Sabrina Stella

Metabolic scaling in solid tumours

Tumour metabolism is an outstanding topic of cancer research, as it determines the growth rate and the global activity of tumours. Recently, by combining the diffusion of oxygen, nutrients, and metabolites in the extracellular environment, and the internal motions that mix live and dead cells, we derived a growth law of solid tumours which is linked to parameters at the cellular level1. Here we use this growth law to obtain a metabolic scaling law for solid tumours, which is obeyed by tumours of different histotypes both in vitro and in vivo, and we display its relation with the fractal dimension of the distribution of live cells in the tumour mass. The scaling behaviour is related to measurable parameters, with potential applications in the clinical practice.

Pulsation-limited oxygen diffusion in the tumour microenvironment

Hypoxia is central to tumour evolution, growth, invasion and metastasis. Mathematical models of hypoxia based on reaction-diffusion equations provide seemingly incomplete descriptions as they fail to predict the measured oxygen concentrations in the tumour microenvironment. In an attempt to explain the discrepancies, we consider both the inhomogeneous distribution of oxygen-consuming cells in solid tumours and the dynamics of blood flow in the tumour microcirculation. We find that the low-frequency oscillations play an important role in the establishment of tumour hypoxia. The oscillations interact with consumption to inhibit oxygen diffusion in the microenvironment. This suggests that alpha-blockers–a class of drugs used to treat hypertension and stress disorders, and known to lower or even abolish low-frequency oscillations of arterial blood flow –may act as adjuvant drugs in the radiotherapy of solid tumours by enhancing the oxygen effect.

Computer-Aided Biophysical Modeling: A Quantitative Approach to Complex Biological Systems

When dealing with the biophysics of tumors, analytical and numerical modeling tools have long been regarded as potentially useful but practically immature tools. Further developments could not just overturn this predicament, but lead to completely new perspectives in biology. Here, we give an account of our own computational tool and how we have put it to good use, and we discuss a paradigmatic example to outline a path to making cell biology more quantitative and predictive.

From single-cell dynamics to scaling laws in oncology

We are developing a biophysical model of tumor biology. We follow a strictly quantitative approach where each step of model development is validated by comparing simulation outputs with experimental data. While this strategy may slow down our advancements, at the same time it provides an invaluable reward: we can trust simulation outputs and use the model to explore territories of cancer biology where current experimental techniques fail. Here, we review our multi-scale biophysical modeling approach and show how a description of cancer at the cellular level has led us to general laws obeyed by both in vitro and in vivo tumors.

Collective radioresistance of T47D breast carcinoma cells is mediated by a Syncytin-1 homologous protein

It is generally accepted that radiotherapy must target clonogenic cells, i.e., those cells in a tumour that have self-renewing potential. Focussing on isolated clonogenic cells, however, may lead to an underestimate or even to an outright neglect of the importance of biological mechanisms that regulate tumour cell sensitivity to radiation. We develop a new statistical and experimental approach to quantify the effects of radiation on cell populations as a whole. In our experiments, we change the proximity relationships of the cells by culturing them in wells with different shapes, and we find that the radiosensitivity of T47D human breast carcinoma cells in tight clusters is different from that of isolated cells. Molecular analyses show that T47D cells express a Syncytin-1 homologous protein (SyHP). We observe that SyHP translocates to the external surface of the plasma membrane of cells killed by radiation treatment. The data support the fundamental role of SyHP in the formation of intercellular cytoplasmic bridges and in the enhanced radioresistance of surviving cells. We conclude that complex and unexpected biological mechanisms of tumour radioresistance take place at the cell population level. These mechanisms may significantly bias our estimates of the radiosensitivity of breast carcinomas in vivo and thereby affect treatment plans, and they call for further investigations.

