Biochemical Networks Research Articles

Non-asymptotic transients away from steady states determine cellular responsiveness to dynamic spatial-temporal signals.

Majority of the theory on cell polarization and the understanding of cellular sensing and responsiveness to localized chemical cues has been based on the idea that non-polarized and polarized cell states can be represented by stable asymptotic switching between them. The existing model classes that describe the dynamics of signaling networks underlying polarization are formulated within the framework of autonomous systems. However these models do not simultaneously capture both, robust maintenance of polarized state longer than the signal duration, and retained responsiveness to signals with complex spatial-temporal distribution. Based on recent experimental evidence for criticality organization of biochemical networks, we challenge the current concepts and demonstrate that non-asymptotic signaling dynamics arising at criticality uniquely ensures optimal responsiveness to changing chemoattractant fields. We provide a framework to characterize non-asymptotic dynamics of system's state trajectories through a non-autonomous treatment of the system, further emphasizing the importance of (long) transient dynamics, as well as the necessity to change the mathematical formalism when describing biological systems that operate in changing environments.

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.

Precise Traits from Sloppy Components: Perception and the Origin of Phenotypic Response.

Organisms perceive their environment and respond. The origin of perception-response traits presents a puzzle. Perception provides no value without response. Response requires perception. Recent advances in machine learning may provide a solution. A randomly connected network creates a reservoir of perceptive information about the recent history of environmental states. In each time step, a relatively small number of inputs drives the dynamics of the relatively large network. Over time, the internal network states retain a memory of past inputs. To achieve a functional response to past states or to predict future states, a system must learn only how to match states of the reservoir to the target response. In the same way, a random biochemical or neural network of an organism can provide an initial perceptive basis. With a solution for one side of the two-step perception-response challenge, evolving an adaptive response may not be so difficult. Two broader themes emerge. First, organisms may often achieve precise traits from sloppy components. Second, evolutionary puzzles often follow the same outlines as the challenges of machine learning. In each case, the basic problem is how to learn, either by artificial computational methods or by natural selection.

The membrane surface as a platform that organizes cellular and biochemical processes.

Membranes are essential for life. They act as semi-permeable boundaries that define cells and organelles. In addition, their surfaces actively participate in biochemical reaction networks, where they confine proteins, align reaction partners, and directly control enzymatic activities. Membrane-localized reactions shape cellular membranes, define the identity of organelles, compartmentalize biochemical processes, and can even be the source of signaling gradients that originate at the plasma membrane and reach into the cytoplasm and nucleus. The membrane surface is, therefore, an essential platform upon which myriad cellular processes are scaffolded. In this review, we summarize our current understanding of the biophysics and biochemistry of membrane-localized reactions with particular focus on insights derived from reconstituted and cellular systems. We discuss how the interplay of cellular factors results in their self-organization, condensation, assembly, and activity, and the emergent properties derived from them.

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.

SBOannotator: a Python Tool for the Automated Assignment of Systems Biology Ontology Terms.

The number and size of computational models in biology have drastically increased over the past years and continue to grow. Modeled networks are becoming more complex, and reconstructing them from the beginning in an exchangeable and reproducible manner is challenging. Using precisely defined ontologies enables the encoding of field-specific knowledge and the association of disparate data types. In computational modeling, the medium for representing domain knowledge is the set of orthogonal structured controlled vocabularies named Systems Biology Ontology (SBO). The SBO terms enable modelers to explicitly define and describe model entities, including their roles and characteristics. Here, we present the first standalone tool that automatically assigns SBO terms to multiple entities of a given SBML model, named the SBOannotator. The main focus lies on the reactions, as the correct assignment of precise SBO annotations requires their extensive classification. Our implementation does not consider only top-level terms but examines the functionality of the underlying enzymes to allocate precise and highly specific ontology terms to biochemical reactions. Transport reactions are examined separately and are classified based on the mechanism of molecule transport. Pseudo-reactions that serve modeling purposes are given reasonable terms to distinguish between biomass production and the import or export of metabolites. Finally, other model entities, such as metabolites and genes, are annotated with appropriate terms. Including SBO annotations in the models will enhance the reproducibility, usability, and analysis of biochemical networks. SBOannotator is freely available from https://github.com/draeger-lab/SBOannotator/. Supplementary data are available at Bioinformatics online.

