Modelling nitrogen assimilation of Escherichia coli at low ammonium concentration

Integrated metabonomic-proteomic analysis reveals the effect of glucose stress on metabolic adaptation of Lactococcus lactis ssp. lactis CICC23200

A combined proteomic and metabonomic approach was used to investigate the metabolism of Lactococcus lactis ssp. lactis subjected to glucose stress treatment. A proteomic method was used to determine 1,427 altered proteins, including 278 proteins with increased expression and 255 proteins with decreased expression. A metabonomic approach was adopted to identify 98 altered metabolites, including 62 metabolites with increased expression and 26 metabolites with decreased expression. The integrated analysis indicated that the RNA and DNA mismatch repair process and energy metabolism were enhanced in response to high-glucose stress in L. lactis. Lactococcus lactis responded to glucose stress by up-regulating oxidoreductase activity, which acted on glycosyl bonds, hydrolase activity, and organic acid transmembrane transporter activity. This led to an improvement in the metabolic flux from glucose to pyruvate, lactate, acetate, and maltose. Down-regulation of amino acid transmembrane transporter, aminoacyl-transfer RNA ligase, hydroxymethyl-, formyl-, and related transferase activities resulted in a decrease in the nitrogen metabolism-associated metabolic pathway, which might be related to inhibition of the production of biogenic amines. Overall, we highlight the response of metabolism to glucose stress and provide potential possibilities for the reduced formation of biogenic amines in improved level of sugar in the dairy fermentation industry. Moreover, according to the demand for industrial production, sugar concentration in fermented foods should be higher, or lower, than a set value that is dependent on bacterial strain and biogenic amine yield.

Ranking network mechanisms by how they fit diverse experiments and deciding on E. coli's ammonium transport and assimilation network

The complex ammonium transport and assimilation network of E. coli involves the ammonium transporter AmtB, the regulatory proteins GlnK and GlnB, and the central N-assimilating enzymes together with their highly complex interactions. The engineering and modelling of such a complex network seem impossible because functioning depends critically on a gamut of data known at patchy accuracy. We developed a way out of this predicament, which employs: (i) a constrained optimization-based technology for the simultaneous fitting of models to heterogeneous experimental data sets gathered through diverse experimental set-ups, (ii) a 'rubber band method' to deal with different degrees of uncertainty, both in experimentally determined or estimated parameter values and in measured transient or steady-state variables (training data sets), (iii) integration of human expertise to decide on accuracies of both parameters and variables, (iv) massive computation employing a fast algorithm and a supercomputer, (v) an objective way of quantifying the plausibility of models, which makes it possible to decide which model is the best and how much better that model is than the others. We applied the new technology to the ammonium transport and assimilation network, integrating recent and older data of various accuracies, from different expert laboratories. The kinetic model objectively ranked best, has E. coli's AmtB as an active transporter of ammonia to be assimilated with GlnK minimizing the futile cycling that is an inevitable consequence of intracellular ammonium accumulation. It is 130 times better than a model with facilitated passive transport of ammonia.

Parameter estimation of qualitative biological regulatory networks on high performance computing hardware

BACKGROUND

Biological Regulatory Networks (BRNs) are responsible for developmental and maintenance related functions in organisms. These functions are implemented by the dynamics of BRNs and are sensitive to regulations enforced by specific activators and inhibitors. The logical modeling formalism by René Thomas incorporates this sensitivity with a set of logical parameters modulated by available regulators, varying with time. With the increase in complexity of BRNs in terms of number of entities and their interactions, the task of parameters estimation becomes computationally expensive with existing sequential SMBioNET tool. We extend the existing sequential implementation of SMBioNET by using a data decomposition approach using a Java messaging library called MPJ Express. The approach divides the parameters space into different regions and each region is then explored in parallel on High Performance Computing (HPC) hardware.

RESULTS

The performance of the parallel approach is evaluated on BRNs of different sizes, and experimental results on multicore and cluster computers showed almost linear speed-up. This parallel code can be executed on a wide range of concurrent hardware including laptops equipped with multicore processors, and specialized distributed memory computer systems. To demonstrate the application of parallel implementation, we selected a case study of Hexosamine Biosynthetic Pathway (HBP) in cancer progression to identify potential therapeutic targets against cancer. A set of logical parameters were computed for HBP model that directs the biological system to a state of recovery. Furthermore, the parameters also suggest a potential therapeutic intervention that restores homeostasis. Additionally, the performance of parallel application was also evaluated on a network (comprising of 23 entities) of Fibroblast Growth Factor Signalling in Drosophila melanogaster.

CONCLUSIONS

Qualitative modeling framework is widely used for investigating dynamics of biological regulatory networks. However, computation of model parameters in qualitative modeling is computationally intensive. In this work, we presented results of our Java based parallel implementation that provides almost linear speed-up on both multicore and cluster platforms. The parallel implementation is available at https://psmbionet.github.io .

