Evolutionary design of functional networks robust against noise

2017

Eur. Phys. J. B

Abstract. We present a study about the construction of functional flow processing networks that produce prescribed output patterns (target functions). The constructions are performed with a process of mutations and selections by an annealing-like algorithm. We consider the coevolution of the prescribed target functions during the optimization processes. We propose three different paths for these coevolutions in order to evolve from a simple initial function to a more complex final one. We compute several network properties during the optimizations by using the different path-coevolutions as mean values over network ensembles. As a function of the number of iterations of the optimization we find a similar behavior like a phase transition in the network structures. This result can be seen clearly in the mean motif distributions of the constructed networks. Coevolution allows to identify that feed-forward loops are responsible for the development of the temporal response of these systems. Finally, we observe that with a large number of iterations the optimized networks present similar properties despite the path-coevolution we employed.

Section: Introductionmentioning

confidence: 99%

Coevolution of functional flow processing networks

2017

Eur. Phys. J. B

“…The constructive approach for studies of bionetworks has been followed in our recent publications [6][7][8]. Using a simplified model representing an abstraction of biological signal processing, ensembles of networks with the same output functions have been generated, which were additionally designed to be self-correcting against deletion of individual links or nodes, or against the application of distributed quenched noise.…”

mentioning

confidence: 99%

Self-correcting networks: Function, robustness, and motif distributions in biological signal processing

Chaos: An Interdisciplinary Journal of Nonlinear Science

Vingron

Mikhailov

2008

Statistical properties of large ensembles of networks, all designed to have the same functions of signal processing, but robust against different kinds of perturbations, are analyzed. We find that robustness against noise and random local damage plays a dominant role in determining motif distributions of networks and may underlie their classification into network superfamilies.High robustness against local damage and noise is a fundamental property of biological networks [1]. In gene knock-out experiments where effects of genes are studied by making inoperative one gene per experiment, it has been found that only 13% of genes in yeast are essential, so that their absence is lethal [2]. Because many subsystems of the cell are small, they are subject to strong stochastic fluctuations, but this also does not prevent regular cell functioning.To suppress fluctuations and increase stability of dynamical systems, negative feedback loops can be used (cf. [3]) and such mechanisms of noise reduction are indeed employed by the cells. Alternatively, robustness against random damage can be achieved through the use of self-correcting network architectures. Can small self-correcting networks with only tens of elements, typical for functional modules of biological cells, be constructed? What are characteristic statistical properties of functional networks capable of self-correction? Here, these questions are addressed for a class of networks that should produce predefined responses following activation of different input elements. In the realm of biology, this class includes not only signal-transduction networks of the cell, but also genetic networks. Moreover, the entire nervous system of primitive mul-2 ticellular organisms may also be viewed as belonging to such a class. In our analysis, an approach that can be described as constructive biology is chosen.Instead of investigating real biological networks, we consider a simple flow distribution model of signal processing where nonlinear feedbacks are excluded.By running evolutionary optimization algorithms, large ensembles of networks with the same size and the same output patterns, but robust against different kinds of local damage or distributed noise, are designed. This allows us to analyze how statistical properties of self-correcting networks depend on the kind of random perturbations against which they are robust. Special attention is paid to motif distributions of networks [4]. We discover that our designed networks, robust against random link deletions, have motif distributions characteristic for the second superfamily previously identified by U. Alon and coworkers [4], which includes signal transduction and genetic developmental networks of multicellular organisms and the neural system of the nematode Caenorhabditid elegans.Completely different motif distributions, that belong instead to the fourth superfamily, are characteristic for networks designed to be robust against node deletions or against of noise.

“…This kind of optimization, Metropolislike methods, have been used in the construction of genetic networks [13][14][15][16], and, flow processing networks [17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Design of artificial genetic regulatory networks with multiple delayed adaptive responses*

Inoue

2016

Eur. Phys. J. B

Abstract. Genetic regulatory networks with adaptive responses are widely studied in biology. Usually, models consisting only of a few nodes have been considered. They present one input receptor for activation and one output node where the adaptive response is computed. In this work, we design genetic regulatory networks with many receptors and many output nodes able to produce delayed adaptive responses. This design is performed by using an evolutionary algorithm of mutations and selections that minimizes an error function defined by the adaptive response in signal shapes. We present several examples of network constructions with a predefined required set of adaptive delayed responses. We show that an output node can have different kinds of responses as a function of the activated receptor. Additionally, complex network structures are presented since processing nodes can be involved in several input-output pathways.