Multi-scale models of whole cells: progress and challenges.

Whole-cell modeling is "the ultimate goal" of computational systems biology and "a grand challenge for 21st century" (Tomita, Trends in Biotechnology, 2001, 19(6), 205-10). These complex, highly detailed models account for the activity of every molecule in a cell and serve as comprehensive knowledgebases for the modeled system. Their scope and utility far surpass those of other systems models. In fact, whole-cell models (WCMs) are an amalgam of several types of "system" models. The models are simulated using a hybrid modeling method where the appropriate mathematical methods for each biological process are used to simulate their behavior. Given the complexity of the models, the process of developing and curating these models is labor-intensive and to date only a handful of these models have been developed. While whole-cell models provide valuable and novel biological insights, and to date have identified some novel biological phenomena, their most important contribution has been to highlight the discrepancy between available data and observations that are used for the parametrization and validation of complex biological models. Another realization has been that current whole-cell modeling simulators are slow and to run models that mimic more complex (e.g., multi-cellular) biosystems, those need to be executed in an accelerated fashion on high-performance computing platforms. In this manuscript, we review the progress of whole-cell modeling to date and discuss some of the ways that they can be improved.

Uses of Multi-Objective Flux Analysis for Optimization of Microbial Production of Secondary Metabolites.

Secondary metabolites are not essential for the growth of microorganisms, but they play a critical role in how microbes interact with their surroundings. In addition to this important ecological role, secondary metabolites also have a variety of agricultural, medicinal, and industrial uses, and thus the examination of secondary metabolism of plants and microbes is a growing scientific field. While the chemical production of certain secondary metabolites is possible, industrial-scale microbial production is a green and economically attractive alternative. This is even more true, given the advances in bioengineering that allow us to alter the workings of microbes in order to increase their production of compounds of interest. This type of engineering requires detailed knowledge of the "chassis" organism's metabolism. Since the resources and the catalytic capacity of enzymes in microbes is finite, it is important to examine the tradeoffs between various bioprocesses in an engineered system and alter its working in a manner that minimally perturbs the robustness of the system while allowing for the maximum production of a product of interest. The in silico multi-objective analysis of metabolism using genome-scale models is an ideal method for such examinations.

Electrogenetic signaling and information propagation for controlling microbial consortia via programmed lysis.

To probe signal propagation and genetic actuation in microbial consortia, we have coopted the components of both redox and quorum sensing (QS) signaling into a communication network for guiding composition by "programming" cell lysis. Here, we use an electrode to generate hydrogen peroxide as a redox cue that determines consortia composition. The oxidative stress regulon of Escherichia coli, OxyR, is employed to receive and transform this signal into a QS signal that coordinates the lysis of a subpopulation of cells. We examine a suite of information transfer modalities including "monoculture" and "transmitter-receiver" models, as well as a series of genetic circuits that introduce time-delays for altering information relay, thereby expanding design space. A simple mathematical model aids in developing communication schemes that accommodate the transient nature of redox signals and the "collective" attributes of QS signals. We suggest this platform methodology will be useful in understanding and controlling synthetic microbial consortia for a variety of applications, including biomanufacturing and biocontainment.

Rank and Wormhole Attack Detection Model for RPL-Based Internet of Things Using Machine Learning.

The proliferation of the internet of things (IoT) technology has led to numerous challenges in various life domains, such as healthcare, smart systems, and mission-critical applications. The most critical issue is the security of IoT nodes, networks, and infrastructures. IoT uses the routing protocol for low-power and lossy networks (RPL) for data communication among the devices. RPL comprises a lightweight core and thus does not support high computation and resource-consuming methods for security implementation. Therefore, both IoT and RPL are vulnerable to security attacks, which are broadly categorized into RPL-specific and sensor-network-inherited attacks. Among the most concerning protocol-specific attacks are rank attacks and wormhole attacks in sensor-network-inherited attack types. They target the RPL resources and components including control messages, repair mechanisms, routing topologies, and sensor network resources by consuming. This leads to the collapse of IoT infrastructure. In this paper, a lightweight multiclass classification-based RPL-specific and sensor-network-inherited attack detection model called MC-MLGBM is proposed. A novel dataset was generated through the construction of various network models to address the unavailability of the required dataset, optimal feature selection to improve model performance, and a light gradient boosting machine-based algorithm optimized for a multiclass classification-based attack detection. The results of extensive experiments are demonstrated through several metrics including confusion matrix, accuracy, precision, and recall. For further performance evaluation and to remove any bias, the multiclass-specific metrics were also used to evaluate the model, including cross-entropy, Cohn's kappa, and Matthews correlation coefficient, and then compared with benchmark research.

