Systems biology calls for studying system-level properties of genes and proteins rather than their individual chemical/biological properties, regarding the bio-molecules as system components. By characterizing how critical the components are to the system and classifying them accordingly, we can study the underlying complex mechanisms, facilitating researches in drug target selection, metabolic engineering, complex disease, etc. Up to date, most studies aiming at this goal are confined to the topology-based or flux-analysis approaches. However, proteins have tertiary structures and specific functions, especially in metabolic systems. Thus topological properties such as connectivity, path length, etc., are not good surrogates for protein properties. Also, the manner of individual sensitivity analysis in most flux-analysis approaches cannot reveal the simultaneous impacts on collateral components as well as the overall impact on the system, thus lacking in system-level perspective.in silico knockouts, regarding to the conceptual term "criticality". Applying the method to E. coli central carbon metabolic system, we found that multiple enzymes including phosphoglycerate kinase, enolase, transketolase-b, etc., had critical roles in the system in terms of both system states and dynamical stability. In contrast, another set of enzymes including glucose-6-phosphate isomerise, pyruvate kinase, phosphoglucomutase, etc., exerted very little influences when deleted. The finding is consistent with experimental characterization of metabolic essentiality and other studies on E. coli gene essentiality and functions. We also found that enzymes could affect distant metabolites or enzymes even greater than a close neighbour and asymmetry in system-level properties of enzymes catalyzing alternative pathways could give rise to local flux compensation.In the present work, we developed a method to directly assess protein system-level properties based on system dynamics and Our method creates a different angle for evaluating protein criticality to a biological system from the conventional methodologies. Moreover, the method leads to consistent results with experimental references, showing its efficiency in studying protein system-level properties. Besides working on metabolic systems, the application of the method can be extended to other kinds of bio-systems to reveal the constitutive/functional properties of system building blocks. E. coli is the best-studied organism, with knowledge accumulated in each of its biological hierarchies, e.g. genetic regulation, genomic information, metabolism, etc [Systems biology focuses on studying properties of bio-molecules like genes and proteins at the system level, especially their constitutive/functional roles as system components. By exploring their interplay structure in the system, we can evaluate how critical a building block is and how different parts vary in properties ,2. Basedism, etc -7. The cism, etc ,7,8.Up to date, multiple genome-scale networks have been built on the organism with regard to the pathway to reveal essentiality of genes and proteins ,10. HoweE. coli central carbon metabolism is the only system with comprehensive kinetic data, we used it as our model. The system components (bio-molecules) were the enzymes, and we defined the criticality of a component by its in silico knockout. We explored how the deletion of an enzyme influenced the system state, i.e. whether state fluctuations were restricted in a limited area or spread throughout a broader range; and how large their amplitudes were. Moreover, we investigated the dynamical stability of the residual system to see whether the system maintained or lost metabolic robustness after removing the enzyme  to represent system state fluctuation, and we could quantify the overall impact with a measurement formula (see section "Methods" for details). All results here were summarized in Additional file We first simulated the system to obtain metabolite kinetics and flux distributions under normal conditions. Next, we carried out em state ,16. We cWe discovered that many enzymes could exert (upon deletion) large influences on the kinetics of many metabolites, i.e. caused large system state fluctuations if deleted. For example, transketolase-b (TKb), an enzyme catalyzing a coupling branch of the glycolysis and the pentose phosphate pathways, had relatively large influences on many metabolites in the central carbon metabolism, especially for glucose, sedoheptulose-7-phosphate, and erythrose-4-phosphate Figure . PhosphoMeanwhile, we also found that there were another group of enzymes, in contrast to those mentioned above, having very little influences on system states. For example, multiple enzymes such as glucose-6-phosphate isomerase (PGI), pyruvate kinase (PK), phosphoglucomutase (PGM), etc., only resulted in very small state fluctuations when deleted. The amplitudes were slight, and the influences were mostly within limited areas as the amplitudes were negligible at the distance ≥ 2  whether deleting this enzyme made the residual system have no equilibrium; (2) if the residual system still had equilibrium(s), how far its location deviated from eqX; and (3) whether its stability property changed (i.e. if there are changes in the neighborhood orbit structure). Equilibrium(s) was computed by dynamical simulation and optimization methods. When it was located, its deviation from eq Xwas calculated and its neighborhood orbit structure was described by the rules of topological conjugacy (see the "Methods" section for details). As multiple enzyme deletions might generate topologically identical orbit structures, we showed several typical cases as examples here. See Additional file We found that the original system had an asymptotically stable equilibrium point t Figure . This giin silico knockout of TKb, the residual system had large qualitative changes in system dynamics. It exhibited equilibrium far away from eq Xwith very different stability property  uncritical enzymes did not cause any of the flux-sums to drop by 50%  validated studies. Kim ondingly . There wondingly . Since ME. coli gene essentiality studies such as the Keio collection, the genetic footprinting study and the Profiling of E. coli Chromosome (PEC) database, and our results were supported by some of the experimental characterizations. For example, the "critical" enzymes PGK and GAPDH are encoded by genes pgk and gapA respectively. And the 2 genes are both characterized as essential by studies of both the Keio collection and genetic footprinting [eno and this gene is also essential, according to the Keio collection and the PEC database [pgi, pykF and pgm respectively, and the 3 genes are all characterized as nonessential by all the Keio collection, genetic footprinting and the PEC database [rpe. However, this gene is characterized as essential by genetic footprinting but nonessential by the Keio collection and PEC database. Given that Ru5P is critical to the central carbon metabolic system as revealed by our method and verified by the flux-sums of essential metabolites (earlier context), we propose from the viewpoint of criticality that gene rpe might be essential.We also compared our results with other printing ,19. ENO database . Moreovedatabase ,19,20. Fdatabase ,19,20. SStudying system-level properties of bio-molecules is essential to systems biology ,2. But min silico deletions instead of mild perturbations . Because a well-casted biological network usually has parametric properties favouring the robustness in dynamics, critical components may well tolerate mild perturbations (i.e. parameters exhibiting the Lyapunov stability). Therefore, individual sensitivity analysis often fails to identify such critical spots, and its inability to reveal simultaneous influences worsens the situation. That is why we develop the "criticality characterization". In silico deletion is equivalent to investigating how the system would be if the component is forcefully assumed to be absent, eliminating the parametric properties stated earlier. Furthermore, our method's capacity of revealing simultaneous/overall impacts at the system level enables it to distinguish real critical spots from uncritical ones more effectively. In addition, utilizing kinetic model as the analytical basis is a superiority over the stoichiometric flux-balance modeling in traditional flux-analysis methods, enabling us to appropriately explore system behaviours in the real-time scale [tpiA (i.e. the gene encoding TIS) attenuated the cell growth by about 70%. And our method appropriately revealed that TIS could exert large impacts on the system if deleted, because of the designs we made (mentioned above). Hence methodologically, our method creates a different angle from topology-based methods and can be viewed as an improvement of conventional flux-analysis approaches.Differing from topology-based methods, our method characterizes system-level properties on the quantitative basis. But unlike the conventional sensitivity analysis, we employ the network structure information by calculating the distances from the deleted spot to the affected entities besides computing the fluctuations. Moreover, unlike conventional flux-analysis approaches, we explore the system stability and retrieve system dynamics structure. Incorporating the network/dynamics structure information allows us to reveal the simultaneous/collateral influences and the overall impact on the system. Another major difference from the sensitivity analysis is that we use me scale . For exain silico deleting a protein, the residual system is actually a virtual structure. We assume that this structure encodes important information about whether the mutant can maintain its functionality and how it would dynamically behave/evolve provided that it stills operates on its own. The residual system fails to maintain functionality when its kinetics goes beyond regular ranges , or its dynamics is trapped in a mode where the stable equilibrium is sabotaged, as stable equilibrium gives rise to robustness and is an essential prerequisite for valid mathematical formulations of living cell dynamics [After dynamics ,24-26. EE. coli central carbon metabolism, we find that deleting enzymes such as PGK, GAPDH, etc. causes the system to become a very ill-suited structure as some state values soaring to levels beyond the normal range and the trajectories are highly divergent throughout the state space  to reveal the