A benchmarked comparison of software packages for time-lapse image processing of monolayer bacterial population dynamics.

Time-lapse microscopy offers a powerful approach for analyzing cellular activity. In particular, this technique is valuable for assessing the behavior of bacterial populations, which can exhibit growth and intercellular interactions in a monolayer. Such time-lapse imaging typically generates large quantities of data, limiting the options for manual investigation. Several image-processing software packages have been developed to facilitate analysis. It can thus be a challenge to identify the software package best suited to a particular research goal. Here, we compare four software packages that support the analysis of 2D time-lapse images of cellular populations: CellProfiler, SuperSegger-Omnipose, DeLTA, and FAST. We compare their performance against benchmarked results on time-lapse observations of Escherichia coli populations. Performance varies across the packages, with each of the four outperforming the others in at least one aspect of the analysis. Not surprisingly, the packages that have been in development for longer showed the strongest performance. We found that deep learning-based approaches to object segmentation outperformed traditional approaches, but the opposite was true for frame-to-frame object tracking. We offer these comparisons, together with insight into usability, computational efficiency, and feature availability, as a guide to researchers seeking image-processing solutions. IMPORTANCE: Time-lapse microscopy provides a detailed window into the world of bacterial behavior. However, the vast amount of data produced by these techniques is difficult to analyze manually. We have analyzed four software tools designed to process such data and compared their performance, using populations of commonly studied bacterial species as our test subjects. Our findings offer a roadmap to scientists, helping them choose the right tool for their research. This comparison bridges a gap between microbiology and computational analysis, streamlining research efforts.

Heterologous expression of NoxA confers aerotolerance in Clostridium sporogenes.

Clostridium is a genus of gram-positive obligate anaerobic bacteria. Some species of Clostridium, including Clostridium sporogenes, may be of use in bacteria-mediated cancer therapy. Spores of Clostridium are inert in healthy normoxic tissue but germinate when in the hypoxic regions of solid tumors, causing tumor regression. However, such treatments fail to completely eradicate tumors partly because of higher oxygen levels at the tumor's outer rim. In this study, we demonstrate that a degree of aerotolerance can be introduced to C. sporogenes by transfer of the noxA gene from Clostridium aminovalericum. NoxA is a water-forming NADH oxidase enzyme, and so has no detrimental effect on cell viability. In addition to its potential in cancer treatment, the noxA-expressing strain described here could be used to alleviate challenges related to oxygen sensitivity of C. sporogenes in biomanufacturing.

Kinetic modelling of ß-cell metabolism reveals control points in the insulin-regulating pyruvate cycling pathways.

Insulin, a key hormone in the regulation of glucose homoeostasis, is secreted by pancreatic ß-cells in response to elevated glucose levels. Insulin is released in a biphasic manner in response to glucose metabolism in ß-cells. The first phase of insulin secretion is triggered by an increase in the ATP:ADP ratio; the second phase occurs in response to both a rise in ATP:ADP and other key metabolic signals, including a rise in the NADPH:NADP+ ratio. Experimental evidence indicates that pyruvate-cycling pathways play an important role in the elevation of the NADPH:NADP+ ratio in response to glucose. The authors developed a kinetic model for the tricarboxylic acid cycle and pyruvate cycling pathways. The authors successfully validated the model against experimental observations and performed a sensitivity analysis to identify key regulatory interactions in the system. The model predicts that the dicarboxylate carrier and the pyruvate transporter are the most important regulators of pyruvate cycling and NADPH production. In contrast, the analysis showed that variation in the pyruvate carboxylase flux was compensated by a response in the activity of mitochondrial isocitrate dehydrogenase (ICDm ) resulting in minimal effect on overall pyruvate cycling flux. The model predictions suggest starting points for further experimental investigation, as well as potential drug targets for the treatment of type 2 diabetes.

NLoed: A Python Package for Nonlinear Optimal Experimental Design in Systems Biology.

