Complex Biochemical Networks Research Articles

Integrative Metabolome and Proteome Analysis of Cerebrospinal Fluid in Parkinson’s Disease

Parkinson’s disease (PD) is a common neurodegenerative disorder characterized by the loss of dopaminergic neurons in the substantia nigra. Recent studies have highlighted the significant role of cerebrospinal fluid (CSF) in reflecting pathophysiological PD brain conditions by analyzing the components of CSF. Based on the published literature, we created a single network with altered metabolites in the CSF of patients with PD. We analyzed biological functions related to the transmembrane of mitochondria, respiration of mitochondria, neurodegeneration, and PD using a bioinformatics tool. As the proteome reflects phenotypes, we collected proteome data based on published papers, and the biological function of the single network showed similarities with that of the metabolomic network. Then, we analyzed the single network of integrated metabolome and proteome. In silico predictions based on the single network with integrated metabolomics and proteomics showed that neurodegeneration and PD were predicted to be activated. In contrast, mitochondrial transmembrane activity and respiration were predicted to be suppressed in the CSF of patients with PD. This review underscores the importance of integrated omics analyses in deciphering PD’s complex biochemical networks underlying neurodegeneration.

A topological mechanism for robust and efficient global oscillations in biological networks

Long and stable timescales are often observed in complex biochemical networks, such as in emergent oscillations. How these robust dynamics persist remains unclear, given the many stochastic reactions and shorter time scales demonstrated by underlying components. We propose a topological model that produces long oscillations around the network boundary, reducing the system dynamics to a lower-dimensional current in a robust manner. Using this to model KaiC, which regulates the circadian rhythm in cyanobacteria, we compare the coherence of oscillations to that in other KaiC models. Our topological model localizes currents on the system edge, with an efficient regime of simultaneously increased precision and decreased cost. Further, we introduce a new predictor of coherence from the analysis of spectral gaps, and show that our model saturates a global thermodynamic bound. Our work presents a new mechanism and parsimonious description for robust emergent oscillations in complex biological networks.

MakeSBML: a tool for converting between Antimony and SBML.

We describe a web-based tool, MakeSBML (https://sys-bio.github.io/makesbml/), that provides an installation-free application for creating, editing, and searching the Biomodels repository for SBML-based models. MakeSBML is a client-based web application that translates models expressed in human-readable Antimony to the System Biology Markup Language (SBML) and vice-versa. Since MakeSBML is a web-based application it requires no installation on the user's part. Currently, MakeSBML is hosted on a GitHub page where the client-based design makes it trivial to move to other hosts. This model for software deployment also reduces maintenance costs since an active server is not required. The SBML modeling language is often used in systems biology research to describe complex biochemical networks and makes reproducing models much easier. However, SBML is designed to be computer-readable, not human-readable. We therefore employ the human-readable Antimony language to make it easy to create and edit SBML models.

MetaLo: metabolic analysis of Logical models extracted from molecular interaction maps.

Molecular interaction maps (MIMs) are static graphical representations depicting complex biochemical networks that can be formalized using one of the Systems Biology Graphical Notation languages. Regardless of their extensive coverage of various biological processes, they are limited in terms of dynamic insights. However, MIMs can serve as templates for developing dynamic computational models. We present MetaLo, an open-source Python package that enables the coupling of Boolean models inferred from process description MIMs with generic core metabolic networks. MetaLo provides a framework to study the impact of signaling cascades, gene regulation processes, and metabolic flux distribution of central energy production pathways. MetaLo computes the Boolean model's asynchronous asymptotic behavior, through the identification of trap-spaces, and extracts metabolic constraints to contextualize the generic metabolic network. MetaLo is able to handle large-scale Boolean models and genome-scale metabolic models without requiring kinetic information or manual tuning. The framework behind MetaLo enables in depth analysis of the regulatory model, and may allow tackling a lack of omics data in poorly addressed biological fields to contextualize generic metabolic networks along with improper automatic reconstructions of cell- and/or disease-specific metabolic networks. MetaLo is available at https://pypi.org/project/metalo/ under the terms of the GNU General Public License v3.