constitutive/functional properties of system building blocks.E. coli central carbon metabolism and adopted a previous modeling framework as our working platform [We utilized existing kinetic data in platform . The kinAll kinetic rate equations were formulated according to biochemical mechanisms . Most ofX denoted system state and P denoted kinetic parameters. R was a function vector collocated by all rate equations, and A was the stoichiometric matrix. B was the term standing for extra reactions . Most parameters were found in published studies and the rest could be estimated using the experimental conditions, steady-state reaction rates and concentrations reported in previous studies [Here vector  studies ,31,32. F0X, i.e. time-courses of metabolite concentrations under normal conditions. After deleting an enzyme, we computed the kinetics of the residual system e Xto see how it deviated from the original state. Thus the influence of the deleted enzyme could be assessed. Assuming solution X was organized as a matrix and each column represented the kinetics of a metabolite, we could calculate the amplitude of metabolite k's state fluctuation asBy substituting in an initial value, a typical Cauchy problem was formed and numerical integration curves were computed for Eqn (1). We used the Gear method in computation so as to alleviate the stiffness problem of ODEs . With anst distance level metabolites within a direct single reaction were assigned a distance of 2, and so on. We combined the distances and amplitudes to see in which ranges influences occurred and how strong they were. We also computed the flux distributions of the residual system based on the metabolites concentrations and rate equations. Thus we could observe how the flux distributions deviated from the original system and assessed them in the same way as Eqn (2). The distances of effects could be directly counted from the enzyme-centric network. Furthermore, we could combine the amplitude and distance data into a single measurement for assessing the overall impact, both for metabolite-centric network and enzyme-centric network (Eqn (3)).We could calculate the distances of metabolites from the deleted enzyme by the structure of metabolite-centric network. Metabolites directly associating with the enzyme were assigned a distance of 1; metabolites not directly associating with the enzyme but associating with the 1eq Xwere calculated by the Euclid norm.Normal bio-systems are subjected to robustness as they structurally consist of abundant alternative pathways and feedback loops ,17,24. TWe defined the dynamical stability following the concept of the Lyapunov stability, which has explicit physical/chemical context and is suitable for describing metabolic robustness ,26. The s Neigenvalues with negative real parts and u Neigenvalues with positive real parts, the trajectories of Eqn (1) near the equilibrium are topologically equivalent to those of Eqn (5). Here β is a part of kinetic parameters and σ is +1 according to our system. In other words, the orbit structure (near the equilibrium) of Eqn (5) is topologically conjugate with that of Eqn (1). Because Eqn (5) is much simpler, we could investigate it instead of the complex Eqn (1). In this way, we explicitly drew the orbit structure of Eqn (5) near the equilibrium and could know the qualitative system dynamics of Eqn (1) accordingly.The Hartman-Grobman Theorem and Center Manifold Theorem prove that if the Jacobian matrix evaluated at an equilibrium has 2 conjugate purely imaginary eigenvalues, in silico deletion (parameter modification) yields multiple equilibriums, the impact on dynamical stability is regarded as large if anyone of the equilibriums exhibit qualitative difference from eq Xin dynamical properties.If the bifurcation caused by an MOMA suggested that metabolic systems were subjected to biological robustness. When perturbed, it could adjust itself towards a state that was relatively close to the original state. We could formulate the process as an optimization problem asμ Pwas the parameter set with the relevant enzymatic parameters deleted, 0 Xwas the original state and 0 Cwas the initial value. Some states that were close to 0 Xin the feasible space could be solved with the genetic algorithm, a heuristic numerical approach that can alleviate computation difficulty in large variable space to some extent.Here et al.'s work on E. coli metabolism [k was defined asWe adopted the definition of essential metabolite and flux-sum in Kim tabolism . The 12 k Ωwas the index set of reactions producing metabolite k.where After MOMA computation, we obtained one (or more) set of parameters and system states. Using rate equations, we simulated the fluxes and calculate flux-sums according to Eqn (7).The authors declare that they have no competing interests.Conceiving and designing the research: RDL and LL. Data acquisition and analysis: RDL. Drafting the manuscript: RDL and LL.E. coli central carbon metabolic networkThe metabolite-centric and enzyme-centric views of the . The file is in the format of *.png, with figures included showing the metabolite-centric (Page 1) and enzyme-centric metabolic (Page 2) networks. In the metabolite-centric view, enzymes/reactions are abbreviated as symbols and denoted by rectangles; metabolites are also abbreviations; inhibitors/activators are drawn as circles beside the reactions. Synth1 is a lumped reaction for synthesizing chorismate and murine; Synth2 is lumped reaction for synthesizing isoleucine, alanine, ketoisovalerate, and diaminopimelate [pimelate . In the Click here for fileImpacts on system states in the metabolite-centric view. The file is in the format of *.pdf, with each plot showing the impacts of enzymes deletions on the metabolite kinetics. The metabolite indexes, impact distances and state uation amplitudes form the 3 dimensions. Legends are the same as those in Figure Click here for fileImpacts on system states in the enzyme-centric view. The file is in the format of *.pdf, with each plot showing the impacts of enzymes deletions on the kinetic fluxes. The reaction/enzyme indexes, impact distances and flux fluctuation amplitudes form the 3 dimensions. Legends are the same as those in Additional file Click here for fileThe catalogue of the results of dynamical stability analysis. The file categorizes all enzymes that generate topologically equivalent system orbit structures, when deleted. Enzymes in the same category exert similar impacts on the qualitative dynamics of the system. The file is in the format of MS Word electronic table (*.doc).Click here for fileThe flux-sum validations of critical and uncritical enzymes. The file is in the format of *.pdf, with each plot showing the flux-sums of the essential metabolites before and after the deletion of an enzyme. Deletions of all presented enzymes are shown. The metabolite symbols and flux-sum values form the lateral and vertical dimensions. Legends are the same as those in Figure Click here for fileDescription of the modeling. The file is the supplementary text of the modeling. It is in the format of *.doc and includes detailed descriptions of metabolites, enzymes, kinetic rate equations and ODEs.Click here for fileHigh resolution images of Figure Click here for fileHigh resolution images of Figure Click here for file


Focusing our analysis on a K-ras dependent transformation system, we show that enhanced proliferation and evasion from apoptosis are closely linked, and present findings that indicate how a large metabolic remodeling sustains the enhanced growth ability. Network analysis of transcriptional profiling gives the first indication on this remodeling, further supported by biochemical investigations and metabolic flux analysis (MFA). Enhanced glycolysis, down-regulation of TCA cycle, decoupling of glucose and glutamine utilization, with increased reductive carboxylation of glutamine, so to yield a sustained production of growth building blocks and glutathione, are the hallmarks of enhanced proliferation. Low glucose availability specifically induces cell death in K-ras transformed cells, while PKA activation reverts this effect, possibly through at least two mitochondrial targets. The central role of mitochondria in determining the two investigated cancer cell properties is finally discussed. Taken together the findings reported herein indicate that a system-level property is sustained by a cascade of interconnected biochemical pathways that behave differently in normal and in transformed cells.Systems Biology holds that complex cellular functions are generated as system-level properties endowed with robustness, each involving large networks of molecular determinants, generally identified by “omics” analyses. In this paper we describe four basic cancer cell properties that can easily be investigated  High-throughput technologies  aim to obtain a global molecular description of complex biological processes and to reach a deeper understanding of their behavior. The application of these technologies has characterized the last decade of biological research, called “the post-genomic era” and each post-genomic technique has already significantly contributed to molecular investigation of many aspects of physiological and pathological processes in many cellular types.An important, widely analyzed target of post-genomic techniques is the very diversified cancer phenotype. Gene expression microarrays allowed to quantitatively characterize genome-wide transcriptional profiles of various cancer types  modulated by internal and external cues  and the dynamics  of the circuit. The analysis of circuit behavior in biological systems is quite well established from providing insight into signaling pathways  as well as quantitative estimation of the system-level property, under various experimental conditions; to test the adequacy of the model by simulation of system's behavior under a variety of conditions; to run sensitivity analysis to assess the relevance of each element of the model in determining the system-level property.The strategy that we follow and that—as outlined in section From “Omics” Data to Networks and Beyond” above—has been employed with success to investigate yeast growth and cycle can be summarized as follows: to use genome-wide analysis and molecular-tailored experiments to ascertain which molecular components are involved in the function under evaluation; to take into consideration all pertinent literature and make use of chemical and/or