Bacterial exometabolites influence Chlamydomonas cell cycle and double algal productivity.

Algal-bacterial interactions provide clues to algal physiology, but mutualistic interactions are complicated by dynamic exchange. We characterized the response of Chlamydomonas reinhardtii to the presence of a putative alga-benefitting commensal bacterium (Arthrobacter strain 'P2b'). Co-cultivation promoted chlorophyll content, biomass, average cell size, and number of dividing cells, relative to axenic cultures. Addition of bacterial spent medium (whole, size-fractionated and heat-treated) had similar effects, indicating P2b does not require algal interaction to promote growth. Nutrients and pH were excluded as putative effectors, collectively indicating a commensal interaction mediated by Arthrobacter-released small exometabolite(s). Proteogenomic comparison revealed similar response to co-cultivation and spent media, including differential cell cycle regulation, extensive downregulation of flagellar genes and histones, carbonic anhydrase and RubisCO downregulation, upregulation of some chlorophyll, amino acid and carbohydrate biosynthesis genes, and changes to redox and Fe homeostasis. Further, Arthrobacter protein expression indicated some highly expressed putative secondary metabolites. Together, these results revealed that low molecular weight bacterial metabolites can elicit major physiological changes in algal cell cycle regulation, perhaps through a more productive G1 phase, that lead to substantial increases in photosynthetically-produced biomass. This work illustrates that model commensal interactions can be used to shed light on algal response to stimulating bacteria.

A Beginner's Guide to the COBRA Toolbox.

COBRA toolbox is one of the most popular tools for systems biology analyses using genome-scale metabolic reconstructions. The toolbox permits the use of many constraint-based analytical methods for examining characteristics of metabolism in the biosystems ranging in complexity from single cells to microbial communities and ultimately multicellular organisms. The toolbox has a number of different variants that can be used depending on a user's choice of programming language. Here, I provide a basic tutorial for beginners that plan to use the original MATLAB version of the toolbox.

Curating COBRA Models of Microbial Metabolism.

Constraint-based reconstruction and analysis (COBRA) methods have been used for over 20 years to generate genome-scale models of metabolism in biological systems. The COBRA models have been utilized to gain new insights into the biochemical conversions that occur within organisms and allow their survival and proliferation. Using these models, computational biologists can conduct a variety of different analyses such as examining network structures, predicting metabolic capabilities, resolving unexplained experimental observations, generating and testing new hypotheses, assessing the nutritional requirements of a biosystem and approximating its environmental niche, identifying missing enzymatic functions in the annotated genomes, and engineering desired metabolic capabilities in model organisms. This chapter details the protocol for developing curated system-level COBRA models of metabolism in microbes.

5G and IoT Based Reporting and Accident Detection (RAD) System to Deliver First Aid Box Using Unmanned Aerial Vehicle.

Internet of Things (IoT) and 5G are enabling intelligent transportation systems (ITSs). ITSs promise to improve road safety in smart cities. Therefore, ITSs are gaining earnest devotion in the industry as well as in academics. Due to the rapid increase in population, vehicle numbers are increasing, resulting in a large number of road accidents. The majority of the time, casualties are not appropriately discovered and reported to hospitals and relatives. This lack of rapid care and first aid might result in life loss in a matter of minutes. To address all of these challenges, an intelligent system is necessary. Although several information communication technologies (ICT)-based solutions for accident detection and rescue operations have been proposed, these solutions are not compatible with all vehicles and are also costly. Therefore, we proposed a reporting and accident detection system (RAD) for a smart city that is compatible with any vehicle and less expensive. Our strategy aims to improve the transportation system at a low cost. In this context, we developed an android application that collects data related to sound, gravitational force, pressure, speed, and location of the accident from the smartphone. The value of speed helps to improve the accident detection accuracy. The collected information is further processed for accident identification. Additionally, a navigation system is designed to inform the relatives, police station, and the nearest hospital. The hospital dispatches UAV (i.e., drone with first aid box) and ambulance to the accident spot. The actual dataset from the Road Safety Open Repository is used for results generation through simulation. The proposed scheme shows promising results in terms of accuracy and response time as compared to existing techniques.

