Ever since its foundational years, synthetic biology has been focused on the implementation of biological computing structures. In the beginning, engineered biological computation has mainly been ...based on uncoupled monoclonal cellular populations. Implementations of such computing structures were mostly inspired by digital electronic circuits and revealed many constraints that limited the advance of the field to relatively simple information processing structures. The focus has recently shifted towards the implementation of biological computing structures within coupled intercellular circuits composed of engineered cellular modules. These circuits have, however, advanced only to a certain point, namely to consist of a few engineered bacterial strains, which perform the computation. It is now time to make a transition from modules and relatively simple systems of biological processing structures to networks composing different strains each presenting a designated computing structure. In such networks, each strain is analogous to a logic chip on a breadboard circuit and is connected to other strains by means of intercellular communication mechanisms rather than copper wires. This analogy can be driven further to use a set of engineered biological modules to construct a complex computing system, such as a multicellular biological processor. We review the state of the art of distributed cellular computation, communication mechanisms, and computational analysis and design approaches for distributed biological computing. We demonstrate the potential next step in engineered biological computation by a proposal of a design of a multicellular biological processor. We demonstrate an analysis of the proposed computing network using in silico simulation and optimisation approaches. Finally, we discuss the potential applications of the reviewed distributed cellular computing structures to the field of neural computing.
Boolean descriptions of gene regulatory networks can provide an insight into interactions between genes. Boolean networks hold predictive power, are easy to understand, and can be used to simulate ...the observed networks in different scenarios.
We review fundamental and state-of-the-art methods for inference of Boolean networks. We introduce a methodology for a straightforward evaluation of Boolean inference approaches based on the generation of evaluation datasets, application of selected inference methods, and evaluation of performance measures to guide the selection of the best method for a given inference problem. We demonstrate this procedure on inference methods REVEAL (REVerse Engineering ALgorithm), Best-Fit Extension, MIBNI (Mutual Information-based Boolean Network Inference), GABNI (Genetic Algorithm-based Boolean Network Inference) and ATEN (AND/OR Tree ENsemble algorithm), which infers Boolean descriptions of gene regulatory networks from discretised time series data.
Boolean inference approaches tend to perform better in terms of dynamic accuracy, and slightly worse in terms of structural correctness. We believe that the proposed methodology and provided guidelines will help researchers to develop Boolean inference approaches with a good predictive capability while maintaining structural correctness and biological relevance.
Boolean network inference; Gene regulatory networks; Static validation; Dynamic validation; Systems biology
Data-driven methods that automatically learn relations between attributes from given data are a popular tool for building mathematical models in computational biology. Since measurements are prone to ...errors, approaches dealing with uncertain data are especially suitable for this task. Fuzzy models are one such approach, but they contain a large amount of parameters and are thus susceptible to over-fitting. Validation methods that help detect over-fitting are therefore needed to eliminate inaccurate models.
We propose a method to enlarge the validation datasets on which a fuzzy dynamic model of a cellular network can be tested. We apply our method to two data-driven dynamic models of the MAPK signalling pathway and two models of the mammalian circadian clock. We show that random initial state perturbations can drastically increase the mean error of predictions of an inaccurate computational model, while keeping errors of predictions of accurate models small.
With the improvement of validation methods, fuzzy models are becoming more accurate and are thus likely to gain new applications. This field of research is promising not only because fuzzy models can cope with uncertainty, but also because their run time is short compared to conventional modelling methods that are nowadays used in systems biology.
Gene regulatory networks with different topological and/or dynamical properties might exhibit similar behavior. System that is less perceptive for the perturbations of its internal and external ...factors should be preferred. Methods for sensitivity and robustness assessment have already been developed and can be roughly divided into local and global approaches. Local methods focus only on the local area around nominal parameter values. This can be problematic when parameters exhibits the desired behavior over a large range of parameter perturbations or when parameter values are unknown. Global methods, on the other hand, investigate the whole space of parameter values and mostly rely on different sampling techniques. This can be computationally inefficient. To address these shortcomings 'glocal' approaches were developed that apply global and local approaches in an effective and rigorous manner.
Herein, we present a computational approach for 'glocal' analysis of viable parameter regions in biological models. The methodology is based on the exploration of high-dimensional viable parameter spaces with global and local sampling, clustering and dimensionality reduction techniques. The proposed methodology allows us to efficiently investigate the viable parameter space regions, evaluate the regions which exhibit the largest robustness, and to gather new insights regarding the size and connectivity of the viable parameter regions. We evaluate the proposed methodology on three different synthetic gene regulatory network models, i.e. the repressilator model, the model of the AC-DC circuit and the model of the edge-triggered master-slave D flip-flop.