genetic perturbations to reconstruct the sequence of molecular events that underlay the function under analysis; to design a concept map of the function describing also relevant spatio-temporal coordinates; use the concept map to construct a mathematical model, in terms of chemical kinetics; to estimate, both from literature and from Of course, as said before, a model can be constructed at various levels, considering different molecular players, as shown for instance in the case of the circuit “osmostat” in the homeostatic response to hyperosmotic stress  and Guanine nucleotide Exchange Factors (GEFs). Deregulation of either GAP or GEF activity may result in hypo- or hyper-activation of downstream pathway(s), so that for instance over-expression of a GEF or inactivation a GAP may both result in cell transformation  that converge to activate cyclin D1 synthesis and stabilization, whose increase together with the down-regulation of cyclin-dependent kinase inhibitors, p27 and p21 promotes entrance into S phase    and of 4 mM glutamine (HQ) is provided to cells growing under optimal condition, while glucose limitation is imposed by an initial concentration of 1 mM (LG) and glutamine limitation by one of 0.5 mM (LQ). As previously described, By comparing transformed to normal cells, in the next sections we analyze the molecular players of the very large module “enhanced cell growth” as well as those of the two connected modules “evasion from apoptosis” and “LG-induced apoptosis” considering both findings from our laboratory and literature data.in vivo and in vitro, normal cells, when they reach the correct dimension of the organ to which they belong or the critical cell density—confluence—respectively, stop growing upon contact inhibition   promotes Drp1 phosphorylation, increases mitochondrial fusion and reduces ROS production  mitochondria are no longer susceptible to apoptosis promoted by glucose deprivation. It is interesting to recall at this point that a myc-inducible human Burkitt lymphoma cell line does not show LG-induced apoptosis, its survival being sustained by glutamine metabolism  is a recent development of metabolomic techniques. By combining the use of isotopic tracers and computer algorithms, MFA allows to actually estimate in quantitative terms intracellular fluxes along metabolic pathways  by pyruvate dehydrogenase kinase largely expressed in these an other cancer cells  by siRNA or LDHA inhibition by a small-molecule inhibitor induces significant cell death and inhibits progression of human lymphoma and pancreatic cancer xenografts , possibly relieving the burden on NADH reoxidation given by the dysfunction of Complex I, previously discussed to be a relevant characteristic of K-s Figure  -2-hydroxyglutarate (2HG)   in all eukaryotic cells, both in conditions of homoplasmia or heteroplasmia. Since evolution resulted in loss of several bacterial pathways—and their encoding genes—modern mitochondria encode only a small number of genes. The 16.6 Kb human mtDNA encodes 2 ribosomal RNA required for assembling of the 70 S mitochondrial ribosomes and 22 tRNA, that is the minimal requirement for the autonomous protein synthesizing machinery of the mitochondria. Only 13 of the 85 proteins composing the OXPHOS complexes are encoded by human mtDNA. All other mitochondrial proteins are encoded by the nuclear genome  lists 1164 and 1265 proteins in the human and mouse mitochondrial proteome, respectively.Mitochondria are semi-autonomous organelles that change in shape, size and association in different tissues or physio-pathological conditions. They derive from an ancestral endosymbiotic bacterium. Accordingly, mitochondria Figure  are charMitochondria play a key role in cellular bioenergetics, ion homeostasis, carbohydrate, and fatty acids metabolism and cell signaling. They are at the center of several inter-connected important metabolic pathways, which are further connected to other important cellular functions Schatz,  Figure . ConsequGeneral issues for mitochondrial proteomics do not differ from general proteomics  and are suitable for hydrophobic proteins, such as membrane proteins . Sample is digested in solution to peptides that are fractionated before being directly analyzed by MS. With the shotgun approach, protein is not lost with extraction from the gel, however, high abundant proteins are digested prior to MS, so these high abundant fragments can blunt many fractions.heavy and light tags specifically designed to react with a particular amino acid (cysteine or lysine) of proteins extracted from two samples, induces a mass shift of the labeled peptides which will appear as a doublet on the mass spectrum, allowing the calculation of the peptide ratio and hence up- or down-regulation of the corresponding protein —a high-throughput proteomic technique that allows the quantification of a given marker in minute amounts of protein from biological specimens  subunits has been reported during prostate cancer progression , O-linked-β-N-acetylglucosamine glycosylation (O-GlcNAc), glutathiolation, sumoylation, ubiquitination are the most relevant PTMs of mitochondrial proteins. PTM MS-based proteomics is in most cases sensitive enough to identify low abundant proteins only after extensive upstream fractionation. Comparative techniques have been developed to cope with the differential abundance and the high sensitivity.Lam et al., recently reported an adaptable, sensitive, specific and robust workflow for quantification of endogenous phosphopeptides from outer and inner mitochondrial membrane proteins  are abundant β-barrel, channel-forming proteins located in the outer mitochondrial membrane, but also present in the plasma membrane   of the electron transport chain to proton extrusion from the mitochondrial matrix, 10 protons being ejected for each 2 electrons transferred from NADH to oxygen. Following the concentration gradient, protons return to the matrix via the Fo subunit of the FoF1 ATP synthase (Complex V). The proton flux induces conformational changes of ATP synthase, so to allow endoergonic ATP synthesis  and H2O2. Mitochondria are selectively vulnerable to oxidative damage  need to be analyzed. Because cells and organisms fine tune their metabolism according to genetic, developmental and environmental clues, changes in flux result from interactions among proteins and metabolites, as well as from regulatory genetic and biochemical interactions, structural and allosteric enzyme regulation, etc., as reviewed in Sauer  labeled substrates are fed to a growing cell population until the isotope label is distributed throughout the network, originating specific metabolite labeling patterns—detected through either MS or nuclear magnetic resonance (NMR)—according to flux distribution in the studied experimental situation. Obtaining fluxes from labeling data is not straightforward and requires computer model interpretation that rely on either parameter fitting procedures  in any given condition allowing to detect non-canonical pathways. For instance in microorganisms it allowed to detect the unexpected activity of the Entner–Doudoroff pathway   and Magnetic Resonance (MR) is increasing. Since PET allows to visualize modifications in cell metabolism or tissue microenvironment, it represents a unique tool for a better understanding of cancer biology, the set up of novel diagnostic procedures and for early assessment of the efficacy of target-directed therapies.Application to cancer of In recent years a growing number of radiopharmaceuticals have been developed and validated at both preclinical and clinical levels for the management of patients with different cancers. Different tracers allow to visualize and measure hallmark cancer phenotypes including glucose and fatty acid metabolism, cell proliferation or regional hypoxia.18F)-D-glucose (FDG) is widely used in clinical practice for staging, restaging and early prediction and assessment of response to pharmacological treatment. The lack of the hydroxyl group in position 2 blocks FDG metabolism past GLUT-mediated glucose transport and phosphorylation by hexokinases (HK): hence FDG uptake specifically reflects the levels of GLUT and HK activity . The potential use of 3′-deoxy-3′-[18F]fluorothymidine (FLT)—whose uptake reflects the activity of cytosolic thymidine kinase-1 (TK-1) which is the first enzyme in the salvage pathway of DNA synthesis activated during the S-phase of the cell cycle 4-fluoroglutamine and l-[5-11C]-glutamine  that allows quantification of different tissue metabolites including glutamine and glutamate. However, MRS indicates only the global levels of metabolites inside tissue and not their relative fluxes between intracellular and extracellular spaces. This measurement is possible and traditionally performed in vitro using isotopically labeled compounds. In the field of MR, hyperpolarized 13C MR represents an emerging tool for the real-time monitoring of single step reactions along metabolic pathways. In hyperpolarized MR, a molecule is labeled with an NMR-active nucleus and then hyperpolarized using dynamic nuclear polarization. Nuclear spin hyperpolarization can dramatically increase the sensitivity of 13C MRS, allowing dynamic measurements of the metabolism of hyperpolarized 13C-labeled substrates in vivo. To date, [5-13C]-glutamine, [1-13C]-glutamate, and [5-13C-4-2H2]-glutamine have been successfully used in cells, but their implementation in vivo at preclinical stage remains limited. Polarization tends to decay relatively rapidly following injection into living tissues minimizing the time frame during which the signal can be detected. Strategies allowing improvements in polarization levels of these agents might pave the way for wider in vivo implementation  paradigm. In a first study, FBA has been used to characterize the optimal flux distributions for maximal ATP production in mitochondria with the aim to offer a systemic perspective regarding the effect of stoichiometric constraints and specific metabolic fluxes on mitochondrial function , which inactivates PDH converting pyruvate to Acetyl-CoA  will produce NADPH at the expenses of NADPFrom this simple and so far still incomplete analysis, it is possible to justify many aspects of the “enhanced growth” phenotype and show that this function/system-level property is sustained by a large number of biochemical pathways that interact often in ways that are not coincident with canonical indications. At first glance this behavior