Gene regulatory networks' dynamics are modified by transient stimuli GRNs have several different types of memory, including associative conditioning Evolution favored GRN memory, and differentiated cells have the most memory capacity Training GRNs offers a novel biomedical strategy not dependent on genetic rewiring
Trainability, in any substrate, refers to the ability to change future behavior based on past experiences. An understanding of such capacity within biological cells and tissues would enable a particularly powerful set of methods for prediction and control of their behavior through specific patterns of stimuli. This top-down mode of control (as an alternative to bottom-up modification of hardware) has been extensively exploited by computer science and the behavioral sciences; in biology however, it is usually reserved for organism-level behavior in animals with brains, such as training animals towards a desired response. Exciting work in the field of basal cognition has begun to reveal degrees and forms of unconventional memory in non-neural tissues and even in subcellular biochemical dynamics. Here, we characterize biological gene regulatory circuit models and protein pathways and find them capable of several different kinds of memory. We extend prior results on learning in binary transcriptional networks to continuous models and identify specific interventions (regimes of stimulation, as opposed to network rewiring) that abolish undesirable network behavior such as drug pharmacoresistance and drug sensitization. We also explore the stability of created memories by assessing their long-term behavior and find that most memories do not decay over long time periods. Additionally, we find that the memory properties are quite robust to noise; surprisingly, in many cases noise actually increases memory potential. We examine various network properties associated with these behaviors and find that no one network property is indicative of memory. Random networks do not show similar memory behavior as models of biological processes, indicating that generic network dynamics are not solely responsible for trainability. Rational control of dynamic pathway function using stimuli derived from computational models opens the door to empirical studies of proto-cognitive capacities in unconventional embodiments and suggests numerous possible applications in biomedicine, where behavior shaping of pathway responses stand as a potential alternative to gene therapy.
Gene regulatory network (GRN) is produced as a result of regulatory interactions between different genes through their coded proteins in cellular context. Having immense importance in disease detection and drug finding, GRN has been modelled through various mathematical and computational schemes and reported in survey articles. Neural and neuro-fuzzy models have been the focus of attraction in bioinformatics. Predominant use of meta-heuristic algorithms in training neural models has proved its excellence. Considering these facts, this paper is organized to survey neural modelling schemes of GRN and the efficacy of meta-heuristic algorithms towards parameter learning (i.e. weighting connections) within the model. This survey paper renders two different structure-related approaches to infer GRN which are global structure approach and substructure approach. It also describes two neural modelling schemes, such as artificial neural network/recurrent neural network based modelling and neuro-fuzzy modelling. The meta-heuristic algorithms applied so far to learn the structure and parameters of neutrally modelled GRN have been reviewed here.
Gene Regulatory Network (GRN) is a virtual network in a cellular context of an organism, comprising a set of genes and their internal relationships to regulate protein production rate (gene expression level) of each other through coded proteins. Computational Reconstruction of GRN from gene expression data is a widely-applied research area. Recurrent Neural Network (RNN) is a useful modeling scheme for GRN reconstruction. In this research, the RNN formulation of GRN reconstruction having single objective function has been modified to incorporate a new objective function. An existing multi-objective meta-heuristic algorithm, Archived Multi Objective Simulated Annealing (AMOSA) has been modified and applied to this bi-objective RNN formulation. Executing the resulting algorithm (called AMOSA-GRN) on a gene expression dataset, a collection (termed as Archive) of non-dominated GRNs has been obtained. Ensemble averaging has been applied on the archives, obtained through a sequence of executions of AMOSA-GRN. Accuracy of GRNs in the averaged archive, with respect to gold standard GRN, varies in the range 0.875 1.0 (87.5% - 100%).
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