Control Of Biological Networks Research Articles

Symbolic approach to verification and control of deterministic/probabilistic Boolean networks

A Boolean network (BN) is well known as one of the models of biological networks such as gene regulatory networks, and has been extensively studied. In this study, for a BN, the verification/control problems are discussed. First, a probabilistic model including both synchronous and asynchronous Boolean dynamics is derived. This model can be generalised as a probabilistic BN. Next, a solution method of the verification/control problems is proposed, based on a probabilistic model checker PRISM. Finally, the PRISM-based method is applied to an apoptosis network and a WNT5A network. The proposed approach provides us an easy and convenient tool for analysis and control of biological networks.

Special issue on “Analysis and Control of Biological Networks”

Special issue on “Analysis and Control of Biological Networks”

Piecewise affine systems approach to control of biological networks

In terms of a piecewise affine system representation, which is a kind of hybrid system model, this article discusses a series of approaches to modelling, analysing and synthesizing a biological network such as a gene-regulatory network. First, the input assignment problem, the controllable state set problem (CSP) and the input trajectory generation problem are emphasized as control problems to be addressed for biological networks. Subsequently, after the modelling issue on biological networks developed in the systems and control community is briefly explained, the CSP is described in detail with reference to control of the quorum-sensing system in the pathogen Pseudomonas aeruginosa. Finally, an optimal control design method to the quorum-sensing system is proposed as a solution to the input trajectory generation problem.

Using a Quantitative Blueprint to Reprogram the Dynamics of the Flagella Gene Network

Detailed understanding and control of biological networks will require a level of description similar to that of electronic engineering blueprints. Currently, however, even the best-studied systems are usually described using qualitative arrow diagrams. A quantitative blueprint requires in vivo measurements of (1) the relative strength of the interactions (numbers on the arrows) and (2) the functions that integrate multiple inputs. Here, we address this using a well-studied system, the flagella biosynthesis transcription network in Escherichia coli. We use theory and high-resolution experiments to obtain a quantitative blueprint with (1) numbers on the arrows, finding different hierarchies of activation coefficients for the two regulators, FlhDC and FliA; and (2) cis-regulatory input functions, which summate the input from the two regulators (SUM gates). We then demonstrate experimentally how this blueprint can be used to reprogram temporal expression patterns in this system, using controlled expression of the regulators or point mutations in their binding sites. The present approach can be used to define blueprints of other gene networks and to quantitatively reprogram their dynamics.