Design Principles for Biological Adaptation: A Systems and Control-Theoretic Treatment.

Establishing a mapping between (from and to) the functionality of interest and the underlying network structure (design principles) remains a crucial step toward understanding and design of bio-systems. Perfect adaptation is one such crucial functionality that enables every living organism to regulate its essential activities in the presence of external disturbances. Previous approaches to deducing the design principles for adaptation have either relied on computationally burdensome brute-force methods or rule-based design strategies detecting only a subset of all possible adaptive network structures. This chapter outlines a scalable and generalizable method inspired by systems theory that unravels an exhaustive set of adaptation-capable structures. We first use the well-known performance parameters to characterize perfect adaptation. These performance parameters are then mapped back to a few parameters (poles, zeros, gain) characteristic of the underlying dynamical system constituted by the rate equations. Therefore, the performance parameters evaluated for the scenario of perfect adaptation can be expressed as a set of precise mathematical conditions involving the system parameters. Finally, we use algebraic graph theory to translate these abstract mathematical conditions to certain structural requirements for adaptation. The proposed algorithm does not assume any particular dynamics and is applicable to networks of any size. Moreover, the results offer a significant advancement in the realm of understanding and designing complex biochemical networks.

Metabolic flux phenotyping of secondary metabolism in cyanobacteria.

Cyanobacteria generate energy from photosynthesis and produce various secondary metabolites with diverse commercial and pharmaceutical applications. Unique metabolic and regulatory pathways in cyanobacteria present new challenges for researchers to enhance their product yields, titers, and rates. Therefore, further advancements are critically needed to establish cyanobacteria as a preferred bioproduction platform. Metabolic flux analysis (MFA) quantitatively determines the intracellular flows of carbon within complex biochemical networks, which elucidate the control of metabolic pathways by transcriptional, translational, and allosteric regulatory mechanisms. The emerging field of systems metabolic engineering (SME) involves the use of MFA and other omics technologies to guide the rational development of microbial production strains. This review highlights the potential of MFA and SME to optimize the production of cyanobacterial secondary metabolites and discusses the technical challenges that lie ahead.

Analytical refractory period distribution for a class of time-variant biochemical systems with second-order reactions.

Refractory period (RP), the waiting time between signals, can induce complex signaling dynamics, such as acceleration, adaptation, and oscillation, within many cellular biochemical networks. However, its underlying molecular mechanisms are still unclear. Rigorously estimating the RP distribution may be essential to identify its causal regulatory mechanisms. Traditional methods of estimating the RP distribution depend on solving the underlying Chemical Master Equations (CMEs), the dominant modeling formalism of biochemical systems. However, exact solutions of the CME are only known for simple reaction systems with zero- and first-order reactions or specific systems with second-order reactions. General solutions still need to be derived for systems with bimolecular reactions. It is even more challenging if large state-space and nonconstant reaction rates are involved. Here, we developed a direct method to gain the analytical RP distribution for a class of second-order reaction systems with nonconstant reaction rates and large state space. Instead of using the CME, we used an equivalent path-wise representation, which is the solution to a transformed martingale problem of the CME. This allowed us to bypass solving a CME. We then applied the method to derive the analytical RP distribution of a real complex biochemical network with second-order reactions, the Drosophila phototransduction cascade. Our approach provides an alternative to the CMEs in deriving the analytical RP distributions of a class of second-order reaction systems. Since the bimolecular reactions are common in biological systems, our approach could enhance understanding real-world biochemical processes.

Biomimetic Approaches in Cardiac Tissue Engineering: Replicating the Native Heart Microenvironment.

