Emulation of Quantitative Systems Pharmacology models to accelerate virtual population inference in immuno-oncology.

Quantitative Systems Pharmacology (QSP) models are increasingly being applied for target discovery and dose selection in immuno-oncology (IO). Typical application involves virtual trial, a simulation of a virtual population of hundreds of model instances with model inputs reflecting individual variability. While the structure of the model and initial parameterisation are based on literature describing the underlying biology, calibration of the virtual population by existing clinical data is frequently required to create tumour and patient population specific model instances. Since comparison of a virtual trial with clinical output requires hundreds of large-scale, non-linear model evaluations, the inference of a virtual population is computationally expensive, frequently becoming a bottleneck. Here, we present novel approach to virtual population inference in IO using emulation of the QSP model and an objective function based on Kolmogorov-Smirnov statistics to maximise congruence of simulated and observed clinical tumour size distributions. We sample the parameter space of a QSP IO model to collect a set of tumour growth time profiles. We evaluate performance of several machine learning approaches in interpolating these time profiles and create a surrogate model, which computes tumor growth profiles faster than the original model and allows examination of tens of millions of virtual patients. We use the surrogate model to infer a virtual population maximising congruence with the waterfall plot of a pembrolizumab clinical trial. We believe that our approach is applicable not only in QSP IO, but also in other applications where virtual populations need to be inferred for computationally expensive mechanistic models.

Mitochondrial Metabolism in the Spotlight: Maintaining Balanced RNAP III Activity Ensures Cellular Homeostasis.

RNA polymerase III (RNAP III) holoenzyme activity and the processing of its products have been linked to several metabolic dysfunctions in lower and higher eukaryotes. Alterations in the activity of RNAP III-driven synthesis of non-coding RNA cause extensive changes in glucose metabolism. Increased RNAP III activity in the S. cerevisiae maf1Δ strain is lethal when grown on a non-fermentable carbon source. This lethal phenotype is suppressed by reducing tRNA synthesis. Neither the cause of the lack of growth nor the underlying molecular mechanism have been deciphered, and this area has been awaiting scientific explanation for a decade. Our previous proteomics data suggested mitochondrial dysfunction in the strain. Using model mutant strains maf1Δ (with increased tRNA abundance) and rpc128-1007 (with reduced tRNA abundance), we collected data showing major changes in the TCA cycle metabolism of the mutants that explain the phenotypic observations. Based on 13C flux data and analysis of TCA enzyme activities, the present study identifies the flux constraints in the mitochondrial metabolic network. The lack of growth is associated with a decrease in TCA cycle activity and downregulation of the flux towards glutamate, aspartate and phosphoenolpyruvate (PEP), the metabolic intermediate feeding the gluconeogenic pathway. rpc128-1007, the strain that is unable to increase tRNA synthesis due to a mutation in the C128 subunit, has increased TCA cycle activity under non-fermentable conditions. To summarize, cells with non-optimal activity of RNAP III undergo substantial adaptation to a new metabolic state, which makes them vulnerable under specific growth conditions. Our results strongly suggest that balanced, non-coding RNA synthesis that is coupled to glucose signaling is a fundamental requirement to sustain a cell's intracellular homeostasis and flexibility under changing growth conditions. The presented results provide insight into the possible role of RNAP III in the mitochondrial metabolism of other cell types.

The prognostic significance of specific HOX gene expression patterns in ovarian cancer.