Metabolic Adjustment of High Intertidal Alga Pelvetia canaliculata to the Tidal Cycle Includes Oscillations of Soluble Carbohydrates, Phlorotannins, and Citric Acid Content.

The brown alga Pelvetia canaliculata is one of the species successfully adapted to intertidal conditions. Inhabiting the high intertidal zone, Pelvetia spends most of its life exposed to air, where it is subjected to desiccation, light, and temperature stresses. However, the physiological and biochemical mechanisms allowing this alga to tolerate such extreme conditions are still largely unknown. The objective of our study is to compare the biochemical composition of Pelvetia during the different phases of the tidal cycle. To our knowledge, this study is the first attempt to draft a detailed biochemical network underneath the complex physiological processes, conferring the successful survival of this organism in the harsh conditions of the high intertidal zone of the polar seas. We considered the tide-induced changes in relative water content, stress markers, titratable acidity, pigment, and phlorotannin content, as well as the low molecular weight metabolite profiles (GC-MS-based approach) in Pelvetia thalli. Thallus desiccation was not accompanied by considerable increase in reactive oxygen species content. Metabolic adjustment of P. canaliculata to emersion included accumulation of soluble carbohydrates, various phenolic compounds, including intracellular phlorotannins, and fatty acids. Changes in titratable acidity accompanied by the oscillations of citric acid content imply that some processes related to the crassulacean acid metabolism (CAM) may be involved in Pelvetia adaptation to the tidal cycle.

Distinct localization of chiral proofreaders resolves organellar translation conflict in plants

Plants have two endosymbiotic organelles originated from two bacterial ancestors. The transition from an independent bacterium to a successful organelle would have required extensive rewiring of biochemical networks for its integration with archaeal host. Here, using Arabidopsis as a model system, we show that plant D-aminoacyl-tRNA deacylase 1 (DTD1), of bacterial origin, is detrimental to organellar protein synthesis owing to its changed tRNA recognition code. Plants survive this conflict by spatially restricting the conflicted DTD1 to the cytosol. In addition, plants have targeted archaeal DTD2 to both the organelles as it is compatible with their translation machinery due to its strict D-chiral specificity and lack of tRNA determinants. Intriguingly, plants have confined bacterial-derived DTD1 to work in archaeal-derived cytosolic compartment whereas archaeal DTD2 is targeted to bacterial-derived organelles. Overall, the study provides a remarkable example of the criticality of optimization of biochemical networks for survival and evolution of plant mitochondria and chloroplast.

Metabolic Modeling of Hermetia illucens Larvae Resource Allocation for High-Value Fatty Acid Production.

All plant and animal kingdom organisms use highly connected biochemical networks to facilitate sustaining, proliferation, and growth functions. While the biochemical network details are well known, the understanding of the intense regulation principles is still limited. We chose to investigate the Hermetia illucens fly at the larval stage because this stage is a crucial period for the successful accumulation and allocation of resources for the subsequent organism's developmental stages. We combined iterative wet lab experiments and innovative metabolic modeling design approaches to simulate and explain the H. illucens larval stage resource allocation processes and biotechnology potential. We performed time-based growth and high-value chemical compound accumulation wet lab chemical analysis experiments on larvae and the Gainesville diet composition. We built and validated the first H. illucens medium-size, stoichiometric metabolic model to predict the effects of diet-based alterations on fatty acid allocation potential. Using optimization methods such as flux balance and flux variability analysis on the novel insect metabolic model, we predicted that doubled essential amino acid consumption increased the growth rate by 32%, but pure glucose consumption had no positive impact on growth. In the case of doubled pure valine consumption, the model predicted a 2% higher growth rate. In this study, we describe a new framework for researching the impact of dietary alterations on the metabolism of multi-cellular organisms at different developmental stages for improved, sustainable, and directed high-value chemicals.

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.

The ups and downs of biological oscillators: a comparison of time-delayed negative feedback mechanisms.

Many biochemical oscillators are driven by the periodic rise and fall of protein concentrations or activities. A negative feedback loop underlies such oscillations. The feedback can act on different parts of the biochemical network. Here, we mathematically compare time-delay models where the feedback affects production and degradation. We show a mathematical connection between the linear stability of the two models, and derive how both mechanisms impose different constraints on the production and degradation rates that allow oscillations. We show how oscillations are affected by the inclusion of a distributed delay, of double regulation (acting on production and degradation) and of enzymatic degradation.

