complex phenomena of microcompartmentation and metabolic channeling, and although critical evaluation of the Selivanov paper [28] also reveals some weak points and shortcomings, due to assumptions for modelling that are only indirectly derived from experimental data. The fact that compartmentation and interactions between myofibrillar components are taken into account in his model calculations makes this approach a more realistic one, which is likely to better reflect the in vivo situation. Apparent diffusion coefficients Dapp and diffusion factors DF (see above), as well as compartmentation, are system level properties not predictable from isolated components but arising from their interactions [3, 3034]. The real problems and challenges for further studies are both to measure local concentrations of metabolites, including those of ATP in different cellular microcompartments and its metabolic channelling within microdomains (local fluxes), as well as to fully understand the nature of these restrictions of diffusion upon which intracellular compartmentation is based. This difficult work is necessary for reasonable computer modeling of the hierarchical modules of metabolic networks as a part of Systems Biology [32, 33]. High throughput methods have now been developed to determine thousands of protein-protein interactions within cells at a given metabolic state, and it may soon be possible to also define changes in protein-protein interactions, for example, as a consequence of cell stimulation and cell signaling and thus being able to study the dynamics of metabolons. This is possible by cross-linking proteins via isotopically tagged chemical cross-linkers in cells in situ and, upon proteolytic digestion and determintation of the isotopically labeled peptides by mass spectrometry, sift through large protein sequence data bases to identify the cross-linked peptides and attribute them to the original partner proteins that at the time and conditions of chemical cross-linking did interact with each other [110].


The authors acknowledge the fruitful discussions of this paper with Dr. Petras Dzeja, Division of Cardiovascular Diseases, Departments of Medicine, Molecular Pharmacology and Expermental Therapeutics, Mayo Clinic College of Medicine, Rochester, MN, USA., and Dr. Marko Vendelin, Institute of Cybernetics, Tallinn, Estonia. This work was supported by grants N 6142 and 7117 from Estonian Science Foundation, by grant from Region Rhone Alpes for N.B., and by grant from Agence Nationale de la Recherche, France, project ANR-07-BLAN-0086-01 for VS. This work was also supported by funding from Swiss National Science Foundation grant No. 3100A0-114137 to T.W.


  1. Welch, GR. On the Role of Organized Multienzyme Systems in Cellular Metabolism: a General Synthesis. Prog. Biophys. Mol. Biol. 1977, 32, 103–191. [Google Scholar]
  2. Welch, GR. Organized multienzyme systems; Academic Press: Orlando, USA, 1985; pp. 1–447. [Google Scholar]
  3. Saks, V. Molecular System Bioenergetics; Wiley-VCH: Weinheim, Germany, 2007; pp. 1–604. [Google Scholar]
  4. Fulton, AB. How Crowded is the Cytoplasm? Cell 1982, 30, 345–347. [Google Scholar]
  5. Srere, PA. Macromolecular Interactions: Tracing the Roots. Trends Biochem. Sci. 2000, 25, 150–153. [Google Scholar]
  6. Ovàdi, J. Cell Architecture and Metabolic Channeling; R.G. Landes Co.: Austin, TX, USA, 1995. [Google Scholar]
  7. Minton, AP. The Influence of Macromolecular Crowding and Macromolecular Confinement on Biochemical Reactions in Biological Media. J. Biol. Chem. 2001, 276, 10577–10580. [Google Scholar]
  8. Hall, D; Minton, AP. Macromolecular Crowding: Qualitative and Semiquantitative Successes, Quantitative Challenges. Biochim. Biophys. Acta 2003, 1649, 127–139. [Google Scholar]
  9. Schliwa, M. The Evolving Complexity of Cytoplasmic Structure. Nat. Rev. Mol. Cell Boil. 2002, 3, 1–6. [Google Scholar]
  10. Schnell, S; Turner, TE. Reaction Kinetics in Intracellular Environments with Macromolecular Crowding: Simulations and Rate Laws. Progr. Biophys. Mol. Biol. 2004, 85, 235–260. [Google Scholar]
  11. Channelling in Intermediary Metabolism; Agius, L; Sherratt, HSA (Eds.) Portland Press: London, UK, 1996; pp. 237–268.