HOX genes are vital for all aspects of mammalian growth and differentiation, and their dysregulated expression is related to ovarian carcinogenesis. The aim of the current study was to establish the prognostic value of HOX dysregulation as well as its role in platinum resistance. The potential to target HOX proteins through the HOX/PBX interaction was also explored in the context of platinum resistance. HOX gene expression was determined in ovarian cancer cell lines and primary EOCs by QPCR, and compared to expression in normal ovarian epithelium and fallopian tube tissue samples. Statistical analysis included one-way ANOVA and t-tests, using statistical software R and GraphPad. The analysis identified 36 of the 39 HOX genes as being overexpressed in high grade serous EOC compared to normal tissue. We detected a molecular HOX gene-signature that predicted poor outcome. Overexpression of HOXB4 and HOXB9 was identified in high grade serous cell lines after platinum resistance developed. Targeting the HOX/PBX dimer with the HXR9 peptide enhanced the cytotoxicity of cisplatin in platinum-resistant ovarian cancer. In conclusion, this study has shown the HOX genes are highly dysregulated in ovarian cancer with high expression of HOXA13, B6, C13, D1 and D13 being predictive of poor clinical outcome. Targeting the HOX/PBX dimer in platinum-resistant cancer represents a potentially new therapeutic option that should be further developed and tested in clinical trials.

Lipid metabolism and Type VII secretion systems dominate the genome scale virulence profile of Mycobacterium tuberculosis in human dendritic cells.

BACKGROUND: Mycobacterium tuberculosis continues to kill more people than any other bacterium. Although its archetypal host cell is the macrophage, it also enters, and survives within, dendritic cells (DCs). By modulating the behaviour of the DC, M. tuberculosis is able to manipulate the host's immune response and establish an infection. To identify the M. tuberculosis genes required for survival within DCs we infected primary human DCs with an M. tuberculosis transposon library and identified mutations with a reduced ability to survive. RESULTS: Parallel sequencing of the transposon inserts of the surviving mutants identified a large number of genes as being required for optimal intracellular fitness in DCs. Loci whose mutation attenuated intracellular survival included those involved in synthesising cell wall lipids, not only the well-established virulence factors, pDIM and cord factor, but also sulfolipids and PGL, which have not previously been identified as having a direct virulence role in cells. Other attenuated loci included the secretion systems ESX-1, ESX-2 and ESX-4, alongside many PPE genes, implicating a role for ESX-5. In contrast the canonical ESAT-6 family of ESX substrates did not have intra-DC fitness costs suggesting an alternative ESX-1 associated virulence mechanism. With the aid of a gene-nutrient interaction model, metabolic processes such as cholesterol side chain catabolism, nitrate reductase and cysteine-methionine metabolism were also identified as important for survival in DCs. CONCLUSION: We conclude that many of the virulence factors required for survival in DC are shared with macrophages, but that survival in DCs also requires several additional functions, such as cysteine-methionine metabolism, PGLs, sulfolipids, ESX systems and PPE genes.

Mycobacterium leprae genomes from a British medieval leprosy hospital: towards understanding an ancient epidemic.

BACKGROUND: Leprosy has afflicted humankind throughout history leaving evidence in both early texts and the archaeological record. In Britain, leprosy was widespread throughout the Middle Ages until its gradual and unexplained decline between the 14th and 16th centuries. The nature of this ancient endemic leprosy and its relationship to modern strains is only partly understood. Modern leprosy strains are currently divided into 5 phylogenetic groups, types 0 to 4, each with strong geographical links. Until recently, European strains, both ancient and modern, were thought to be exclusively type 3 strains. However, evidence for type 2 strains, a group normally associated with Central Asia and the Middle East, has recently been found in archaeological samples in Scandinavia and from two skeletons from the medieval leprosy hospital (or leprosarium) of St Mary Magdalen, near Winchester, England. RESULTS: Here we report the genotypic analysis and whole genome sequencing of two further ancient M. leprae genomes extracted from the remains of two individuals, Sk14 and Sk27, that were excavated from 10th-12th century burials at the leprosarium of St Mary Magdalen. DNA was extracted from the surfaces of bones showing osteological signs of leprosy. Known M. leprae polymorphisms were PCR amplified and Sanger sequenced, while draft genomes were generated by enriching for M. leprae DNA, and Illumina sequencing. SNP-typing and phylogenetic analysis of the draft genomes placed both of these ancient strains in the conserved type 2 group, with very few novel SNPs compared to other ancient or modern strains. CONCLUSIONS: The genomes of the two newly sequenced M. leprae strains group firmly with other type 2F strains. Moreover, the M. leprae strain most closely related to one of the strains, Sk14, in the worldwide phylogeny is a contemporaneous ancient St Magdalen skeleton, vividly illustrating the epidemic and clonal nature of leprosy at this site. The prevalence of these type 2 strains indicates that type 2F strains, in contrast to later European and associated North American type 3 isolates, may have been the co-dominant or even the predominant genotype at this location during the 11th century.