The proposed methodology provides a rigorous assessment of the shape and size of viable parameter regions based on (1) the mathematical description of the biological system of interest, (2) constraints that define feasible parameter regions and (3) cost function that defines the desired or observed behavior of the system. These insights can be used to assess the robustness of biological systems, even in the case when parameter values are unknown and more importantly, even when there are multiple poorly connected viable parameter regions in the solution space. Moreover, the methodology can be efficiently applied to the analysis of biological systems that exhibit multiple modes of the targeted behavior.
Boolean networks provide an effective mechanism for describing interactions and dynamics of gene regulatory networks (GRNs). Deriving accurate Boolean descriptions of GRNs is a challenging task. The ...number of experiments is usually much smaller than the number of genes. In addition, binarization leads to a loss of information and inconsistencies arise in binarized time-series data. The inference of Boolean networks from binarized time-series data alone often leads to complex and overfitted models. To obtain relevant Boolean models of gene regulatory networks, inference methods could incorporate data from multiple sources and prior knowledge in terms of general network structure and/or exact interactions. We propose the Boolean network inference method SAILoR (Structure-Aware Inference of Logic Rules). SAILoR incorporates time-series gene expression data in combination with provided reference networks to infer accurate Boolean models. SAILoR automatically extracts topological properties from reference networks. These can describe a more general structure of the GRN or can be more precise and describe specific interactions. SAILoR infers a Boolean network by learning from both continuous and binarized time-series data. It navigates between two main objectives, topological similarity to reference networks and correspondence with gene expression data. By incorporating the NSGA-II multi-objective genetic algorithm, SAILoR relies on the wisdom of crowds. Our results indicate that SAILoR can infer accurate and biologically relevant Boolean descriptions of GRNs from both a static and a dynamic perspective. We show that SAILoR improves the static accuracy of the inferred network compared to the network inference method dynGENIE3. Furthermore, we compared the performance of SAILoR with other Boolean network inference approaches including Best-Fit, REVEAL, MIBNI, GABNI, ATEN, and LogBTF. We have shown that by incorporating prior knowledge about the overall network structure, SAILoR can improve the structural correctness of the inferred Boolean networks while maintaining dynamic accuracy. To demonstrate the applicability of SAILoR, we inferred context-specific Boolean subnetworks of female Drosophila melanogaster before and after mating.
In this article we present an extended
quantum-dot cellular automaton (QCA) cell. The classical QCA cell is extended in the sense of an enlarged range of its possible stable and usable states. ...Indeed, in the classical QCA cell the electrons, owing to electrostatic repulsion, align along one of the two diagonal configurations that correspond to their maximal spatial separation. The QCA cell thus has the ability to encode two states – two logic values (0 and 1). By extending the QCA cell with four additional quantum dots, we introduce the extended QCA (EQCA) cell and analyze its behavior, the analysis of which is based on the semi-classical modeling approach. Experiments showed that by using a special interpretation of electron configurations in the EQCA, the range of possible states can be increased from two to three, giving the EQCA cell the ability to encode the logic values (0, 1/2 and 1). The primary motive of this article is to promote the idea of finally switching focus from pure miniaturization and the top-down concept to the bottom-up concept and start extending the currently available approaches to allow for ‘richer’ processing and data storage capabilities without a major increase in space requirements.
COVID-19 presents a complex disease that needs to be addressed using systems medicine approaches that include genome-scale metabolic models (GEMs). Previous studies have used a single model ...extraction method (MEM) and/or a single transcriptomic dataset to reconstruct context-specific models, which proved to be insufficient for the broader biological contexts. We have applied four MEMs in combination with five COVID-19 datasets. Models produced by GIMME were separated by infection, while tINIT preserved the biological variability in the data and enabled the best prediction of the enrichment of metabolic subsystems. Vitamin D3 metabolism was predicted to be down-regulated in one dataset by GIMME, and in all by tINIT. Models generated by tINIT and GIMME predicted downregulation of retinol metabolism in different datasets, while downregulated cholesterol metabolism was predicted only by tINIT-generated models. Predictions are in line with the observations in COVID-19 patients. Our data indicated that GIMME and tINIT models provided the most biologically relevant results and should have a larger emphasis in further analyses. Particularly tINIT models identified the metabolic pathways that are a part of the host response and are potential antiviral targets. The code and the results of the analyses are available to download from https://github.com/CompBioLj/COVID_GEMs_and_MEMs.
•Model extraction method (MEM) significantly affected enrichment of metabolic pathways.•The MEM which preserved separation by biological factors was selected.•PCA or tSNE analysis aided the selection of the best performing MEM.•Model predictions were in line with observations in COVID-19 patients.•tINIT models predicted lower metabolism of several vitamins in COVID-19 patients.
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