57 research outputs found
Quantifying the entropic cost of cellular growth control
We quantify the amount of regulation required to control growth in living
cells by a Maximum Entropy approach to the space of underlying metabolic states
described by genome-scale models. Results obtained for E. coli and human cells
are consistent with experiments and point to different regulatory strategies by
which growth can be fostered or repressed. Moreover we explicitly connect the
`inverse temperature' that controls MaxEnt distributions to the growth
dynamics, showing that the initial size of a colony may be crucial in
determining how an exponentially growing population organizes the phenotypic
space.Comment: 3 page
Quantitative constraint-based computational model of tumor-to-stroma coupling via lactate shuttle
Cancer cells utilize large amounts of ATP to sustain growth, relying primarily on non-oxidative,
fermentative pathways for its production. In many types of cancers this leads, even in the presence
of oxygen, to the secretion of carbon equivalents (usually in the form of lactate) in the cellâs
surroundings, a feature known as the Warburg effect. While the molecular basis of this phenomenon
are still to be elucidated, it is clear that the spilling of energy resources contributes to creating a
peculiar microenvironment for tumors, possibly characterized by a degree of toxicity. This suggests
that mechanisms for recycling the fermentation products (e.g. a lactate shuttle) may be active,
effectively inducing a mutually beneficial metabolic coupling between aberrant and non-aberrant
cells. Here we analyze this scenario through a large-scale in silico metabolic model of interacting
human cells. By going beyond the cell-autonomous description, we show that elementary physico-
chemical constraints indeed favor the establishment of such a coupling under very broad conditions.
The characterization we obtained by tuning the aberrant cellâs demand for ATP, amino-acids and
fatty acids and/or the imbalance in nutrient partitioning provides quantitative support to the idea
that synergistic multi-cell effects play a central role in cancer sustainmen
Modeling Networks of Coupled Enzymatic Reactions Using the Total Quasi-Steady State Approximation
In metabolic networks, metabolites are usually present in great excess over the enzymes that catalyze their interconversion, and describing the rates of these reactions by using the MichaelisâMenten rate law is perfectly valid. This rate law assumes that the concentration of enzymeâsubstrate complex (C) is much less than the free substrate concentration (S (0)). However, in protein interaction networks, the enzymes and substrates are all proteins in comparable concentrations, and neglecting C with respect to S (0) is not valid. Borghans, DeBoer, and Segel developed an alternative description of enzyme kinetics that is valid when C is comparable to S (0). We extend this description, which Borghans et al. call the total quasi-steady state approximation, to networks of coupled enzymatic reactions. First, we analyze an isolated GoldbeterâKoshland switch when enzymes and substrates are present in comparable concentrations. Then, on the basis of a real example of the molecular network governing cell cycle progression, we couple two and three GoldbeterâKoshland switches together to study the effects of feedback in networks of protein kinases and phosphatases. Our analysis shows that the total quasi-steady state approximation provides an excellent kinetic formalism for protein interaction networks, because (1) it unveils the modular structure of the enzymatic reactions, (2) it suggests a simple algorithm to formulate correct kinetic equations, and (3) contrary to classical MichaelisâMenten kinetics, it succeeds in faithfully reproducing the dynamics of the network both qualitatively and quantitatively
An Industrial Application of Business Intelligence Approach to the Electronic Defence Sector
In the age of digital transformation, the availability of data is growing ex-ponentially leading companies to struggle in processing big data while not missing out useful insights to focus on their business development strate-gy. In this scenario, always more often companies are making use of Busi-ness Intelligence platforms that could allow them to collect, analyses and disseminate data in real time to face the dynamic of the market. This paper aims to apply a Business Intelligence approach that adopts OSINT (open-source intelligence) and SOCMINT (Social Media Intelligence) techniques to Defence Electronics Market to analyse how this technology could facili-tate Companies decision-making process by providing them with a distinct competitive advantage. In this frame we used QUIPO intelligence platform for an industrial scenario analysis in the Defence Electronics sector. This is an initial research to study the correlation between the experimental OSINT analysis carried out by the intelligence platform and the information based on the internal experience and know-how of the company for the use case study
The Brain on Low Power Architectures - Efficient Simulation of Cortical Slow Waves and Asynchronous States
Efficient brain simulation is a scientific grand challenge, a
parallel/distributed coding challenge and a source of requirements and
suggestions for future computing architectures. Indeed, the human brain
includes about 10^15 synapses and 10^11 neurons activated at a mean rate of
several Hz. Full brain simulation poses Exascale challenges even if simulated
at the highest abstraction level. The WaveScalES experiment in the Human Brain
Project (HBP) has the goal of matching experimental measures and simulations of
slow waves during deep-sleep and anesthesia and the transition to other brain
states. The focus is the development of dedicated large-scale
parallel/distributed simulation technologies. The ExaNeSt project designs an