recalls that of a “Rube Goldberg machine,” which performs a given task in a very complex fashion, interlocking in an unexpected way elementary sequences of events that realize a chain reaction to perform the required task. The interest of the chain reaction is given by the fact that the first reaction generates a product that determines the behavior of the second reaction and so on. For instance, when glucose uptake and glycolytic flux are low, pyruvate is utilized by the TCA cycle and sustains OXPHOS-dependent ATP synthesis. Instead when the glycolytic flux strongly increases, pyruvate is mostly diverted to lactate production. Therefore the energy released at the various reaction steps allows the system to reach one or the other end point, as a function of the conditions generated during the chain.ras transformed cells. The first step to be undertaken afterwards is to verify its completeness and eventually to add other relevant pathways, not yet considered. Then, efforts of constructing mathematical models, at the appropriate level of detail, may be undertaken.Of course Figure + ratio, due to Complex I partial inactivation, on the insurgence and maintenance of the mitochondrial remodeling of the glutamine utilization in cancer cells.As discussed in section “Modeling Mitochondrial Activities,” models of mitochondrial functions are quite complex and their granularity depends upon the problems one would like to address. A first interesting question to investigate could be the role of the altered NADH/NADA neat definition, at molecular level, of the structure of the complex molecular machine underlying “enhanced growth,” compared to those of both growing and resting normal cells could allow to identify the step(s) to be inhibited, if one wants to specifically arrest growth of cancer cells, leaving unaffected normal ones.ras transformed cells as compared with myc-dependent Burkitt lymphoma cell lines which instead are able to survive using only glutamine metabolism (Le et al., ras transformed cells in low glucose will be required.Following the same line of thought, Figure in silico simulations can be carried on, and their predictions compared and validated with experimental results and data sets. As noted above, multiscale modeling and simulation tools play an essential role. The identification of the molecular steps that, inhibited by a new drug, may be able to selectively kill K-ras dependent cancer cells, offers hope to become able to eradicate this type of K-ras-addicted cancers.Putting together this information, one should be able to construct first a concept map, then a mathematical model of “LG-induced cell death.” Based on this model, extensive In conclusion, as discussed in this paper, post-genomic analysis of cancer cells becomes able to yield understanding and predictive ability for understanding of multifactorial diseases and hence develop efficient new therapeutic treatments, only by integration of network analysis with dynamic modeling of physiologically relevant system-level properties, thus modifying the initial, too optimistic, expectation (Henney and Superti-Furga, The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


While functional genomics, focused on gene functions and gene-gene interactions, has become a very active field of research in molecular biology, equivalent methodologies embracing the environment and gene-environment interactions are relatively less developed. Understanding the function of environmental factors is, however, of paramount importance given the complex, interactive nature of environmental and genetic factors across multiple time scales.Here, we propose a systems biology framework, where the function of environmental factors is set at its core. We set forth a "reverse" functional analysis approach, whereby cellular functions are reconstructed from the analysis of dynamic envirome data. Our results show these data sets can be mapped to less than 20 core cellular functions in a typical mammalian cell culture, while explaining over 90% of flux data variance. A functional enviromics map can be created, which provides a template for manipulating the environmental factors to induce a desired phenotypic trait.Our results support the feasibility of cellular function reconstruction guided by the analysis and manipulation of dynamic envirome data. The phenotype of a cell results from the combined effect of environmental and genetic factors ,2. WhileThe relationship between genes and environment can be analysed by computational methods founded on the principle that the biochemical habitat of a cell is primarily "engraved" in its DNA sequence. Genes may be associated with metabolic enzymes, membrane transporters, signal transduction or regulatory control. Combined with basic biochemical information currently available in several databases  setting the universe of cellular functions and envirome components, ii) collecting informative envirome data over time, and iii) systems level analysis of dynamic envirome data to find relationships between environmental variables and cellular functions.In functional genomics, the goal is genome-wide cellular function reconstruction through the collection and analysis of transcriptome or proteome data over time. In functional enviromics, we attempt to bridge environmental factors and function through the collection and analysis of dynamic envirome data. The necessary elements to such an analysis are: To support such a framework, we have developed a computational algorithm called "envirome-guided projection to latent pathways (PLP)". "Cellular function" has been defined in different ways, here we adopted elementary flux modes (EM) as function descriptors -18 becau6 [A medium scale network has typically a very large number of EMs in the order of ~106 . It is h6 . It is ti) variance of measured phenotypic trait explained by each EM and ii) degree of correlation of each EM with the environmental state. By maximising these two criteria, the algorithm delivers a ranking of EMs in increasing order of percentage of explained variance in the measured flux data, and a functional enviromics map (FEM) representing the strength of up- or down-regulation of EMs by environmental factors. Mathematical details of the method can be found in the Methods section. In what follows we illustrate the methodology with an application to a recombinant BHK cell line expressing the fusion glycoprotein IgG1-IL2 (see Methods for details).In conformity with the above enumerated principles, PLP was designed to maximise the covariance between environmental state (envirome data sets) and observed phenotypic trait (rate of change of envirome variables) under the constraint of known genes translated into a plausible set of cellular functions Figure . Simply The methodology was first applied to the analysis of a data set comprising measurements of 27 environmental factors collected from six independent, differently operated culture experiments as described in Methods and Table A high degree of correlation was observed between measured envirome components and relative weight of discriminated EMs, ultimately reflected in high correlation between measured and predicted extracellular rates Figure . Within Several other methods were proposed to calculate EMs weighting factors from flux vectors but none of these methods use the correlation with the envirome as criterion for EMs discrimination. As pointed out by Wiback et al. , the extOn the whole, these results show that the most significant pathways in cultured mammalian cells could be identified from a larger, redundant set of possible routes using the correlation with the environmental state as the selection criterion. Further, the weighting factors of selected pathways are highly correlated with the environmental state denoting either a causal or effector relationship between environmental state and active pathways. Finally, such envirome-correlated pathways explain more than 90% of flux data variance.quasi steady state is observed, according to the profiles of extracellular metabolite concentrations.Pathways may be up- or down-regulated either as a consequence of changes in the environment or driven by changes in the internal state ultimately reflected in a particular environmental "footprint". To monitor such regulatory processes, the PLP algorithm was applied to a moving time window, from the beginning to the end of each fed-batch experiment, to identify EM rearrangements over time. Each culture was partitioned into sequential metabolic phases, where Table We further analyse in more detail the cases of the reversible EM 4 (Gln ↔ Glu + Amm) and EM 7 (Asn ↔ Amm + Asp), linked to amino acids metabolism, which were selected in all phases but at some point changed direction  was performed to compare intracellular flux distributions of the original BHK metabolic network and PLP reconstructed metabolic networks. Also the consistency index  was calcThe excluded reactions  refer to the catabolism of amino acids . The concept of FEM is illustrated in Figure Functional enviromics maps are intended to compile large-scale quantitative information of the interactions between environmental factors and cellular functions. A FEM consists of a data matrix, FEM = {IAnalysing these data, it can be observed that several envirome factors correlate positively with the flux for biomass synthesis through EM 20, namely the concentrations of glucose, lactate, glutamine and other 8 amino acids. On the other hand, extracellular pH and the concentrations of product and histidine correlate negatively. While glucose is known to be mandatory for many cell types in culture, BHK cells are capable of glutamine synthesis from glutamate . HoweverIn terms of product synthesis (EM 1), negatively correlated factors include the pH and glucose concentration, while osmolality and the concentrations of valine, isoleucine, leucine and lysine correlate positively with this flux. The negative correlation with glucose concentration is explained by bioreactor operation factors, as it decreases (or is maintained) along culture time whereas product formation rate increases, rather than by an inhibition mechanism on product synthesis. The effect of pH deserves further attention, since it also correlates negatively with biomass formation. As described in Methods, the bulk pH was allowed to vary between 7 and 7.25. Some reports agree that maximum growth rates  and recoDespite using data from six independent experiments, some