Community detection in the human connectome: Method types, differences and their impact on inference.

Community structure is a fundamental topological characteristic of optimally organized brain networks. Currently, there is no clear standard or systematic approach for selecting the most appropriate community detection method. Furthermore, the impact of method choice on the accuracy and robustness of estimated communities (and network modularity), as well as method-dependent relationships between network communities and cognitive and other individual measures, are not well understood. This study analyzed large datasets of real brain networks (estimated from resting-state fMRI from n $$ n $$ = 5251 pre/early adolescents in the adolescent brain cognitive development [ABCD] study), and n $$ n $$ = 5338 synthetic networks with heterogeneous, data-inspired topologies, with the goal to investigate and compare three classes of community detection methods: (i) modularity maximization-based (Newman and Louvain), (ii) probabilistic (Bayesian inference within the framework of stochastic block modeling (SBM)), and (iii) geometric (based on graph Ricci flow). Extensive comparisons between methods and their individual accuracy (relative to the ground truth in synthetic networks), and reliability (when applied to multiple fMRI runs from the same brains) suggest that the underlying brain network topology plays a critical role in the accuracy, reliability and agreement of community detection methods. Consistent method (dis)similarities, and their correlations with topological properties, were estimated across fMRI runs. Based on synthetic graphs, most methods performed similarly and had comparable high accuracy only in some topological regimes, specifically those corresponding to developed connectomes with at least quasi-optimal community organization. In contrast, in densely and/or weakly connected networks with difficult to detect communities, the methods yielded highly dissimilar results, with Bayesian inference within SBM having significantly higher accuracy compared to all others. Associations between method-specific modularity and demographic, anthropometric, physiological and cognitive parameters showed mostly method invariance but some method dependence as well. Although method sensitivity to different levels of community structure may in part explain method-dependent associations between modularity estimates and parameters of interest, method dependence also highlights potential issues of reliability and reproducibility. These findings suggest that a probabilistic approach, such as Bayesian inference in the framework of SBM, may provide consistently reliable estimates of community structure across network topologies. In addition, to maximize robustness of biological inferences, identified network communities and their cognitive, behavioral and other correlates should be confirmed with multiple reliable detection methods.

Optimizing Renewable Ammonia Production for a Sustainable Fertilizer Supply Chain Transition.

Local renewable ammonia production using electrolytic hydrogen is an emerging approach to alleviate emissions attributed to synthetic nitrogen fertilizer production while also insulating against fluctuations in fertilizer prices and mitigating transportation costs and emissions. However, replacing ammonia currently produced using fossil fuels will not be immediate. To this end, we develop a supply chain transition model, which first optimizes the design and hourly operation of new renewable ammonia facilities to minimize production costs and then optimizes the annual installation timing, production scale, and location of these new renewable facilities along with ammonia transportation to meet county resolution demands. The objective is to augment and eventually replace conventional ammonia market imports in an economically competitive manner. We performed a case study for Minnesota's ammonia supply chain and found that a full transition to in-state renewable production by 2032 is optimal. This is incentivized by the U.S. federal government's clean hydrogen production credits. This transition results in 99 % reduction in carbon intensity along with stable supply costs below $475 per metric tonne. New renewable production facilities are an order of magnitude smaller than existing conventional plants. They use both wind and solar resources and operate dynamically to minimize expensive battery and hydrogen storage capacities.

A hybrid mechanistic-empirical model for in silico mammalian cell bioprocess simulation.

In cell culture processes cell growth and metabolism drive changes in the chemical environment of the culture. These environmental changes elicit reactor control actions, cell growth response, and are sensed by cell signaling pathways that influence metabolism. The interplay of these forces shapes the culture dynamics through different stages of cell cultivation and the outcome greatly affects process productivity, product quality, and robustness. Developing a systems model that describes the interactions of those major players in the cell culture system can lead to better process understanding and enhance process robustness. Here we report the construction of a hybrid mechanistic-empirical bioprocess model which integrates a mechanistic metabolic model with subcomponent models for cell growth, signaling regulation, and the bioreactor environment for in silico exploration of process scenarios. Model parameters were optimized by fitting to a dataset of cell culture manufacturing process which exhibits variability in metabolism and productivity. The model fitting process was broken into multiple steps to mitigate the substantial numerical challenges related to the first-principles model components. The optimized model captured the dynamics of metabolism and the variability of the process runs with different kinetic profiles and productivity. The variability of the process was attributed in part to the metabolic state of cell inoculum. The model was then used to identify potential mitigation strategies to reduce process variability by altering the initial process conditions as well as to explore the effect of changing CO2 removal capacity in different bioreactor scales on process performance. By incorporating a mechanistic model of cell metabolism and appropriately fitting it to a large dataset, the hybrid model can describe the different metabolic phases in culture and the variability in manufacturing runs. This approach of employing a hybrid model has the potential to greatly facilitate process development and reactor scaling.