  12. Xia, Y; Yu, H; Jansen, R; Seringhaus, M; Baxter, S; Greenbaum, D; Zhao, H; Gerstein, M. Analyzing Cellular Biochemistry in Terms of Molecular Networks. Annu. Rev. Biochem. 2004, 73, 1051–1087. [Google Scholar]
  13. Ovàdi, J; Saks, V. On the Origin of Intracellular Compartmentation and Organized Metabolic Systems. Mol. Cell. Biochem. 2004, 256/257, 5–12. [Google Scholar]
  14. Verkman, AS. Solute and Macromolecule Diffusion in Cellular Aqueous Compartments. Trends Biochem. Sci. 2002, 27, 27–33. [Google Scholar]
  15. Alberts, B. The Cell as a Collection of Protein Machines: Preparing the Next Generation of Molecular Biologists. Cell 1998, 92, 291–294. [Google Scholar]
  16. Clegg, JS. Cellular Infrastructure and Metabolic Organization. Curr. Top. Cell. Regul. 1992, 33, 3–14. [Google Scholar]
  17. Gaertner, FH. Unique Catalytic Properties of Enzyme Clusters. Trends Biochem. Sci. 1978, 3, 63–65. [Google Scholar]
  18. Ovàdi, J; Srere, AP. Macromolecular Compartmentation and Channeling. Intern. Rev. Cytol. 2000, 192, 255–280. [Google Scholar]
  19. Penman, S. Rethinking Cell Structure. Proc. Natl. Acad. Sci. U. S. A. 1995, 92, 5251–5257. [Google Scholar]
  20. Clegg, JS. Properties and Metabolism of the Aqueous Cytoplasm and Its Boundaries. Am. J. Physiol. 1984, 246, R133–R151. [Google Scholar]
  21. Hudder, A; Nathanson, L; Deutscher, MP. Organization of Mammalian Cytoplasm. Mol. Cell. Biol. 2003, 23, 9318–9326. [Google Scholar]
  22. Srere, PA. The Metabolon. Trends Biochem. Sci. 1985, 10, 109–110. [Google Scholar]
  23. Saks, V; Kuznetsov, A; Andrienko, T; Usson, Y; Appaix, F; Guerrero, K; Kaambre, T; Sikk, P; Lemba, M; Vendelin, M. Heterogeneity of ADP Diffusion and Regulation of Respiration in Cardiac Cells. Biophys. J. 2003, 84, 3436–3456. [Google Scholar]
  24. Wallimann, T; Tokarska-Schlattner; Neumann, D; Epand, R; Andres, RH; Widmer, HR; Hornemann, T; Saks, V; Agarkova, I; Schlattner, U. The Phosphocreatine Circuit: Molecular and Cellular Physiology of Creatine Kinases, Sensitivity to Free Radicals, and Enhancement by Creatine Supplementation. In Molecular System Bioenergetics. Energy for Life; Saks, V, Ed.; Wiley-VCH: Weinheim, Germany, 2007; pp. 195–264. [Google Scholar]
  25. Saks, VA; Vendelin, M; Aliev, MK; Kekelidze, T; Engelbrecht, J. Mechanisms and Modeling of Energy Transfer Between and Among Intracellular Compartments. In Handbook of Neurochemistry and Molecular Neurobiology, 3rd Ed; Dienel, G, Gibson, G, Eds.; Springer Science and Business Media: New York-Boston, USA, 2007; Volume 5, pp. 815–860. [Google Scholar]
  26. Saks, V; Anmann, T; Guzun, R; Kaambre, T; Sikk, P; Schlattner, U; Wallimann, T; Aliev, M; Vendelin, M. The Creatine Kinase Phosphotransfer Network: Thermodynamic and Kinetic Considerations, the Impact of the Mitochondrial Outer Membrane and Modelling Approaches. In Creatine and Creatine Kinase in Health and Disease; Wyss, M, Salomons, G, Eds.; Springer: Dordrecht, Netherlands, 2007; pp. 27–66. [Google Scholar]
