A data integration approach for cell cycle analysis oriented to model simulation in systems biology

Alfieri, Roberta; Merelli, Ivan; Mosca, Ettore; Milanesi, Luciano

doi:10.1186/1752-0509-1-35

Cited by 24 publications

(11 citation statements)

References 63 publications

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“…The only study reported to date is a genome-wide location analysis to identify binding sites for transcription factors, which suggested that the Fkh1 forkhead transcription factor binds to Clb4 (Simon et al, 2001). Moreover, Fkh1 binds to the CLB4 promoter (CCDB database, Alfieri et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

Sic1 plays a role in timing and oscillatory behaviour of B-type cyclins

Barberis

Linke

Adrover

et al. 2012

Biotechnology Advances

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Budding yeast cell cycle oscillates between states of low and high cyclin-dependent kinase activity, driven by association of Cdk1 with B-type (Clb) cyclins. Various Cdk1-Clb complexes are activated and inactivated in a fixed, temporally regulated sequence, inducing the behaviour known as "waves of cyclins". The transition from low to high Clb activity is triggered by degradation of Sic1, the inhibitor of Cdk1-Clb complexes, at the entry to S phase. The G(1) phase is characterized by low Clb activity and high Sic1 levels. High Clb activity and Sic1 proteolysis are found from the beginning of the S phase until the end of mitosis. The mechanism regulating the appearance on schedule of Cdk1-Clb complexes is currently unknown. Here, we analyse oscillations of Clbs, focusing on the role of their inhibitor Sic1. We compare mathematical networks differing in interactions that Sic1 may establish with Cdk1-Clb complexes. Our analysis suggests that the wave-like cyclins pattern derives from the binding of Sic1 to all Clb pairs rather than from Clb degradation. These predictions are experimentally validated, showing that Sic1 indeed interacts and coexists in time with Clbs. Intriguingly, a sic1Δ strain looses cell cycle-regulated periodicity of Clbs, which is observed in the wild type, whether a SIC1-0P strain delays the formation of Clb waves. Our results highlight an additional role for Sic1 in regulating Cdk1-Clb complexes, coordinating their appearance.

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Section: Introductionmentioning

confidence: 99%

Sic1 plays a role in timing and oscillatory behaviour of B-type cyclins

Barberis

Linke

Adrover

et al. 2012

Biotechnology Advances

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“…This type of modeling has been the basis in computational biology research for analyzing interaction networks and simulating the behavior of each cellular component over time. Goel et al 2006;Hornberg et al 2006;Alfieri et al 2007). (b) Dynamic, stable and unstable system models.…”

Section: Modeling Tumor Growth and Disease Progressionmentioning

confidence: 91%

PKPD and Disease Modeling: Concepts and Applications to Oncology

Pasqua

2010

Clinical Trial Simulations

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Despite the transition from chemotherapy with cytotoxic and cytostatic drugs to targeted therapy with monoclonal antibodies, clinical decisions regarding the benefit of pharmacological interventions still rely on the concept of a maximum tolerated dose (MTD). In this chapter, it is shown that a model-based approach can be used in oncology to optimize dose selection and characterize drug effect on tumor growth, overall survival and safety. Furthermore, modeling and simulation also provides insight into the underlying mechanisms of action, enabling translation of the differences in pharmacology, safety and disease processes from preclinical experiments to clinical trials. A paradigm shift is proposed to bring the benefits of model-based drug development to cancer patients, in which biomarkers of safety and prognostic markers of overall survival are assessed to predict treatment outcome and disease progression. General Concepts and History of Model-Based Research in OncologyModels aim to represent and simplify complex systems. The accurate representation and simplification of the complexity and heterogeneity of biological systems have characterized oncology modeling efforts in clinical pharmacology and translational medicine research (Rew 2000a;Quaranta et al. 2005). Although clinical oncologists can appreciate the value and potential implications of experimental models, the justification for the use of mathematical models remains unclear to most of them. Mathematical modeling can be a powerful tool for analyzing

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“…Mathematical models of signal transduction pathways have provided new information on the basic properties of signalling cascades in connection with their targets (see Klipp and Liebermeister, 2006 for a review). Another area where the use of mathematical models has facilitated the understanding of how a complex network of genes and proteins interacts to regulate and execute cellular functions is that of cell cycle (Alfieri et al, 2007;Allen et al, 2006;Barberis et al, 2007;Brazhnik and Tyson, 2006;Lau et al, 2007;Novak and Tyson, 2003;Sible and Tyson, 2007;Zi and Klipp, 2007).…”

Section: Pathways Reconstructionmentioning

confidence: 99%

Mathematical formalisms based on approximated kinetic representations for modeling genetic and metabolic pathways

Alves

Vilaprinyó

Hernández‐Bermejo

et al. 2008

Biotechnology and Genetic Engineering Reviews

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Citing articles: 16 View citing articles Biotechnology and Genetic Engineering Reviews -Vol. 25, 1-40 (2008) AbstractThere is a renewed interest in obtaining a systemic understanding of metabolism, gene expression and signal transduction processes, driven by the recent research focus on Systems Biology. From a biotechnological point of view, such a systemic understanding of how a biological system is designed to work can facilitate the rational manipulation of specific pathways in different cell types to achieve specific goals. Due to the intrinsic complexity of biological systems, mathematical models are a central tool for understanding and predicting the integrative behavior of those systems. Particularly, models are essential for a rational development of biotechnological applications and in understanding system's design from an evolutionary point of view. Mathematical models can be obtained using many different strategies. In each case, their utility will depend upon the properties of the mathematical representation and on the possibility of obtaining meaningful parameters from available data. In practice, there are several issues at stake when one has to decide which mathematical model is more appropriate for the study of a given problem. First, one needs a model that can represent the aspects of the system one wishes to study. Second, one must choose a mathematical representation that allows an accurate analysis of the system with respect to different aspects of interest (for example, robustness of the system, dynamical behavior, optimization of the system with respect to some production goal, parameter value determination, etc). Third, before choosing between alternative and equally appropriate mathematical representations for the system, one should compare representations with respect to easiness of automation for model set-up, simulation, and analysis of results. Fourth, one should also consider how to facilitate model transference and re-usability by other researchers and for distinct purposes. Finally, one factor that is important for all four aspects is the regularity in the mathematical structure of the equations because it facilitates computational manipulation. This regularity is a mark of kinetic representations based on approximation theory. The use of approximation theory to derive mathematical representations with regular structure for modeling purposes has a long tradition in science. In most applied fields, such as engineering and physics, those approximations are often required to obtain practical solutions to complex problems. In this paper we review some of the more popular mathematical representations that have been derived using approximation theory and are used for modeling in molecular systems biology. We will focus on formalisms that are theoretically supported by the Taylor Theorem. These include the Power-law formalism, the recently proposed (log)linear and Lin-log formalisms as well as some closely related alternatives. We will analyze the similarities and differences between...

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A data integration approach for cell cycle analysis oriented to model simulation in systems biology

Cited by 24 publications

References 63 publications

Sic1 plays a role in timing and oscillatory behaviour of B-type cyclins

Sic1 plays a role in timing and oscillatory behaviour of B-type cyclins

PKPD and Disease Modeling: Concepts and Applications to Oncology

Mathematical formalisms based on approximated kinetic representations for modeling genetic and metabolic pathways

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