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Arsenate-reducing microorganisms may mobilize arsenic into groundwater. Phylogenetic DNA sequence information cannot exclusively be used to infer arsenate reduction, as the spotted homologies may not translate to biochemical activities. We here develop a cultivation-based metagenomic strategy for inferring actual activities: we complement a cultivation-independent microbial community survey covering 22 arsenic-contaminated drinking water wells in Bangladesh, with the characterization of enrichments of anaerobic arsenate reducers. All investigated samples revealed microbial arsenate reduction activity. The enriched microbial communities were phylogenetically diverse within and between the enrichments. The enrichment also unveiled species: the arsenate-reducing enrichments held 16S rRNA gene sequences closest to Dechloromonas sp., Azonexus fungiphilus, Youngiibacter fragile, Methanogenic prokaryote and Fusibacter sp., in addition to arsenate-reducing Sulfurospirillum strains NP4, b10 and dissimilatory iron-reducing Geobacter species. The enriched arsenate reductase gene (arrA) sequences were closest to sequences in known arsenate reducing Sulfurospirillum barnesii. This study bridges the knowledge gap between cultivation-independent and cultivation-dependent analyses. It clarifies the mechanism for arsenic mobilization and redistribution in rural drinking water wells, as a balance between bioremediation and harmful activities. The identified, potentially dissimilatory arsenate and iron-reducing microbes, constitute a risk for arsenic bioremediation. Appropriate metagenomic and dynamic analyses should help design and maintain safer, effective bioremediation.

Dynamic variables in the non-equilibrium systems of life are determined by catalytic activities. These relate to the expression of the genome. The extent to which such a variable depends on the catalytic activity defined by a gene has become more and more important in view of the possibilities to modulate gene expression or intervene with enzyme function through the use of medicinal drugs. With all the complexity of cellular systems biology, there are still some very simple principles that guide the control of variables such as fluxes, concentrations, and half-times. Using time-unit invariance we here derive a multitude of laws governing the sums of the control coefficients that quantify the control of multiple variables by all the catalytic activities. We show that the sum of the control coefficients of any dynamic variable over all catalytic activities is determined by the control of the same property by time. When the variable is at a maximum, minimum or steady, this limits the sums to simple integers like 0, -1, 1, and -2, depending on the variable under consideration. Some of the implications for biological control are discussed as is the dependence of these results on the precise definition of control.

How the network around ROS protects against oxidative stress and Parkinson’s disease (PD), and how processes at the minutes timescale cause disease and aging after decades, remains enigmatic. Challenging whether the ROS network is as complex as it seems, we built a fairly comprehensive version thereof which we disentangled into a hierarchy of only five simpler subnetworks each delivering one type of robustness. The comprehensive dynamic model described in vitro data sets from two independent laboratories. Notwithstanding its five-fold robustness, it exhibited a relatively sudden breakdown, after some 80 years of virtually steady performance: it predicted aging. PD-related conditions such as lack of DJ-1 protein or increased α-synuclein accelerated the collapse, while antioxidants or caffeine retarded it. Introducing a new concept (aging-time-control coefficient), we found that as many as 25 out of 57 molecular processes controlled aging. We identified new targets for “life-extending interventions”: mitochondrial synthesis, KEAP1 degradation, and p62 metabolism.

Most attention has been focused in recent years with respect to the issues of the medical students suffering from excess of stress during their studies, which may constrain their extent of learning, acquisition of knowledge, and consequently their academic performance. A cross-sectional descriptive study was conducted between July 2016 and June 2017 to explore the activities which helped to reduce the burden of stress among the undergraduate medical students of four government and two private medical colleges in Bangladesh. A self-administered semi-structured questionnaire was used to collect the data on the students' perception. A total of 1145 students included in this study of which 63 % were female. The study revealed that the students' perceptions of stress were highest with respect to the existing policy concerning vacation/holiday (87%), common room facilities (78%), counselling service (64%) and accommodation facilities (60%). The most common coping strategies adopted by the students were discussing problems with family members and friends, gossiping with friends and using social media. Neither alcohol nor drug consumption was mentioned by the students as an effective stress reduction strategy. To generalize the findings, a further longitudinal study including prospective and multi-center focus group discussions along with quantitative research is recommended. Bangladesh Journal of Medical Education Vol.10(1) 2019: 20-24