Modeling in systems and synthetic biology relies on accurate parameter estimates and predictions. Accurate model calibration relies, in turn, on data and on how well suited the available data are to a particular modeling task. Optimal experimental design (OED) techniques can be used to identify experiments and data collection procedures that will most efficiently contribute to a given modeling objective. However, implementation of OED is limited by currently available software tools that are not well suited for the diversity of nonlinear models and non-normal data commonly encountered in biological research. Moreover, existing OED tools do not make use of the state-of-the-art numerical tools, resulting in inefficient computation. Here, we present the NLoed software package and demonstrate its use with in vivo data from an optogenetic system in Escherichia coli. NLoed is an open-source Python library providing convenient access to OED methods, with particular emphasis on experimental design for systems biology research. NLoed supports a wide variety of nonlinear, multi-input/output, and dynamic models and facilitates modeling and design of experiments over a wide variety of data types. To support OED investigations, the NLoed package implements maximum likelihood fitting and diagnostic tools, providing a comprehensive modeling workflow. NLoed offers an accessible, modular, and flexible OED tool set suited to the wide variety of experimental scenarios encountered in systems biology research. We demonstrate NLoed's capabilities by applying it to experimental design for characterization of a bacterial optogenetic system.

Deep reinforcement learning for optimal experimental design in biology.

The field of optimal experimental design uses mathematical techniques to determine experiments that are maximally informative from a given experimental setup. Here we apply a technique from artificial intelligence-reinforcement learning-to the optimal experimental design task of maximizing confidence in estimates of model parameter values. We show that a reinforcement learning approach performs favourably in comparison with a one-step ahead optimisation algorithm and a model predictive controller for the inference of bacterial growth parameters in a simulated chemostat. Further, we demonstrate the ability of reinforcement learning to train over a distribution of parameters, indicating that this approach is robust to parametric uncertainty.

Calibrating spatiotemporal models of microbial communities to microscopy data: A review.

Spatiotemporal models that account for heterogeneity within microbial communities rely on single-cell data for calibration and validation. Such data, commonly collected via microscopy and flow cytometry, have been made more accessible by recent advances in microfluidics platforms and data processing pipelines. However, validating models against such data poses significant challenges. Validation practices vary widely between modelling studies; systematic and rigorous methods have not been widely adopted. Similar challenges are faced by the (macrobial) ecology community, in which systematic calibration approaches are often employed to improve quantitative predictions from computational models. Here, we review single-cell observation techniques that are being applied to study microbial communities and the calibration strategies that are being employed for accompanying spatiotemporal models. To facilitate future calibration efforts, we have compiled a list of summary statistics relevant for quantifying spatiotemporal patterns in microbial communities. Finally, we highlight some recently developed techniques that hold promise for improved model calibration, including algorithmic guidance of summary statistic selection and machine learning approaches for efficient model simulation.

Advancing undergraduate synthetic biology education: insights from a Canadian iGEM student perspective.

The last two decades have seen vigorous activity in synthetic biology research and the ever-increasing applications of these technologies. However, pedagogical research pertaining to teaching synthetic biology is scarce, especially when compared to other science and engineering disciplines. Within Canada, there are only three universities that offer synthetic biology programs, two of which are at the undergraduate level. Rather than taking place in formal academic settings, many Canadian undergraduate students are introduced to synthetic biology through participation in the annual International Genetically Engineered Machine (iGEM) competition. Although the iGEM competition has had a transformative impact on synthetic biology training in other nations, its impact in Canada has been relatively modest. Consequently, the iGEM competition remains a major setting for synthetic biology education in Canada. To promote further development of synthetic biology education, we surveyed undergraduate students from the Canadian iGEM design teams of 2019. We extracted insights from these data using qualitative analysis to provide recommendations for best teaching practices in synthetic biology undergraduate education, which we describe through our proposed Framework for Transdisciplinary Synthetic Biology Education (FTSBE).

A Keller-Segel model for C elegans L1 aggregation.