QSSPN: dynamic simulation of molecular interaction networks describing gene regulation, signalling and whole-cell metabolism in human cells.

MOTIVATION: Dynamic simulation of genome-scale molecular interaction networks will enable the mechanistic prediction of genotype-phenotype relationships. Despite advances in quantitative biology, full parameterization of whole-cell models is not yet possible. Simulation methods capable of using available qualitative data are required to develop dynamic whole-cell models through an iterative process of modelling and experimental validation. RESULTS: We formulate quasi-steady state Petri nets (QSSPN), a novel method integrating Petri nets and constraint-based analysis to predict the feasibility of qualitative dynamic behaviours in qualitative models of gene regulation, signalling and whole-cell metabolism. We present the first dynamic simulations including regulatory mechanisms and a genome-scale metabolic network in human cell, using bile acid homeostasis in human hepatocytes as a case study. QSSPN simulations reproduce experimentally determined qualitative dynamic behaviours and permit mechanistic analysis of genotype-phenotype relationships. AVAILABILITY AND IMPLEMENTATION: The model and simulation software implemented in C++ are available in supplementary material and at http://sysbio3.fhms.surrey.ac.uk/qsspn/.

Diverse control of metabolism and other cellular processes in Streptomyces coelicolor by the PhoP transcription factor: genome-wide identification of in vivo targets.

Streptomycetes sense and respond to the stress of phosphate starvation via the two-component PhoR-PhoP signal transduction system. To identify the in vivo targets of PhoP we have undertaken a chromatin-immunoprecipitation-on-microarray analysis of wild-type and phoP mutant cultures and, in parallel, have quantified their transcriptomes. Most (ca. 80%) of the previously in vitro characterized PhoP targets were identified in this study among several hundred other putative novel PhoP targets. In addition to activating genes for phosphate scavenging systems PhoP was shown to target two gene clusters for cell wall/extracellular polymer biosynthesis. Furthermore PhoP was found to repress an unprecedented range of pathways upon entering phosphate limitation including nitrogen assimilation, oxidative phosphorylation, nucleotide biosynthesis and glycogen catabolism. Moreover, PhoP was shown to target many key genes involved in antibiotic production and morphological differentiation, including afsS, atrA, bldA, bldC, bldD, bldK, bldM, cdaR, cdgA, cdgB and scbR-scbA. Intriguingly, in the PhoP-dependent cpk polyketide gene cluster, PhoP accumulates substantially at three specific sites within the giant polyketide synthase-encoding genes. This study suggests that, following phosphate limitation, Streptomyces coelicolor PhoP functions as a 'master' regulator, suppressing central metabolism, secondary metabolism and developmental pathways until sufficient phosphate is salvaged to support further growth and, ultimately, morphological development.

Equation-free analysis of two-component system signalling model reveals the emergence of co-existing phenotypes in the absence of multistationarity.

Phenotypic differences of genetically identical cells under the same environmental conditions have been attributed to the inherent stochasticity of biochemical processes. Various mechanisms have been suggested, including the existence of alternative steady states in regulatory networks that are reached by means of stochastic fluctuations, long transient excursions from a stable state to an unstable excited state, and the switching on and off of a reaction network according to the availability of a constituent chemical species. Here we analyse a detailed stochastic kinetic model of two-component system signalling in bacteria, and show that alternative phenotypes emerge in the absence of these features. We perform a bifurcation analysis of deterministic reaction rate equations derived from the model, and find that they cannot reproduce the whole range of qualitative responses to external signals demonstrated by direct stochastic simulations. In particular, the mixed mode, where stochastic switching and a graded response are seen simultaneously, is absent. However, probabilistic and equation-free analyses of the stochastic model that calculate stationary states for the mean of an ensemble of stochastic trajectories reveal that slow transcription of either response regulator or histidine kinase leads to the coexistence of an approximate basal solution and a graded response that combine to produce the mixed mode, thus establishing its essential stochastic nature. The same techniques also show that stochasticity results in the observation of an all-or-none bistable response over a much wider range of external signals than would be expected on deterministic grounds. Thus we demonstrate the application of numerical equation-free methods to a detailed biochemical reaction network model, and show that it can provide new insight into the role of stochasticity in the emergence of phenotypic diversity.