The growth properties of Azorhizobium caulinodans wild-type and a cytochrome aa

Epigenetics maintains cell-identity specific gene-expression patterns. However, within a population of isogenic cells of the same identity, a substantial variability in gene expression and responsiveness is still observed. Transcription bursting is a substantial source of this gene-expression variability or ‘noise’, contributing to phenotypic heterogeneity and potentially driving both physiological and pathological processes such as differentiation or tumorigenesis and drug resistance. Identification of transcription-bursting dynamics at a genome-wide scale has been restricted to inferring bursts in mRNA production computationally from the heterogeneity of mRNA levels in single cell transcriptomic data. Systematic characterisation of the genomic and epigenetic chromatin context of genes with defined transcription bursting behaviour has been incomplete. Here, we measured the bursting of transcription itself by genome-wide nascent RNA sequencing of breast cancer MCF-7 cells upon synchronisation of transcription with a transcription elongation inhibitor and by calibration using live cell imaging of nascent PP7-tagged GREB1 transcription. Comparing across the entire genome, we find transcription bursting to be ubiquitous, with burst sizes of up to 160 transcripts. Transcription bursting is strongly correlated with steady state gene expression between genes, whereas both burst frequency and nascent transcript degradation only correlate weakly. Individual genes deviate strongly from this trend and engage both in anomalous burst size and frequency. We find that the presence of the TATA box or Inr sequence within gene promoters are significantly associated with a larger burst size, as are promoter-associated YY1 and E2F1 transcription-factor binding motifs. Enrichment of the transcription start site with epigenetic marks such as H3K79me2 and H3K27ac is also strongly associated with the transcription burst size. Finally, we show that in these MCF-7 breast-cancer cells, genes with a larger transcription burst size exhibit a larger immediate transcriptional response following endocrine drug treatment. Our genome-wide transcription-bursting analysis method paves the way to elucidate the dynamic role of epigenetic regulation on dynamic transcription in pathophysiology.

Using standard systems biology methodologies a 14-compartment dynamic model was developed for the Corona virus epidemic. The model predicts that : (i) it will be impossible to limit lockdown intensity such that sufficient herd immunity develops for this epidemic to die down, (ii) the death toll from the SARS-CoV-2 virus decreases very strongly with increasing intensity of the lockdown, but (iii) the duration of the epidemic increases at first with that intensity and then decreases again, such that (iv) it may be best to begin with selecting a lockdown intensity beyond the intensity that leads to the maximum duration, (v) an intermittent lock down strategy should also work and be more acceptable socially and economically, (vi) an initially intensive but adaptive lockdown strategy should be most efficient, both in terms of its low number casualties and shorter duration, (vii) such an adaptive lockdown strategy offers the advantage of being robust to unexpected imports of the virus, e.g. due to international travel, (viii) the eradication strategy may still be superior as it leads to even fewer deaths and a shorter period of economic lockdown maximum, but should have the adaptive strategy as backup in case of unexpected inflation imports (ix) earlier detection of infections is perhaps the most effective way in which the epidemic can be controlled more readily, whilst waiting for vaccines.

International audience; Some biological networks exhibit oscillations in their components to convert stimuli to time-dependent responses. The eukaryotic cell cycle is such a case, being governed by waves of cyclin-dependent kinase (cyclin/Cdk) activities that rise and fall with specific timing and guarantee its timely occurrence. Disruption of cyclin/Cdk oscillations could result in dysfunction through reduced cell division. Therefore, it is of interest to capture properties of network designs that exhibit robust oscillations. Here we show that a minimal yeast cell cycle network is able to oscillate autonomously, and that cyclin/Cdk-mediated positive feedback loops (PFLs) and Clb3-centered regulations sustain cyclin/Cdk oscillations, in known and hypothetical network designs. We propose that Clb3-mediated coordination of cyclin/Cdk waves reconciles checkpoint and oscillatory cell cycle models. Considering the evolutionary conservation of the cyclin/Cdk network across eukaryotes, we hypothesize that functional ("healthy") phenotypes require the capacity to oscillate autonomously whereas dysfunctional (potentially "diseased") phenotypes may lack this capacity. npj Systems Biology and Applications (2020) 6:8 ; https://doi.