We describe a mathematical model for the aggregation of starved first-stage C elegans larvae (L1s). We propose that starved L1s produce and respond chemotactically to two labile diffusible chemical signals, a short-range attractant and a longer range repellent. This model takes the mathematical form of three coupled partial differential equations, one that describes the movement of the worms and one for each of the chemical signals. Numerical solution of these equations produced a pattern of aggregates that resembled that of worm aggregates observed in experiments. We also describe the identification of a sensory receptor gene, srh-2, whose expression is induced under conditions that promote L1 aggregation. Worms whose srh-2 gene has been knocked out form irregularly shaped aggregates. Our model suggests this phenotype may be explained by the mutant worms slowing their movement more quickly than the wild type.

Gene-Centric Model Approaches for Accurate Prediction of Pesticide Biodegradation in Soils.

Pesticides are widely used in agriculture despite their negative impact on ecosystems and human health. Biogeochemical modeling facilitates the mechanistic understanding of microbial controls on pesticide turnover in soils. We propose to inform models of coupled microbial dynamics and pesticide turnover with measurements of the abundance and expression of functional genes. To assess the advantages of informing models with genetic data, we developed a novel "gene-centric" model and compared model variants of differing structural complexity against a standard biomass-based model. The models were calibrated and validated using data from two batch experiments in which the degradation of the pesticides dichlorophenoxyacetic acid (2,4-D) and 2-methyl-4-chlorophenoxyacetic acid (MCPA) were observed in soil. When calibrating against data on pesticide mineralization, the gene-centric and biomass-based models performed equally well. However, accounting for pesticide-triggered gene regulation allows improved performance in capturing microbial dynamics and in predicting pesticide mineralization. This novel modeling approach also reveals a hysteretic relationship between pesticide degradation rates and gene expression, implying that the biodegradation performance in soils cannot be directly assessed by measuring the expression of functional genes. Our gene-centric model provides an effective approach for exploiting molecular biology data to simulate pesticide degradation in soils.

Deep reinforcement learning for the control of microbial co-cultures in bioreactors.

Multi-species microbial communities are widespread in natural ecosystems. When employed for biomanufacturing, engineered synthetic communities have shown increased productivity in comparison with monocultures and allow for the reduction of metabolic load by compartmentalising bioprocesses between multiple sub-populations. Despite these benefits, co-cultures are rarely used in practice because control over the constituent species of an assembled community has proven challenging. Here we demonstrate, in silico, the efficacy of an approach from artificial intelligence-reinforcement learning-for the control of co-cultures within continuous bioreactors. We confirm that feedback via a trained reinforcement learning agent can be used to maintain populations at target levels, and that model-free performance with bang-bang control can outperform a traditional proportional integral controller with continuous control, when faced with infrequent sampling. Further, we demonstrate that a satisfactory control policy can be learned in one twenty-four hour experiment by running five bioreactors in parallel. Finally, we show that reinforcement learning can directly optimise the output of a co-culture bioprocess. Overall, reinforcement learning is a promising technique for the control of microbial communities.

Effective implicit finite-difference method for sensitivity analysis of stiff stochastic discrete biochemical systems.

Simulation of cellular processes is achieved through a range of mathematical modelling approaches. Deterministic differential equation models are a commonly used first strategy. However, because many biochemical processes are inherently probabilistic, stochastic models are often called for to capture the random fluctuations observed in these systems. In that context, the Chemical Master Equation (CME) is a widely used stochastic model of biochemical kinetics. Use of these models relies on estimates of kinetic parameters, which are often poorly constrained by experimental observations. Consequently, sensitivity analysis, which quantifies the dependence of systems dynamics on model parameters, is a valuable tool for model analysis and assessment. A number of approaches to sensitivity analysis of biochemical models have been developed. In this study, the authors present a novel method for estimation of sensitivity coefficients for CME models of biochemical reaction systems that span a wide range of time-scales. They make use of finite-difference approximations and adaptive implicit tau-leaping strategies to estimate sensitivities for these stiff models, resulting in significant computational efficiencies in comparison with previously published approaches of similar accuracy, as evidenced by illustrative applications.

