Using a mammalian cell cycle simulation to interpret differential kinase inhibition in anti-tumour pharmaceutical development

Chassagnole, Christophe; Jackson, Robert; Hussain, N. Sooraj; Bashir, L.; Derow, Catherine K; Savin, J.; Fell, David A.

doi:10.1016/j.biosystems.2005.04.007

Cited by 33 publications

(22 citation statements)

References 28 publications

(29 reference statements)

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“…On the other hand, our analysis suggests that the modulation of other time-delays considered in our model (related to the nucleo-cytoplasmic shuttling of STAT5 and hypoxiacontrolled physiological dynamics of Epo) is not enough to induce self-oscillations in the system. Interestingly enough, an increase in the timing of proliferation and differentiation has been suggested as an emergent mechanism used by cancer cells to create resistance against DNA damage-related drugs (Jackson 1989;Chassagnole et al 2006). Thus, modulations of this time-delay can have important pathological consequences.…”

Section: Discussionmentioning

confidence: 99%

Integration of sensitivity and bifurcation analysis to detect critical processes in a model combining signalling and cell population dynamics

Николов

Lai

Liebal

et al. 2010

International Journal of Systems Science

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In this article we present and test a strategy to integrate, in a sequential manner, sensitivity analysis, bifurcation analysis and predictive simulations. Our strategy uses some of these methods in a coordinated way such that information, generated in one step, feeds into the definition of further analyses and helps refining the structure of the mathematical model. The aim of the method is to help in the designing of more informative predictive simulations, which focus on critical model parameters and the biological effects of their modulation. We tested our methodology with a multilevel model, accounting for the effect of erythropoietin (Epo)-mediated JAK2-STAT5 signalling in erythropoiesis. Our analysis revealed that time-delays associated with the proliferation-differentiation process are critical to induce pathological sustained oscillations, whereas the modulation of time-delays related to intracellular signalling and hypoxia-controlled physiological dynamics is not enough to induce self-oscillations in the system. Furthermore, our results suggest that the system is able to compensate (through the physiological-level feedback loop on hypoxia) the partial impairment of intracellular signalling processes (downregulation or overexpression of Epo receptor complex and STAT5), but cannot control impairment in some critical physiological-level processes, which provoke the emergence of pathological oscillations.

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Section: Discussionmentioning

confidence: 99%

Integration of sensitivity and bifurcation analysis to detect critical processes in a model combining signalling and cell population dynamics

Николов

Lai

Liebal

et al. 2010

International Journal of Systems Science

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“…Thus, since the mode transition is admissible for every under (A2), is equal to the number of combinations of different sampling times taken at a time, i.e., (22) These with (16) and (18) imply that and . Therefore, the probability that is sampled by Algorithm B, denoted by , is estimated as (23) which completes the proof.…”

Section: Efficient Sampling On Admissible Mode Sequence Setmentioning

confidence: 86%

“…For example, nonlinear terms based on Michaelis-Menten kinetics often appear in gene networks, and the number of the state variables such as the concentration of the protein is at least dozens for the cell cycle model (see, e.g., [23] and [24]). 2) Necessity of input allocation: In general, no input channel is determined in advance or, rather, they should be found.…”

Section: A Observation Of Biosystems From the Viewpoint Of Controllamentioning

confidence: 99%

“…Example 5: Controllability for Periodic Behaviors: As in the case of a cell cycle model (e.g., [23] and [24]) and circadian rhythms (e.g., [31]), the solution behaviors that often appear in the biosystems are periodic. In such a case, if periodic state constraints like , are used, the controllability for periodic behaviors will be analyzed by solving PCSP, where is the admissible state set at time .…”

Section: B Controllability Problem For Biosystemsmentioning

confidence: 99%

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Controllability Analysis of Biosystems Based on Piecewise-Affine Systems Approach

Azuma

Yanagisawa

Imura

2008

IEEE Trans. Automat. Contr.

