Control of Spatially Heterogeneous and Time-Varying Cellular Reaction Networks: A New Summation Law

A hallmark of a plethora of intracellular signaling pathways is the spatial separation of activation and deactivation processes that potentially results in precipitous gradients of activated proteins. The classical Metabolic Control Analysis (MCA), which quantifies the influence of an individual process on a system variable as the control coefficient, cannot be applied to spatially separated protein networks. The present paper unravels the principles that govern the control over the fluxes and intermediate concentrations in spatially heterogeneous reaction networks. Our main results are two types of the control summation theorems. The first type is a non-trivial generalization of the classical theorems to systems with spatially and temporally varying concentrations. In this generalization, the process of diffusion, which enters as the result of spatial concentration gradients, plays a role similar to other processes such as chemical reactions and membrane transport. The second summation theorem is completely novel. It states that the control by the membrane transport, the diffusion control coefficient multiplied by two, and a newly introduced control coefficient associated with changes in the spatial size of a system (e.g., cell), all add up to one and zero for the control over flux and concentration. Using a simple example of a kinase/phosphatase system in a spherical cell, we speculate that unless active mechanisms of intracellular transport are involved, the threshold cell size is limited by the diffusion control, when it is beginning to exceed the spatial control coefficient significantly.Comment: 19 pages, AMS-LaTeX, 6 eps figures included with geompsfi.st

Control of spatially heterogeneous and time-varying cellular reaction networks: a new summation law

A hallmark of a plethora of intracellular signaling pathways is the spatial separation of activation and deactivation processes that potentially results in precipitous gradients of activated proteins. The classical Metabolic Control Analysis (MCA), which quantifies the influence of an individual process on a system variable as the control coefficient, cannot be applied to spatially separated protein networks. The present paper unravels the principles that govern the control over the fluxes and intermediate concentrations in spatially heterogeneous reaction networks. Our main results are two types of the control summation theorems. The first type is a non-trivial generalization of the classical theorems to systems with spatially and temporally varying concentrations. In this generalization, the process of diffusion, which enters as the result of spatial concentration gradients, plays a role similar to other processes such as chemical reactions and membrane transport. The second summation theorem is completely novel. It states that the control by the membrane transport, the diffusion control coefficient multiplied by two, and a newly introduced control coefficient associated with changes in the spatial size of a system (e.g., cell), all add up to one and zero for the control over flux and concentration. Using a simple example of a kinase/phosphatase system in a spherical cell, we speculate that unless active mechanisms of intracellular transport are involved, the threshold cell size is limited by the diffusion control, when it is beginning to exceed the spatial control coefficient significantly

‘Domino’ systems biology and the ‘A’ of ATP

AbstractWe develop a strategic ‘domino’ approach that starts with one key feature of cell function and the main process providing for it, and then adds additional processes and components only as necessary to explain provoked experimental observations. The approach is here applied to the energy metabolism of yeast in a glucose limited chemostat, subjected to a sudden increase in glucose. The puzzles addressed include (i) the lack of increase in Adenosine triphosphate (ATP) upon glucose addition, (ii) the lack of increase in Adenosine diphosphate (ADP) when ATP is hydrolyzed, and (iii) the rapid disappearance of the ‘A’ (adenine) moiety of ATP. Neither the incorporation of nucleotides into new biomass, nor steady de novo synthesis of Adenosine monophosphate (AMP) explains. Cycling of the ‘A’ moiety accelerates when the cell's energy state is endangered, another essential domino among the seven required for understanding of the experimental observations. This new domino analysis shows how strategic experimental design and observations in tandem with theory and modeling may identify and resolve important paradoxes. It also highlights the hitherto unexpected role of the ‘A’ component of ATP

Application of differential metabolic control analysis to identify new targets in cancer treatment

