Engineering multicellular living systems-a Keystone Symposia report.

The ability to engineer complex multicellular systems has enormous potential to inform our understanding of biological processes and disease and alter the drug development process. Engineering living systems to emulate natural processes or to incorporate new functions relies on a detailed understanding of the biochemical, mechanical, and other cues between cells and between cells and their environment that result in the coordinated action of multicellular systems. On April 3-6, 2022, experts in the field met at the Keystone symposium "Engineering Multicellular Living Systems" to discuss recent advances in understanding how cells cooperate within a multicellular system, as well as recent efforts to engineer systems like organ-on-a-chip models, biological robots, and organoids. Given the similarities and common themes, this meeting was held in conjunction with the symposium "Organoids as Tools for Fundamental Discovery and Translation".

A Method for Deciphering Major Drivers of Bacterial Iron Stress Response in the Presence of Oxidative Stressors.

This paper describes a method for deciphering major drivers of bacterial stress response using an empirically informed computational approach. We develop a working model of iron flux regulation and concomitant oxidative stress response in Escherichia coli. The integrated model is used to investigate the temporal effects of iron and hydrogen peroxide stress on bacterial growth and metabolism. We employ a sensitivity analysis platform and, using various measures, probe for major mechanistic drivers of the bacterial response to iron stress.

Compounding effect of vitamin D3 diet, supplementation, and alcohol exposure on macrophage response to mycobacterium infection.

Vitamin D3 is known to be a key component in the defense against Mycobacterium tuberculosis (Mtb) infection through the regulation of cytokine and effector molecules. Conversely, alcohol exposure has been recognized as an immune dysregulator. Macrophages were extracted from D3 deficient and sufficient diet mice and supplemented with D3 or exposed to ethanol during ex vivo infection using M. bovis BCG, as a surrogate for Mtb. Results of our study indicate that while exogenous supplementation or alcohol exposure did alter immune response, in vivo diet was the greatest determinant of cytokine and effector molecule production. Alcohol exposure was found to profoundly dysregulate primary murine macrophages, with ethanol-exposed cells generally characterized as hyper- or hyporesponsive. Exogenous D3 supplementation had a normative effect for diet deficient host, however supplementation was not sufficient to compensate for the effects of diet deficiency. Vitamin D3 sufficient diet resulted in reduced cell cytotoxicity for the majority of time points. Results provide insight into the ramifications of both the individual and combined health risks of D3 deficiency or alcohol exposure. Given the clinical relevance of D3 deficiency and alcohol use comorbidities, outcomes of this study have implications in therapeutic approaches for the treatment of tuberculosis disease.

Spatiotemporal Analysis of Mycobacterium-Dependent Macrophage Response.

There are three main outcomes of Mycobacterium tuberculosis infection: clearance, dissemination, and containment - in which the immune system physically isolates the invading microbes in lesions called granulomas. These structures are a hallmark of the disease and play an important role in the progression of infection. However, current in vitro and in vivo methods are ill adapted for spatial and temporal quantification of host-pathogen dynamics, which are necessary for the development of granulomas. We have developed an integrated 3D in vitro and computational platform with longterm time-lapse confocal imaging to provide a semi-automatic analysis of host-pathogen interaction data. Through exploratory data analysis, we conduct a preliminary investigation of how the intracellular bacterial load of macrophages can impact cellular spatiotemporal dynamics during Mycobacterium infection.

A Multiscale Agent-Based Model for the Investigation of E. coli K12 Metabolic Response During Biofilm Formation.

Bacterial biofilm formation is an organized collective response to biochemical cues that enables bacterial colonies to persist and withstand environmental insults. We developed a multiscale agent-based model that characterizes the intracellular, extracellular, and cellular scale interactions that modulate Escherichia coli MG1655 biofilm formation. Each bacterium's intracellular response and cellular state were represented as an outcome of interactions with the environment and neighboring bacteria. In the intracellular model, environment-driven gene expression and metabolism were captured using statistical regression and Michaelis-Menten kinetics, respectively. In the cellular model, growth, death, and type IV pili- and flagella-dependent movement were based on the bacteria's intracellular state. We implemented the extracellular model as a three-dimensional diffusion model used to describe glucose, oxygen, and autoinducer 2 gradients within the biofilm and bulk fluid. We validated the model by comparing simulation results to empirical quantitative biofilm profiles, gene expression, and metabolic concentrations. Using the model, we characterized and compared the temporal metabolic and gene expression profiles of sessile versus planktonic bacterial populations during biofilm formation and investigated correlations between gene expression and biofilm-associated metabolites and cellular scale phenotypes. Based on our in silico studies, planktonic bacteria had higher metabolite concentrations in the glycolysis and citric acid cycle pathways, with higher gene expression levels in flagella and lipopolysaccharide-associated genes. Conversely, sessile bacteria had higher metabolite concentrations in the autoinducer 2 pathway, with type IV pili, autoinducer 2 export, and cellular respiration genes upregulated in comparison with planktonic bacteria. Having demonstrated results consistent with in vitro static culture biofilm systems, our model enables examination of molecular phenomena within biofilms that are experimentally inaccessible and provides a framework for future exploration of how hypothesized molecular mechanisms impact bulk community behavior.

