Data-based method for creating electricity use load profiles using large amount of customer-specific hourly measured electricity use data

The recent technological developments monitoring the electricity use of small customers provides with a whole new view to develop electricity distribution systems, customer-specific services and to increase energy efficiency. The analysis of customer load profile and load estimation is an important and popular area of electricity distribution technology and management. In this paper, we present an efficient methodology, based on self-organizing maps (SOM) and clustering methods (K-means and hierarchical clustering), capable of handling large amounts of time-series data in the context of electricity load management research. The proposed methodology was applied on a dataset consisting of hourly measured electricity use data, for 3989 small customers located in Northern-Savo, Finland. Information for the hourly electricity use, for a large numbers of small customers, has been made available only recently. Therefore, this paper presents the first results of making use of these data. The individual customers were classified into user groups based on their electricity use profile. On this basis, new, data-based load curves were calculated for each of these user groups. The new user groups as well as the new-estimated load curves were compared with the existing ones, which were calculated by the electricity company, on the basis of a customer classification scheme and their annual demand for electricity. The index of agreement statistics were used to quantify the agreement between the estimated and observed electricity use. The results indicate that there is a clear improvement when using data-based estimations, while the new-estimated load curves can be utilized directly by existing electricity power systems for more accurate load estimates.Electricity use Load curves Load profiling Time-series clustering Self-organizing map Energy efficiency

Modeling genotypes in their microenvironment to predict single- and multi-cellular behavior

A cell’s phenotype is the set of observable characteristics resulting from the interaction of the genotype with the surrounding environment, determining cell behavior. Deciphering genotype-phenotype relationships has been crucial to understanding normal and disease biology. Analysis of molecular pathways has provided an invaluable tool to such understanding; however, typically it does not consider the physical microenvironment, which is a key determinant of phenotype. In this study, we present a novel modeling framework that enables the study of the link between genotype, signaling networks, and cell behavior in a three-dimensional microenvironment. To achieve this, we bring together Agent-Based Modeling, a powerful computational modeling technique, and gene networks. This combination allows biological hypotheses to be tested in a controlled stepwise fashion, and it lends itself naturally to model a heterogeneous population of cells acting and evolving in a dynamic microenvironment, which is needed to predict the evolution of complex multi-cellular dynamics. Importantly, this enables modeling co-occurring intrinsic perturbations, such as mutations, and extrinsic perturbations, such as nutrient availability, and their interactions. Using cancer as a model system, we illustrate how this framework delivers a unique opportunity to identify determinants of single-cell behavior, while uncovering emerging properties of multi-cellular growth. This framework is freely available at http://www.microc.org

Metabolic symbiosis between oxygenated and hypoxic tumour cells: An agent-based modelling study.

Deregulated metabolism is one of the hallmarks of cancer. It is well-known that tumour cells tend to metabolize glucose via glycolysis even when oxygen is available and mitochondrial respiration is functional. However, the lower energy efficiency of aerobic glycolysis with respect to mitochondrial respiration makes this behaviour, namely the Warburg effect, counter-intuitive, although it has now been recognized as source of anabolic precursors. On the other hand, there is evidence that oxygenated tumour cells could be fuelled by exogenous lactate produced from glycolysis. We employed a multi-scale approach that integrates multi-agent modelling, diffusion-reaction, stoichiometric equations, and Boolean networks to study metabolic cooperation between hypoxic and oxygenated cells exposed to varying oxygen, nutrient, and inhibitor concentrations. The results show that the cooperation reduces the depletion of environmental glucose, resulting in an overall advantage of using aerobic glycolysis. In addition, the oxygen level was found to be decreased by symbiosis, promoting a further shift towards anaerobic glycolysis. However, the oxygenated and hypoxic populations may gradually reach quasi-equilibrium. A sensitivity analysis using Latin hypercube sampling and partial rank correlation shows that the symbiotic dynamics depends on properties of the specific cell such as the minimum glucose level needed for glycolysis. Our results suggest that strategies that block glucose transporters may be more effective to reduce tumour growth than those blocking lactate intake transporters

Liver glycogen phosphorylase is upregulated in glioblastoma and provides a metabolic vulnerability to high dose radiation