Finite element simulation and statistical investigation of an orthodontic mini-implant's stability in a novel screw design.

One of the essential aspects of the mini-implant's successful application is its stability after being installed in the bone. The stability of the mini-implant affected the most by its geometry. In the present research, the effect of the geometry-related parameters of the mini-implant on its lateral displacement is investigated by Finite Element (FE) modeling using ABAQUS software. The parameters studied include length, diameter, pitch, and depth of the screw threads; besides, length and angle of the conical section. The Taguchi method was used to prevent many experiments. The mesh convergence tests and experimental tests confirmed the FE model quantitatively and qualitatively. Mean of means and variance analysis determined the parameters significance and their contribution on the stability. The screw diameter and length have the most contribution to mini-implant' displacement. The effect of screw pitch was less than that for length and diameter. The conical section improved the initial stability by creating compressive stress and additional friction in its surrounding bone. No significant effects on the stability of the mini-implant have been observed for the non-threaded part. By examining the effect of thread depth on its stability by defining the ratio of thread depth to the internal diameter and to maintain the strength of the screw the optimal value for internal to external ratio is set at about 0.7.

Collagen Turnover in Relation to Risk Factors and Hemodynamics in Human Intracranial Aneurysms.

Background and Purpose- Determinants for molecular and structural instability, that is, impending growth or rupture, of intracranial aneurysms (IAs) remain uncertain. To elucidate this, we endeavored to estimate the actual turnover rates of the main molecular constituent in human IA (collagen) on the basis of radiocarbon (14C) birth dating in relation to IA hemodynamics. Methods- Collagen turnover rates in excised human IA samples were calculated using mathematical modeling of 14C birth dating data of collagen in relation to risk factors and histological markers for collagen maturity/turnover in selected IA. Hemodynamics were simulated using image-based computational fluid dynamics. Correlation, logistic regression, and receiver operating characteristic analyses were performed. Results- Collagen turnover rates were estimated in 46 IA (43 patients); computational fluid dynamics could be performed in 20 IA (20 patients). The mean collagen turnover rate (γ) constituted 126% (±1% error) per year. For patients with arterial hypertension, γ was greater than 2600% annually, whereas γ was distinctly lower with 32% (±1% error) per year for patients without risk factors, such as smoking and hypertension. There was a distinct association between histological presence of rather immature collagen in human IA and the presence of modifiable risk factors. Spatial-temporal averaged wall shear stress predicted rapid collagen turnover (odds ratio, 1.6 [95% CI, 1.0-2.7]). Receiver operating characteristic analysis demonstrated a good test accuracy (area under the curve, 0.798 [95% CI, 0.598-0.998]) for average wall shear stress with a threshold ≥4.9 Pa for rapid collagen turnover. Conclusions- Our data indicate that turnover rates and stability of collagen in human IA are strongly associated with the presence of modifiable risk factors and aneurysmal hemodynamics. These findings underline the importance of strict risk factor modification in patients with unruptured IA. Future should include more detailed risk factor data to establish a more causal understanding of hemodynamics and the rupture risk of individual IA.

Investigation of the optimal design of orthodontic mini-implants based on the primary stability: A finite element analysis.