The dynamic immunomodulatory effects of vitamin D3 during Mycobacterium infection.

Mycobacterium tuberculosis ( Mtb), is a highly infectious airborne bacterium. Previous studies have found vitamin D3 to be a key factor in the defense against Mtb infection, through its regulation of the production of immune-related cytokines, chemokines and effector molecules. Mycobacterium smegmatis was used in our study as a surrogate of Mtb. We hypothesized that the continuous presence of vitamin D3, as well as the level of severity of infection would differentially modulate host cell immune response in comparison with control and the vehicle, ethanol. We found that vitamin D3 conditioning promotes increased bacterial clearance during low-level infection, intracellular containment during high-level infection, and minimizes host cytotoxicity. In the presence of vitamin D3 host cell production of cytokines and effector molecules was infection-level dependent, most notably IL-12, which increased during high-level infection and decreased during low-level infection, and NO, which had a rate of change positively correlated to IL-12. Our study provides evidence that vitamin D3 modulation is context-dependent and time-variant, as well as highly correlated to level of infection. This study furthers our mechanistic understanding of the dual role of vitamin D3 as a regulator of bactericidal molecules and protective agent against host cell damage.

Oxygen Availability and Metabolic Dynamics During Mycobacterium tuberculosis Latency.

OBJECTIVE: In vitro models of Mycobacterium tuberculosis (Mtb) nonreplicating persistence (NRP) suggest the rate of oxygen ( O2 ) depletion is a significant determinant in persistence. However, few studies have characterized the metabolic association between slow and rapid O2 depletion rates and successful versus failed persistence. METHODS: We developed a theoretical model of Mtb metabolic adaptation that includes O2 driven genetic modulation of enzymes in the tricarboxylic acid cycle, energy, and redox recycling pathways. We conducted an in silico study of Mtb adaptation, and investigated the metabolic dynamics that enable persistence during slow versus rapid anaerobiosis. RESULTS: Consistent with in vitro studies, during rapid anaerobiosis the most significant enzymatic changes occurred during the active growth period while the majority of the slow anaerobic system's adaptive response occurred during the first phase of aerobic shiftdown (NRP1). The characteristic response of the two conditions differed, with the slow anaerobic system exhibiting positive adaptation response, upregulating six enzymes during NRP1, while the rapid system exhibited a negative adaptation response, decreasing the levels of seven enzymes during active growth. CONCLUSION: Results of our study illustrate the intricate metabolic balance Mtb achieves during adaptation to anaerobic conditions, and how failure in redox recycling correlates to failure in persistence. SIGNIFICANCE: We have provided the first theoretical description of the genetic and metabolic death profile for Mtb during anaerobic growth. Insight into Mtbs dynamic adaptation response provides a unique systems framework for the development of targeted therapies to reduce the likelihood of mycobacterial persistence and the related incidence of latent tuberculosis infection.

Investigating the Role of TNF-α and IFN-γ Activation on the Dynamics of iNOS Gene Expression in LPS Stimulated Macrophages.

Macrophage produced inducible nitric oxide synthase (iNOS) is known to play a critical role in the proinflammatory response against intracellular pathogens by promoting the generation of bactericidal reactive nitrogen species. Robust and timely production of nitric oxide (NO) by iNOS and analogous production of reactive oxygen species are critical components of an effective immune response. In addition to pathogen associated lipopolysaccharides (LPS), iNOS gene expression is dependent on numerous proinflammatory cytokines in the cellular microenvironment of the macrophage, two of which include interferon gamma (IFN-γ) and tumor necrosis factor alpha (TNF-α). To understand the synergistic effect of IFN-γ and TNF-α activation, and LPS stimulation on iNOS expression dynamics and NO production, we developed a systems biology based mathematical model. Using our model, we investigated the impact of pre-infection cytokine exposure, or priming, on the system. We explored the essentiality of IFN-γ priming to the robustness of initial proinflammatory response with respect to the ability of macrophages to produce reactive species needed for pathogen clearance. Results from our theoretical studies indicated that IFN-γ and subsequent activation of IRF1 are essential in consequential production of iNOS upon LPS stimulation. We showed that IFN-γ priming at low concentrations greatly increases the effector response of macrophages against intracellular pathogens. Ultimately the model demonstrated that although TNF-α contributed towards a more rapid response time, measured as time to reach maximum iNOS production, IFN-γ stimulation was significantly more significant in terms of the maximum expression of iNOS and the concentration of NO produced.