environmental factors  show similar variation trends, which precludes disclosure of their true contribution to cellular phenotype. It is clear that the identifiability of environment-EMs relationships is conditional to sufficient "system excitation", which can only be assured by model-based design of experiments. Within proper circumstances of experimental design and physiological range of physical and biochemical variables, the information of EMs regression coefficients can be used to direct the phenotype to a desired state by manipulating genes, envirome or both. It should be noted that EMs represent clusters of genes, thus the EM regression coefficients translate the lumped effect of kinetic and genetic regulation induced by the envirome.While in functional genomics the aim is to unravel gene functions from the analysis of transcriptome and proteome dynamical data -40, hereWhen applied to a recombinant BHK cell line, environmental data finds correspondence to less than 20 elementary flux modes, while explaining over 90% of flux variance. Most of the discriminated elementary flux modes are typical routes known to be active in cultured mammalian cells, while excluded routes are related with the catabolism of those amino acids not in excess in the medium, thus being directly used for protein synthesis. Furthermore, metabolic flux distributions of reduced metabolic networks, accounting for the discriminated core cellular functions only, were shown to be consistent with metabolic flux distributions of the original, unreduced metabolic network. These results essentially show that through the life of the culture, phenotypic variability is almost completely traceable by monitoring changes in envirome data. The remaining 10% of unexplained variance corresponds mostly to experimental error, thus leaving little room for genetically induced variability that cannot be traced to envirome data. On the whole, such results support the feasibility of a function reconstruction approach guided by the collection and analysis of envirome data sets.et al. [When applied to a sliding time window along a single culture experiment, it was observed that several discriminated EMs are not active during all culture phases while others may change direction at some point. Such short-term dynamic changes correspond to metabolic adaptation induced by environmental stimuli. While some metabolic adaptation effects are readily interpretable in terms of exhaustion or limitation of essential substrates, some other have nontrivial interpretation and may correspond to unknown functions of environmental factors. The analysis of EM rearrangements can provide insights about the mechanisms underlying such metabolic adaptation. Despite the variability in observed EMs with culture time, a high degree of conservation of cellular function among the six different cultivation experiments was obtained . These observations support the results presented by Ma et al.  who usedWhile our intention was to demonstrate the principle of cell functional enviromics, its full potential can be realized when large-scale, high-throughput analysis techniques are employed for envirome-wide reconstruction of cellular functions eventually leading to high resolution functional enviromics maps. This approach has many interesting practical applications ranging from drug design to macroscopic process control. A point to be made is that finding a statistical correlation, even if linked with a metabolic structure, does not allow one to discriminate between a "cause" and an "effect". Such discrimination is only possible after a systematic analysis on the basis of a sound experimental design, which might include simultaneous environmental and genetic perturbations.Fundamental issues, which remain to be clarified, include the degree of conservation of envirome functions among different species. One can anticipate that, given the role of environmental factors in the evolution of life, some degree of conservation may exist between species, as implicitly acknowledged by those who defend the reverse ecological principle . A systeThe need for envirome-driven systems biology approaches to address human diseases has long been recognized . Others r, of a given metabolic network operating in steady state can be described as a non-negative weighted sum of a finite number of elementary flux modes, ei :, which represents additional measured information in our method that is not used in other methods. The objective function in our case consists of:Elementary modes thus represent unique and non-decomposable flux solutions, from which all possible system solutions can be obtained through the proper determination of elementary modes weighting factors, r. e.g. -29). As . As λi. r.  and x(t)), andi) maximising correlation between ii) minimising redundancy, i.e. eliminating all elementary modes with weak correlations with the envirome.The basic idea is that the genome sets the structure of the elementary flux modes, while the relative weights of elementary flux modes are to a large extent set by the environment.According to the above criteria, the PLP algorithm was designed to maximise the covariance between an input data matrix, X = {x(t)}, of independent envirome observations, x(t), and an output response matrix, R = {r(t)}, of observed steady state reaction rates, r(t), under the constraint of a plausible set of elementary flux modes. This problem can be expressed mathematically by the following constrained linear program:i} a nr × nem matrix of nem elementary flux modes, ei (dim(ei) = nr) } a np × nx matrix of np independent observations of envirome vectors x(t) (dim(x) = nx), R = {r(t)} a np × nr matrix of np independent observations of reaction rates, r(t) (dim(r) = nr), E = {eMaximising covariance implies two concomitant goals, namely maximising variance and minimising redundancy. Unconstrained maximisation of covariance can be achieved by the very popular projection to latent structures method, also known as partial least squares (PLS). Since PLP is derived from PLS, in the lines below we first review the structure of PLS and then show how it can be modified to PLP.X, called the predictor, and an output response matrix, which here is the target rate data, R. It differs from traditional multivariate linear regression in that it decomposes iteratively both the predictor and the response matrices into a reduced set of uncorrelated latent variables, thereby eliminating redundancy in the input and output data sets. More specifically, the matrices X and R are decomposed into loadings matrices (W and Q), scores matrices (T and U) and residuals matrices (EX and ER):PLS is a multivariate linear regression technique that maximises the covariance between an input matrix, W and Q are the latent variables in which the input and output matrices are decomposed, respectively. Additionally, the outputs scores matrix U is linearly regressed against the inputs scores matrix T:The columns of the loadings matrices B holds the regression coefficients determined by minimising the residuals EU. Finally, the explained variance in target data  by the model is given by the following equation:where i and j denoting observation and flux indexes respectively. Solving system Eqs. (3a-c), which imply minimising residuals EX. ER and EU, can be effectively performed by the very popular Non Iterative Partial Least Squares (NIPALS) algorithm. For more details about PLS and NIPALS see, for instance, Geladi and Kowalski [with indexes Kowalski .ei can be viewed as PLS latent variables equivalents, while the elementary modes weighting factors λi can be interpreted as PLS latent variables score values. Following this analogy, a straightforward modification to Eq. (3b) can be introducedPLP can be viewed as a constrained version of PLS by the inclusion of the elementary flux modes constraints. Given their equivalent mathematical structures, an interesting comparison may be made between projection of target reaction rate data, R, onto latent variables Q (performed in PLS according to Eq. (3b)), and projection of flux vectors onto elementary modes weighting factors according to Eq. (1). Indeed, elementary flux modes E are no longer degrees of freedom in PLP as Q was in PLS. Instead, E represents a constraint to the relationship between X and R imposed by the metabolic network structure and ultimately by the genome of the cells.by substituting PLS loading matrix, Q, by the elementary modes matrix, E. This means that the latent structures An advantage of PLP over PLS is that both the latent variables and score values of the target matrix have in PLP a physical meaning. The latent variables are latent pathways while scores U are equivalent to Λ, i.e. they represent the relative weighting factors of latent pathways. For this reason, the algorithm is called projection to latent pathways. Moreover, the regression coefficients B can be used to deduce the functional enviromics matrix, FEM, as follows:FEM is a nx × nem matrix comprising the regression coefficients of elementary flux modes against envirome components, thus providing information of how elementary flux modes are up- or down-regulated by envirome components.a priori according to the elementary modes structure, E. The calculation of the elementary modes is not automatically integrated in PLP. For that we used the METATOOL 5.0 [The PLP algorithm was coded in MATLAB  as a modified version of the NIPALS algorithm {Geladi, 1986 #15}, wherein the loadings of the outputs are fixed TOOL 5.0  as expla2. The batch culture started at 48 mM of glucose, 1.2 mM of Glutamine, 2.8 mM of Glutamate, 1.3 mM of Serine and 0.75 mM of Aspartate. As for the fed-batch cultures, the initial concentrations and feeding strategies are given in Table A BHK-21 cell line constitutively expressing the fusion glycoprotein IgG1-IL2 (antibody type one linked to Interleukin two) was used in this study . Six celet al. [et al. [27 envirome components were profiled, namely temperature, pH, osmolality and concentrations of 24 extracellular compounds determined as follows. Cell counts were determined in a Fuchs-Rosenthal haemocytometer; cell viability was assessed by the trypan blue dye exclusion method. Product concentration was determined by ELISA . AmmoniThe BHK metabolic network adopted in this study comprised 57 metabolic reactions (including transport/diffusion across the plasma membrane), 35 intracellular metabolites and 24 extracellular metabolites and other