  27. Saks, V; Monge, C; Anmann, T; Dzeja, P. Integrated and Organized Cellular Energetic Systems: Theories of Cell Energetics, Compartmentation and Metabolic Channeling. In Molecular System Bioenergetics. Energy for Life; Saks, V, Ed.; Wiley-VCH: Weinheim, Germany, 2007; pp. 59–110. [Google Scholar]
  28. Selivanov, VA; Krause, S; Roca, J; Cascante, M. Modeling of Spatial Metabolite Distribution in the Cardiac Sarcomere. Biophysical J. 2007, 92, 3492–3500. [Google Scholar]
  29. Barros, LF; Martinez, C. An enquiry into metabolite domains. Biophys. J. 2007, 92, 3878–3884. [Google Scholar]
  30. Noble, D. The music of life. Biology beyond the genome; Oxford University Press: Oxford, UK, 2006. [Google Scholar]
  31. Kitano, H. Systems Biology: a Brief Overview. Science 2002, 295, 1662–1664. [Google Scholar]
  32. Noble, D. Modeling the Heart-from Genes to Cells to the Whole Organ. Science 2002, 295, 1678–1682. [Google Scholar]
  33. Hunter, P; Nielsen, P. A Strategy for Integrative Computational Physiology. Physiology 2005, 20, 316–325. [Google Scholar]
  34. Saks, V; Dzeja, P; Schlattner, U; Vendelin, M; Terzic, A; Wallimann, T. Cardiac System Bioenergetics: Metabolic Basis of Frank-Starling Law. J. Physiol. 2006, 571, 253–273. [Google Scholar]
  35. Iancu, RV; Jones, SW; Harvey, RD. Compartmentation of cAMP Signaling in Cardiac Myocytes: a Computational Study. Biophys J. 2007, 92, 3317–3331. [Google Scholar]
  36. Karpen, JW; Rich, TC. Resolution of cAMP Signals in Three-dimensional Microdomains Using Novel, Real-time Sensors. Proc. West. Pharmacol. Soc. 2004, 47, 1–5. [Google Scholar]
  37. Zaccolo, M; Pozzan, T. Discrete Microdomains with High Concentration of cAMP in Stimulated Rat Neonatal Cardiac Myocytes. Science 2002, 295, 1711–1715. [Google Scholar]
  38. Agutter, PS; Malone, PC; Wheatley, DN. Diffusion Theory in Biology: a Relic of Mechanistic Materialism. J. Hist Biol. 2000, 33, 71–111. [Google Scholar]
  39. Wheatley, DN. Diffusion Theory, the Cell and the Synapse. Biosystems 1998, 45, 151–63. [Google Scholar]
  40. Agutter, PS; Malone, PC; Wheatley, DN. Intracellular Transport Mechanisms: a Critique of Diffusion Theory. J. Theor Biol. 1995, 176, 261–72. [Google Scholar]
  41. Fick, A. Über Diffusion. Ann. Phys. Leipzig 1855, 94, 59–86. [Google Scholar]
  42. Einstein, A. Von der Molerulärkinetichen Theorie der Wärme Gefordete Bewegung von in Ruhenden Flüssigkeiten Suspendierten Teilchen. Ann. der Physik 1905, 17, 549–554. [Google Scholar]
  43. Smoluchowski, M. von Zür Kinetichen Theorie der Brownschen Molekulärbewegung und der Suspensionen. Ann. der Physik 1906, 21, 756–780. [Google Scholar]
  44. Renn, J. Einstein's Invention of Brownin Motion. Ann. Phys. (Leipzig) 2005, 14, Supplement. 23–37. [Google Scholar]
  45. West, IC. Molecular and Physicochemical Aspects. In Channelling in Intermediary metabolism; Agius, L, Sherratt, HSA, Eds.; Portland Press: London, UK, 1997; pp. 13–39. [Google Scholar]