Oxygen Modulates the Effectiveness of Granuloma Mediated Host Response to Mycobacterium tuberculosis: A Multiscale Computational Biology Approach.

Mycobacterium tuberculosis associated granuloma formation can be viewed as a structural immune response that can contain and halt the spread of the pathogen. In several mammalian hosts, including non-human primates, Mtb granulomas are often hypoxic, although this has not been observed in wild type murine infection models. While a presumed consequence, the structural contribution of the granuloma to oxygen limitation and the concomitant impact on Mtb metabolic viability and persistence remains to be fully explored. We develop a multiscale computational model to test to what extent in vivo Mtb granulomas become hypoxic, and investigate the effects of hypoxia on host immune response efficacy and mycobacterial persistence. Our study integrates a physiological model of oxygen dynamics in the extracellular space of alveolar tissue, an agent-based model of cellular immune response, and a systems biology-based model of Mtb metabolic dynamics. Our theoretical studies suggest that the dynamics of granuloma organization mediates oxygen availability and illustrates the immunological contribution of this structural host response to infection outcome. Furthermore, our integrated model demonstrates the link between structural immune response and mechanistic drivers influencing Mtbs adaptation to its changing microenvironment and the qualitative infection outcome scenarios of clearance, containment, dissemination, and a newly observed theoretical outcome of transient containment. We observed hypoxic regions in the containment granuloma similar in size to granulomas found in mammalian in vivo models of Mtb infection. In the case of the containment outcome, our model uniquely demonstrates that immune response mediated hypoxic conditions help foster the shift down of bacteria through two stages of adaptation similar to the in vitro non-replicating persistence (NRP) observed in the Wayne model of Mtb dormancy. The adaptation in part contributes to the ability of Mtb to remain dormant for years after initial infection.

Multiscale Modeling in the Clinic: Drug Design and Development.

A wide range of length and time scales are relevant to pharmacology, especially in drug development, drug design and drug delivery. Therefore, multiscale computational modeling and simulation methods and paradigms that advance the linkage of phenomena occurring at these multiple scales have become increasingly important. Multiscale approaches present in silico opportunities to advance laboratory research to bedside clinical applications in pharmaceuticals research. This is achievable through the capability of modeling to reveal phenomena occurring across multiple spatial and temporal scales, which are not otherwise readily accessible to experimentation. The resultant models, when validated, are capable of making testable predictions to guide drug design and delivery. In this review we describe the goals, methods, and opportunities of multiscale modeling in drug design and development. We demonstrate the impact of multiple scales of modeling in this field. We indicate the common mathematical and computational techniques employed for multiscale modeling approaches used in pharmacometric and systems pharmacology models in drug development and present several examples illustrating the current state-of-the-art models for (1) excitable systems and applications in cardiac disease; (2) stem cell driven complex biosystems; (3) nanoparticle delivery, with applications to angiogenesis and cancer therapy; (4) host-pathogen interactions and their use in metabolic disorders, inflammation and sepsis; and (5) computer-aided design of nanomedical systems. We conclude with a focus on barriers to successful clinical translation of drug development, drug design and drug delivery multiscale models.

A method for modeling oxygen diffusion in an agent-based model with application to host-pathogen infection.

This paper describes a method for incorporating a diffusion field modeling oxygen usage and dispersion in a multi-scale model of Mycobacterium tuberculosis (Mtb) infection mediated granuloma formation. We implemented this method over a floating-point field to model oxygen dynamics in host tissue during chronic phase response and Mtb persistence. The method avoids the requirement of satisfying the Courant-Friedrichs-Lewy (CFL) condition, which is necessary in implementing the explicit version of the finite-difference method, but imposes an impractical bound on the time step. Instead, diffusion is modeled by a matrix-based, steady state approximate solution to the diffusion equation. Presented in figure 1 is the evolution of the diffusion profiles of a containment granuloma over time.

In silico Kinetic Model of iNOS expression in macrophages.