Cardiovascular diseases, including heart failure, pose significant challenges in medical practice, necessitating innovative approaches for cardiac repair and regeneration. Cardiac tissue engineering has emerged as a promising solution, aiming to develop functional and physiologically relevant cardiac tissue constructs. Replicating the native heart microenvironment, with its complex and dynamic milieu necessary for cardiac tissue growth and function, is crucial in tissue engineering. Biomimetic strategies that closely mimic the natural heart microenvironment have gained significant interest due to their potential to enhance synthetic cardiac tissue functionality and therapeutic applicability. Biomimetic approaches focus on mimicking biochemical cues, mechanical stimuli, coordinated electrical signaling, and cell-cell/cell-matrix interactions of cardiac tissue. By combining bioactive ligands, controlled delivery systems, appropriate biomaterial characteristics, electrical signals, and strategies to enhance cell interactions, biomimetic approaches provide a more physiologically relevant environment for tissue growth. The replication of the native cardiac microenvironment enables precise regulation of cellular responses, tissue remodeling, and the development of functional cardiac tissue constructs. Challenges and future directions include refining complex biochemical signaling networks, paracrine signaling, synchronized electrical networks, and cell-cell/cell-matrix interactions. Advancements in biomimetic approaches hold great promise for cardiovascular regenerative medicine, offering potential therapeutic strategies and revolutionizing cardiac disease modeling. These approaches contribute to the development of more effective treatments, personalized medicine, and improved patient outcomes. Ongoing research and innovation in biomimetic approaches have the potential to revolutionize regenerative medicine and cardiac disease modeling by replicating the native heart microenvironment, advancing functional cardiac tissue engineering, and improving patient outcomes.

Thermoring-based heat activation switches in the TRPV1 biothermometer

Non-covalent interactions in bio-macromolecules are individually weak but collectively important. How they take a concerted action in a complex biochemical reaction network to realize their thermal stability and activity is still challenging to study. Here graph theory was used to investigate how the temperature-dependent non-covalent interactions as identified in the 3D structures of the thermo-gated capsaicin receptor TRPV1 could form a systemic fluidic grid-like mesh network with topological grids constrained as the thermo-rings to govern heat-sensing. The results showed that the heat-evoked melting of the biggest grid initiated a matched temperature threshold to release the lipid from the active vanilloid site for channel activation. Meanwhile, smaller grids were required to stabilize heat efficacy. Altogether, the change in the total grid sizes upon the change in the total noncovalent interactions along the lipid-dependent gating pathway was necessary for the matched temperature sensitivity. Therefore, this grid thermodynamic model may be broadly significant for the structural thermostability and the functional thermoactivity of bio-macromolecules.

Quantifying Information of Dynamical Biochemical Reaction Networks.

A large number of complex biochemical reaction networks are included in the gene expression, cell development, and cell differentiation of in vivo cells, among other processes. Biochemical reaction-underlying processes are the ones transmitting information from cellular internal or external signaling. However, how this information is measured remains an open question. In this paper, we apply the method of information length, based on the combination of Fisher information and information geometry, to study linear and nonlinear biochemical reaction chains, respectively. Through a lot of random simulations, we find that the amount of information does not always increase with the length of the linear reaction chain; instead, the amount of information varies significantly when this length is not very large. When the length of the linear reaction chain reaches a certain value, the amount of information hardly changes. For nonlinear reaction chains, the amount of information changes not only with the length of this chain, but also with reaction coefficients and rates, and this amount also increases with the length of the nonlinear reaction chain. Our results will help to understand the role of the biochemical reaction networks in cells.

Crosstalk between biochemical signalling network architecture and trafficking governs AMPAR dynamics in synaptic plasticity.