Background. The design of an orthodontic mini-implant is a significant factor in determining its primary stability and its clinical success. The aim of this study was to measure the relative effect of mini-implant design factors on primary stability of orthodontic mini-implants. Methods. Thirty-two 3-dimensional assemblies of mini-implant models with their surrounding bone were generated using finite element analysis software. The maximum displacement of each mini-implant model was measured as they were loaded with a 2-N horizontal force. Employing Taguchi's design of experiments as a statistical method, the contribution of each design factor to primary stability was calculated. As a result of the great effect of the upper diameter and length, to better detect the impact of the remaining design factors, another set of 25 models with a fixed amount of length and diameter was generated and evaluated. Results. The diameter and length showed a great impact on the primary stability in the first set of experiments (P<0.05). According to the second set of experiments, increased taper angle in the threaded and non-threaded area decreased the primary stability. There was also an optimum amount of 2.5 mm for threaded taper length beyond which the primary stability decreased. Conclusion. It is advisable to increase the diameter and length if primary stability is at risk. In the second place, a minimum amount of taper angle, both in the threaded and non-threaded area with an approximate proportion of 20% of threaded taper length to MI length, would be desirable for MIs with a moderate size.

System-level analysis of metabolic trade-offs during anaerobic photoheterotrophic growth in Rhodopseudomonas palustris.

BACKGROUND: Living organisms need to allocate their limited resources in a manner that optimizes their overall fitness by simultaneously achieving several different biological objectives. Examination of these biological trade-offs can provide invaluable information regarding the biophysical and biochemical bases behind observed cellular phenotypes. A quantitative knowledge of a cell system's critical objectives is also needed for engineering of cellular metabolism, where there is interest in mitigating the fitness costs that may result from human manipulation. RESULTS: To study metabolism in photoheterotrophs, we developed and validated a genome-scale model of metabolism in Rhodopseudomonas palustris, a metabolically versatile gram-negative purple non-sulfur bacterium capable of growing phototrophically on various carbon sources, including inorganic carbon and aromatic compounds. To quantitatively assess trade-offs among a set of important biological objectives during different metabolic growth modes, we used our new model to conduct an 8-dimensional multi-objective flux analysis of metabolism in R. palustris. Our results revealed that phototrophic metabolism in R. palustris is light-limited under anaerobic conditions, regardless of the available carbon source. Under photoheterotrophic conditions, R. palustris prioritizes the optimization of carbon efficiency, followed by ATP production and biomass production rate, in a Pareto-optimal manner. To achieve maximum carbon fixation, cells appear to divert limited energy resources away from growth and toward CO2 fixation, even in the presence of excess reduced carbon. We also found that to achieve the theoretical maximum rate of biomass production, anaerobic metabolism requires import of additional compounds (such as protons) to serve as electron acceptors. Finally, we found that production of hydrogen gas, of potential interest as a candidate biofuel, lowers the cellular growth rates under all circumstances. CONCLUSIONS: Photoheterotrophic metabolism of R. palustris is primarily regulated by the amount of light it can absorb and not the availability of carbon. However, despite carbon's secondary role as a regulating factor, R. palustris' metabolism strives for maximum carbon efficiency, even when this increased efficiency leads to slightly lower growth rates.

Combining multiple functional annotation tools increases coverage of metabolic annotation.

BACKGROUND: Genome-scale metabolic modeling is a cornerstone of systems biology analysis of microbial organisms and communities, yet these genome-scale modeling efforts are invariably based on incomplete functional annotations. Annotated genomes typically contain 30-50% of genes without functional annotation, severely limiting our knowledge of the "parts lists" that the organisms have at their disposal. These incomplete annotations may be sufficient to derive a model of a core set of well-studied metabolic pathways that support growth in pure culture. However, pathways important for growth on unusual metabolites exchanged in complex microbial communities are often less understood, resulting in missing functional annotations in newly sequenced genomes. RESULTS: Here, we present results on a comprehensive reannotation of 27 bacterial reference genomes, focusing on enzymes with EC numbers annotated by KEGG, RAST, EFICAz, and the BRENDA enzyme database, and on membrane transport annotations by TransportDB, KEGG and RAST. Our analysis shows that annotation using multiple tools can result in a drastically larger metabolic network reconstruction, adding on average 40% more EC numbers, 3-8 times more substrate-specific transporters, and 37% more metabolic genes. These results are even more pronounced for bacterial species that are phylogenetically distant from well-studied model organisms such as E. coli. CONCLUSIONS: Metabolic annotations are often incomplete and inconsistent. Combining multiple functional annotation tools can greatly improve genome coverage and metabolic network size, especially for non-model organisms and non-core pathways.