Desulfitobacterium hafniense Y51 has been widely used in investigations of perchloroethylene (PCE) biodegradation, but limited information exists on its other physiological capabilities. We investigated how D. hafniense Y51 confronts the debilitating limitations of not having enough electron donor (lactate), or electron acceptor (fumarate) during cultivation in chemostats. The residual concentrations of the substrates supplied in excess were much lower than expected. Transcriptomics, proteomics and fluxomics were integrated to investigate how this phenomenon was regulated. Through diverse regulation at both transcriptional and translational levels, strain Y51 turned to fermenting the excess lactate and disproportionating the excess fumarate under fumarate- and lactate-limiting conditions respectively. Genes and proteins related to the utilization of a variety of alternative electron donors and acceptors absent from the medium were induced, apparently involving the Wood–Ljungdahl pathway. Through this metabolic flexibility, D. hafniense Y51 may be able to switch between different metabolic capabilities under limiting conditions.

The potent transcription inhibitor Actinomycin D is used with several cancers. Here, we report the discovery that this naturally occurring antibiotic inhibits two human neutral aminopeptidases, the cell-surface alanine aminopeptidase and intracellular methionine aminopeptidase type 2. These metallo-containing exopeptidases participate in tumor cell expansion and motility and are targets for anticancer therapies. We show that the peptide portions of Actinomycin D and Actinomycin X2 are not required for effective inhibition, but the loss of these regions changes the mechanism of interaction. Two structurally less complex Actinomycin D analogs containing the phenoxazone chromophores, Questiomycin A and Actinocin, appear to be competitive inhibitors of both aminopeptidases, with potencies similar to the non-competitive macrocyclic parent compound (Ki in the micromolar range). The mode of action for all four compounds and both enzymes was demonstrated by molecular modeling and docking in the corresponding active sites. This knowledge gives new perspectives to Actinomycin D’s action on tumors and suggests new avenues and molecules for medical applications.

Summary Age-related skeletal degeneration in patients with osteoporosis is characterized by decreased bone mass and occurs concomitant with an increase in bone marrow adipocytes. Using microarray expression profiling with high temporal resolution, we identified gene regulatory events in early stages of osteogenic and adipogenic lineage commitment of human mesenchymal stromal cells (hMSCs). Data analysis revealed three distinct phases when cells adopt a committed expression phenotype: initiation of differentiation (0–3 hr, phase I), lineage acquisition (6–24 hr, phase II), and early lineage progression (48–96 hr, phase III). Upstream regulator analysis identified 34 transcription factors (TFs) in phase I with a role in hMSC differentiation. Interestingly, expression levels of identified TFs did not always change and indicate additional post-transcriptional regulatory mechanisms. Functional analysis revealed that forced expression of IRF2 enhances osteogenic differentiation. Thus, IRF2 and other early-responder TFs may control osteogenic cell fate of MSCs and should be considered in mechanistic models that clarify bone-anabolic changes during clinical progression of osteoporosis., Graphical Abstract, Highlights • Determination of high temporal gene expression changes in differentiating hMSCs • Three transcriptional phases during early osteoblast and adipocyte differentiation • Transcription factors that change activity during early hMSC differentiation • IRF2 enhances osteogenic differentiation of hMSCs, In this article, van Leeuwen and colleagues determine high temporal gene expression changes in human mesenchymal stromal cells upon induction of adipocyte and osteoblast differentiation. They identify three early differentiation phases within the first 4 days. Furthermore, novel transcription factors were identified that may control cell fate of hMSCs and should be considered in mechanistic models that clarify bone-anabolic changes.