QSP Designer: Quantitative systems pharmacology modeling with modular biological process map notation and multiple language code generation.

Typical Quantitative Systems Pharmacology (QSP) workflows involve discussion of biology, supported by graphical diagrams, followed by construction of large Ordinary Differential Equation models. QSP Designer facilitates this process by providing enhanced graphical notation, which enables hierarchical presentation with modules and handling of combinatorial complexity with diagram node arrays. Whereas the software includes a simulation engine, a major feature is full model code generation in MATLAB, R, C, and Julia to support multiple modeling communities.

Learn-confirm in model-informed drug development: Assessing an immunogenicity quantitative systems pharmacology platform.

Immunogenicity against therapeutic proteins frequently causes attrition owing to its potential impact on pharmacokinetics, pharmacodynamics, efficacy, and safety. Predicting immunogenicity is complex because of its multifactorial drivers, including compound properties, subject characteristics, and treatment parameters. To integrate these, the Immunogenicity Simulator was developed using published, predominantly late-stage trial data from 15 therapeutic proteins. This single-blinded evaluation with subject-level data from 10 further monoclonals assesses the Immunogenicity Simulator's credibility for application during the drug development process.

CalcTav--integration of a spreadsheet and Taverna workbench.

MOTIVATION: Taverna workbench is an environment for construction, visualization and execution of bioinformatic workflows that integrates specialized tools available on the Internet. It already supports major bioinformatics services and is constantly gaining popularity. However, its user interface requires considerable effort to learn, and sometimes requires programming or scripting experience from its users. We have integrated Taverna with OpenOffice Calc, making the functions of the scientific workflow system available in the spreadsheet. In CalcTav, one can define workflows using the spreadsheet interface and analyze the results using the spreadsheet toolset. RESULTS: Technically, CalcTav is a plugin for OpenOffice Calc, which provides the functionality of Taverna available in the form of spreadsheet functions. Even basic familiarity with spreadsheets already suffices to define and use spreadsheet workflows with Taverna services. The data processed by the Taverna components is automatically transferred to and from spreadsheet cells, so all the visualization and data analysis tools of OpenOffice Calc are available to the workflow creator within one, consistent user interface. AVAILABILITY: CalcTav is available under GPLv2 from http://code.google.com/p/calctav/ CONTACT: sroka@mimuw.edu.pl.

SurreyFBA: a command line tool and graphics user interface for constraint-based modeling of genome-scale metabolic reaction networks.

UNLABELLED: Constraint-based modeling of genome-scale metabolic networks has been successfully used in numerous applications such as prediction of gene essentiality and metabolic engineering. We present SurreyFBA, which provides constraint-based simulations and network map visualization in a free, stand-alone software. In addition to basic simulation protocols, the tool also implements the analysis of minimal substrate and product sets, which is useful for metabolic engineering and prediction of nutritional requirements in complex in vivo environments, but not available in other commonly used programs. The SurreyFBA is based on a command line interface to the GLPK solver distributed as binary and source code for the three major operating systems. The command line tool, implemented in C++, is easily executed within scripting languages used in the bioinformatics community and provides efficient implementation of tasks requiring iterative calls to the linear programming solver. SurreyFBA includes JyMet, a graphics user interface allowing spreadsheet-based model presentation, visualization of numerical results on metabolic networks represented in the Petri net convention, as well as in charts and plots. AVAILABILITY: SurreyFBA is distributed under GNU GPL license and available from http://sysbio3.fhms.surrey.ac.uk/SurreyFBA.zip.