Rapid countermeasure discovery against Francisella tularensis based on a metabolic network reconstruction.

In the future, we may be faced with the need to provide treatment for an emergent biological threat against which existing vaccines and drugs have limited efficacy or availability. To prepare for this eventuality, our objective was to use a metabolic network-based approach to rapidly identify potential drug targets and prospectively screen and validate novel small-molecule antimicrobials. Our target organism was the fully virulent Francisella tularensis subspecies tularensis Schu S4 strain, a highly infectious intracellular pathogen that is the causative agent of tularemia and is classified as a category A biological agent by the Centers for Disease Control and Prevention. We proceeded with a staggered computational and experimental workflow that used a strain-specific metabolic network model, homology modeling and X-ray crystallography of protein targets, and ligand- and structure-based drug design. Selected compounds were subsequently filtered based on physiological-based pharmacokinetic modeling, and we selected a final set of 40 compounds for experimental validation of antimicrobial activity. We began screening these compounds in whole bacterial cell-based assays in biosafety level 3 facilities in the 20th week of the study and completed the screens within 12 weeks. Six compounds showed significant growth inhibition of F. tularensis, and we determined their respective minimum inhibitory concentrations and mammalian cell cytotoxicities. The most promising compound had a low molecular weight, was non-toxic, and abolished bacterial growth at 13 µM, with putative activity against pantetheine-phosphate adenylyltransferase, an enzyme involved in the biosynthesis of coenzyme A, encoded by gene coaD. The novel antimicrobial compounds identified in this study serve as starting points for lead optimization, animal testing, and drug development against tularemia. Our integrated in silico/in vitro approach had an overall 15% success rate in terms of active versus tested compounds over an elapsed time period of 32 weeks, from pathogen strain identification to selection and validation of novel antimicrobial compounds.

D-Lactate production as a function of glucose metabolism in Saccharomyces cerevisiae.

Methylglyoxal, a reactive, toxic dicarbonyl, is generated by the spontaneous degradation of glycolytic intermediates. Methylglyoxal can form covalent adducts with cellular macromolecules, potentially disrupting cellular function. We performed experiments using the model organism Saccharomyces cerevisiae, grown in media containing low, moderate and high glucose concentrations, to determine the relationship between glucose consumption and methylglyoxal metabolism. Normal growth experiments and glutathione depletion experiments showed that metabolism of methylglyoxal by log-phase yeast cultured aerobically occurred primarily through the glyoxalase pathway. Growth in high-glucose media resulted in increased generation of the methylglyoxal metabolite D-lactate and overall lower efficiency of glucose utilization as measured by growth rates. Cells grown in high-glucose media maintained higher glucose uptake flux than cells grown in moderate-glucose or low-glucose media. Computational modelling showed that increased glucose consumption may impair catabolism of triose phosphates as a result of an altered NAD⁺:NADH ratio.

Genome-level transcription data of Yersinia pestis analyzed with a new metabolic constraint-based approach.