How cancer cells utilize nutrients to support their growth and proliferation in complex nutritional systems is still an open question. However, it is certainly determined by both genetics and an environmental-specific context. The interactions between them lead to profound metabolic specialization, such as consuming glucose and glutamine and producing lactate at prodigious rates. To investigate whether and how glucose and glutamine availability impact metabolic specialization, we integrated computational modeling on the genome-scale metabolic reconstruction with an experimental study on cell lines. We used the most comprehensive human metabolic network model to date, Recon3D, to build cell line-specific models. RNA-Seq data was used to specify the activity of genes in each cell line and the uptake rates were quantitatively constrained according to nutrient availability. To integrated both constraints we applied a novel method, named GENSI (Gene Expression and Nutrients Simultaneous Integration), that translates the relative importance of gene expression and nutrient availability data into the metabolic fluxes based on an observed experimental feature(s). We applied GENSI to study hepatocellular carcinoma addiction to glucose/glutamine. We were able to identify that proliferation, and lactate production is associated with the presence of glucose but does not necessarily increase with its concentration when the latter exceeds the physiological concentration. There was no such association with glutamine. We show that the integration of gene expression and nutrient availability data into genome-wide models improves the prediction of metabolic phenotypes.

The eminently complex regulatory network protecting the cell against oxidative stress, surfaces in several disease maps, including that of Parkinson’s disease (PD). How this molecular networking achieves its various functionalities and how processes operating at the seconds-minutes time scale cause a disease at a time scale of multiple decennia is enigmatic.By computational analysis, we here disentangle the reactive oxygen species (ROS) regulatory network into a hierarchy of subnetworks that each correspond to a different functionality. The detailed dynamic model of ROS management obtained integrates these functionalities and fitsin vitrodata sets from two different laboratories.The model shows effective ROS-management for a century, followed by a sudden system’s collapse due to the loss of p62 protein. PD related conditions such as lack of DJ-1 protein or increased α-synuclein accelerated the system’s collapse. Variousin-silicointerventions (e.g. addition of antioxidants or caffeine) slowed down the collapse of the systemin silico, suggesting the model may help discover new medicinal and nutritional therapies.

By their definition, inadvertent exposure to endocrine disrupting compounds (EDCs) intervenes with the endocrine signalling system, even at low dose. On the one hand, some EDCs are used as important pharmaceutical drugs that one would not want to dismiss. On the other hand, these pharmaceutical drugs are having off-target effects and increasingly significant exposure to the general population with unwanted health implications. Flutamide, one of the top pharmaceutical products marketed all over the world for the treatment of prostate cancer, is also a pollutant. Its therapeutic action mainly depends on targeting the androgen receptors and inhibiting the androgen action that is essential for growth and survival of prostate tissue. Currently flutamide is of concern with respect to its categorization as an endocrine disruptor. In this work we have developed a physiologically based pharmacokinetic (PBPK) model of flutamide that could serve as a standard tool for its human risk assessment. First we built the model for rat (where many parameters have been measured). The rat PBPK model was extrapolated to human where the re-parameterization involved human-specific physiology, metabolic kinetics derived from in-vitro studies, and the partition coefficient same as the rat model. We have harmonized the model by integrating different sets of in-vitro, in-vivo and physiological data into a PBPK model. Then the model was used to simulate different exposure scenarios and the results were compared against the observed data. Both uncertainty and sensitivity analysis was done. Since this new whole-body PBPK model can predict flutamide concentrations not only in plasma but also in various organs, the model may have clinical applications in efficacy and safety assessment of flutamide. The model can also be used for reverse dosimetry in the context of interpreting the available biomonitoring data to estimate the degree to which the population is currently being exposed, and a tool for the pharmaceutical companies to validate the estimated Permitted Daily Exposure (PDE) for flutamide.