Synaptic plasticity involves modification of both biochemical and structural components of neurons. Many studies have revealed that the change in the number density of the glutamatergic receptor AMPAR at the synapse is proportional to synaptic weight update; an increase in AMPAR corresponds to strengthening of synapses while a decrease in AMPAR density weakens synaptic connections. The dynamics of AMPAR are thought to be regulated by upstream signalling, primarily the calcium-CaMKII pathway, trafficking to and from the synapse, and influx from extrasynaptic sources. Previous work in the field of deterministic modelling of CaMKII dynamics has assumed bistable kinetics, while experiments and rule-based modelling have revealed that CaMKII dynamics can be either monostable or ultrasensitive. This raises the following question: how does the choice of model assumptions involving CaMKII dynamics influence AMPAR dynamics at the synapse? To answer this question, we have developed a set of models using compartmental ordinary differential equations to systematically investigate contributions of different signalling and trafficking variations, along with their coupled effects, on AMPAR dynamics at the synaptic site. We find that the properties of the model including network architecture describing different stability features of CaMKII and parameters that capture the endocytosis and exocytosis of AMPAR significantly affect the integration of fast upstream species by slower downstream species. Furthermore, we predict that the model outcome, as determined by bound AMPAR at the synaptic site, depends on (1) the choice of signalling model (bistable CaMKII or monostable CaMKII dynamics), (2) trafficking versus influx contributions and (3) frequency of stimulus. KEY POINTS: The density of AMPA receptors (AMPARs) at the postsynaptic density of the synapse provides a readout of synaptic plasticity, which involves crosstalk between complex biochemical signalling networks including CaMKII dynamics and trafficking pathways including exocytosis and endocytosis. Here we build a model that integrates CaMKII dynamics and AMPAR trafficking to explore this crosstalk. We compare different models of CaMKII that result in monostable or bistable kinetics and their impact on AMPAR dynamics. Our results show that AMPAR density depends on the coupling between aspects of biochemical signalling and trafficking. Specifically, assumptions regarding CaMKII dynamics and its stability features can alter AMPAR density at the synapse. Our model also predicts that the kinetics of trafficking versus influx of AMPAR from the extrasynaptic space can further impact AMPAR density. Thus, the contributions of both signalling and trafficking should be considered in computational models.

Formal verification confirms the role of p53 protein in cell fate decision mechanism

The bio-cell cycle is controlled by a complex biochemical network of signaling pathways. Modeling such challenging networks accurately is imperative for the understanding of their detailed dynamical behavior. In this paper, we construct, analyze, and verify a hybrid Petri net (HPN) model of a complex biochemical network that captures the role of an important protein (namely p53) in deciding the fate of the cell. We model the behavior of the cell nucleus and cytoplasm as two stochastic and continuous Petri nets, respectively, combined together into a single HPN. We use simulative model checking to verify three different properties that capture the dynamical behavior of p53 protein with respect to the intensity of the ionizing radiation (IR) to which the cell is exposed. For each IR dose, 1000 simulation runs are carried out to verify each property. Our verification results showed that the fluctuations in p53, which relies on IR intensity, are compatible with the findings of the preceding simulation studies that have previously examined the role of p53 in cell fate decision.

The chemical Langevin equation for biochemical systems in dynamic environments.

Modeling and simulation of complex biochemical reaction networks form cornerstones of modern biophysics. Many of the approaches developed so far capture temporal fluctuations due to the inherent stochasticity of the biophysical processes, referred to as intrinsic noise. Stochastic fluctuations, however, predominantly stem from the interplay of the network with many other-and mostly unknown-fluctuating processes, as well as with various random signals arising from the extracellular world; these sources contribute extrinsic noise. Here, we provide a computational simulation method to probe the stochastic dynamics of biochemical systems subject to both intrinsic and extrinsic noise. We develop an extrinsic chemical Langevin equation (CLE)-a physically motivated extension of the CLE-to model intrinsically noisy reaction networks embedded in a stochastically fluctuating environment. The extrinsic CLE is a continuous approximation to the chemical master equation (CME) with time-varying propensities. In our approach, noise is incorporated at the level of the CME, and it can account for the full dynamics of the exogenous noise process, irrespective of timescales and their mismatches. We show that our method accurately captures the first two moments of the stationary probability density when compared with exact stochastic simulation methods while reducing the computational runtime by several orders of magnitude. Our approach provides a method that is practical, computationally efficient, and physically accurate to study systems that are simultaneously subject to a variety of noise sources.