BACKGROUND: Constraint-based computational approaches, such as flux balance analysis (FBA), have proven successful in modeling genome-level metabolic behavior for conditions where a set of simple cellular objectives can be clearly articulated. Recently, the necessity to expand the current range of constraint-based methods to incorporate high-throughput experimental data has been acknowledged by the proposal of several methods. However, these methods have rarely been used to address cellular metabolic responses to some relevant perturbations such as antimicrobial or temperature-induced stress. Here, we present a new method for combining gene-expression data with FBA (GX-FBA) that allows modeling of genome-level metabolic response to a broad range of environmental perturbations within a constraint-based framework. The method uses mRNA expression data to guide hierarchical regulation of cellular metabolism subject to the interconnectivity of the metabolic network. RESULTS: We applied GX-FBA to a genome-scale model of metabolism in the gram negative bacterium Yersinia pestis and analyzed its metabolic response to (i) variations in temperature known to induce virulence, and (ii) antibiotic stress. Without imposition of any a priori behavioral constraints, our results show strong agreement with reported phenotypes. Our analyses also lead to novel insights into how Y. pestis uses metabolic adjustments to counter different forms of stress. CONCLUSIONS: Comparisons of GX-FBA predicted metabolic states with fluxomic measurements and different reported post-stress phenotypes suggest that mass conservation constraints and network connectivity can be an effective representative of metabolic flux regulation in constraint-based models. We believe that our approach will be of aid in the in silico evaluation of cellular goals under different conditions and can be used for a variety of analyses such as identification of potential drug targets and their action.

Development of constraint-based system-level models of microbial metabolism.

Genome-scale models of metabolism are valuable tools for using genomic information to predict microbial phenotypes. System-level mathematical models of metabolic networks have been developed for a number of microbes and have been used to gain new insights into the biochemical conversions that occur within organisms and permit their survival and proliferation. Utilizing these models, computational biologists can (1) examine network structures, (2) predict metabolic capabilities and resolve unexplained experimental observations, (3) generate and test new hypotheses, (4) assess the nutritional requirements of the organism and approximate its environmental niche, (5) identify missing enzymatic functions in the annotated genome, and (6) engineer desired metabolic capabilities in model organisms. This chapter details the protocol for developing genome-scale models of metabolism in microbes as well as tips for accelerating the model building process.

Applications of system-level models of metabolism for analysis of bacterial physiology and identification of new drug targets.

For nearly all of the 20th century, biologists gained considerable insights into the fundamental principles of cellular dynamics by examining select modules of biochemical processes. This form of analysis provides detailed information about the workings of the examined pathways. However, any attempt to alter the normal function of bacteria (perhaps for industrial or medicinal goals) requires a detailed global understanding of cellular mechanisms. The reductionist mode of analysis cannot provide the required information for developing the needed perspective on the complex interactions of biochemical pathways. Thankfully, the increasing availability of microbial genomic, transcriptomic, proteomic and other high-throughput data permits system-level analyses of microbiology. During the past two decades, systems biologists have developed constraint-based genome-scale models (GSM) of metabolism for a variety of pathogens. These models are important tools for assessing the metabolic capabilities of various genotypes. Simultaneously, new computational methods have been developed that use these network reconstructions to answer an array of important immunological questions. The objective of this article is to briefly review some of the uses of GSMs for studying bacterial metabolism under different conditions and to discuss how the calculated solutions can be used for rational design of drugs.

Yeast dynamic metabolic flux measurement in nutrient-rich media by HPLC and accelerator mass spectrometry.

Metabolic flux, the flow of metabolites through networks of enzymes, represents the dynamic productive output of cells. Improved understanding of intracellular metabolic fluxes will enable targeted manipulation of metabolic pathways of medical and industrial importance to a greater degree than is currently possible. Flux balance analysis (FBA) is a constraint-based approach to modeling metabolic fluxes, but its utility is limited by a lack of experimental measurements. Incorporation of experimentally measured fluxes as system constraints will significantly improve the overall accuracy of FBA. We applied a novel, two-tiered approach in the yeast Saccharomyces cerevisiae to measure nutrient consumption rates (extracellular fluxes) and a targeted intracellular flux using a (14)C-labeled precursor with HPLC separation and flux quantitation by accelerator mass spectrometry (AMS). The use of AMS to trace the intracellular fate of (14)C-glutamine allowed the calculation of intracellular metabolic flux through this pathway, with glutathione as the metabolic end point. Measured flux values provided global constraints for the yeast FBA model which reduced model uncertainty by more than 20%, proving the importance of additional constraints in improving the accuracy of model predictions and demonstrating the use of AMS to measure intracellular metabolic fluxes. Our results highlight the need to use intracellular fluxes to constrain the models. We show that inclusion of just one such measurement alone can reduce the average variability of model predicted fluxes by 10%.