Macrophages are a key component in the host innate response and are major contributors to the proinflammatory response against pathogens. One of the key players in the proinflammatory response is induced nitric oxide synthase (iNOS), an enzyme that provides the nitric oxide needed by phagocytic cells to create reactive nitrogen species, which are highly damaging to intracellular pathogens. To model the macrophage intracellular mechanism of iNOS gene expression, we use a systems biology approach to capture the dynamics of the iNOS gene expression system stimulated by bacterial lipopolysaccharide (LPS) and IFN-γ. Our simulation results agree with in vitro assays of iNOS gene expression and provide a platform for further investigating the potential impact of LPS and IFN-γ variations on macrophage effector function.

A systems chemical biology study of malate synthase and isocitrate lyase inhibition in Mycobacterium tuberculosis during active and NRP growth.

The ability of Mycobacterium tuberculosis (Mtb) to survive in low oxygen environments enables the bacterium to persist in a latent state within host tissues. In vitro studies of Mtb growth have identified changes in isocitrate lyase (ICL) and malate synthase (MS) that enable bacterial persistence under low oxygen and other environmentally limiting conditions. Systems chemical biology (SCB) enables us to evaluate the effects of small molecule inhibitors not only on the reaction catalyzed by malate synthase and isocitrate lyase, but the effect on the complete tricarboxylic acid cycle (TCA) by taking into account complex network relationships within that system. To study the kinetic consequences of inhibition on persistent bacilli, we implement a systems-chemical biology (SCB) platform and perform a chemistry-centric analysis of key metabolic pathways believed to impact Mtb latency. We explore consequences of disrupting the function of malate synthase (MS) and isocitrate lyase (ICL) during aerobic and hypoxic non-replicating persistence (NRP) growth by using the SCB method to identify small molecules that inhibit the function of MS and ICL, and simulating the metabolic consequence of the disruption. Results indicate variations in target and non-target reaction steps, clear differences in the normal and low oxygen models, as well as dosage dependent response. Simulation results from singular and combined enzyme inhibition strategies suggest ICL may be the more effective target for chemotherapeutic treatment against Mtb growing in a microenvironment where oxygen is slowly depleted, which may favor persistence.

Computational systems chemical biology.

There is a critical need for improving the level of chemistry awareness in systems biology. The data and information related to modulation of genes and proteins by small molecules continue to accumulate at the same time as simulation tools in systems biology and whole body physiologically based pharmacokinetics (PBPK) continue to evolve. We called this emerging area at the interface between chemical biology and systems biology systems chemical biology (SCB) (Nat Chem Biol 3: 447-450, 2007).The overarching goal of computational SCB is to develop tools for integrated chemical-biological data acquisition, filtering and processing, by taking into account relevant information related to interactions between proteins and small molecules, possible metabolic transformations of small molecules, as well as associated information related to genes, networks, small molecules, and, where applicable, mutants and variants of those proteins. There is yet an unmet need to develop an integrated in silico pharmacology/systems biology continuum that embeds drug-target-clinical outcome (DTCO) triplets, a capability that is vital to the future of chemical biology, pharmacology, and systems biology. Through the development of the SCB approach, scientists will be able to start addressing, in an integrated simulation environment, questions that make the best use of our ever-growing chemical and biological data repositories at the system-wide level. This chapter reviews some of the major research concepts and describes key components that constitute the emerging area of computational systems chemical biology.

Coding theory based models for protein translation initiation in prokaryotic organisms.

Our research explores the feasibility of using communication theory, error control (EC) coding theory specifically, for quantitatively modeling the protein translation initiation mechanism. The messenger RNA (mRNA) of Escherichia coli K-12 is modeled as a noisy (errored), encoded signal and the ribosome as a minimum Hamming distance decoder, where the 16S ribosomal RNA (rRNA) serves as a template for generating a set of valid codewords (the codebook). We tested the E. coli based coding models on 5' untranslated leader sequences of prokaryotic organisms of varying taxonomical relation to E. coli including: Salmonella typhimurium LT2, Bacillus subtilis, and Staphylococcus aureus Mu50. The model identified regions on the 5' untranslated leader where the minimum Hamming distance values of translated mRNA sub-sequences and non-translated genomic sequences differ the most. These regions correspond to the Shine-Dalgarno domain and the non-random domain. Applying the EC coding-based models to B. subtilis, and S. aureus Mu50 yielded results similar to those for E. coli K-12. Contrary to our expectations, the behavior of S. typhimurium LT2, the more taxonomically related to E. coli, resembled that of the non-translated sequence group.