Model-Driven Engineering of N-Linked Glycosylation in Chinese Hamster Ovary Cells.

Chinese hamster ovary (CHO) cells are used for industrial production of protein-based therapeutics (i.e., "biologics"). Here we describe a method for combining systems-level kinetic models with a synthetic biology platform for multigene overexpression to rationally perturb N-linked glycosylation. Specifically, we sought to increase galactose incorporation on a secreted Immunoglobulin G (IgG) protein. We rationally design, build, and test a total of 23 transgenic cell pools that express single or three-gene glycoengineering cassettes comprising a total of 100 kilobases of engineered DNA sequence. Through iterative engineering and model refinement, we rationally increase the fraction of bigalactosylated glycans five-fold from 11.9% to 61.9% and simultaneously decrease the glycan heterogeneity on the secreted IgG. Our approach allows for rapid hypothesis testing and identification of synergistic behavior from genetic perturbations by bridging systems and synthetic biology.

Kinetic model optimization and its application to mitigating the Warburg effect through multiple enzyme alterations.

Pathway engineering is a powerful tool in biotechnological and clinical applications. However, many phenomena cannot be rewired with a single enzyme change, and in a complex network like energy metabolism, the selection of combinations of targets to engineer is a daunting task. To facilitate this process, we have developed an optimization framework and applied it to a mechanistic kinetic model of energy metabolism. We then identified combinations of enzyme alternations that led to the elimination of the Warburg effect seen in the metabolism of cancer cells and cell lines, a phenomenon coupling rapid proliferation to lactate production. Typically, optimization approaches use integer variables to achieve the desired flux redistribution with a minimum number of altered genes. This framework uses convex penalty terms to replace these integer variables and improve computational tractability. Optimal solutions are identified which substantially reduce or eliminate lactate production while maintaining the requirements for cellular proliferation using three or more enzymes.

Topology Effects on Sparse Control of Complex Networks with Laplacian Dynamics.

Ease of control of complex networks has been assessed extensively in terms of structural controllability and observability, and minimum control energy criteria. Here we adopt a sparsity-promoting feedback control framework for undirected networks with Laplacian dynamics and distinct topological features. The control objective considered is to minimize the effect of disturbance signals, magnitude of control signals and cost of feedback channels. We show that depending on the cost of feedback channels, different complex network structures become the least expensive option to control. Specifically, increased cost of feedback channels favors organized topological complexity such as modularity and centralization. Thus, although sparse and heterogeneous undirected networks may require larger numbers of actuators and sensors for structural controllability, networks with Laplacian dynamics are shown to be easier to control when accounting for the cost of feedback channels.

An integrated platform for mucin-type O-glycosylation network generation and visualization.

Mucin-type O-glycans have profound effects on the structure and stability of glycoproteins. O-Glycans on the cell surface proteins also modulate the cell's interactions with the surrounding environments and other cells. The synthetic pathway of O-glycans involves a large number of enzymes with diverse substrate specificity. The expression pattern of these enzymes is cell and tissue-specific, thus making the pathway highly diverse. To facilitate pathway analysis in a cell and tissue-specific fashion, we developed an integrated platform of RING (Rule Input Network Generator) and O-GlycoVis. RING uses an English-like reaction language to describe the substrate specificity of enzymes and additional constraints on the formation of the glycan products. Using this information, the RING generates a list of possible glycans, which is used as input into O-Glycovis. O-GlycoVis displays the glycan distribution in the pathway and potential reaction paths leading to each glycan. With the input glycan data, O-GlycoVis also traces all possible reaction paths leading to each glycan and outputs pathway maps with the relative abundance levels of glycans overlaid. O-Glycan profiles from two breast cancer cell lines, MCF7 and T47d, human umbilical vascular endothelium cells, Chinese Hamster Ovary cells were generated based on transcriptional data and compared with experimentally observed O-glycans. This RING-based program allows rules to be added or subtracted for network generation and visualization of networks of O-glycosylation network of different tissues and species.

Zeolitic imidazolate framework membranes made by ligand-induced permselectivation.