  46. Islam, MA. Einstein-Smoluchowski Diffusion Equation: a Discussion. Physica Scripta 2004, 70, 120–125. [Google Scholar]
  47. Philbert, J. One and Half Century of Diffusion: Fick, Einstein, Before and Beyond. Diffusion Fundamentals 2006, 4, 6.1–6.19. [Google Scholar]
  48. Srivastava, DK; Bernhard, SA. Metabolite Transfer via Enzyme-Enzymz Complexes. Science 1986, 234, 1081–1086. [Google Scholar]
  49. Qian, H; Elson, EL. Single-molecule Enzymology: Stochastic Michaelis-Menten Kinetics. Biophys. Chem. 2002, 101/102, 565–576. [Google Scholar]
  50. Vendelin, M; Eimre, M; Seppet, E; Peet, N; Andrienko, T; Lemba, M; Engelbrecht, J; Seppet, EK; Saks, VA. Intracellular Diffusion of Adenosine Phosphates is Locally Restricted in Cardiac muscle. Mol. Cell. Biochem. 2004, 256/257, 229–241. [Google Scholar]
  51. Abraham, MR; Selivanov, VA; Hodgson, DM; Pucar, D; Zingman, LV; Wieringa, B; Dzeja, P; Alekseev, AE; Terzic, A. Coupling of Cell Energetics with Membrane Metabolic Sensing. Integrative Signaling through Creatine Kinase Phosphotransfer Disrupted by M-CK Gene Knock-out. J. Biol. Chem. 2002, 277, 24427–24434. [Google Scholar]
  52. Selivanov, VA; Alekseev, AE; Hodgson, DM; Dzeja, PP; Terzic, A. Nucleotide-gated KATP Channels Integrated with Creatine and Adenylate Kinases: Amplification, Tuning and Sensing of Energetics Signals in the Compartmentalized Cellular Environment. Mol. Cell. Biol. 2004, 256/257, 243–256. [Google Scholar]
  53. Scalettar, BA; Abney, JR; Hackenbrock, CR. Dynamics, Structure, and Function are Coupled in the Mitochondrial Matrix. Proc. Natl. Acad. Sci. U. S. A. 1991, 88, 8057–8061. [Google Scholar]
  54. Medalia, O; Weber, I; Frangakis, AS; Nicastro, D; Gerish, G; Baumeister, W. Macromolecular archtecture in eukariotic cells visualized by cryoelectron tomography. Science 2002, 298, 1209–1213. [Google Scholar]
  55. Ridgway, D; Broderick, G; Lopez-Campistrous, A; Ru’aini, M; Winter, P; Hamilton, M; Boulanger, P; Kovalenko, A; Ellison, MJ. Coarse-grained molecular simulation of diffusion and reaction kinetics in a crowded virtual cytoplasm. Biophys. J. 2008, 94, 3748–3759. [Google Scholar]
  56. Xia, Y; Yu, H; Jansen, R; Seringhaus, M; Baxter, S; Greenbaum, D; Zhao, H; Gerstein, M. Analyzing Cellular Biochemistry in Terms of Molecular Networks. Annu. Rev. Biochem. 2004, 73, 1051–1087. [Google Scholar]
  57. de Graaf, RA; Van Kranenburg, A; Nicolay, K. In vivo 31P-NMR Spectroscopy of ATP and Phosphocreatine in Rat Skeletal Muscle. Biophys. J. 2000, 78, 1657–1664. [Google Scholar]
  58. Kinsey, ST; Locke, BR; Benke, B; Moerland, TS. Diffusional Anisotropy is Induced by Subcellular Barriers in Skeletal Muscle. NMR Biomed. 1999, 12, 1–7. [Google Scholar]
  59. Kaldis, P; Kamp, G; Piendl, T; Wallimann, T. Functions of Creatine Kinase Isoenzymes in Spermatozoa. Advances in Developmental Biochemistry 1997, 5, 275–312. [Google Scholar]