STRENDA DB, freely available at http://www.strenda-db.org, is an online validation and storage system for functional enzyme data that aims at being integrated into the publication practices of the scientific community and into the publication processes of journals. It provides a simple-to-use web submission tool and searchable database allowing the sharing, comparison and accurate reporting of enzyme kinetics data. The submission tool incorporates the STandards for Reporting ENzymology DAta (STRENDA), Guidelines which specify minimum information requested in the reporting of enzyme function data, including kinetic parameter values and full experimental conditions under which they were acquired. STRENDA DB checks the manuscript data entered by the author for compliance with the STRENDA Guidelines. If data is submitted prior to or during the publication process, the submission tool aids the author of a manuscript in the submission of kinetic parameters, ensuring that all required data and metadata are supplied. Data sets compliant with the Guidelines are assigned a STRENDA Registry Number and registered a Direct Object Identifier (DOI), which provides a perennial and resolvable identifier for each dataset. The data will normally be publicly available in STRENDA DB only after the corresponding article has been peer-reviewed and published in a journal. Data can also be submitted after publication. By promoting the practice of simultaneously submitting articles to journals and kinetics data to STRENDA DB, reviewers of journal articles as well as authors and consumers of data will benefit from the availability of standardised data in multiple ways.

With genome sequencing of thousands of organisms, a scaffold has become available for data integration: molecular information can now be organized by attaching it to the genes and their gene-expression products. It is however, the genome that is selfish not the gene, making it necessary to organize the information into maps that enable functional interpretation of the fitness of the genome. Using flux balance analysis one can calculate the theoretical capabilities of the living organism. Here we examine whether according to this genome organized information, organisms such as the ones present when life on Earth began, are able to assimilate the Gibbs energy and carbon that life needs for its reproduction and maintenance, from a relatively poor Gibbs-energy environment. We shall address how Clostridium ljungdahlii may use at least two special features and one special pathway to this end: gear-shifting, electron bifurcation and the Wood-Ljungdahl pathway. Additionally, we examined whether the C. ljungdahlii map can also help solve the problem of waste management. We find that there is a definite effect of the choices of redox equivalents in the Wood-Ljungdahl pathway and the hydrogenase on the yield of interesting products like hydroxybutyrate. We provide a drawing of a subset of the metabolic network that may be utilized to project flux distributions onto by the community in future works. Furthermore, we make all the code leading to the results discussed here publicly available for the benefit of future work.

In the August 2013 issue of this journal, Barzel & Barabási reported a method for reconstructing network topologies. Here we show that the Barzel & Barabási method is a variant of a previously published method, modular response analysis (MRA). We also demonstrate that the implementation of their algorithm using statistical similarity measures as a proxy for global network responses to perturbations is erroneous and its performance is overestimated.

In the second half of 2014 the Manchester Institute of Biotechnology, based in Manchester (UK), hosted the first SupraBiology congress, an event attended by representatives of different academic institutions and industry based in both the UK and China. The congress was aimed to serve as a platform to discuss and promote potential collaborations between the UK and China on the subject of Systems Biology and High Performance Computing. The event, sponsored by the " BBSRC China Partnering Awards " and ISBE, was organised as a sequence of talks addressing the different aspects of Systems Biology that can benefit from High Performance Computing. A general discussion session followed where the scientific, technical , and logistic aspects of the prospected UK-China collaborations were examined. In what follows we summarize the contributions of the different attendees to the congress. In particular, the material hereby presented is organized around five main areas of interest: Systems Biology in medicine Computational methods of Systems Biology Computational Biology for Industry Computational Systems Biology in Education Computational Biology on the Tianhe-2 Supercom-puter The report ends with a discussion section relating the ideas that have been pushed forward to establish collaborations between UK and China

Optimization of experimental conditions is critical in ensuring robust experimental reproducibility. Through detailed metabolomic analysis we found that cell culture conditions significantly impacted on glutaminase (GLS1) sensitivity resulting in variable sensitivity and irreproducibility in data. Baseline metabolite profiling highlighted that untreated cells underwent significant changes in metabolic status. Both the extracellular levels of glutamine and lactate and the intracellular levels of multiple metabolites changed drastically during the assay. We show that these changes compromise the robustness of the assay and make it difficult to reproduce. We discuss the implications of the cells’ metabolic environment when studying the effects of perturbations to cell function by any type of inhibitor. We then devised ‘metabolically rationalized standard’ assay conditions, in which glutaminase-1 inhibition reduced glutamine metabolism differently in both cell lines assayed, and decreased the proliferation of one of them. The adoption of optimized conditions such as the ones described here should lead to an improvement in reproducibility and help eliminate false negatives as well as false positives in these assays.