COPASI and its applications in biotechnology

COPASI is software used for the creation, modification, simulation and computational analysis of kinetic models in various fields. It is open-source, available for all major platforms and provides a user-friendly graphical user interface, but is also controllable via the command line and scripting languages. These are likely reasons for its wide acceptance. We begin this review with a short introduction describing the general approaches and techniques used in computational modeling in the biosciences. Next we introduce the COPASI package, and its capabilities, before looking at typical applications of COPASI in biotechnology.

Development of an accurate kinetic model for the central carbon metabolism of Escherichia coli

Background

A kinetic model provides insights into the dynamic response of biological systems and predicts how their complex metabolic and gene regulatory networks generate particular functions. Of many biological systems, Escherichia coli metabolic pathways have been modeled extensively at the enzymatic and genetic levels, but existing models cannot accurately reproduce experimental behaviors in a batch culture, due to the inadequate estimation of a specific cell growth rate and a large number of unmeasured parameters.

Results

In this study, we developed a detailed kinetic model for the central carbon metabolism of E. coli in a batch culture, which includes the glycolytic pathway, tricarboxylic acid cycle, pentose phosphate pathway, Entner-Doudoroff pathway, anaplerotic pathway, glyoxylate shunt, oxidative phosphorylation, phosphotransferase system (Pts), non-Pts and metabolic gene regulations by four protein transcription factors: cAMP receptor, catabolite repressor/activator, pyruvate dehydrogenase complex repressor and isocitrate lyase regulator. The kinetic parameters were estimated by a constrained optimization method on a supercomputer. The model estimated a specific growth rate based on reaction kinetics and accurately reproduced the dynamics of wild-type E. coli and multiple genetic mutants in a batch culture.

Conclusions

This model overcame the intrinsic limitations of existing kinetic models in a batch culture, predicted the effects of multilayer regulations (allosteric effectors and gene expression) on central carbon metabolism and proposed rationally designed fast-growing cells based on understandings of molecular processes.

Electronic supplementary material

The online version of this article (doi:10.1186/s12934-016-0511-x) contains supplementary material, which is available to authorized users.

Nitrogen assimilation in Escherichia coli: putting molecular data into a systems perspective

We present a comprehensive overview of the hierarchical network of intracellular processes revolving around central nitrogen metabolism in Escherichia coli. The hierarchy intertwines transport, metabolism, signaling leading to posttranslational modification, and transcription. The protein components of the network include an ammonium transporter (AmtB), a glutamine transporter (GlnHPQ), two ammonium assimilation pathways (glutamine synthetase [GS]-glutamate synthase [glutamine 2-oxoglutarate amidotransferase {GOGAT}] and glutamate dehydrogenase [GDH]), the two bifunctional enzymes adenylyl transferase/adenylyl-removing enzyme (ATase) and uridylyl transferase/uridylyl-removing enzyme (UTase), the two trimeric signal transduction proteins (GlnB and GlnK), the two-component regulatory system composed of the histidine protein kinase nitrogen regulator II (NRII) and the response nitrogen regulator I (NRI), three global transcriptional regulators called nitrogen assimilation control (Nac) protein, leucine-responsive regulatory protein (Lrp), and cyclic AMP (cAMP) receptor protein (Crp), the glutaminases, and the nitrogen-phosphotransferase system. First, the structural and molecular knowledge on these proteins is reviewed. Thereafter, the activities of the components as they engage together in transport, metabolism, signal transduction, and transcription and their regulation are discussed. Next, old and new molecular data and physiological data are put into a common perspective on integral cellular functioning, especially with the aim of resolving counterintuitive or paradoxical processes featured in nitrogen assimilation. Finally, we articulate what still remains to be discovered and what general lessons can be learned from the vast amounts of data that are available now.

Mathematical model of the binding of allosteric effectors to the Escherichia coli PII signal transduction protein GlnB

PII proteins are important regulators of nitrogen metabolism in a wide variety of organisms: the binding of the allosteric effectors ATP, ADP, and 2-oxoglutarate (2-OG) to PII proteins affects their ability to interact with target proteins. We modeled the simultaneous binding of ATP, ADP, and 2-OG to one PII protein, namely GlnB of Escherichia coli, using a modeling approach that allows the prediction of the proportions of individual binding states. Four models with different binding rules were compared. We selected one of these models (that assumes that the binding of the first nucleotide to GlnB makes it harder for subsequent nucleotides to bind) and used it to explore how physiological concentrations of ATP, ADP, and 2-OG would affect the proportions of those states of GlnB that interact with the target proteins ATase and NtrB. Our simulations indicate that GlnB can, as suggested by previous researchers, act as a sensor of both 2-OG and the ATP:ADP ratio. We conclude that our modeling approach will be an important tool in future studies concerning the PII binding states and their interactions with target proteins.

On parameter synthesis by parallel model checking

An important problem in current computational systems biology is to analyze models of biological systems dynamics under parameter uncertainty. This paper presents a novel algorithm for parameter synthesis based on parallel model checking. The algorithm is conceptually universal with respect to the modeling approach employed. We introduce the algorithm, show its scalability, and examine its applicability on several biological models.