Robustness and state-space structure of Boolean gene regulatory models

Willadsen, Kai; Wiles, Janet

doi:10.1016/j.jtbi.2007.09.004

Cited by 43 publications

(43 citation statements)

References 29 publications

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“…Boolean networks allow integration of qualitative information into the modelling process and have been successfully applied to many relevant biological systems [6,96,98]. Despite being deterministic, the Boolean framework allows inclusion of stochastic components in the models, either in the states of nodes [99,100] or in the logic functions [101]. Together with Boolean models, multiple valued logic models [97,102] are another type of discrete logic models that allow considering more than two levels, e.g.…”

Section: Discrete Modelsmentioning

confidence: 99%

On Different Aspects of Network Analysis in Systems Biology

et al. 2013

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Network analysis is an essential component of systems biology approaches toward understanding the molecular and cellular interactions underlying biological systems functionalities and their perturbations in disease. Regulatory and signalling pathways involve DNA, RNA, proteins and metabolites as key elements to coordinate most aspects of cellular functioning. Cellular processes depend on the structure and dynamics of gene regulatory networks and can be studied by employing a network representation of molecular interactions. This chapter describes several types of biological networks, how combination of different analytic approaches can be used to study diseases, and provides a list of selected tools for network visualization and analysis. It also introduces proteinprotein interaction networks, gene regulatory networks, signalling networks and metabolic networks to illustrate concepts underlying network representation of cellular processes and molecular interactions. It finally discusses how the level of An erratum to this chapter is available at

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Section: Discrete Modelsmentioning

confidence: 99%

On Different Aspects of Network Analysis in Systems Biology

et al. 2013

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“…A system's current state is defined as a point within the attractor space. An example of attractor structure in a real world biological network can be seen in Figure (Li et al, 2004) and visualised in (Willadsen & Wiles, 2007). Each node in the graph represents a state of the system, and an edge between nodes represent a dynamic transition between states, with a loop showing a cyclic attractor.…”

Section: Complex Systems Analysismentioning

confidence: 99%

The artificial epigenetic network

Turner

Lones

Fuente

et al. 2013

2013 IEEE International Conference on Evolvable Systems (ICES)

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The term epigenetics refers to typically heritable biological mechanisms which facilitate stable yet reversible modifications of gene expression or phenotype state, without alteration of the underlying genetic code. More specifically, epigenetic mechanisms allow organisms to control which genes are active at a given time. In eukaryotes, epigenetic mechanisms have essential roles in gene regulation, cellular differentiation and genetic packaging. These epigenetic mechanisms give rise to functionality which DNA alone is generally incapable of providing.This thesis takes inspiration from the fields of genetics and epigenetics, and builds a computational model which captures the beneficial properties of epigenetics in silico. This computational model is referred to as the artificial epigenetic network. The artificial epigenetic network can dynamically control which genes within the network are active at a given time, allowing certain groups of genes to become specialised towards specific aspects of a task.Hence, the artificial epigenetic network can contain many different regulatory circuits, each with specific properties. This gives the networks the ability to more readily express a wider range of dynamical behaviours, which were found to produce a number computational benefits. The artificial epigenetic network is applied to a diverse range of control tasks, each with varying dynamics, to ascertain how the functionality of the artificial epigenetic structures effects the functionality of the network. An emergent property is that the epigenetic structures can partition the network into functional units corresponding to the logical decomposition of the tasks, and control these units with a switch like behaviour. This provides an interface, where a user can gain control over the complex dynamics of the target domain via the activation or deactivation of these switches.3

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“…Harary et al 1988;Bagley and Glass 1996;Mendoza and Alvarez-Buylla 1998;Willadsen and Wiles 2007). Schemata are mostly utilized to obtain a compact representation of state space.…”

Section: Symbolic Steady Statesmentioning

confidence: 99%

“…Schemata representations can also be used as a measure of complexity. In Willadsen and Wiles (2007), basins of attraction of a system are represented by a set of schemata constructed based on a minimum description principle. The size of the schemata set reflects the complexity of the structure of the basin of attraction in state space.…”

Section: Symbolic Steady Statesmentioning

confidence: 99%

Analysis of Discrete Bioregulatory Networks Using Symbolic Steady States

Siebert

2010

Bull Math Biol

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A discrete model of a biological regulatory network can be represented by a discrete function that contains all available information on interactions between network components and the rules governing the evolution of the network in a finite state space. Since the state space size grows exponentially with the number of network components, analysis of large networks is a complex problem. In this paper, we introduce the notion of symbolic steady state that allows us to identify subnetworks that govern the dynamics of the original network in some region of state space. We state rules to explicitly construct attractors of the system from subnetwork attractors. Using the results, we formulate sufficient conditions for the existence of multiple attractors resp. a cyclic attractor based on the existence of positive resp. negative feedback circuits in the graph representing the structure of the system. In addition, we discuss approaches to finding symbolic steady states. We focus both on dynamics derived via synchronous as well as asynchronous update rules. Lastly, we illustrate the results by analyzing a model of T helper cell differentiation.

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Robustness and state-space structure of Boolean gene regulatory models

Cited by 43 publications

References 29 publications

On Different Aspects of Network Analysis in Systems Biology

On Different Aspects of Network Analysis in Systems Biology

The artificial epigenetic network

Analysis of Discrete Bioregulatory Networks Using Symbolic Steady States

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