Differential producibility analysis (DPA) of transcriptomic data with metabolic networks: deconstructing the metabolic response of M. tuberculosis.

A general paucity of knowledge about the metabolic state of Mycobacterium tuberculosis within the host environment is a major factor impeding development of novel drugs against tuberculosis. Current experimental methods do not allow direct determination of the global metabolic state of a bacterial pathogen in vivo, but the transcriptional activity of all encoded genes has been investigated in numerous microarray studies. We describe a novel algorithm, Differential Producibility Analysis (DPA) that uses a metabolic network to extract metabolic signals from transcriptome data. The method utilizes Flux Balance Analysis (FBA) to identify the set of genes that affect the ability to produce each metabolite in the network. Subsequently, Rank Product Analysis is used to identify those metabolites predicted to be most affected by a transcriptional signal. We first apply DPA to investigate the metabolic response of E. coli to both anaerobic growth and inactivation of the FNR global regulator. DPA successfully extracts metabolic signals that correspond to experimental data and provides novel metabolic insights. We next apply DPA to investigate the metabolic response of M. tuberculosis to the macrophage environment, human sputum and a range of in vitro environmental perturbations. The analysis revealed a previously unrecognized feature of the response of M. tuberculosis to the macrophage environment: a down-regulation of genes influencing metabolites in central metabolism and concomitant up-regulation of genes that influence synthesis of cell wall components and virulence factors. DPA suggests that a significant feature of the response of the tubercle bacillus to the intracellular environment is a channeling of resources towards remodeling of its cell envelope, possibly in preparation for attack by host defenses. DPA may be used to unravel the mechanisms of virulence and persistence of M. tuberculosis and other pathogens and may have general application for extracting metabolic signals from other "-omics" data.

Mechanistic Models of COVID-19: Insights into Disease Progression, Vaccines, and Therapeutics.

The COVID-19 pandemic has severely impacted health systems and economies worldwide. Significant global efforts are therefore ongoing to improve vaccine efficacies, optimize vaccine deployment, and develop new antiviral therapies to combat the pandemic. Mechanistic viral dynamics and quantitative systems pharmacology models of SARS-CoV-2 infection, vaccines, immunomodulatory agents, and antiviral therapeutics have played a key role in advancing our understanding of SARS-CoV-2 pathogenesis and transmission, the interplay between innate and adaptive immunity to influence the outcomes of infection, effectiveness of treatments, mechanisms and performance of COVID-19 vaccines, and the impact of emerging SARS-CoV-2 variants. Here, we review some of the critical insights provided by these models and discuss the challenges ahead.

Acorn: a grid computing system for constraint based modeling and visualization of the genome scale metabolic reaction networks via a web interface.

BACKGROUND: Constraint-based approaches facilitate the prediction of cellular metabolic capabilities, based, in turn on predictions of the repertoire of enzymes encoded in the genome. Recently, genome annotations have been used to reconstruct genome scale metabolic reaction networks for numerous species, including Homo sapiens, which allow simulations that provide valuable insights into topics, including predictions of gene essentiality of pathogens, interpretation of genetic polymorphism in metabolic disease syndromes and suggestions for novel approaches to microbial metabolic engineering. These constraint-based simulations are being integrated with the functional genomics portals, an activity that requires efficient implementation of the constraint-based simulations in the web-based environment. RESULTS: Here, we present Acorn, an open source (GNU GPL) grid computing system for constraint-based simulations of genome scale metabolic reaction networks within an interactive web environment. The grid-based architecture allows efficient execution of computationally intensive, iterative protocols such as Flux Variability Analysis, which can be readily scaled up as the numbers of models (and users) increase. The web interface uses AJAX, which facilitates efficient model browsing and other search functions, and intuitive implementation of appropriate simulation conditions. Research groups can install Acorn locally and create user accounts. Users can also import models in the familiar SBML format and link reaction formulas to major functional genomics portals of choice. Selected models and simulation results can be shared between different users and made publically available. Users can construct pathway map layouts and import them into the server using a desktop editor integrated within the system. Pathway maps are then used to visualise numerical results within the web environment. To illustrate these features we have deployed Acorn and created a web server allowing constraint based simulations of the genome scale metabolic reaction networks of E. coli, S. cerevisiae and M. tuberculosis. CONCLUSIONS: Acorn is a free software package, which can be installed by research groups to create a web based environment for computer simulations of genome scale metabolic reaction networks. It facilitates shared access to models and creation of publicly available constraint based modelling resources.

