Modeling positive regulatory feedbacks in cell–cell interactions

Applications of personalised signalling network models in precision oncology

As our ability to provide in-depth, patient-specific characterisation of the molecular alterations within tumours rapidly improves, it is becoming apparent that new approaches will be required to leverage the power of this data and derive the full benefit for each individual patient. Systems biology approaches are beginning to emerge within this field as a potential method of incorporating large volumes of network level data and distilling a coherent, clinically-relevant prediction of drug response. However, the initial promise of this developing field is yet to be realised. Here we argue that in order to develop these precise models of individual drug response and tailor treatment accordingly, we will need to develop mathematical models capable of capturing both the dynamic nature of drug-response signalling networks and key patient-specific information such as mutation status or expression profiles. We also review the modelling approaches commonly utilised within this field, and outline recent examples of their use in furthering the application of systems biology for a precision medicine approach to cancer treatment.

Computational Systems Biology in Cancer: Modeling Methods and Applications

In recent years it has become clear that carcinogenesis is a complex process, both at the molecular and cellular levels. Understanding the origins, growth and spread of cancer, therefore requires an integrated or system-wide approach. Computational systems biology is an emerging sub-discipline in systems biology that utilizes the wealth of data from genomic, proteomic and metabolomic studies to build computer simulations of intra and intercellular processes. Several useful descriptive and predictive models of the origin, growth and spread of cancers have been developed in an effort to better understand the disease and potential therapeutic approaches. In this review we describe and assess the practical and theoretical underpinnings of commonly-used modeling approaches, including ordinary and partial differential equations, petri nets, cellular automata, agent based models and hybrid systems. A number of computer-based formalisms have been implemented to improve the accessibility of the various approaches to researchers whose primary interest lies outside of model development. We discuss several of these and describe how they have led to novel insights into tumor genesis, growth, apoptosis, vascularization and therapy.

Mathematical model of heterogeneous cancer growth with an autocrine signalling pathway

OBJECTIVES

Cancer is a complex biological occurrence which is difficult to describe clearly and explain its growth development. As such, novel concepts, such as of heterogeneity and signalling pathways, grow exponentially and many mathematical models accommodating the latest knowledge have been proposed. Here, we present a simple mathematical model that exhibits many characteristics of experimental data, using prostate carcinoma cell spheroids under treatment.

MATERIALS AND METHODS

We have modelled cancer as a two-subpopulation system, with one subpopulation representing a cancer stem cell state, and the other a normal cancer cell state. As a first approximation, these follow a logistical growth model with self and competing capacities, but they can transform into each other by using an autocrine signalling pathway.

RESULTS AND CONCLUSION

By analysing regulation behaviour of each of the system parameters, we show that the model exhibits many characteristics of actual cancer growth curves. Features reproduced in this model include delayed phase of evolving cancer under 17AAG treatment, and bi-stable behaviour under treatment by irradiation. In addition, our interpretation of the system parameters corresponds well with known facts involving 17AAG treatment. This model may thus provide insight into some of the mechanisms behind cancer.

Predicting outcomes of prostate cancer immunotherapy by personalized mathematical models

BACKGROUND

Therapeutic vaccination against disseminated prostate cancer (PCa) is partially effective in some PCa patients. We hypothesized that the efficacy of treatment will be enhanced by individualized vaccination regimens tailored by simple mathematical models.

METHODOLOGY/PRINCIPAL FINDINGS

We developed a general mathematical model encompassing the basic interactions of a vaccine, immune system and PCa cells, and validated it by the results of a clinical trial testing an allogeneic PCa whole-cell vaccine. For model validation in the absence of any other pertinent marker, we used the clinically measured changes in prostate-specific antigen (PSA) levels as a correlate of tumor burden. Up to 26 PSA levels measured per patient were divided into each patient's training set and his validation set. The training set, used for model personalization, contained the patient's initial sequence of PSA levels; the validation set contained his subsequent PSA data points. Personalized models were simulated to predict changes in tumor burden and PSA levels and predictions were compared to the validation set. The model accurately predicted PSA levels over the entire measured period in 12 of the 15 vaccination-responsive patients (the coefficient of determination between the predicted and observed PSA values was R(2) = 0.972). The model could not account for the inconsistent changes in PSA levels in 3 of the 15 responsive patients at the end of treatment. Each validated personalized model was simulated under many hypothetical immunotherapy protocols to suggest alternative vaccination regimens. Personalized regimens predicted to enhance the effects of therapy differed among the patients.

CONCLUSIONS/SIGNIFICANCE

Using a few initial measurements, we constructed robust patient-specific models of PCa immunotherapy, which were retrospectively validated by clinical trial results. Our results emphasize the potential value and feasibility of individualized model-suggested immunotherapy protocols.

Long-term prediction of fish growth under varying ambient temperature using a multiscale dynamic model

Background

Feed composition has a large impact on the growth of animals, particularly marine fish. We have developed a quantitative dynamic model that can predict the growth and body composition of marine fish for a given feed composition over a timespan of several months. The model takes into consideration the effects of environmental factors, particularly temperature, on growth, and it incorporates detailed kinetics describing the main metabolic processes (protein, lipid, and central metabolism) known to play major roles in growth and body composition.