Verifiable biology.

The formalization of biological systems using computational modelling approaches as an alternative to mathematical-based methods has recently received much interest because computational models provide a deeper mechanistic understanding of biological systems. In particular, formal verification, complementary approach to standard computational techniques such as simulation, is used to validate the system correctness and obtain critical information about system behaviour. In this study, we survey the most frequently used computational modelling approaches and formal verification techniques for computational biology. We compare a number of verification tools and software suites used to analyse biological systems and biochemical networks, and to verify a wide range of biological properties. For users who have no expertise in formal verification, we present a novel methodology that allows them to easily apply formal verification techniques to analyse their biological or biochemical system of interest.

Abstract 359: Multiscale Model Reveals Importance Of Cumulative Vascular Smooth Muscle Cell Responses In Aortic Hypercontractility

Diabetes more than doubles the risk of cardiovascular disease (CVD), and the chronic hyperglycemia leads to vascular maladaptations prior to the diagnostic threshold. While endothelium-independent vascular smooth muscle cell (VSMC) hypercontractility is well-established at the tissue-scale, the mechanical phenotype is inconsistently observed at the cell-scale. Our strategy consisted of experimental characterization at the tissue-scale via an isometric ring contraction assay of WT and OGT KO aortas, the latter an established chronic hyperglycemia model, from male mice and at the cell-scale via published cellular microbiaxial stretching data, coupled with the construction of a multiscale computational model. We hypothesize that the biochemical signaling changes due to increased blood glucose are sufficient to predict VSMC hypercontractility. The multiscale model operates off a biochemical signaling network that determines the activation and contraction of actin stress fibers. The stress fibers are connected in either 2D (cell-scale) or 3D (tissue-scale) cytoskeletal networks where for the tissue-scale, the cytoskeletal mechanics are fit to a macroscale constrained mixture model. The multiscale model correctly predicts the cell-scale euglycemic contractility, however the hyperglycemic condition is too subtle to be resolved at this scale. At the tissue-scale, the model correctly predicts both euglycemic and hyperglycemic active force generation. The model does not currently consider ECM alterations, so the changes in passive tissue mechanics cannot be predicted. The model predictions suggest that while the impetus of hyperglycemia-driven hypercontractility occurs at the cell-scale, these small-scale changes must be amplified across length scales to be experimentally-observable. These results demonstrate the power of the multiscale model to connect cell- and tissue-scale vascular mechanical phenotypes due to chronic hyperglycemia.

Molecular Interaction Networks and Cardiovascular Disease Risk: The Role of Food Bioactive Small Molecules.

Diet is a well-known modifiable risk factor for cardiovascular diseases, which are the leading cause of death worldwide. However, our current understanding of the human diet is still limited in terms of fully capturing the role of dietary compounds in the intraspecies and interspecies biochemical networks that determine our health. This is due, in part, to a lack of detailed information on the presence of small molecules in food (molecular weight, ≤1000 daltons), their amounts, and their interactions with known protein targets. As a result, our ability to develop a mechanistic understanding of how food chemicals impact our health is limited. In recent years, the Foodome project has tackled several aspects of this challenging universe, leveraging the expertise of a diverse group of scientific communities, from computer science to epidemiology. Here, we review the most recent efforts of the Foodome project in mapping the chemical complexity of food and predicting its effect on human health. Leveraging the network medicine framework applied to Amla-a medicinal plant-we offer a rationale for future research on the mechanism of action of food bioactive small molecules, whose designing principles could inspire next-generation drug discovery and combinations.

Editorial for the special issue “Control‐theoretic approaches for systems in the life sciences”

Editorial for the special issue “Control<scp>‐theoretic</scp> approaches for systems in the life sciences”

Universal structures for adaptation in biochemical reaction networks

At the molecular level, the evolution of life is driven by the generation and diversification of adaptation mechanisms. A universal description of adaptation-capable chemical reaction network (CRN) structures has remained elusive until now, since currently-known criteria for adaptation apply only to a tiny subset of possible CRNs. Here we identify the definitive structural requirements that characterize all adaptation-capable collections of interacting molecules, however large or complex. We show that these network structures implement a form of integral control in which multiple independent integrals can collaborate to confer the capacity for adaptation on specific molecules. Using an algebraic algorithm informed by these findings, we demonstrate the existence of embedded integrals in a variety of biologically important CRNs that have eluded previous methods, and for which adaptation has been observed experimentally. This definitive picture of biological adaptation at the level of intermolecular interactions represents a blueprint for adaptation-capable signaling networks across all domains of life, and for the design of synthetic biosystems.