In the quest for anti-cancer drugs with high efficacy and low toxicity, cancer metabolism has increasingly been a focus of interest in clinical research. Enhanced glycolysis and robust production of lactate constitute characteristic traits that discriminate many cancerous cells from their normal counterparts. This, in principle, may provide researchers with a general handle on such a complex disease, regardless of the intrinsic genotypic heterogeneity of the single transformed cells. The work carried out during this project and presented in this thesis consists of developing and applying analytical approaches, mainly drawn from the field of metabolic control analysis (MCA), to the study of cancer metabolism. The ultimate goal is to assess whether, and to what extent, the metabolic features of cancer cells may be exploited in the attempt to attack the malignancy more specifically than through traditional clinical approaches. The underlying idea consists of identifying enzymes that represent points of fragility specifically characterising the cancerous metabolic phenotype. These enzymes are such that an alteration in their activity (due for example to the action of an anticancer drug) would elicit the desired response in cancer cells, without affecting their normal counterparts. The application of MCA relies on a mathematical representation of the system under study. Creating such a model is often hampered by the lack of data about the precise kinetic laws governing the different reaction steps and the value of their corresponding parameters. The most important result reached during this project shows that the metabolic quantities defining the normal and cancer phenotypes (such as fluxes and metabolite concentrations), together with heuristic assumptions about the properties of typical enzyme-catalyzed reactions, already allow for a fast and efficient way to explore the effectiveness of putative drug targets with respect to criteria of high efficacy and low toxicity. The relevance of this result lies in the fact that the quantities defining a metabolic phenotype are experimentally more accessible than the kinetic parameters of the different enzymatic steps in the system.EThOS - Electronic Theses Online ServiceBBSRCEPSRCGBUnited Kingdo

Predictable Irreversible Switching Between Acute and Chronic Inflammation

Many a disease associates with inflammation. Upon binding of antigen-antibody complexes to immunoglobulin-like receptors, mast cells release tumor necrosis factor-α and proteases, causing fibroblasts to release endogenous antigens that may be cross reactive with exogenous antigens. We made a predictive dynamic map of the corresponding extracellular network. In silico, this map cleared bacterial infections, via acute inflammation, but could also cause chronic inflammation. In the calculations, limited inflammation flipped to strong inflammation when cross-reacting antigen exceeded an “On threshold.” Subsequent reduction of the antigen load to below this “On threshold” did not remove the strong inflammation phenotype unless the antigen load dropped below a much lower and subtler “Off” threshold. In between both thresholds, the network appeared caught either in a “low” or a “high” inflammatory state. This was not simply a matter of bi-stability, however, the transition to the “high” state was temporarily revertible but ultimately irreversible: removing antigen after high exposure reduced the inflammatory phenotype back to “low” levels but if then the antigen dosage was increased only a little, the high inflammation state was already re-attained. This property may explain why the high inflammation state is indeed “chronic,” whereas only the naive low-inflammation state is “acute.” The model demonstrates that therapies of chronic inflammation such as with anti-IgLC should require fibroblast implantation (or corresponding stem cell activation) for permanence in order to redress the irreversible transition

Recurrent design patterns in the feedback regulation of the mammalian signalling network

Biochemical networks are characterized by recurrent patterns and motifs, but the design principles underlying the dynamics of the mammalian intracellular signalling network remain unclear. We systematically analysed decay rates of 134 signalling proteins and investigated their gene expression profiles in response to stimulation to get insights into transcriptional feedback regulation. We found a clear separation of the signalling pathways into flexible and static parts: for each pathway a subgroup of unstable signal inhibitors is transcriptionally induced upon stimulation, while the other constitutively expressed signalling proteins are long-lived. Kinetic modelling suggests that this design principle allows for swift feedback regulation and establishes latency phases after signalling, and that it might be an optimal design due to a trade-off between energy efficiency and flexibility

Dupuytren's: a systems biology disease

Dupuytren's disease (DD) is an ill-defined fibroproliferative disorder of the palm of the hands leading to digital contracture. DD commonly occurs in individuals of northern European extraction. Cellular components and processes associated with DD pathogenesis include altered gene and protein expression of cytokines, growth factors, adhesion molecules, and extracellular matrix components. Histology has shown increased but varying levels of particular types of collagen, myofibroblasts and myoglobin proteins in DD tissue. Free radicals and localised ischaemia have been suggested to trigger the proliferation of DD tissue. Although the existing available biological information on DD may contain potentially valuable (though largely uninterpreted) information, the precise aetiology of DD remains unknown. Systems biology combines mechanistic modelling with quantitative experimentation in studies of networks and better understanding of the interaction of multiple components in disease processes. Adopting systems biology may be the ideal approach for future research in order to improve understanding of complex diseases of multifactorial origin. In this review, we propose that DD is a disease of several networks rather than of a single gene, and show that this accounts for the experimental observations obtained to date from a variety of sources. We outline how DD may be investigated more effectively by employing a systems biology approach that considers the disease network as a whole rather than focusing on any specific single molecule