Modelling and analysis of gene regulatory networks

Karlebach, Guy; Shamir, Ron

doi:10.1038/nrm2503

Cited by 1,118 publications

(918 citation statements)

References 123 publications

Supporting

Mentioning

863

Contrasting

Unclassified

Order By: Relevance

“…The recent development of advanced experimental techniques in molecular biology has increased the amount of available experimental data on gene regulation which has led to a rapidly growing interest in mathematical modelling methods for the study and analysis of gene regulation [9,15,27,29,40,47]. One of the very first mathematical approaches is the framework of boolean networks [30,32,42,46] which is based on three assumptions: (i) the state of each gene can be either ON or OFF, (ii) the regulatory control of gene expression can be approximated by Boolean logical rules and (iii) all genes update their ON and OFF state synchronously [40].…”

Section: Introductionmentioning

confidence: 99%

Comparing different ODE modelling approaches for gene regulatory networks

Polynikis

Hogan

Bernardo

2009

Journal of Theoretical Biology

156

103

View full text Add to dashboard Cite

A fundamental step in synthetic biology and systems biology is to derive appropriate mathematical models for the purposes of analysis and design. For example, to synthesize a gene regulatory network, the derivation of a mathematical model is important in order to carry out in silico investigations of the network dynamics and to investigate parameter variations and robustness issues. Different mathematical frameworks have been proposed to derive such models. In particular, the use of sets of nonlinear ordinary differential equations (ODEs) has been proposed to model the dynamics of the concentrations of mRNAs and proteins. These models are usually characterized by the presence of highly nonlinear Hill function terms. A typical simplification is to reduce the number of equations by means of a quasi-steady-state assumption on the mRNA concentrations. This yields a class of simplified ODE models. A radically different approach is to replace the Hill functions by piecewise-linear approximations [5]. A further modelling approach is the use of discretetime maps [7] where the evolution of the system is modelled in discrete, rather than continuous, time. The aim of this paper is to discuss and compare these different modelling approaches, using a representative gene regulatory network. We will show that different models often lead to conflicting conclusions concerning the existence and stability of equilibria and stable oscillatory behaviours. Moreover, we shall discuss, where possible, the viability of making certain modelling approximations (e.g. quasi-steady-state mRNA dynamics or piecewise-linear approximations of Hill functions) and their effects on the overall system dynamics.

show abstract

Section: Introductionmentioning

confidence: 99%

Comparing different ODE modelling approaches for gene regulatory networks

Polynikis

Hogan

Bernardo

2009

Journal of Theoretical Biology

156

103

View full text Add to dashboard Cite

show abstract

“…For instance, understanding interactions at cellular levels may help in identifying genes and pathways involved in the progression of specific human diseases [33]. Also, understanding how genes regulate themselves in gene regulatory networks may explain how cell differentiation is carried out in living organisms [80,[91][92][93]]. …”

Section: Network Connectivity From Time Seriesmentioning

confidence: 99%

Network Dynamics as an Inverse Problem

Casadiego

Timme

2015

Mathematical Technology of Networks

View full text Add to dashboard Cite

“…At the cellular level, networks emerge via interacting proteins, and other macro-molecules, resulting in various biochemical nets, such as gene regulatory networks 5,6 , protein-protein interaction networks 7 and protein residue networks 8,9 . In this regards, the metabolic process in particular plays a fundamental role, providing the building blocks (nucleic acids and amino acids) that enable genes to interact effectively, and thus for the cell to function properly.…”

Section: Introductionmentioning

confidence: 99%

Network motif frequency vectors reveal evolving metabolic network organisation

2015

View full text Add to dashboard Cite

At the systems level many organisms of interest may be described by their patterns of interaction, and as such, are perhaps best characterised via network or graph models. Metabolic networks, in particular, are fundamental to the proper functioning of many important biological processes, and thus, have been widely studied over the past decade or so. Such investigations have revealed a number of shared topological features, such as a short characteristic path-length, large clustering coefficient and hierarchical modular structure. However, the extent to which evolutionary and functional properties of metabolism manifest via this underlying network architecture remains unclear. In this paper, we employ a novel graph embedding technique, based upon low-order network motifs, to compare metabolic network structure for 383 bacterial species categorised according to a number of biological features. In particular, we introduce a new global significance score which enables us to quantify important evolutionary relationships that exist between organisms and their physical environments. Using this new approach, we demonstrate a number of significant correlations between environmental factors, such as growth conditions and habitat variability, and network motif structure, providing evidence that organism adaptability leads to increased complexities in the resultant metabolic networks.

show abstract

Modelling and analysis of gene regulatory networks

Cited by 1,118 publications

References 123 publications

Comparing different ODE modelling approaches for gene regulatory networks

Comparing different ODE modelling approaches for gene regulatory networks

Network Dynamics as an Inverse Problem

Network motif frequency vectors reveal evolving metabolic network organisation

Contact Info

Product

Resources

About