MotivationMetabolic reprogramming is a general feature of cancer cells. Regrettably, the comprehensive quantification of metabolites in biological specimens does not promptly translate into knowledge on the utilization of metabolic pathways. Computational models hold the promise to bridge this gap, by estimating fluxes across metabolic pathways. Yet they currently portray the average behavior of intermixed subpopulations, masking their inherent heterogeneity known to hinder cancer diagnosis and treatment. If complemented with the information on single-cell transcriptome, now enabled by RNA sequencing (scRNA-seq), metabolic models of cancer populations are expected to empower the characterization of the mechanisms behind metabolic heterogeneity. To this aim, we propose single-cell Flux Balance Analysis (scFBA) as a computational framework to translate sc-transcriptomes into single-cell fluxomes.ResultsWe show that the integration of scRNA-seq profiles of cells derived from lung ade-nocarcinoma and breast cancer patients, into a multi-scale stoichiometric model of cancer population: 1) significantly reduces the space of feasible single-cell fluxomes; 2) allows to identify clusters of cells with different growth rates within the population; 3) points out the possible metabolic interactions among cells via exchange of metabolites.AvailabilityThe scFBA suite of MATLAB functions is available at https://github.com/BIMIB-DISCo/scFBA, as well as the case study datasets.Contactchiara.damiani@unimib.it

This chapter reviews the models of microbial growth that do not relate growth kinetics to cellular biochemistry, but are merely descriptive phenomenological. The new approaches to the modeling of microbial growth have appeared, which aimed specifically at closing the gap between biochemistry and microbial growth kinetics. The chapter discusses the position of these approaches relative to other models of microbial growth. A growing bacterium may be considered as an energy transducer that consumes substrates and produces biomass and other products. It is important to note that for the sake of description, all cells are together represented by one "super-cell". The contents of the vessel containing cells and medium are essentially divided into two compartments: the biotic and the abiotic compartments. The MNET model, with its intermediate complexity, is very well suited to analyze the efficiency of bacterial metabolism and, in particular, to pinpoint via relatively few experimental measurements where the quantitatively important "metabolic leaks" occur.

Classical Thermodynamics restricts engineering. These restrictions are independent of mechanism and kinetics, and thereby inescapable. Forgetting these restrictions can lead to over-optimistic designs for making bio-plastics from waste, and to erroneous ideas on early or new Life on this or other planets. This can be rectified by putting the thermodynamics in place. Is every biochemical network design feasible, provided one puts classical thermodynamics in place? Or, are there other, ill-recognized, generic restrictions to bioengineering? For a while a Non-Equilibrium Thermodynamics (NET) has been trying to discover behaviors of dynamical systems away from equilibrium that are completely independent of kinetics and mechanism and thereby not engineerable. The principles discovered were of limited use to bioengineering however. We here show that processes away from equilibrium must indeed depend on kinetics and mechanism, but, importantly, not on all kinetic and mechanistic details: There are limitations to what the engineering of mechanisms and kinetics can achieve. It is of course better to recognize what is impossible before trying to engineer the impossible. Importantly, the new NET methodology also shows that system properties that are possible, can be engineered only in certain ways. The new NET methodology also enables to understand, and perhaps engineer towards, a performance that, by adjusting the network, remains optimal when conditions are changing. Using our in silico discovery tool, we show that this may indeed occur in the Archeon S. solfataricus. What we call ‘variomatic’ gear shifting is a way that some cells may use to self-engineer their ways to maximal growth rates in environments that lack robust resources, such as in environments with fluctuating oxygen levels. Population heterogeneity is another mechanism that can increase the robustness of a cell factory. We discuss a NET principle that suggests ways in which one can engineer the cells’ diversity. Transcription burst size rather than kinetics should be modulated for making a diverse population perform much better than its average.