Application of quantitative systems pharmacology to guide the optimal dosing of COVID-19 vaccines.

Optimal use and distribution of coronavirus disease 2019 (COVID-19) vaccines involves adjustments of dosing. Due to the rapidly evolving pandemic, such adjustments often need to be introduced before full efficacy data are available. As demonstrated in other areas of drug development, quantitative systems pharmacology (QSP) is well placed to guide such extrapolation in a rational and timely manner. Here, we propose for the first time how QSP can be applied in the context of COVID-19 vaccine development.

Quantitative Systems Pharmacology Approaches for Immuno-Oncology: Adding Virtual Patients to the Development Paradigm.

Drug development in oncology commonly exploits the tools of molecular biology to gain therapeutic benefit through reprograming of cellular responses. In immuno-oncology (IO) the aim is to direct the patient's own immune system to fight cancer. After remarkable successes of antibodies targeting PD1/PD-L1 and CTLA4 receptors in targeted patient populations, the focus of further development has shifted toward combination therapies. However, the current drug-development approach of exploiting a vast number of possible combination targets and dosing regimens has proven to be challenging and is arguably inefficient. In particular, the unprecedented number of clinical trials testing different combinations may no longer be sustainable by the population of available patients. Further development in IO requires a step change in selection and validation of candidate therapies to decrease development attrition rate and limit the number of clinical trials. Quantitative systems pharmacology (QSP) proposes to tackle this challenge through mechanistic modeling and simulation. Compounds' pharmacokinetics, target binding, and mechanisms of action as well as existing knowledge on the underlying tumor and immune system biology are described by quantitative, dynamic models aiming to predict clinical results for novel combinations. Here, we review the current QSP approaches, the legacy of mathematical models available to quantitative clinical pharmacologists describing interaction between tumor and immune system, and the recent development of IO QSP platform models. We argue that QSP and virtual patients can be integrated as a new tool in existing IO drug development approaches to increase the efficiency and effectiveness of the search for novel combination therapies.

Metabolic and evolutionary insights into the closely-related species Streptomyces coelicolor and Streptomyces lividans deduced from high-resolution comparative genomic hybridization.

BACKGROUND: Whilst being closely related to the model actinomycete Streptomyces coelicolor A3(2), S. lividans 66 differs from it in several significant and phenotypically observable ways, including antibiotic production. Previous comparative gene hybridization studies investigating such differences have used low-density (one probe per gene) PCR-based spotted arrays. Here we use new experimentally optimised 104,000 × 60-mer probe arrays to characterize in detail the genomic differences between wild-type S. lividans 66, a derivative industrial strain, TK24, and S. coelicolor M145. RESULTS: The high coverage and specificity (detection of three nucleotide differences) of the new microarrays used has highlighted the macroscopic genomic differences between two S. lividans strains and S. coelicolor. In a series of case studies we have validated the microarray and have identified subtle changes in genomic structure which occur in the Asp-activating adenylation domains of CDA non-ribosomal peptide synthetase genes which provides evidence of gene shuffling between these domains. We also identify single nucleotide sequence inter-species differences which exist in the actinorhodin biosynthetic gene cluster. As the glyoxylate bypass is non-functional in both S. lividans strains due to the absence of the gene encoding isocitrate lyase it is likely that the ethylmalonyl-CoA pathway functions as the alternative mechanism for the assimilation of C2 compounds. CONCLUSIONS: This study provides evidence for widespread genetic recombination, rather than it being focussed at 'hotspots', suggesting that the previously proposed 'archipelago model' of genomic differences between S. coelicolor and S. lividans is unduly simplistic. The two S. lividans strains investigated differ considerably in genetic complement, with TK24 lacking 175 more genes than its wild-type parent when compared to S. coelicolor. Additionally, we confirm the presence of bldB in S. lividans and deduce that S. lividans 66 and TK24, both deficient in the glyoxylate bypass, possess an alternative metabolic mechanism for the assimilation of C2 compounds. Given that streptomycetes generally display high genetic instability it is envisaged that these high-density arrays will find application for rapid assessment of genome content (particularly amplifications/deletions) in mutational studies of S. coelicolor and related species.