Parametric Sensitivity Analysis of Oscillatory Delay Systems with an Application to Gene Regulation.

A parametric sensitivity analysis for periodic solutions of delay-differential equations is developed. Because phase shifts cause the sensitivity coefficients of a periodic orbit to diverge, we focus on sensitivities of the extrema, from which amplitude sensitivities are computed, and of the period. Delay-differential equations are often used to model gene expression networks. In these models, the parametric sensitivities of a particular genotype define the local geometry of the evolutionary landscape. Thus, sensitivities can be used to investigate directions of gradual evolutionary change. An oscillatory protein synthesis model whose properties are modulated by RNA interference is used as an example. This model consists of a set of coupled delay-differential equations involving three delays. Sensitivity analyses are carried out at several operating points. Comments on the evolutionary implications of the results are offered.

Predictive Modeling of a Batch Filter Mating Process.

Quantitative characterizations of horizontal gene transfer are needed to accurately describe gene transfer processes in natural and engineered systems. A number of approaches to the quantitative description of plasmid conjugation have appeared in the literature. In this study, we seek to extend that work, motivated by the question of whether a mathematical model can accurately predict growth and conjugation dynamics in a batch process. We used flow cytometry to make time-point observations of a filter-associated mating between two E. coli strains, and fit ordinary differential equation models to the data. A model comparison analysis identified the model formulation that is best supported by the data. Identifiability analysis revealed that the parameters were estimated with acceptable accuracy. The predictive power of the model was assessed by comparison with test data that demanded extrapolation from the training experiments. This study represents the first attempt to assess the quality of model predictions for plasmid conjugation. Our successful application of this approach lays a foundation for predictive modeling that can be used both in the study of natural plasmid transmission and in model-based design of engineering approaches that employ conjugation, such as plasmid-mediated bioaugmentation.

An efficient finite-difference strategy for sensitivity analysis of stochastic models of biochemical systems.

Sensitivity analysis characterizes the dependence of a model's behaviour on system parameters. It is a critical tool in the formulation, characterization, and verification of models of biochemical reaction networks, for which confident estimates of parameter values are often lacking. In this paper, we propose a novel method for sensitivity analysis of discrete stochastic models of biochemical reaction systems whose dynamics occur over a range of timescales. This method combines finite-difference approximations and adaptive tau-leaping strategies to efficiently estimate parametric sensitivities for stiff stochastic biochemical kinetics models, with negligible loss in accuracy compared with previously published approaches. We analyze several models of interest to illustrate the advantages of our method.

Microbial herd protection mediated by antagonistic interaction in polymicrobial communities.

In the host and natural environments, microbes often exist in complex multispecies communities. The molecular mechanisms through which such communities develop and persist - despite significant antagonistic interactions between species - are not well understood. The type VI secretion system (T6SS) is a lethal weapon commonly employed by Gram-negative bacteria to inhibit neighboring species through delivery of toxic effectors. It is well established that intra-species protection is conferred by immunity proteins that neutralize effector toxicities. By contrast, the mechanisms for interspecies protection are not clear. Here we use two T6SS active antagonistic bacteria, Aeromonas hydrophila (AH) and Vibrio cholerae (VC), to demonstrate that interspecies protection is dependent on effectors. AH and VC do not share conserved immunity genes but could equally co-exist in a mixture. However, mutants lacking the T6SS or effectors were effectively eliminated by the other competing wild type. Time-lapse microscopy analyses show that mutually lethal interactions drive the segregation of mixed species into distinct single-species clusters by eliminating interspersed single cells. Cluster formation provides herd protection by abolishing lethal interaction inside each cluster and restricting it to the boundary. Using an agent-based modeling approach, we simulated the antagonistic interactions of two hypothetical species. The resulting simulations recapitulate our experimental observation. These results provide mechanistic insights for the general role of microbial weapons in determining the structures of complex multispecies communities. IMPORTANCE: Investigating the warfare of microbes allows us to better understand the ecological relationships in complex microbial communities such as the human microbiota. Here we use the T6SS, a deadly bacterial weapon, as a model to demonstrate the importance of lethal interactions in determining community structures and exchange of genetic materials. This simplified model elucidates a mechanism of microbial herd protection by which competing antagonistic species coexist in the same niche despite their diverse mutually destructive activities. Our results also suggest that antagonistic interaction imposes a strong selection that could promote multicellular like social behaviors and contribute to the transition to multicellularity during evolution.