Thermodynamic bounds on ultrasensitivity in covalent switching

Switch-like motifs are among the basic building blocks of biochemical networks. A common motif that can serve as an ultrasensitive switch consists of two enzymes acting antagonistically on a substrate, one making and the other removing a covalent modification. To work as a switch, such covalent modification cycles must be held out of thermodynamic equilibrium by continuous expenditure of energy. Here, we exploit the linear framework for timescale separation to establish tight bounds on the performance of any covalent-modification switch in terms of the chemical potential difference driving the cycle. The bounds apply to arbitrary enzyme mechanisms, not just Michaelis-Menten, with arbitrary rate constants and thereby reflect fundamental physical constraints on covalent switching.

Semigroup models for biochemical reaction networks

The catalytic reaction system (CRS) formalism by Hordijk and Steel is a versatile method to model autocatalytic biochemical reaction networks. It is particularly suited, and has been widely used, to study self-sustainment and self-generation properties. Its distinguishing feature is the explicit assignment of a catalytic function to chemicals that are part of the system. In this work, it is shown that the subsequent and simultaneous catalytic functions give rise to an algebraic structure of a semigroup with the additional compatible operation of idempotent addition and a partial order. The aim of this article is to demonstrate that such semigroup models are a natural setup to describe and analyze self-sustaining CRS. The basic algebraic properties of the models are established and the notion of the function of any set of chemicals on the whole CRS is made precise. This leads to a natural discrete dynamical system on the power set of chemicals, which is obtained by iteratively considering the self-action on a set of chemicals by its own function. The fixed points of this dynamical system are proven to correspond to self-sustaining sets of chemicals, which are functionally closed. Finally, as the main application, a theorem on the maximal self-sustaining set and a structure theorem on the set of functionally closed self-sustaining sets of chemicals are proven.

A framework for deriving analytic steady states of biochemical reaction networks.

The long-term behaviors of biochemical systems are often described by their steady states. Deriving these states directly for complex networks arising from real-world applications, however, is often challenging. Recent work has consequently focused on network-based approaches. Specifically, biochemical reaction networks are transformed into weakly reversible and deficiency zero generalized networks, which allows the derivation of their analytic steady states. Identifying this transformation, however, can be challenging for large and complex networks. In this paper, we address this difficulty by breaking the complex network into smaller independent subnetworks and then transforming the subnetworks to derive the analytic steady states of each subnetwork. We show that stitching these solutions together leads to the analytic steady states of the original network. To facilitate this process, we develop a user-friendly and publicly available package, COMPILES (COMPutIng anaLytic stEady States). With COMPILES, we can easily test the presence of bistability of a CRISPRi toggle switch model, which was previously investigated via tremendous number of numerical simulations and within a limited range of parameters. Furthermore, COMPILES can be used to identify absolute concentration robustness (ACR), the property of a system that maintains the concentration of particular species at a steady state regardless of any initial concentrations. Specifically, our approach completely identifies all the species with and without ACR in a complex insulin model. Our method provides an effective approach to analyzing and understanding complex biochemical systems.

The unraveling of balanced complexes in metabolic networks

Balanced complexes in biochemical networks are at core of several theoretical and computational approaches that make statements about the properties of the steady states supported by the network. Recent computational approaches have employed balanced complexes to reduce metabolic networks, while ensuring preservation of particular steady-state properties; however, the underlying factors leading to the formation of balanced complexes have not been studied, yet. Here, we present a number of factorizations providing insights in mechanisms that lead to the origins of the corresponding balanced complexes. The proposed factorizations enable us to categorize balanced complexes into four distinct classes, each with specific origins and characteristics. They also provide the means to efficiently determine if a balanced complex in large-scale networks belongs to a particular class from the categorization. The results are obtained under very general conditions and irrespective of the network kinetics, rendering them broadly applicable across variety of network models. Application of the categorization shows that all classes of balanced complexes are present in large-scale metabolic models across all kingdoms of life, therefore paving the way to study their relevance with respect to different properties of steady states supported by these networks.