Results

For validation, we compared our model's predictions with the results of several experimental studies. We showed that the model gives reliable predictions of growth, nutrient utilization (including amino acid retention), and body composition over a timespan of several months, longer than most of the previously developed predictive models.

Conclusion

We demonstrate that, despite the difficulties involved, multiscale models in biology can yield reasonable and useful results. The model predictions are reliable over several timescales and in the presence of strong temperature fluctuations, which are crucial factors for modeling marine organism growth. The model provides important improvements over existing models.

Alveolar recruitment and arterial desflurane concentration during bariatric surgery

BACKGROUND

We investigated whether reversal of intraoperative atelectasis with the lung recruitment maneuver (RM) affects desflurane arterial concentrations during bariatric surgery.

METHODS

After anesthetic induction and maintenance with propofol, patients were randomized to receive alveolar RM at intervals (RM group) or not (controls). Desflurane 6% was initiated, and rate of increase of alveolar desflurane concentration (ratio of end-expiratory to inspiratory concentrations, F(A)/F(I)) and desflurane blood concentrations were measured in both groups. Blood and end-tidal desflurane concentrations were also measured after the discontinuation of anesthesia.

RESULTS

The RM group had higher intraoperative Pao(2)/Fio(2) compared with the control group (both, P < 0.001). During induction, the rate of increase in blood desflurane concentrations was rapid in both groups. At comparable mechanical ventilation settings, median times to achieve 0.5 mM (approximately 3%) were 2.1 and 1.59 min (P = 0.09) in the control and RM group, respectively. The times to achieve 0.7 mM (approximately 4.2%) desflurane were 15.9 and 9.3 min in the control and RM group, respectively (P = 0.08). Desflurane blood concentrations tended to be higher during the first 30 min after induction in the RM group (P = 0.066). During maintenance or emergence, the blood desflurane concentrations were not different between control and RM groups. Consequently, the time to eye opening did not differ between groups.

CONCLUSION

Although the RM during bariatric surgery represents an effective method for improving intraoperative oxygenation, it does not significantly affect the desflurane blood concentrations during anesthesia or its elimination during emergence.

Signaling between cells attached to a surface

We present a kinetic model allowing one to classify likely scenarios of protein-mediated communication between attached cells of two distinct types. In our treatment, messenger proteins, synthesized in type-1 cells, are considered to penetrate the external membrane of these cells, diffuse in the extracellular medium, associate with the receptors in the external membrane of cells of both types, and induce intracellular signal transduction cascades, influencing the development of cells. Protein degradation inside and outside cells is taken into account as well.

Mathematical modeling of cancer radiovirotherapy

Cancer virotherapy represents a dynamical system that requires mathematical modeling for complete understanding of the outcomes. The combination of virotherapy with radiation (radiovirotherapy) has been recently shown to successfully eliminate tumors when virotherapy alone failed. However, it introduces a new level of complexity. We have developed a mathematical model, based on population dynamics, that captures the essential elements of radiovirotherapy. The existence of corresponding equilibrium points related to complete cure, partial cure, and therapy failure is proved and discussed. The parameters of the model were estimated by fitting to experimental data. By using simulations we analyzed the influence of parameters that describe the interaction between virus and tumor cell on the outcome of the therapy. Furthermore, we evaluated relevant therapeutic scenarios for radiovirotherapy, and offered elements for optimization.

Tumor growth instability and the onset of invasion

Motivated by experimental observations, we develop a mathematical model of chemotactically directed tumor growth. We present an analytical study of the model as well as a numerical one. The mathematical analysis shows that: (i) tumor cell proliferation by itself cannot generate the invasive branching behavior observed experimentally, (ii) heterotype chemotaxis provides an instability mechanism that leads to the onset of tumor invasion, and (iii) homotype chemotaxis does not provide such an instability mechanism but enhances the mean speed of the tumor surface. The numerical results not only support the assumptions needed to perform the mathematical analysis but they also provide evidence of (i), (ii), and (iii). Finally, both the analytical study and the numerical work agree with the experimental phenomena.

Simulation of proliferation of neural stem cells on a surface with emphasis on spatial constraints on cell division

We present Monte Carlo lattice simulations of proliferation of cells on a surface in the situation when the cell-cell adhesion is relatively strong and the cells may form islands and/or flattened hemispheres. The model parameters were chosen to mimic proliferation of adult rat neural stem cells (or, more specifically, adult hippocampal progenitor cells) deposited on polyornithine and laminin coated polystyrene. The results obtained show that the spatial constraints on cell division may result in slowdown of the exponential growth. Depending on the rules used for cell division, this effect may be either nearly negligible or appreciable. In the latter case, the scale of the deviations from the exponential growth is comparable with that observed in our experiments. In the simulations, the slowdown of the growth starts however somewhat earlier and occurs in a less abrupt manner. This seems to indicate that the spatial constraints on division of cells are not the main factor behind the experimentally observed termination of the growth.