Exploiting stoichiometric redundancies for computational efficiency and network reduction.

Analysis of metabolic networks typically begins with construction of the stoichiometry matrix, which characterizes the network topology. This matrix provides, via the balance equation, a description of the potential steady-state flow distribution. This paper begins with the observation that the balance equation depends only on the structure of linear redundancies in the network, and so can be stated in a succinct manner, leading to computational efficiencies in steady-state analysis. This alternative description of steady-state behaviour is then used to provide a novel method for network reduction, which complements existing algorithms for describing intracellular networks in terms of input-output macro-reactions (to facilitate bioprocess optimization and control). Finally, it is demonstrated that this novel reduction method can be used to address elementary mode analysis of large networks: the modes supported by a reduced network can capture the input-output modes of a metabolic module with significantly reduced computational effort.

Modeling of cell culture damage and recovery leads to increased antibody and biomass productivity in CHO cell cultures.

The development of an efficient and productive cell-culture process requires a deep understanding of intracellular mechanisms and extracellular conditions for optimal product synthesis. Mathematical modeling provides an effective strategy to predict, control, and optimize cell performance under a range of culture conditions. In this study, a mathematical model is proposed for the investigation of cell damage of a Chinese hamster ovary cell culture secreting recombinant anti-RhD monoclonal antibody (mAb). Irreversible cell damage was found to be correlated with a reduction in pH. This irreversible damage to cellular function is described mathematically by a Tessier-based model, in which the actively growing fraction of cells is dependent on an intracellular metabolic product acting as a growth inhibitor. To further verify the model, an offline model-based optimization of mAb production in the cell culture was carried out, with the goal of minimizing cell damage and thereby enhancing productivity through intermittent refreshment of the culture medium. An experimental implementation of this model-based strategy resulted in a doubling of the yield as compared to the batch operation and the resulting biomass and productivity profiles agreed with the model predictions.

Effects of multimerization on the temporal variability of protein complex abundance.

We explore whether the process of multimerization can be used as a means to regulate noise in the abundance of functional protein complexes. Additionally, we analyze how this process affects the mean level of these functional units, response time of a gene, and temporal correlation between the numbers of expressed proteins and of the functional multimers. We show that, although multimerization increases noise by reducing the mean number of functional complexes it can reduce noise in comparison with a monomer, when abundance of the functional proteins are comparable. Alternatively, reduction in noise occurs if both monomeric and multimeric forms of the protein are functional. Moreover, we find that multimerization either increases the response time to external signals or decreases the correlation between number of functional complexes and protein production kinetics. Finally, we show that the results are in agreement with recent genome-wide assessments of cell-to-cell variability in protein numbers and of multimerization in essential and non-essential genes in Escherichia coli, and that the effects of multimerization are tangible at the level of genetic circuits.

Modeling the coupled extracellular and intracellular environments in mammalian cell culture.

The regulation of metabolism in mammalian cell culture is closely linked to the process of apoptosis-programmed cell death. Apoptosis negatively impacts culture viability, product yield, and quality. An improved understanding of the interaction between apoptosis and metabolism will give rise to better control over the culture process, and thus improvements in product yield. This study presents a mathematical model that describes both the metabolic fluxes involving the extracellular metabolites and the progression of apoptosis in terms of intracellular caspases, and thus highlights the interactions between these two processes. The model is trained and validated against experimental observations of Chinese Hamster Ovary cell culture producing monoclonal antibody. Importantly, the model describes the continued production of monoclonal antibody in post exponential phase by incorporating different rates of antibody production for separate sub-populations within the culture. A parameter estimability test was applied on the combined model to assess the confidence in parameter estimates.