Parallel ant colony optimization for the training of cell signaling networks

Acquiring a functional comprehension of the deregulation of cell signaling networks in disease allows progress in the development of new therapies and drugs. Computational models are becoming increasingly popular as a systematic tool to analyze the functioning of complex biochemical networks, such as those involved in cell signaling. CellNOpt is a framework to build predictive logic-based models of signaling pathways by training a prior knowledge network to biochemical data obtained from perturbation experiments. This training can be formulated as an optimization problem that can be solved using metaheuristics. However, the genetic algorithm used so far in CellNOpt presents limitations in terms of execution time and quality of solutions when applied to large instances. Thus, in order to overcome those issues, in this paper we propose the use of a method based on ant colony optimization, adapted to the problem at hand and parallelized using a hybrid approach. The performance of this novel method is illustrated with several challenging benchmark problems in the study of new therapies for liver cancer.

Spatially modulated illumination microscopy: application perspectives in nuclear nanostructure analysis

Thousands of genes and the complex biochemical networks for their transcription are packed in the micrometer sized cell nucleus. To control biochemical processes, spatial organization plays a key role. Hence the structure of the cell nucleus of higher organisms has emerged as a main topic of advanced light microscopy. So far, a variety of methods have been applied for this, including confocal laser scanning fluorescence microscopy, 4Pi-, STED- and localization microscopy approaches, as well as (laterally) structured illumination microscopy (SIM). Here, we summarize the state of the art and discuss application perspectives for nuclear nanostructure analysis of spatially modulated illumination (SMI). SMI is a widefield-based approach to using axially structured illumination patterns to determine the axial extension (size) of small, optically isolated fluorescent objects between less than or equal to 200 nm and greater than or equal to 40 nm diameter with a precision down to the few nm range; in addition, it allows the axial positioning of such structures down to the 1 nm scale. Combined with SIM, a three-dimensional localization precision of less than or equal to 1 nm is expected to become feasible using fluorescence yields typical for single molecule localization microscopy applications. Together with its nanosizing capability, this may eventually be used to analyse macromolecular complexes and other nanostructures with a topological resolution, further narrowing the gap to Cryoelectron microscopy. This article is part of the Theo Murphy meeting issue ‘Super-resolution structured illumination microscopy (part 2)’.

Study of the bioenergetics to identify the novel pathways as a drug target against Mycobacterium tuberculosis using Petri net

Tuberculosis is one of the life-threatening diseases globally, caused by the bacteria Mycobacterium tuberculosis. In order to control this epidemic globally, there is an urgent need to discover new drugs with novel mechanism of action that can help in shortening the duration of treatment for both drug resistant and drug sensitive tuberculosis. Mycobacterium essentially depends on oxidative phosphorylation for its growth and establishment of pathogenesis. This pathway is unique in Mycobacterium tuberculosis as compared to host due to the differences in some of the enzyme complexes carrying electron transfer. Hence, it serves as an important drug target area. The uncouplers which inhibit adenosine triphosphate synthesis, could play a vital role in serving as antimycobacterial agents and thus could help in eradicating this deadly disease. In this article, the bioenergetics of Mycobacterium tuberculosis are studied with and without uncouplers using Petri net. Petri net is among the most widely used mathematical and computational tools to model and study the complex biochemical networks. We first represented the bioenergetic pathway as a Petri net which is then validated and analyzed using invariant analysis techniques of Petri net. The valid mathematical models presented here are capable to explain the molecular mechanism of uncouplers and the processes occurring within the electron transport chain of Mycobacterium tuberculosis. The results explained the net behavior in agreement with the biological results and also suggested some possible processes and pathways to be studied as a drug target for developing antimycobacterials.

Systems Chemistry in Self‐Healing Materials

AbstractSelf‐healing materials are a kind of intelligent materials that have the ability to repair themselves after damage. By using chemical reaction networks to temporarily control the dynamics of covalent or coordination bonds, systems chemistry has recently shown great promise to reconcile the contradiction between the kinetic stability/inertness and intrinsic healing ability of synthetic materials. When compared to the traditional self‐healing strategies for synthetic materials, the regulation of the healing ability of materials by systems chemistry looks clumsy, but it represents a way similar to the self‐healing in biological systems, which is essentially regulated by complex biochemical reaction networks. Therefore, this field is worthy of further studies. In this Minireview, the significance, achievements and expectations of systems chemistry in regulating the healing ability of synthetic materials are discussed. We hope that it would be an inspiration for creating smart self‐healing materials with life‐like properties.

