COVID-19 and cholesterol biosynthesis: Towards innovative decision support systems

With COVID-19 becoming endemic, there is a continuing need to find biomarkers characterizing the disease and aiding in patient stratification. We studied the relation between COVID-19 and cholesterol biosynthesis by comparing 10 intermediates of cholesterol biosynthesis during the hospitalization of 164 patients (admission, disease deterioration, discharge) admitted to the University Medical Center of Ljubljana. The concentrations of zymosterol, 24-dehydrolathosterol, desmosterol, and zymostenol were significantly altered in COVID-19 patients. We further developed a predictive model for disease severity based on clinical parameters alone and their combination with a subset of sterols. Our machine learning models applying 8 clinical parameters predicted disease severity with excellent accuracy (AUC = 0.96), showing substantial improvement over current clinical risk scores. After including sterols, model performance remained better than COVID-GRAM. This is the first study to examine cholesterol biosynthesis during COVID-19 and shows that a subset of cholesterol-related sterols is associated with the severity of COVID-19.

A computational design of a programmable biological processor

Basic synthetic information processing structures, such as logic gates, oscillators and flip-flops, have already been implemented in living organisms. Current implementations of these structures have yet to be extended to more complex processing structures that would constitute a biological computer. We make a step forward towards the construction of a biological computer. We describe a model-based computational design of a biological processor that uses transcription and translation resources of the host cell to perform its operations. The proposed processor is composed of an instruction memory containing a biological program, a program counter that is used to address this memory, and a biological oscillator that triggers the execution of the next instruction in the memory. We additionally describe the implementation of a biological compiler that compiles a sequence of human-readable instructions into ordinary differential equation-based models, which can be used to simulate and analyse the dynamics of the processor. The proposed implementation presents the first programmable biological processor that exploits cellular resources to execute the specified instructions. We demonstrate the application of the described processor on a set of simple yet scalable biological programs. Biological descriptions of these programs can be produced manually or automatically using the provided compiler.

Review and assessment of Boolean approaches for inference of gene regulatory networks

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.

Field-programmable biological circuits and configurable (bio)logic blocks for distributed biological computing

Synthetic biology applications often require engineered computing structures, which can be programmed to process the information in a given way. However, programming of these structures usually requires significant amount of trial-and-error genetic engineering. This process is to some degree analogous to the design of application-specific integrated circuits (ASIC) in the domain of digital electronic circuits, which often require complex and time-consuming workflows to obtain a desired response. We describe a design of programmable biological circuits that can be configured without additional genetic engineering. Their configuration can be changed in vivo, i.e. during the execution of their biological program, simply with an introduction of programming inputs. These, e.g., increase the degradation rates of selected proteins that store the current configuration of the circuit. Programming can be thus performed in the field as in the case of field-programmable gate array (FPGA) circuits, which present an attractive alternative of ASICs in digital electronics. We describe a basic programmable unit, which we denote configurable (bio)logical block (CBLB) inspired by the architecture of configurable logic blocks (CLBs), basic functional units within the FPGA circuits. The design of a CBLB is based on distributed cellular computing modules, which makes its biological implementation easier to achieve. We establish a computational model of a CBLB and analyse its response with a given set of biologically feasible parameter values. Furthermore, we show that the proposed CBLB design exhibits correct behaviour for a vast range of kinetic parameter values, different population ratios, and as well preserves this response in stochastic simulations.

Computational analysis of viable parameter regions in models of synthetic biological systems

Background

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.

Results

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.

Conclusions

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.