compounds. Considered reactions explain the major fluxes of carbon and nitrogen, namely glycolysis, glutaminolysis, TCA cycle, pentose-phosphate pathway, recombinant product synthesis and biosynthesis of cellular components . The catabolism of all amino acids except tryptophan is also considered. The network was simplified by lumping series of metabolic reactions, free of branching points, without loss of representativeness under steady-state. A detailed description can be found in Additional File FluxAnalyzer software [For metabolic flux analysis (MFA), the difference between the number of reactions and the number of intracellular metabolites is 57-35 = 22, which means that at least 22 measured fluxes are required to obtain a determined MFA system. Envirome profiling provided data of 24 exchange fluxes, which means that the MFA system becomes redundant with 24-22 = 2 degrees of freedom. As such, the full vector of 57 fluxes was partitioned into known (extracellular) and unknown (intracellular) subsets of 24 and 33 fluxes respectively. Measured fluxes for different phases of fed-batch cultures are presented as Table software .h, was calculated according to the method described in Wang and Stephanopoulos [h were compared to the χ2 statistical test for two degrees of freedom. Whenever h is below the χ2 threshold value the system is said to be consistent. These tasks were performed using FluxAnalyzer [Given that the MFA system is redundant with two degrees of freedom, the consistency index method  can be anopoulos . Then, tAnalyzer .FluxAnalyzer [The BHK metabolic network was firstly manipulated according to the guidelines in Gagneur and Klamt , and theAnalyzer . This meFor the present BHK problem, the input data set X comprises the above enumerated 27 envirome factors. The target data-set R comprises measurements of the same 24 exchange fluxes used for MFA. The universe of EMs is formed by the 251 EMs obtained for BHK (see Additional File The project was conceived by RO and experiments planned by AT, RO, PA and MJTC. Experiments were performed by AT and NC. MS and AC supervised the bioreaction and JC developed software to support fed-batch control. PLP algorithm was developed by JD, MVS and RO. Data analysis and manuscript writing were performed by AT, JD, NC and RO. All authors read and approved the final manuscript.BHK elementary modes. List of elementary modes obtained from the BHK metabolic network (additional file Click here for fileBHK metabolic network. Biochemical reactions/pathways, enzymes and biomass composition considered in the metabolic model of BHK cellsClick here for file


Synechocystis sp. PCC6803 is a cyanobacterium considered as a candidate photo-biological production platform - an attractive cell factory capable of using CO2 and light as carbon and energy source, respectively. In order to enable efficient use of metabolic potential of Synechocystis sp. PCC6803, it is of importance to develop tools for uncovering stoichiometric and regulatory principles in the Synechocystis metabolic network.Synechocystis sp. PCC6803 available, iSyn669, which includes 882 reactions, associated with 669 genes, and 790 metabolites. The model includes a detailed biomass equation which encompasses elementary building blocks that are needed for cell growth, as well as a detailed stoichiometric representation of photosynthesis. We demonstrate applicability of iSyn669 for stoichiometric analysis by simulating three physiologically relevant growth conditions of Synechocystis sp. PCC6803, and through in silico metabolic engineering simulations that allowed identification of a set of gene knock-out candidates towards enhanced succinate production. Gene essentiality and hydrogen production potential have also been assessed. Furthermore, iSyn669 was used as a transcriptomic data integration scaffold and thereby we found metabolic hot-spots around which gene regulation is dominant during light-shifting growth regimes.We report the most comprehensive metabolic model of iSyn669 provides a platform for facilitating the development of cyanobacteria as microbial cell factories. Relevance of this phylum covers from evolutionary studies [Synechocystis sp. PCC6803 is a cyanobacterium that is considered as a good candidate for developing a photo-biological cell factory towards production of a variety of molecules of socio-economic interest, with CO2 (and/or sugars) as carbon source and light (and/or sugars) as energy source [Cyanobacteria, which have been model organisms since the early 70s of the past century , are a w studies  to biote studies . Synechoy source ,10 - an y source .Synechocystis sp. PCC6803 is capable of growing under three different growth conditions as marked by the utilized carbon source (/s) [viz., i) photoautotrophy, where energy comes from light and carbon from CO2; ii) heterotrophy, where energy and carbon source is a saccharide, for instance glucose; and, iii) mixotrophy, a combination of the above two, where light is present as well as a combination of two carbon sources: glucose and CO2. Reconstruction of a genome-scale metabolic model for this model photo-synthetic bacterium is one of the main goals of the current study. Genome-scale metabolic network reconstruction is, in essence, a systematic assembly and organization of all the reactions which build up the metabolism of a given organism; and has been of great interest in the post-genomic era. The variety of applications of such a metabolic model [in silico model that allows probing possible flux distributions inside the cell under different environmental conditions and for a given genetic make-up. Towards this end, a variety of tools/algorithms are available [rce (/s) . This caic model  includesvailable , includivailable ,16, minivailable , regulatvailable  and metavailable ,20.Synechocystis sp. PCC6803 genome was sequenced, annotated and made publicly available in 1996 [et al [et al. These two works considered one lumped reaction for the photosynthesis of the system. More recently, an uncurated reaction list with a biomass composition represented by central carbon metabolites has been published [ in 1996 ,22 and h in 1996 ,24. The 6 [et al  focused ublished . This moSynechocystis sp. PCC6803 and demonstrates some of its potential applications.The large quantity of information featured in public databases, like details about genomes , pathwayiSyn669 is a valuable tool towards the development of a photo-biological production platform. The model will also contribute to the existing set of genome-scale models with a virtue of being one of the first stoichiometric models that account for photosynthesis.The present model features a detailed biomass equation which encompasses all the building blocks that are needed for a flux distribution simulation that reflects observed phenotype. No lumped reactions are present and photosynthesis is described as a set of 19 reactions, thus enabling the tracing of the corresponding fluxes. Furthermore, different analyses are performed by using this metabolic reconstruction, including reaction knock-out simulations, flux variability analysis and identification of transcriptional regulatory hotspots. Overall, Synechocystis sp. PCC6803. For a thorough overview of the process of metabolic model reconstruction, refer to very instructive work by Forster et al [et al [Synechocystis sp. PCC6803 [A complete literature examination, including databases, biochemistry textbooks and the annotated genome sequence, was needed in order to extract the current state of the art on known metabolic reactions within the metabolic network of er et al  as well l [et al . In deta PCC6803 ,22. Thes PCC6803  in order PCC6803  and KEGG PCC6803 . Reactio PCC6803 . Additio PCC6803 . If no cIn the reconstruction of the metabolic model, many reactions  were included. Additionally, a total of 79 reactions were included on the basis of biochemical evidence or physiological considerations, but currently with no annotated Open Reading Frame (ORF). iSyn669 genome-scale metabolic model is available in Additional file The product of this reconstruction process was a set of reactions that encompass all the known metabolite conversions that take place in iSyn669 spans all the biologically relevant flux nodes in the Synechocystis metabolism. Pyruvate, phosphoenolpyruvate (PEP), 3-phosphoglycerate, erythrose-4-phosphate and 2-oxoglutarate are main flux nodes for amino acids biosynthesis. Acetyl-CoA is an important flux node for fatty acids production, with high relevance for metabolic engineering towards biofuel production. Biosynthesis of nucleic acids comes from different metabolites, namely, ribose-5-phosphate, 5-phospho-beta-D-ribosyl-amine, L-histidine and L-glutamine. Moreover, with the information publicly available on databases, we can conclude that Synechocystis sp. PCC6803 bears an incomplete tricarboxylic acid cycle (TCA cycle), as it lacks 2-ketoglutarate dehydrogenase (EC 1.2.4.2). It has been published that glyoxylate shunt completes this cycle [2.6.1.1a in iSyn669) can interconvert 2-ketoglutarate and oxaloacetate, thus bridging the gap of 2-ketoglutarate dehydrogenase, but short-circuiting TCA cycle.is cycle , permittiSyn669 displays the connectivity distribution pattern similar to that of the other microbial genome-scale networks, e.g. yeast [Escherichia coli [From the network topology perspective, g. yeast  and Eschhia coli  : carbon dioxide and light (autotrophic), sugars (heterotrophic), carbon dioxide, light and sugars (mixotrophic).lgorithm . The FBAlgorithm ,24. Neve results . Due to Growth under pure heterotrophy, or dark heterotrophy (in the absence of light) is a subject under study ,43, bein-1. This growth rate corresponds to a light input of 0.8 mE gDW-1 h-1 and to a net carbon flux of 3.4 mmol gDW-1 h-1 into the cell, with HCO3- and CO2 as carbon sources. For the sake of comparison across the different conditions, uptake rates for the corresponding carbon sources were matched based on normalization per number of carbon atoms . Results of the subsequent FBA simulations for the three different growth conditions are presented in the following. Some of the reactions that are physiologically relevant for each of the conditions are summarized in Table All FBA simulations were carried out under the appropriate constraints so as to match an autotrophic specific growth rate of 0.09 hDW-1 h-1, entering the system through glcP glucose transporter (reaction TRANS-RXN59G-152 in iSyn669). With