  60. Huang, X; Holden, HM; Raushel, FM. Channeling of Substrates and Intermediates in Enzyme-catalyzed Reactions. Annu. Rev. Biochem. 2001, 70, 149–180. [Google Scholar]
  61. Saks, VA; Khuchua, ZA; Vasilyeva, EV; Belikova, Yu, O; Kuznetsov, A. Metabolic Compartmentation and Substrate Channeling in Muscle Cells. Role of Coupled Creatine Kinases in vivo Regulation of Cellular Respiration-a Synthesis. Mol. Cell. Biochem. 1994, 133/134, 155–192. [Google Scholar]
  62. Friedrich, P. Dynamic Compartmentation in Soluble Multienzyme System. Organized multienzyme systems. In Catalytic properties; Welch, GR, Ed.; Academic Press: New-York-London, 1985; pp. 141–176. [Google Scholar]
  63. Ottaway, JH; Mowbray, J. The Role of Compartmentation in the Control of Glycolysis. Curr. Top. Cell Regul. 1977, 12, 107–208. [Google Scholar]
  64. Maughan, DW; Henkin, JA; Vigoreaux, JO. Concentrations of Glycolytic Enzymes and Other Glycolytic Proteins in the Diffusible Fraction of a Vertebrate Muscle Proteome. Mol. Cell. Proteomics 2005, 4, 1541–1549. [Google Scholar]
  65. Waingeh, VF; Gustafson, CD; Kozliak, EI; Lowe, SL; Knull, HR; Thomasson, KA. Glycolytic Enzyme Interactions with Yeast and Skeletal Muscle F-actin. Biophys. J. 2006, 90, 1371–1384. [Google Scholar]
  66. Arnold, H; Pette, D. Binding of Glycolytic Enzymes to Structure Proteins in Muscle. Eur. J. Biochem. 1968, 6, 163–171. [Google Scholar]
  67. Kurganov, BI; Sugrobova, NP; Mil’man, LS. Supramolecular Organization of Glycolytic Enzymes. J. Theor. Biol. 1985, 116, 509–526. [Google Scholar]
  68. Saier, MH. Vectorial Metabolism and the Evolution of Transport Systems. J. Bacteriol. 2000, 182, 5029–5035. [Google Scholar]
  69. Vélot, C; Mixon, MB; Teige, M; Srere, P. Model of a Quinary Structure between Krebs TCA Cycle Enzymes: a Model for Metabolon. Biochemistry 1997, 36, 14271–14276. [Google Scholar]
  70. Haggie, PM; Verkman, AS. Diffusion of Tricarboxylic Acid Cycle Enzymes in the Mitochondrial Matrix in vivo. J. Biol. Chem. 2002, 277, 40782–40788. [Google Scholar]
  71. Eaton, S; Bursby, T; Middleton, B; Pourfarzam, M; Mills, K; Johnson, AW; Bartlett, K. The Mitochondrial Trifunctional Protein: Centre of a β-oxidation Metabolon? Biochem. Soc. Transactions 2000, 28, 177–182. [Google Scholar]
  72. Edwards, MR. Metabolite Channeling in the Origin of Life. J. Theor. Biol. 1996, 179, 313–322. [Google Scholar]
  73. Aliev, MK; Saks, VA. Quantitative Analysis of the “Phosphocreatine Shuttle”. I. A Probability Approach to the Description of Phosphocreatine Production in the Coupled Creatine Kinase-ATP/ADP Translocase-oxidative Phosphorylation Reaction in Heart Mitochondria. Biochim. Biophys. Acta 1993, 1143, 291–300. [Google Scholar]
  74. Mitchell, P. The Ninth Sir Hans Krebs Lecture. Compartmentation and Communication in Living Systems. Ligand Conduction: a General Catalytic Principle in Chemical, Osmotic and Chemiosmotic Reaction Systems. Eur. J. Biochem. 1979, 95, 1–20. [Google Scholar]