ARM-based, low-power HPC architecture scalable to million of cores, developing
a dedicated scalable interconnect system, and SWA/AW simulations are included
among the driving benchmarks. At the joint between both projects is the INFN
proprietary Distributed and Plastic Spiking Neural Networks (DPSNN) simulation
engine. DPSNN can be configured to stress either the networking or the
computation features available on the execution platforms. The simulation
stresses the networking component when the neural net - composed by a
relatively low number of neurons, each one projecting thousands of synapses -
is distributed over a large number of hardware cores. When growing the number
of neurons per core, the computation starts to be the dominating component for
short range connections. This paper reports about preliminary performance
results obtained on an ARM-based HPC prototype developed in the framework of
the ExaNeSt project. Furthermore, a comparison is given of instantaneous power,
total energy consumption, execution time and energetic cost per synaptic event
of SWA/AW DPSNN simulations when executed on either ARM- or Intel-based server
platforms
Transient anomalous diffusion MRI measurement discriminates porous polymeric matrices characterized by different sub-microstructures and fractal dimension
Considering the current development of new nanostructured and complex materials and gels, it is critical to develop a sub-micro-scale sensitivity tool to quantify experimentally new parameters describing sub-microstructured porous systems. Diffusion NMR, based on the measurement of endogenous waterâs diffusion displacement, offers unique information on the structural features of materials and tissues. In this paper, we applied anomalous diffusion NMR protocols to quantify the subdiffusion of water and to measure, in an alternative, non-destructive and non-invasive modality, the fractal dimension dw of systems characterized by micro and sub-micro geometrical structures. To this end, three highly heterogeneous porous-polymeric matrices were studied. All the three matrices composed of glycidylmethacrylate-divynilbenzene porous monoliths obtained through the High Internal Phase Emulsion technique were characterized by pores of approximately spherical symmetry, with diameters in the range of 2â10 ÎŒm. Pores were interconnected by a plurality of window holes present on pore walls, which were characterized by size coverings in the range of 0.5â2 ÎŒm. The walls were characterized by a different degree of surface roughness. Moreover, complementary techniques, namely Field Emission Scanning Electron Microscopy (FE-SEM) and dielectric spectroscopy, were used to corroborate the NMR results. The experimental results showed that the anomalous diffusion α parameter that quantifies subdiffusion and dw = 2/α changed in parallel to the specific surface area S (or the surface roughness) of the porous matrices, showing a submicroscopic sensitivity. The results reported here suggest that the anomalous diffusion NMR method tested may be a valid experimental tool to corroborate theoretical and simulation results developed and performed for describing highly heterogeneous and complex systems. On the other hand, non-invasive and non-destructive anomalous subdiffusion NMR may be a useful tool to study the characteristic features of new highly heterogeneous nanostructured and complex functional materials and gels useful in cultural heritage applications, as well as scaffolds useful in tissue engineering
Quantitative analysis reveals how EGFR activation and downregulation are coupled in normal but not in cancer cells
Ubiquitination of the epidermal growth factor receptor (EGFR) that occurs when Cbl and Grb2 bind to three phosphotyrosine residues (pY1045, pY1068 and pY1086) on the receptor displays a sharp threshold effect as a function of EGF concentration. Here we use a simple modelling approach together with experiments to show that the establishment of the threshold requires both the multiplicity of binding sites and cooperative binding of Cbl and Grb2 to the EGFR. While the threshold is remarkably robust, a more sophisticated model predicted that it could be modulated as a function of EGFR levels on the cell surface. We confirmed experimentally that the system has evolved to perform optimally at physiological levels of EGFR. As a consequence, this system displays an intrinsic weakness that causesâat the supraphysiological levels of receptor and/or ligand associated with cancerâuncoupling of the mechanisms leading to signalling through phosphorylation and attenuation through ubiquitination
Gaussian and exponential lateral connectivity on distributed spiking neural network simulation
We measured the impact of long-range exponentially decaying intra-areal
lateral connectivity on the scaling and memory occupation of a distributed
spiking neural network simulator compared to that of short-range Gaussian
decays. While previous studies adopted short-range connectivity, recent
experimental neurosciences studies are pointing out the role of longer-range
intra-areal connectivity with implications on neural simulation platforms.
Two-dimensional grids of cortical columns composed by up to 11 M point-like
spiking neurons with spike frequency adaption were connected by up to 30 G
synapses using short- and long-range connectivity models. The MPI processes
composing the distributed simulator were run on up to 1024 hardware cores,
hosted on a 64 nodes server platform. The hardware platform was a cluster of
IBM NX360 M5 16-core compute nodes, each one containing two Intel Xeon Haswell
8-core E5-2630 v3 processors, with a clock of 2.40 G Hz, interconnected through
an InfiniBand network, equipped with 4x QDR switches.Comment: 9 pages, 9 figures, added reference to final peer reviewed version on
conference paper and DO
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