the purpose of having a pure heterotrophic state, photon uptake rate was constrained to 0; this caused photosynthesis fluxes to be shut down. In this case, glucose will be the source for the formation of carbon backbones for the building blocks of the cell, depicted in the biomass equation. The glycolytic and the oxidative mode of the pentose phosphate pathway were found to be active. Oxidative pentose phosphate pathway is the major pathway for glucose catabolism as was reported in reference [4.1.1.31 in iSyn669) is the main anaplerotic flux to the TCA cycle. Carbon fixation efficiency is around 60%, the rest being released in the form of CO2, as reported in our previous work [Heterotrophy was simulated by considering glucose as the sole carbon source with uptake rate of 0.567 mmol geference . PEP car4.1.1.39), fixing all the CO2 that was released in dark heterotrophy, boosting carbon efficiency to a theoretical 100%. In this case, global flux distribution as well as flux ranges resemble that of autotrophy more than that of the dark heterotrophy. Carbon skeletons are still produced through glycolysis and NAD(P)H is reduced along the glycolysis, pyruvate metabolism and TCA cycle. On the other hand, pentose phosphate pathway has shifted to the reductive mode due to RuBisCO activation and the corresponding flux is increased in magnitude. Carbon fixation happens at the RuBisCO level, thereby assimilating the CO2 produced by the glucose metabolism, and the production of ATP and NADPH through photosynthesis relieves the oxidative phosphorylation from draining NADPH to generate ATP.In contrast to dark heterotrophy, if a light-activated heterotrophy simulation is run, light enters the system and RuBisCO enzyme is active  and photosystem II (reaction _lightII) being 0.8 mE gDW-1 h-1. Carbon sources used in simulating photoautotrophy conditions were carbon dioxide and carbonic acid, and its entrance to the system was mediated by RuBisCO (reaction 4.1.1.39 in iSyn669) and carbonic anhydrase (reaction 4.2.1.1b) respectively. As iSyn669 biomass equation encompasses all essential metabolite precursors, these will be the sinks of our network, while photons, carbon dioxide and/or carbonic acid will be the sources. Thus autotrophic fluxes will flow in the gluconeogenic direction and through the Calvin cycle, which is the reductive mode of the pentose phosphate pathway. PEP carboxylase is the main anaplerotic flux to the TCA cycle and glyoxylate shunt is inactive.Photoautotrophy was initially simulated considering an illumination of 0.15 mE ms 0.5 pg  and its s 0.5 pg , we estiurements . First, 2 and glucose inputs to the same carbon uptake flux as in the case of the autotrophy and the heterotrophy. Photon uptake rates were also normalized in a similar manner to match the autotrophic state. Having the same metabolic sinks as the two previous modes and the sources from the both of them, it is logical to think that the resulting flux distribution will be a mixture of the autotrophic and heterotrophic simulations. Indeed, we observed that the mixotrophic flux distribution lies in-between the previous two states, being a bit closer to the heterotrophy. Glycolysis is present and glyoxylate is shut down; an active photosynthesis is present, oxidative phosphorylation is less stressed than in heterotrophy as the energy can be produced from the photon uptake; and Calvin cycle is active, as carbon sources are CO2 and glucose.Photons, carbon dioxide and glucose are independent feed fluxes in this simulation. These fluxes entered the system through the same reactions as described for the previous growth modes. Carbon source presents, in this case, one more degree of freedom than in the rest of the conditions. In order to keep a comparative criterion across conditions, we normalized COSynechocystis metabolism due to (un)availability of light, we have compared the autotrophic growth with the other two by using flux variability analysis . However, the predicted intra-cellular flux distribution is not necessarily unique due to the presence of multiple pathways that are equivalent in terms of their overall stoichiometry. Thus, often the system exhibits multiple optimal solutions and further elucidation requires additional constraints based on experimental evidences (e.g. carbon labeling data). Alternatively, physiological insight can be still obtained by studying the variability at each flux node given the objective function value - a procedure referred to as flux variability analysis. In order to gain insight into the flux changes underlying the changes in the s Figure . InteresiSyn669 and FBA simulations enabled us to further analyze the characteristics and potential of the Synechocystis metabolic network. This can be oriented towards the study of the reactions (and thereby the corresponding genes) that are necessary for the growth, or to in silico metabolic engineering for identification of targets for maximization of a given metabolite of socio-economic interest.The comprehensive set of reconstructed biochemical equations of iSyn669 network consists of 790 metabolites and 882 reactions. Among these, 350 genes  [Saccharomyces cerevisiae  [iSyn669, we find that Synechocystis has a significantly larger fraction of metabolic genes whose deletion obliterates biomass formation . One possible explanation for the difference in the relative proportion of essential genes in these three organisms would be an incomplete/incorrect annotation of the genome of Synechocystis sp. PCC6803. For example, if only one of the isoenzymes corresponding to a reaction is annotated, the corresponding in silico knock-out will result in a false negative prediction. It is important to note that the computational predictions of gene essentiality based on FBA are highly dependent on the growth medium used for the simulations. Thus, the comparison across different species may not be straight-forward. Moreover, it is also possible that the natural growth conditions of Synechocystis may have dictated selection for a relatively high proportion of essential genes. Such hypotheses need careful consideration of several factors and are beyond the scope of this work.Interestingly, if we compare the proportion of the essential genes under FBA simulation in the metabolic networks of e total)  and Sacce total)  with iSySynechocystis sp. PCC6803 is considered as a candidate photobiological production platform - it can potentially produce molecules of interest by using CO2 and light [iSyn669 can be used to perform simulations, not only for assessing the feasibility of producing a given compound, but also to identify potential metabolic engineering targets towards improved productivity. For example, FBA simulations can help in estimating maximum theoretical yields for the products/intermediates of interest. A product of obvious interest is hydrogen. In our previous work [In silico studies can direct the efforts and counsel the scientists towards a hydrogen producing cyanobacteria that could be of impact. iSyn669 predicts, in autotrophic conditions, a theoretical H2 evolution rate of 0.17 mmol gDW-1 h-1 obliterating biomass growth. Else, the stoichiometry permits the evolution of 0.156 mmol gDW-1 h-1 of hydrogen with a biomass growth of 10% of the wild type (0.007 mmol gDW-1 h-1).in silico metabolic engineering strategy to improve the production of succinate. The underlying idea is to design a succinate over-producing metabolic network (through reaction knock-out simulations), whereas the intracellular fluxes are distributed so as to maximize the biological objective function (e.g. growth) [design objective function which copes with the metabolite of interest, succinate, has been determined maintaining the biological objective function as the biomass formation.Succinate is an important metabolite for its biotechnological applications as well as for being a metabolite that bridges the TCA cycle with the electron transfer chain. As an example of the usefulness of the present metabolic model we have designed an  growth) . To this growth)  was used growth)  as a bioDW-1 h-1. The best single knock-out was found to be the mutant of pyruvate kinase (reaction 2.7.1.40c in iSyn669 and genes sll0587 and sll1275) that has a succinate evolution of 0.5695 mmol gDW-1 h-1 with a growth rate of 0.0714 mmol gDW-1 h-1. Blocking this reaction, preventing pyruvate and phosphoenolpyruvate from using GTP and GDP would drive a high increase in succinate production. The flux between pyruvate and phosphoenolpyruvate can still be accomplished with reactions 2.7.1.40a and 2.7.9.2, but using ATP and ADP as cofactors. Double deletion did not improve the results from the single knock-out strain, evolving the same succinate production with the same growth rate. The best triple knock-out was found to be the combination of pyruvate kinase (reaction 2.7.1.40c in iSyn669 and genes sll0018 and sll0587), fructose-bisphosphate aldolase (reaction 4.1.2.13b in iSyn669 and genes slr0943 and sll1275) and succinate dehydrogenase . This simulated strain has a succinate evolution of 0.6999 mmol gDW-1 h-1 with a growth rate of 0.0688 mmol gDW-1 h-1. This design combines the blocking of the oxidation of succinate on the electron chain transfer through succinate dehydrogenase with the prevention of using GTP between pyruvate and phosphoenolpyruvate and the lack of an aldolase needed in the reductive mode of the pentose phosphate pathway. This leads to a situation where flux is directed to TCA cycle in order to meet with an overproduction of succinate.OptGene simulations for single, double and triple knock-out strategies were performed to obtain solutions with improved succinate production, but without drastically diminishing the biomass production. We used mixotrophic conditions, for which wild type optimal growth rate was 0.17909 mmol gin vivo knock-out building will ultimately be through gene manipulations. This is the reason underlying the fact that we found 2.7.1.40c knock-out as the best result. This design would hint at the idea of selection of a mutated pyruvate kinase protein specific for ATP cofactor. This may be difficult to achieve on the bench, but has high biotechnological expectations.These studies on knock-outs are reaction centered, even though the omics datasets, such as transcriptome and metabolome. An example of algorithms for carrying out such an integrative analysis through the use of genome-scale metabolic networks is Reporter Features [omics data with