  75. Mitchell, P. Coupling of Phosphorylation to Electron Transfer by a Chemiosmotic Type of Mechanism. Nature 1961, 191, 144–148. [Google Scholar]
  76. Neely, JR; Rovetto, MJ; Whitmer, JT; Morgan, H. Effects of Ischemia on Function and Mtabolism of the Isolated Working Rat Heart. Am. J. Physiol. 1973, 22, 651–658. [Google Scholar]
  77. Neely, JR; Grotyohann, LW. Role of Glycolytic Products in Damage to Ischemic Myocardium. Dissociation of Adenosine Triphosphate Levels and Recovery of Function of Reperfused Ischemic Myocardium. Circ. Res. 1984, 55, 816–824. [Google Scholar]
  78. Gudbjarnason, S; Mathes, P; Raven, KG. Functional Compartmentation of ATP and Creatine Phosphate in Heart Muscle. J. Mol. Cell. Cardiol. 1970, 1, 325–339. [Google Scholar]
  79. Neubauer, S. The Failing Heart-an Engine out of Fuel. New Engl. J. Med. 2007, 356, 1140–1151. [Google Scholar]
  80. Bricknell, OL; Opie, LH. A Relationship between Adenosine Triphosphate, Glycolysis and Ischemic Contracture in the Isolated Rat Heart. J. Mol. Cell. Card. 1981, 13, 941–945. [Google Scholar]
  81. Miller, DS; Horowitz, SB. Intracellular Compartmentalization of Adenosine Triphosphate. J. Biol Chem. 1986, 261, 13911–13915. [Google Scholar]
  82. Kennedy, HJ; Pouli, AE; Ainscow, EK; Jouaville, LS; Rizzuto, R; Rutter, GA. Glucose Generates Sub-plasma Membrane ATP Microdomains in Single Islet -cells. J. Biol. Chem. 1999, 274, 13291–13291. [Google Scholar]
  83. Bessman, SP; Geiger, PJ. Transport of Energy in Muscle: the Phosphorylcreatine Shuttle. Science 1981, 21, 448–452. [Google Scholar]
  84. Wallimann, T; Wyss, M; Brdiczka, D; Nicolay, K; Eppenberger, HM. Intracellular Compartmentation, Structure and Function of Creatine Kinase Isoenzymes in Tissues with High and Fluctuating Energy Demands: the ‘Phosphocreatine Circuit’ for Cellular Energy Homeostasis. Biochem. J. 1992, 281, 21–40. [Google Scholar]
  85. Cellular Bioenergetics. Role of coupled creatine kinases; Saks, VA; Ventura-Clapier, R (Eds.) Kluver Academic Publishers: Dordrecht-Boston, 1994; pp. 1–348.
  86. Saks, VA; Ventura-Clapier, R; Aliev, MK. Metabolic Control and Metabolic Capacity: Two Aspects of Creatine Kinase Functioning in the Cells. Biochim. Biophys. Acta 1996, 1274, 81–92. [Google Scholar]
  87. Ventura-Clapier, R; Veksler, V; Hoerter, JA. Myofibrillar Creatine Kinase and Cardiac Contraction. Mol. Cell. Biochem. 1994, 133, 125–144. [Google Scholar]
  88. Wyss, M; Smeitink, J; Wevers, RA; Wallimann, T. Mitochondrial Creatine Kinase: a Key Enzyme of Aerobic Energy Metabolism. Biochim. Biophys. Acta 1992, 1102, 119–166. [Google Scholar]
  89. Wyss, M; Kaddurah-Daouk, R. Creatine and Creatinine Metabolism. Physiol. Rev. 2000, 80, 1107–1213. [Google Scholar]
  90. Creatine and Creatine Kinase in Health and Disease; Wyss, M; Salomons, G (Eds.) Springer: Dordrecht, Ntherlands, 2007; pp. 1–334.