PO-126 Survival probability of human breast carcinoma cells to radiation treatment: role of cell fusion and of a syncytin1-homologous protein

Introduction The success of radiotherapy depends on the ability to inhibit tumour growth, and relapse after therapy is determined by cells that retain their clonogenic potential. The radiation sensitivity of isolated tumour cell clones in vitro is routinely determined with clonogenic assays. In solid tumours, however, clonogenic cells are not isolated and we carried out experiments to measure the influences of cell-cell contact on their proliferative potential. To this end we developed a new experimental approach to measure the effects of radiation on tumour cell populations. The observations can be understood with the help of a novel stochastic model with a well-defined biological basis. Material and methods T47D cells (human breast carcinoma) were grown at various concentrations in F(flat)-bottom and V-bottom wells of 96-well culture plates. The spheroid outgrowth method was also used to obtain densely-packed tissue cell cultures. A Gammacell40 irradiator equipped with a 137Cs source was used to treat cell cultures. Cell fusion was assessed by confocal microscopy. Syncytin 1 expression was assessed by RT-PCR and by flow cytometry using an anti-HERV antibody (clone ab7115, Abcam). Results and discussions The probability of cell survival after 8 Gy radiation treatment increased ~4.7 times when the cells were grown in V-bottom wells as compared to cells grown in F-bottom wells (p(survival)=0.0113 and 0.0024, respectively). Microscopic inspections of tissue-like cultures showed that after treatment cell populations were mostly composed of giant cells with multiple nuclei. Cytoplasmic bridges joining different cells were clearly visible. Giant cells and cytoplasmic bridges disappeared at later times (>600 hours) when the cells displayed normal morphology and started to proliferate again. Sequence analysis of cloned RT-PCR products showed that cells expressed a Syncytin 1 homologous protein (Sp). Flow cytometry assays confirmed cytoplasmic expression of Sp and revealed that Sp translocated to the cell surface of irradiated cells committed to death. The fraction of cells surviving 8 Gy treatment was significantly reduced in cultures treated with anti-Sp antibodies. Conclusion Our experimental findings indicate that recovery of breast tumours from radiation is very likely to involve complex pathways that act at the cell population level and that include events of cell fusion mediated by a protein homologous to Syncytin 1.

Using graph theory for automated electric circuit solving

Graph theory plays many important roles in modern physics and in many different contexts, spanning diverse topics such as the description of scale-free networks and the structure of the universe as a complex directed graph in causal set theory. Graph theory is also ideally suited to describe many concepts in computer science. Therefore it is increasingly important for physics students to master the basic concepts of graph theory. Here we describe a student project where we develop a computational approach to electric circuit solving which is based on graph theoretic concepts. This highly multidisciplinary approach combines abstract mathematics, linear algebra, the physics of circuits, and computer programming to reach the ambitious goal of implementing automated circuit solving.

Neighbor search algorithm for lattice-free simulations with short-range forces

We have recently developed a lattice-free simulation program in computational cell biology which needs the introduction and management of the biomechanical interactions of cells. These interactions are associated with short range forces which act on nearest-neighbors only. The forces act in the rearrangement of cells due to proliferation and cell growth and this requires a recalculation of the proximity relations at each time step. Here we describe the implementation of an algorithm to efficiently compute the proximity relations and designed to run on Graphics Processing Units (GPUs). The results of the first test runs on an NVidia Fermi GPU are encouraging: the algorithm has the potential to significantly boost the simulation program and to map the disordered lattice also on other multicore machines with hypercubic connectivity.

Use of GPUs to boost the performance of a lattice-free tumour growth model

We recently developed a computational model of tumour growth. It is a cell- based model that can simulate the growth of multicellular tumour spheroids up to more than one million cells. The simulation program is very demanding and simulation time severely limits the integration of additional biological details, and indeed, at the moment, a typical simulation run requires tens of days to be completed. A new version of the code that exploits Graphics Processing Units (GPUs) to boost performance is being developed. In this paper we describe the design and implementation of a nearest-neighbour search (NNS) algorithm suitable to run on GPU. The algorithm will be integrated in the original code to manage the geometrical calculation in the simulation of the spheroid. Initially the stand alone NNS algorithm was tested for spheroids of different size: better efficency was obtained for bigger spheroids. Eventually the code was integrated in the whole simulation code and preliminary runs gave a speed up of about 5 for spheroids of relatively small size (15000 cells).

Efficient and extendible class scheme for the combined reaction–diffusion of multiple molecular species

Abstract When dealing with large numbers of cells in biophysical simulations, it is important to properly manage the different substances that diffuse and react in and around cells. Although in an object-oriented programming environment it seems more natural to define cells as the basic objects, it turns out that individual substances are better suited to take this role. Here we describe the biophysical problem and our computational solution, and display the results obtained with a toy model. We find that the new implementation does not decrease performance and yet it leads to a much better structured and modular code. This will make more realistic programs with many molecular pathways much more modular and readily extendible.