Zeolitic imidazolate framework (ZIF) membranes are emerging as a promising energy-efficient separation technology. However, their reliable and scalable manufacturing remains a challenge. We demonstrate the fabrication of ZIF nanocomposite membranes by means of an all-vapor-phase processing method based on atomic layer deposition (ALD) of ZnO in a porous support followed by ligand-vapor treatment. After ALD, the obtained nanocomposite exhibits low flux and is not selective, whereas after ligand-vapor (2-methylimidazole) treatment, it is partially transformed to ZIF and shows stable performance with high mixture separation factor for propylene over propane (an energy-intensive high-volume separation) and high propylene flux. Membrane synthesis through ligand-induced permselectivation of a nonselective and impermeable deposit is shown to be simple and highly reproducible and holds promise for scalability.

Automated network generation and analysis of biochemical reaction pathways using RING.

This paper describes how Rule Input Network Generator (RING), a network generation computational tool, can be adopted to generate a variety of complex biochemical reaction networks. The reaction language incorporated in RING allows representation of chemical compounds in biological systems with various structural complexity. Complex molecules such as oligosaccharides in glycosylation pathways can be described using a simplified representation of their monosaccharide building blocks and glycosidic bonds. The automated generation and topological network analysis features in RING also allow for: (1) constructing biochemical reaction networks in a rule-based manner, (2) generating graphical representations of the networks, (3) querying molecules containing a particular structural pattern, (4) finding the shortest synthetic pathways to a user-specified species, and (5) performing enzyme knockout to study their effect on the reaction network. Case studies involving three biochemical reaction systems: (1) Synthesis of 2-ketoglutarate from xylose in bacterial cells, (2) N-glycosylation in mammalian cells, and (3) O-glycosylation in mammalian cells are presented to demonstrate the capabilities of RING for robust and exhaustive network generation and the advantages of its post-processing features.

Multiplicity of steady states in glycolysis and shift of metabolic state in cultured mammalian cells.

Cultured mammalian cells exhibit elevated glycolysis flux and high lactate production. In the industrial bioprocesses for biotherapeutic protein production, glucose is supplemented to the culture medium to sustain continued cell growth resulting in the accumulation of lactate to high levels. In such fed-batch cultures, sometimes a metabolic shift from a state of high glycolysis flux and high lactate production to a state of low glycolysis flux and low lactate production or even lactate consumption is observed. While in other cases with very similar culture conditions, the same cell line and medium, cells continue to produce lactate. A metabolic shift to lactate consumption has been correlated to the productivity of the process. Cultures that exhibited the metabolic shift to lactate consumption had higher titers than those which didn't. However, the cues that trigger the metabolic shift to lactate consumption state (or low lactate production state) are yet to be identified. Metabolic control of cells is tightly linked to growth control through signaling pathways such as the AKT pathway. We have previously shown that the glycolysis of proliferating cells can exhibit bistability with well-segregated high flux and low flux states. Low lactate production (or lactate consumption) is possible only at a low glycolysis flux state. In this study, we use mathematical modeling to demonstrate that lactate inhibition together with AKT regulation on glycolysis enzymes can profoundly influence the bistable behavior, resulting in a complex steady-state topology. The transition from the high flux state to the low flux state can only occur in certain regions of the steady state topology, and therefore the metabolic fate of the cells depends on their metabolic trajectory encountering the region that allows such a metabolic state switch. Insights from such switch behavior present us with new means to control the metabolism of mammalian cells in fed-batch cultures.

Mechanism for multiplicity of steady states with distinct cell concentration in continuous culture of mammalian cells.

Continuous culture for the production of biopharmaceutical proteins offers the possibility of steady state operations and thus more consistent product quality and increased productivity. Under some conditions, multiplicity of steady states has been observed in continuous cultures of mammalian cells, wherein with the same dilution rate and feed nutrient composition, steady states with very different cell and product concentrations may be reached. At those different steady states, cells may exhibit a high glycolysis flux with high lactate production and low cell concentration, or a low glycolysis flux with low lactate and high cell concentration. These different steady states, with different cell concentration, also have different productivity. Developing a mechanistic understanding of the occurrence of steady state multiplicity and devising a strategy to steer the culture toward the desired steady state is critical. We establish a multi-scale kinetic model that integrates a mechanistic intracellular metabolic model and cell growth model in a continuous bioreactor. We show that steady state multiplicity exists in a range of dilution rate in continuous culture as a result of the bistable behavior in glycolysis. The insights from the model were used to devise strategies to guide the culture to the desired steady state in the multiple steady state region. The model provides a guideline principle in the design of continuous culture processes of mammalian cells.

Bistability in glycolysis pathway as a physiological switch in energy metabolism.