  91. Ishida, I; Riesinger, I; Wallimann, T; Paul, RJ. Compartmentation of ATP Synthesis and Utilization in Smooth Muscle: Roles of Aerobic Glycolysis and Creatine Kinase. Mol. Cell. Biochim. 1994, 133/134, 39–50. [Google Scholar]
  92. Bers, D. Excitation-contraction coupling and cardiac contraction; Kluwer Academic Publishers: Dordrecht, Netherlands, 2001. [Google Scholar]
  93. Noma, A. ATP-regulated K+ Channel in Cardiac Muscle. Nature 1983, 305, 147–148. [Google Scholar]
  94. Wang, SQ; Wei, C; Zhao, G; Brochet, D; Shen, J; Song, LS; Wang, W; Yang, D; Cheng, H. Imaging Microdomain Ca2+ in Muscle Cell. Circ. Res. 2004, 94, 1011–1022. [Google Scholar]
  95. Mannella, C. The relevance of mitochondrial membrane topology to mitochondrial function. Biochim. Biophys. Acta 2006, 1762, 140–147. [Google Scholar]
  96. Frey, TG; Renken, CW; Perkins, GA. Insight into mitochondrial structure and function from electron tomography. Biochim. Biophys. Acta 2002, 1555, 196–203. [Google Scholar]
  97. Dzeja, P; Chung, S; Terzic, A. Integration of adenylate kinase and glycolytic and clycogenolytic circuits in cellular energetics. In Molecular System Bioenergetics. Energy for life; Saks, V, Ed.; Wiley – VCH: Weinheim, Germany, 2007; pp. 195–264. [Google Scholar]
  98. Dzeja, P; Terzic, A. Phosphotransfer networks and cellular energetics. J. Exp Biol. 2003, 206, 2039–2047. [Google Scholar]
  99. Joubert, F; Hoerter, JA; Mazet, JL. Discrimination of cardiac subcellular creatine kinase fluxes by NMR spectroscopy: a new method of analysis. Biophys J. 2001, 81, 2995–3004. [Google Scholar]
  100. Bittl, JA; Ingwall, JS. Reaction rates of creatine kinase and ATP synthesis in the isolated rat heart. A 31P NMR magnetization transfer study. J. Biol Chem. 1985, 260, 3512–3517. [Google Scholar]
  101. O’Connor, RS; Steeds, CM; Wiseman, R; Pavlath, G. Phosphocreatine as an energy source for actin cytoskeletal rearrangements during myoblast fusion. J. Physiol. 2008. published online Apr 17,; DOI: 10.1113/jphysiol.2008.151027. [Google Scholar]
  102. Weiss, JN; Yang, L; Qu, Z. Network Perspectives of Cardiovascular Metabolism. J. Lipid Research 2006, 47, 2355–2366. [Google Scholar]
  103. Ravasz, E; Somera, AL; Mongru, DA; Oltvai, ZN; Barabasi, AL. Hierarchical Organization of Modularity in Metabolic Networks. Science 2002, 297, 1551–1555. [Google Scholar]
  104. Barabasi, AL; Oltvai, ZN. Network Biology: Understanding the Cell's Functional Organization. Nature Rev. Genet. 2004, 24, 101–113. [Google Scholar]
  105. McFarland, EW; Kushmerick, MJ; Moerland, TS. Activity of Creatine Kinase in a Contracting Mammalian Muscle of Uniform Fiber Type. Biophys J. 1994, 67, 1912–1924. [Google Scholar]
  106. Wiseman, RW; Kushmerick, MJ. Creatine Kinase Equilibration Follows Solution Thermodynamics in Skeletal Muscle. 31P NMR Studies Using Creatine Analogs. J. Biol Chem. 1995, 270, 12428–12438. [Google Scholar]
  107. van Deursen, J; Heerschap, A; Oerlemans, F; Ruitenbeek, W; Jap, P; ter Laak, H; Wieringa, B. Skeletal Muscles of Mice Deficient in Muscle Creatine Kinase Lack Burst Activity. Cell. 1993, 74, 621–631. [Google Scholar]
  108. van Deursen, J; Ruitenbeek, W; Heerschap, A; Jap, P; ter Laak, H; Wieringa, B. Creatine Kinase (CK) in Skeletal Muscle Energy Metabolism: a Study of Mouse Mutants with Graded Reduction in Muscle CK Expression. Proc. Natl. Acad. Sci. U. S. A. 1994, 91, 9091–9095. [Google Scholar]
  109. Wallimann, T. 31P-NMR-measured Creatine Kinase Reaction Flux in Muscle: a Caveat! J. Muscle Res. Cell Motil. 1996, 17, 177–181. [Google Scholar]
  110. Rinner, O; Seebacher, J; Walzthoeni, T; Mueller, L; Beck, M; Schmidt, A; Mueller, M; Abersold, R. Identification of cross-linked peptides from large sequence databases. Nat. Methods 2008, 4, 315–318, Epub Mar 9th 2008. [Google Scholar]
Int. J. Mol. Sci. EISSN 1422-0067 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top