Modeling and simulation of the main metabolism in Escherichia coli and its several single-gene knockout mutants with experimental verification.

BACKGROUND: It is quite important to simulate the metabolic changes of a cell in response to the change in culture environment and/or specific gene knockouts particularly for the purpose of application in industry. If this could be done, the cell design can be made without conducting exhaustive experiments, and one can screen out the promising candidates, proceeded by experimental verification of a select few of particular interest. Although several models have so far been proposed, most of them focus on the specific metabolic pathways. It is preferred to model the whole of the main metabolic pathways in Escherichia coli, allowing for the estimation of energy generation and cell synthesis, based on intracellular fluxes and that may be used to characterize phenotypic growth. RESULTS: In the present study, we considered the simulation of the main metabolic pathways such as glycolysis, TCA cycle, pentose phosphate (PP) pathway, and the anapleorotic pathways using enzymatic reaction models of E. coli. Once intracellular fluxes were computed by this model, the specific ATP production rate, the specific CO2 production rate, and the specific NADPH production rate could be estimated. The specific ATP production rate thus computed was used for the estimation of the specific growth rate. The CO2 production rate could be used to estimate cell yield, and the specific NADPH production rate could be used to determine the flux of the oxidative PP pathway. The batch and continuous cultivations were simulated where the changing patterns of extracellular and intra-cellular metabolite concentrations were compared with experimental data. Moreover, the effects of the knockout of such pathways as Ppc, Pck and Pyk on the metabolism were simulated. It was shown to be difficult for the cell to grow in Ppc mutant due to low concentration of OAA, while Pck mutant does not necessarily show this phenomenon. The slower growth rate of the Ppc mutant was properly estimated by taking into account the lower specific ATP production rate. In the case of Pyk mutant, the enzyme level regulation was made clear such that Pyk knockout caused PEP concentration to be up-regulated and activated Ppc, which caused the increase in MAL concentration and backed up reduced PYR through Mez, resulting in the phenotypic growth characteristics similar to the wild type. CONCLUSIONS: It was shown to be useful to simulate the main metabolism of E. coli for understanding metabolic changes inside the cell in response to specific pathway gene knockouts, considering the whole main metabolic pathways. The comparison of the simulation result with the experimental data indicates that the present model could simulate the effect of the specific gene knockouts to the changes in the metabolisms to some extent.

Integration of Omics Data Sources to Inform Mechanistic Modeling of Immune-Oncology Therapies: A Tutorial for Clinical Pharmacologists.

Application of contemporary molecular biology techniques to clinical samples in oncology resulted in the accumulation of unprecedented experimental data. These "omics" data are mined for discovery of therapeutic target combinations and diagnostic biomarkers. It is less appreciated that omics resources could also revolutionize development of the mechanistic models informing clinical pharmacology quantitative decisions about dose amount, timing, and sequence. We discuss the integration of omics data to inform mechanistic models supporting drug development in immuno-oncology. To illustrate our arguments, we present a minimal clinical model of the Cancer Immunity Cycle (CIC), calibrated for non-small cell lung carcinoma using tumor microenvironment composition inferred from transcriptomics of clinical samples. We review omics data resources, which can be integrated to parameterize mechanistic models of the CIC. We propose that virtual trial simulations with clinical Quantitative Systems Pharmacology platforms informed by omics data will be making increasing impact in the development of cancer immunotherapies.