The development of drugs that can inactivate disease-causing cells (e.g. cancer cells or parasites) without causing collateral damage to healthy or to host cells is complicated by the fact that many proteins are very similar between organisms. Nevertheless, due to subtle, quantitative differences between the biochemical reaction networks of target cell and host, a drug can limit the flux of the same essential process in one organism more than in another. We identified precise criteria for this ‘network-based’ drug selectivity, which can serve as an alternative or additive to structural differences. We combined computational and experimental approaches to compare energy metabolism in the causative agent of sleeping sickness, Trypanosoma brucei, with that of human erythrocytes, and identified glucose transport and glyceraldehyde-3-phosphate dehydrogenase as the most selective antiparasitic targets. Computational predictions were validated experimentally in a novel parasite-erythrocytes co-culture system. Glucose-transport inhibitors killed trypanosomes without killing erythrocytes, neurons or liver cells.

Mathematical models are key to systems biology where they typically describe the topology and dynamics of biological networks, listing biochemical entities and their relationships with one another. Some (hyper)thermophilic Archaea contain an enzyme, called non-phosphorylating glyceraldehyde-3-phosphate dehydrogenase (GAPN), which catalyzes the direct oxidation of glyceraldehyde-3-phosphate to 3-phosphoglycerate omitting adenosine 5′-triphosphate (ATP) formation by substrate-level-phosphorylation via phosphoglycerate kinase. In this study we formulate three hypotheses that could explain functionally why GAPN exists in these Archaea, and then construct and use mathematical models to test these three hypotheses. We used kinetic parameters of enzymes of Sulfolobus solfataricus (S. solfataricus) which is a thermo-acidophilic archaeon that grows optimally between 60 and 90 °C and between pH 2 and 4. For comparison, we used a model of Saccharomyces cerevisiae (S. cerevisiae), an organism that can live at moderate temperatures. We find that both the first hypothesis, i.e., that the glyceraldehyde-3-phosphate dehydrogenase (GAPDH) plus phosphoglycerate kinase (PGK) route (the alternative to GAPN) is thermodynamically too much uphill and the third hypothesis, i.e., that GAPDH plus PGK are required to carry the flux in the gluconeogenic direction, are correct. The second hypothesis, i.e., that the GAPDH plus PGK route delivers less than the 1 ATP per pyruvate that is delivered by the GAPN route, is only correct when GAPDH reaction has a high rate and 1,3-bis-phosphoglycerate (BPG) spontaneously degrades to 3PG at a high rate. OA gold

Systems Biology has established numerous approaches for mechanistic modeling of molecular networks in the cell and a legacy of models. The current frontier is the integration of models expressed in different formalisms to address the multi-scale biological system organization challenge. We present MUFINS (MUlti-Formalism Interaction Network Simulator) software, implementing a unique set of approaches for multi-formalism simulation of interaction networks. We extend the constraint-based modeling (CBM) framework by incorporation of linear inhibition constraints, enabling for the first time linear modeling of networks simultaneously describing gene regulation, signaling and whole-cell metabolism at steady state. We present a use case where a logical hypergraph model of a regulatory network is expressed by linear constraints and integrated with a Genome-Scale Metabolic Network (GSMN) of mouse macrophage. We experimentally validate predictions, demonstrating application of our software in an iterative cycle of hypothesis generation, validation and model refinement. MUFINS incorporates an extended version of our Quasi-Steady State Petri Net approach to integrate dynamic models with CBM, which we demonstrate through a dynamic model of cortisol signaling integrated with the human Recon2 GSMN and a model of nutrient dynamics in physiological compartments. Finally, we implement a number of methods for deriving metabolic states from ~omics data, including our new variant of the iMAT congruency approach. We compare our approach with iMAT through the analysis of 262 individual tumor transcriptomes, recovering features of metabolic reprogramming in cancer. The software provides graphics user interface with network visualization, which facilitates use by researchers who are not experienced in coding and mathematical modeling environments.