The effective graph reveals redundancy, canalization, and control pathways in biochemical regulation and signaling

The ability to map causal interactions underlying genetic control and cellular signaling has led to increasingly accurate models of the complex biochemical networks that regulate cellular function. These network models provide deep insights into the organization, dynamics, and function of biochemical systems: for example, by revealing genetic control pathways involved in disease. However, the traditional representation of biochemical networks as binary interaction graphs fails to accurately represent an important dynamical feature of these multivariate systems: some pathways propagate control signals much more effectively than do others. Such heterogeneity of interactions reflects canalization-the system is robust to dynamical interventions in redundant pathways but responsive to interventions in effective pathways. Here, we introduce the effective graph, a weighted graph that captures the nonlinear logical redundancy present in biochemical network regulation, signaling, and control. Using 78 experimentally validated models derived from systems biology, we demonstrate that 1) redundant pathways are prevalent in biological models of biochemical regulation, 2) the effective graph provides a probabilistic but precise characterization of multivariate dynamics in a causal graph form, and 3) the effective graph provides an accurate explanation of how dynamical perturbation and control signals, such as those induced by cancer drug therapies, propagate in biochemical pathways. Overall, our results indicate that the effective graph provides an enriched description of the structure and dynamics of networked multivariate causal interactions. We demonstrate that it improves explainability, prediction, and control of complex dynamical systems in general and biochemical regulation in particular.

Astrocyte-mediated spike-timing-dependent long-term depression modulates synaptic properties in the developing cortex.

Astrocytes have been shown to modulate synaptic transmission and plasticity in specific cortical synapses, but our understanding of the underlying molecular and cellular mechanisms remains limited. Here we present a new biophysicochemical model of a somatosensory cortical layer 4 to layer 2/3 synapse to study the role of astrocytes in spike-timing-dependent long-term depression (t-LTD) in vivo. By applying the synapse model and electrophysiological data recorded from rodent somatosensory cortex, we show that a signal from a postsynaptic neuron, orchestrated by endocannabinoids, astrocytic calcium signaling, and presynaptic N-methyl-D-aspartate receptors coupled with calcineurin signaling, induces t-LTD which is sensitive to the temporal difference between post- and presynaptic firing. We predict for the first time the dynamics of astrocyte-mediated molecular mechanisms underlying t-LTD and link complex biochemical networks at presynaptic, postsynaptic, and astrocytic sites to the time window of t-LTD induction. During t-LTD a single astrocyte acts as a delay factor for fast neuronal activity and integrates fast neuronal sensory processing with slow non-neuronal processing to modulate synaptic properties in the brain. Our results suggest that astrocytes play a critical role in synaptic computation during postnatal development and are of paramount importance in guiding the development of brain circuit functions, learning and memory.

Pyridoxal 5'-Phosphate-Dependent Enzymes at the Crossroads of Host-Microbe Tryptophan Metabolism.

The chemical processes taking place in humans intersects the myriad of metabolic pathways occurring in commensal microorganisms that colonize the body to generate a complex biochemical network that regulates multiple aspects of human life. The role of tryptophan (Trp) metabolism at the intersection between the host and microbes is increasingly being recognized, and multiple pathways of Trp utilization in either direction have been identified with the production of a wide range of bioactive products. It comes that a dysregulation of Trp metabolism in either the host or the microbes may unbalance the production of metabolites with potential pathological consequences. The ability to redirect the Trp flux to restore a homeostatic production of Trp metabolites may represent a valid therapeutic strategy for a variety of pathological conditions, but identifying metabolic checkpoints that could be exploited to manipulate the Trp metabolic network is still an unmet need. In this review, we put forward the hypothesis that pyridoxal 5′-phosphate (PLP)-dependent enzymes, which regulate multiple pathways of Trp metabolism in both the host and in microbes, might represent critical nodes and that modulating the levels of vitamin B6, from which PLP is derived, might represent a metabolic checkpoint to re-orienteer Trp flux for therapeutic purposes.