Channelling of glucose via glycogen, known as the glycogen shunt, may play an important role in the metabolism of brain tumours, especially in hypoxic conditions. We aimed to dissect the role of glycogen degradation in glioblastoma (GBM) response to ionising radiation (IR). Knockdown of the glycogen phosphorylase liver isoform (PYGL), but not the brain isoform (PYGB), decreased clonogenic growth and survival of GBM cell lines and sensitised them to IR doses of 10-12 Gy. Two to five days after IR exposure of PYGL knockdown GBM cells, mitotic catastrophy and a giant multinucleated cell morphology with senescence-like phenotype developed. The basal levels of the lysosomal enzyme alpha-acid glucosidase (GAA), essential for autolysosomal glycogen degradation, and the lipidated forms of gamma-aminobutyric acid receptor-associated protein-like (GABARAPL1 and GABARAPL2) increased in shPYGL U87MG cells, suggesting a compensatory mechanism of glycogen degradation. In response to IR, dysregulation of autophagy was shown by accumulation of the p62 and the lipidated form of GABARAPL1 and GABARAPL2 in shPYGL U87MG cells. IR increased the mitochondrial mass and the colocalisation of mitochondria with lysosomes in shPYGL cells, thereby indicating reduced mitophagy. These changes coincided with increased phosphorylation of AMP-activated protein kinase and acetyl-CoA carboxylase 2, slower ATP generation in response to glucose loading and progressive loss of oxidative phosphorylation. The resulting metabolic deficiencies affected the availability of ATP required for mitosis, resulting in the mitotic catastrophy observed in shPYGL cells following IR. PYGL mRNA and protein levels were higher in human GBM than in normal human brain tissues and high PYGL mRNA expression in GBM correlated with poor patient survival. In conclusion, we show a major new role for glycogen metabolism in GBM cancer. Inhibition of glycogen degradation sensitises GBM cells to high-dose IR indicating that PYGL is a potential novel target for the treatment of GBMs

Metabolic symbiosis simulations with network gene alterations: The gene enriched (+) and knockout (-) status were simulated by setting the respective node of the regulatory network to 1 and 0, respectively.

The gene wild type (WT) status was simulated without setting the respective node to either 0 or 1. Each gene was altered individually. (A). Percentage tumour growth increase due to symbiosis is shown for each gene enrichment status. (B). Percentage tumour growth increase due to symbiosis is shown for each gene knockout status. (C). Whether tumour growth is significantly different (p-value (D). Whether tumour growth is significantly different (p-value < 0.05) between symbiosis and non-symbiosis for each gene knockout status is shown. Clusters of gene alterations can be identified, which enhance tumour growth due to symbiosis while some other gene alterations together with symbiosis adversely affect tumour growth (A, B). Colours indicate percentage growth increase by symbiosis (A, B) and p values (C, D). p values from 0 to 0.05 are shown in red to white colour scale and p values ≥ 0.05 are shown in grey colour.</p

Tumour growth over time at different combinations of GLUT1 and MCT1 inhibitors.

[GLUT1i]/IC50 and [MCT1i]/IC50 were varied from 0 to 100 and 0 to 1000, respectively. (A, B, C). p53wt tumour cells. Note that the tumour is completely disappeared at [GLUT1]/IC50 = 10. (D, E, F). p53- tumour cells. Temporal variations of total cells (A, D), glycolytic cells (B, E), and OXPHOS cells (C, F) are shown. (DOCX)</p

Symbiosis-induced changes of glucose and oxygen of the microenvironment over time.

Here, the Length is the cross section through the center of the tumour. The heat maps show the variation of the percentage change of oxygen and glucose due to metabolic symbiosis under p53wt and p53- status, and at different initial tumour sizes. (A). The symbiosis would increase the glucose level in the medium. (B). The symbiosis would decrease the oxygen level in the medium. (DOCX)</p

The cell regulatory network of our model (microC model).

The modified MAPK network decides the cell phenotype based on inputs obtained from the microenvironment. Oxygen_supply, Glucose_supply, EGFR_stimulus, cMET_stimulus, FGFR_stimulus, TGFBR_stimulus, DNA_damage and Growth_inhibitor are inputs to the network. The inhibitor nodes (EGFRI, cMETI, FGFRI, GLUT1I, MCT1I, and MCT4I) are also inputs. The inhibitor nodes are activated by respective drugs (e.g. GLUT1I is activated by GLUT1D, MCT1I is activated by MCT1D and so on). The Boolean network is updated asynchronously, and cell phenotypical outputs are calculated. The outputs are Proliferation, Apoptosis, Necrosis and Growth_Arrest. The yellow-colored nodes are diffusible substances. The newly added interactions to the original MAPK network taken from [30] are shown in dotted lines. The solid and dotted green lines are positive interactions and solid and dotted red lines are negative interactions. More details about Boolean logical conditions at each node are given in [16,30] and Tables A and B in S1 Text. A high-resolution image of this network is available in S1 and S2 Files. (DOCX)</p

Cell Regulatory Network.

This is the source file to open the network with GINsim software (http://ginsim.org) and all the Boolean logical conditions and respective supporting evidence can be seen there. Download GINsim software from http://ginsim.org and upload S1 File to visualise cell regulatory network and logical conditions. (ZGINML)</p

Different time scales are used for different processes.

The smallest time step is defined as the time for updating one node of the regulatory network (TNetwork). Cell phenotype and diffusion fields are updated at red (TPhynotypes) and green (TDifusion) ticks, respectively. A cell which is older than the cell division time (TDivision) can divide if its phenotype is Proliferation. (DOCX)</p