bio-molecular interaction networks, thereby allowing identification of cellular regulatory focal points (i.e. reporter features), for instance reporter metabolites as regulatory hubs in the metabolic network.Apart from the flux simulations, another important problem in the field of metabolic systems biology that can be addressed by using reconstructed genome-scale models is the integration of the different genome-wide bio-molecular abundance datasets, i.e. Features ,49. RepoSynechocystis sp. PCC6803 network allowing us to infer regulatory principles underlying metabolic flux changes following shifts in growth mode. In particular, we analyzed the data from a work [Synechocystis sp. PCC6803 by shifts from darkness to illumination conditions and back. As it can be understood from the rationale beneath the metabolic capabilities of this cyanobacterium, the presence or absence of light drives big changes in the flux distribution through the network, as discussed in the previous sections. We have focused our study on the relationship between the transcription of Synechocystis sp. PCC6803 genes and the reactions of the metabolic network. Associations between genes and reactions were identified, listing all the genes that performed or were involved in a specific reaction. With this information and the metabolic model, Reporter Features analysis was carried out. In brief, the analysis helped to identify metabolites around which the transcriptional changes are significantly concentrated. These metabolites are termed reporter metabolites as they represent key regulatory nodes in the network.In this work, Reporter Features software was used to integrate transcriptional information over the reconstructed m a work  that repet al [Synechocystis was grown to mid-exponential phase (A730 = 0.6 to 0.8). Then, the lights were extinguished and RNA samples were taken after 24 h in the dark (full dark). Illumination was then turned back on for 100 min (transient light), followed immediately by an additional 100 min in the dark (transient dark).Gill et al  designedet al [We were interested in two aspects of this study: i) to identify metabolites around which regulation is centered during the light regime transitions; and ii) to find the metabolic genes that were collectively significantly co-regulated across these transitions .The analet al . In thisreporter metabolites were found to be quite scattered across the metabolism spanning several metabolic pathways, and thus offering a global view of the transcriptional response in the metabolic network , nucleic acids and its precursors , carbon metabolism metabolites , lipids precursors , cofactors  and photosynthesis metabolites (plastocyanin) pictures a scenario of a global regulation throughout the different metabolic pathways.When all seven arrays were used, iSyn669 network. Furthermore, 50 genes were identified that are strongly co-regulated all along the profile of the experiment . sm Table . Some coiSyn669 reactions. Furthermore, 84 genes were identified that are strongly co-regulated across the three arrays , light a 1,4-alpha-D-glucan_n and its cognate a 1,4-alpha-D-glucan_n1 also stands out as they are involved in carbon reserves catabolism and anabolism.Finally, we considered the arrays that represent the shift from light to dark, data from "90 minutes of light" array to "60 minutes of dark" array. Similar to the previous case study, reporter metabolites were found to be focused on the nucleotide and amino acid metabolism Table . AdditioWith the help of the sub-network search, 133 genes were identified as being significantly co-regulated across those three arrays  and oxidative phosphorylation is the main energy pathway that remains present. Regulation is centered on the energy metabolism shift , withholding amino acids and nucleotide precursors and keeping the cofactors available in a low-profile metabolism.This last set of data array is a scenario where metabolism is being shut down, as a consequence of the darkness and lack of carbohydrate source. Without light, photosynthesis is blocked and carbon fixation is nearly obliterated. Cells strive to build up carbon reserves  will be a valuable tool for the applied and fundamental research of Synechocystis sp. PCC6803, as well as for the broad field of metabolic systems biology. iSyn669 represents an important step for the integration of tools and knowledge from different disciplines towards development of photo-biological cell factories.Altogether, the genome-scale metabolic network of Synechocystis-specific database of genes, proteins, enzymes and metabolites. Synechocystis sp. PCC6803 genome and annotation files were downloaded from NCBI Entrez Genome repository as of date 10 of September of 2008 [Pathway Tools software  was used of 2008 . Pathway of 2008 , KEGG pa of 2008 , BioCyc  of 2008 , BRENDA  of 2008  and UniP of 2008 .Synechocystis network, like the incomplete TCA cycle [Parts that characterize CA cycle ,53, the CA cycle , the intCA cycle  or the cCA cycle -57, wereet al [Saccharomyces cerevisiae metabolic network.At the end of the reconstruction process, four kinds of relationships were present in the database: reaction with cognate genes, reactions that needed to be included in the model in order to have metabolic precursors in the network (with no assigned genes), non-enzymatic reactions that have no related gene, and genes described in the annotations but with no assigned function. For an overview of the underlying process, please refer to Fortser et al  work on The set of biochemical reactions of the genome-scale metabolic model were formulated as a steady state stoichiometric model:et al [S is stoichiometric matrix that contains the stoichiometric coefficients corresponding to all internal  metabolites. v is flux vector that corresponds to the columns of S. Given a set of experimentally-driven constraints, former equation was solved by using linear programming, the approach known as flux balance analysis, or FBA [The details are described elsewhere, for example in Stephanopoulos et al . This mo, or FBA .Since the number of reactions is typically larger than the number of metabolites, the system becomes underdetermined. In order to obtain a feasible solution for the intracellular fluxes, an optimization criterion on metabolic balances has to be imposed. This can be formulated by maximizing one of the biochemical reactions, e.g. biomass equation, subject to the mass balance and the capacity constraints.For instance,j vis the rate of the th jreaction. The elements of the flux vector v were constrained for the definition of reversible and irreversible reactions, j, rev vand j, irrv, respectively. Additionally, two set of equations were established, j, constν, constrained metabolic reactions, and j, uptakeν, uptake reactions, which were bound by experimentally determined values from the literature. Biomass synthesis was considered as a drain of precursors or building blocks into a hypothetical biomass component. Flux through biomass synthesis reaction, being the biomass formation rate, is directly related to growth of the modeled organism [iSyn669 metabolic model.where organism . Table 3DW-1 h-1 [2 uptake rate was found to be 3.7 mmol CO2 gDW-1 h-1 [Simulations were performed with the OptGene software . Some caDW-1 h-1 . MaximumDW-1 h-1 . AdditioDW-1 h-1 , and waset al [Segre et al  introducj) that has minimal distance from a given flux vector w. The goal is to find the vector x ∈Φj such that the Euclidean distanceMOMA algorithm searches for a point in the feasible space of the solutions space of the knock-out .In brief, Reporter algorithm converts the p-value for a given node to a z-score by using the inverse normal cumulative distribution function , i.e.:After scoring each non-feature node in this fashion, we need to calculate the score of each feature jfeature , this value should be corrected for the background distribution of z scores in the data, by subtracting the mean (Nm) and dividing by the standard deviation (Ns) of random aggregates of size N.To evaluate the significance of each zBM: biomass; DCW: dry cell weight; FBA: flux balance analysis; MCA: metabolic control analysis; MOMA: minimization of metabolic adjustments; ORF: Open Reading Frame; PEP: phosphoenolpyruvate; ROOM: regulatory on-off minimization of metabolic fluxes; RuBisCO: Ribulose-1,5-bisphosphate carboxylase oxygenase; TCA cycle: tricarboxylic acid cycleAM and EN conducted the reconstruction and the different analyses. PF and JFU conceived of the study and participated in its design. AM and KRP designed the study and wrote the manuscript. All authors contributed to, read and approved the final manuscript.iSyn669 reactions to gene connections. Excel file with the list of iSyn669 reactions and its cognate list of genes.Click here for fileiSyn669 genome-scale metabolic model in OptGene format. Text file with the stoichiometric model, in OptGene [ OptGene  format, Click here for fileMost connected metabolites with filtered cofactors. Supplementary table with most connected metabolites once the cofactors have been filtered.Click here for fileiSyn669 metabolic fluxes simulated under four conditions. Excel file with all the reactions simulations and resulting flux ranges from the model simulated under four growth conditions: autotrophy, dark o pure heterotrophy, light-activated heterotrophy and mixotrophy.Click here for fileFluxes of reactions around pyruvate. Flux values (in mmol/g DCW/h) for reactions that produce or drain pyruvate in Synechocystis sp. PCC6803 metabolism. Negative sign in bidirectional reactions means pyruvate consumption. Reactions names can be traced in reaction list in Additional files 2 and fluxes can be found in Additional file 4.Click here for fileSynechocystis sp. PCC6803, Escherichia coli and Saccharomyces cerevisiae genome-scale metabolic modelsFBA and MOMA simulation values for biomass growth in . Excel file with the growth values under MOMA simulation for Synechocystis sp. PCC6803, Escherichia coli and Saccharomyces cerevisiae. Data for Synechocystis is original from present work, data for Escherichia coli has been obtained from metabolic model from reference 18 and data for Saccharomyces cerevisiae is from reference 30.Click here for fileiSyn669 groups of correlated genes in the three sets of arrays of light shift experiments. Word file with the list of iSyn669 correlated genes in "All time points", "Dark to light" and "Light to dark" analyses.Click here for file