The flux of glycolysis is tightly controlled by feed-back and feed-forward allosteric regulations to maintain the body's glucose homeostasis and to respond to cell's growth and energetic needs. Using a mathematical model based on reported mechanisms for the allosteric regulations of the enzymes, we demonstrate that glycolysis exhibits multiple steady state behavior segregating glucose metabolism into high flux and low flux states. Two regulatory loops centering on phosphofructokinase and on pyruvate kinase each gives rise to the bistable behavior, and together impose more complex flux control. Steady state multiplicity endows glycolysis with a robust switch to transit between the two flux states. Under physiological glucose concentrations the glycolysis flux does not move between the states easily without an external stimulus such as hormonal, signaling or oncogenic cues. Distinct combination of isozymes in glycolysis gives different cell types the versatility in their response to different biosynthetic and energetic needs. Insights from the switch behavior of glycolysis may reveal new means of metabolic intervention in the treatment of cancer and other metabolic disorders through suppression of glycolysis.

Stochastic simulations of the tetracycline operon.

BACKGROUND: The tetracycline operon is a self-regulated system. It is found naturally in bacteria where it confers resistance to antibiotic tetracycline. Because of the performance of the molecular elements of the tetracycline operon, these elements are widely used as parts of synthetic gene networks where the protein production can be efficiently turned on and off in response to the presence or the absence of tetracycline. In this paper, we investigate the dynamics of the tetracycline operon. To this end, we develop a mathematical model guided by experimental findings. Our model consists of biochemical reactions that capture the biomolecular interactions of this intriguing system. Having in mind that small biological systems are subjects to stochasticity, we use a stochastic algorithm to simulate the tetracycline operon behavior. A sensitivity analysis of two critical parameters embodied this system is also performed providing a useful understanding of the function of this system. RESULTS: Simulations generate a timeline of biomolecular events that confer resistance to bacteria against tetracycline. We monitor the amounts of intracellular TetR2 and TetA proteins, the two important regulatory and resistance molecules, as a function of intrecellular tetracycline. We find that lack of one of the promoters of the tetracycline operon has no influence on the total behavior of this system inferring that this promoter is not essential for Escherichia coli. Sensitivity analysis with respect to the binding strength of tetracycline to repressor and of repressor to operators suggests that these two parameters play a predominant role in the behavior of the system. The results of the simulations agree well with experimental observations such as tight repression, fast gene expression, induction with tetracycline, and small intracellular TetR2 amounts. CONCLUSIONS: Computer simulations of the tetracycline operon afford augmented insight into the interplay between its molecular components. They provide useful explanations of how the components and their interactions have evolved to best serve bacteria carrying this operon. Therefore, simulations may assist in designing novel gene network architectures consisting of tetracycline operon components.

Model reduction of multiscale chemical langevin equations: a numerical case study.

Two very important characteristics of biological reaction networks need to be considered carefully when modeling these systems. First, models must account for the inherent probabilistic nature of systems far from the thermodynamic limit. Often, biological systems cannot be modeled with traditional continuous-deterministic models. Second, models must take into consideration the disparate spectrum of time scales observed in biological phenomena, such as slow transcription events and fast dimerization reactions. In the last decade, significant efforts have been expended on the development of stochastic chemical kinetics models to capture the dynamics of biomolecular systems, and on the development of robust multiscale algorithms, able to handle stiffness. In this paper, the focus is on the dynamics of reaction sets governed by stiff chemical Langevin equations, i.e., stiff stochastic differential equations. These are particularly challenging systems to model, requiring prohibitively small integration step sizes. We describe and illustrate the application of a semianalytical reduction framework for chemical Langevin equations that results in significant gains in computational cost.

Non-linear reduction for kinetic models of metabolic reaction networks.

Kinetic models of metabolic networks are essential for predicting and optimizing the transient behavior of cells in culture. However, such models are inherently high dimensional and stiff due to the large number of species and reactions involved and to kinetic rate constants of widely different orders of magnitude. In this paper we address the problem of deriving non-stiff, reduced-order non-linear models of the dominant dynamics of metabolic networks with fast and slow reactions. We present a method, based on singular perturbation analysis, which allows the systematic identification of quasi-steady-state conditions for the fast reactions, and the derivation of explicit non-linear models of the slow dynamics independent of the fast reaction rate expressions. The method is successfully applied to detailed models of metabolism in human erythrocytes and Saccharomyces cerevisiae.

On the dynamics of nonlinear systems with input constraints.

In this work we deal with the dynamical analysis of nonlinear systems with input constraints. A characterization of the domain of attraction of the region of controllability of an equilibrium point under bounded control is provided and the concept of regions of invariance within such domains of attraction is introduced and characterized. The concepts and results are illustrated through case studies on chemical reactor models. (c) 1999 American Institute of Physics.