The role of modelling in identifying drug targets for diseases of the cell cycle

Abstract

The cell cycle is implicated in diseases that are the leading cause of mortality and morbidity in the developed world. Until recently, the search for drug targets has focused on relatively small parts of the regulatory network under the assumption that key events can be controlled by targeting single pathways. This is valid provided the impact of couplings to the wider scale context of the network can be ignored. The resulting depth of study has revealed many new insights; however, these have been won at the expense of breadth and a proper understanding of the consequences of links between the different parts of the network. Since it is now becoming clear that these early assumptions may not hold and successful treatments are likely to employ drugs that simultaneously target a number of different sites in the regulatory network, it is timely to redress this imbalance. However, the substantial increase in complexity presents new challenges and necessitates parallel theoretical and experimental approaches. We review the current status of theoretical models for the cell cycle in light of these new challenges. Many of the existing approaches are not sufficiently comprehensive to simultaneously incorporate the required extent of couplings. Where more appropriate levels of complexity are incorporated, the models are difficult to link directly to currently available data. Further progress requires a better integration of experiment and theory. New kinds of data are required that are quantitative, have a higher temporal resolution and that allow simultaneous quantitative comparison of the concentration of larger numbers of different proteins. More comprehensive models are required and must accommodate not only substantial uncertainties in the structure and kinetic parameters of the networks, but also high levels of ignorance. The most recent results relating network complexity to robustness of the dynamics provide clues that suggest progress is possible.

Probabilistic Modeling of DNA Mismatch Repair Effects on Cell Cycle Dynamics and Iododeoxyuridine-DNA Incorporation

“…The models for G 1 -S and G 2 -M transitions and restriction points have been recently reviewed by Clyde et al (14). Although there are successful bottom-up models of the cell cycle, there are still several important problems to be overcome for this type of modeling, which include the lack of sufficient quantitative data on molecular concentrations and kinetic parameters (14,15,18). In contrast, McAdams and Shapiro (18) suggest that a top-down modeling paradigm could be a potentially useful approach toward modeling the cell cycle, and this is the approach taken in this work.…”

“…Mathematical modeling of the effects of drugs on the cell cycle may provide insights that can help identify and develop effective drug targets and multiple drug targeting strategies (14). These model-building exercises have been found to be useful in providing quantitative hypotheses that can be experimentally tested (15).…”

“…The current ordinary differential equation (ODE) models of the cell cycle are based on a bottom-up mechanistic approach, i.e., they are based on biochemical modeling of protein dynamics involved in the cell cycle (16,17). The models for G 1 -S and G 2 -M transitions and restriction points have been recently reviewed by Clyde et al (14). Although there are successful bottom-up models of the cell cycle, there are still several important problems to be overcome for this type of modeling, which include the lack of sufficient quantitative data on molecular concentrations and kinetic parameters (14,15,18).…”

Interoperability of time series cytometric data: A cross platform approach for modeling tumor heterogeneity

“…The translation of a system cytometry approach to measure and interpret complex cellular systems with predictive capacity was the primary focus of this study (21,47). Computational modeling of the cell cycle is an iterative process which incorporates sound assumptions, rigorous hypothesis testing and robust parameter value estimation (48).…”

Combining micro-bottom-up and macro-top-down modelling responses to nutrient cycles in complex agricultural systems

“…A universally applied ''best'' practice will not fit a unique system (Kurtz and Snowden 2003). Studies in network topography and in drug pathways that influence the cell cycle provide useful examples for understanding the nature of complex systems (Clyde et al 2006;Faratian et al 2009;Liu et al 2011). …”