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This paper discusses the controllability problem of biosystems based on the piecewise-affine system model representation. First, we consider what kind of controllability problems are useful for analyzing and controlling biosystems, and then the controllable set problem, that is, a problem of finding a state set in which each state can be driven to a given target state, is formulated. It is shown that this problem will be very useful for many kinds of problems on control of biosystems such as the input allocation problem and the stabilization problem. Next, based on our previous probabilistic controllability analysis technique for hybrid dynamical systems, a more sophisticated method for solving the above complex problem in an approximated and suitable way for biosystem analysis is proposed. Finally, the proposed framework is applied to the quorum sensing system of the pathogen Pseudomonas aeruginosa for explaining how it is formulated and what solutions are obtained.

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“…A disease model predicted, correctly, that with the use of multi-drug combinations, and with sufficient treatment intensity, disease progression could be arrested for many years [3]. In oncology, models of the cancer cell cycle [4] and three-dimensional virtual tumours have been used to predict optimal drug combination schedules [5]. Disease models have been described for diabetes, rheumatoid arthritis, hypertension and skin ageing [6].…”

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confidence: 99%

What can systems pharmacology contribute to drug development? Disease modelling as a predictive tool

Jackson¹

2012

Biodiscovery

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The paper "The Apoe-/-Mouse PhysioLab ® Platform: A Validated Physiologically-based Mathematical Model of Atherosclerotic Plaque Progression in the Apoe-/-Mouse" by Jason Chan and colleagues [1] published in BioDiscovery 2012; 3: 2 is significant for several reasons. The pharmaceutical and biotechnology industries have become quite proficient at rational drug design, but rational drug development has not progressed as quickly (and development costs represent at least 70% of the billion dollar cost of taking a new chemical entity from conception to market). The reasons are clear: with the possible exception of antiinfectives, drugs for most therapeutic indications involve perturbations of complex interactive systems, our preclinical models all involve sweeping simplifications, and clinical trials usually involve heterogeneous groups of patients. Systems pharmacology approaches present one approach to addressing this complexity. Genetically engineered animal models offer useful approximations to human disease, but an animal model supplemented by a computational disease model greatly increases the range of questions that can be asked. Atherosclerosis presents a case in point: its aetiology is complex, it is affected by environmental and behavioural factors, including diet and smoking, and it may take several decades for the condition to progress to clinical disease. The Apoe-/-mouse reflects many of the features of human atherosclerosis, but there are important differences: in humans the dominant circulating atherogenic particles consist of LDL, while in the Apoe-/-mouse VLDL and IDL predominate. How do these differences affect the ability of the mouse model to predict the outcome of drug treatment or lifestyle changes in humans? Another limitation of the mouse model is that, despite its biological similarities with human atherosclerosis, it does not lead to the same clinical outcomes of angina and heart attack. Why is that? Given these limitations of the mouse model, can it still guide the development of prevention and treatment regimens to reduce the incidence of human heart disease? Chan et al have presented a computational model of atherosclerotic progression that has the potential to improve the predictive power of animal models of the disease, and (when the model is extended from mice to humans) to enable in silico clinical trials.The Chan et al. model, based upon the Apoe-/-mouse, includes elements of cholesterol and macrophage trafficking, inflammation, oxidative stress, endothelial function, and thrombosis. It has the ability to predict relationships between biomarker data, pharmacodynamic effects and clinical outcomes. The model is the outcome of a collaboration between Entelos, an in silico modelling and simulation company, and Philip Morris, a tobacco company. A primary motive for developing the model appears to have been a desire to explore the relationship between smoking (and smoking cessation) and heart disease. However, the scope of the model is broad enough to enable it to be used

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Using a mammalian cell cycle simulation to interpret differential kinase inhibition in anti-tumour pharmaceutical development

Cited by 33 publications

References 28 publications

Integration of sensitivity and bifurcation analysis to detect critical processes in a model combining signalling and cell population dynamics

Integration of sensitivity and bifurcation analysis to detect critical processes in a model combining signalling and cell population dynamics

Controllability Analysis of Biosystems Based on Piecewise-Affine Systems Approach

What can systems pharmacology contribute to drug development? Disease modelling as a predictive tool

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