Biological feedback control-Respect the loops

Glycolysis model shows that allostery maintains high ATP and limits accumulation of intermediates

Glycolysis is a conserved metabolic pathway that produces ATP and biosynthetic precursors. It is not well understood how the control of mammalian glycolytic enzymes through allosteric feedback and mass action accomplishes various tasks of ATP homeostasis, such as controlling the rate of ATP production, maintaining high and stable ATP levels, ensuring that ATP hydrolysis generates a net excess of energy, and maintaining glycolytic intermediate concentrations within physiological levels. To investigate these questions, we developed a biophysical model of glycolysis based on enzyme rate equations derived from in vitro kinetic data. This is the first biophysical model of human glycolysis that successfully recapitulates the above tasks of ATP homeostasis and predicts absolute concentrations of glycolytic intermediates and isotope tracing kinetics that align with experimental measurements in human cells. We use the model to show that mass action alone is sufficient to control the ATP production rate and maintain the high energy of ATP hydrolysis. Meanwhile, allosteric regulation of hexokinase and phosphofructokinase by ATP, ADP, inorganic phosphate, and glucose-6-phosphate is required to maintain high ATP levels and to prevent uncontrolled accumulation of phosphorylated intermediates of glycolysis. Allosteric feedback achieves the latter by maintaining hexokinase and phosphofructokinase enzyme activity at one-half of ATP demand and, thus, inhibiting the reaction of Harden and Young, which otherwise converts glucose to supraphysiological levels of phosphorylated glycolytic intermediates at the expense of ATP. Our methodology provides a roadmap for a quantitative understanding of how metabolic homeostasis emerges from the activities of individual enzymes.

Stereotyped Subclones Revealed by High-Density Single-Cell Lineage Tracing Support Robust Development

Robust development is essential for multicellular organisms. While various mechanisms contributing to developmental robustness are identified at the subcellular level, those at the intercellular and tissue level remain underexplored. This question is approached using a well-established in vitro directed differentiation model recapitulating the in vivo development of lung progenitor cells from human embryonic stem cells. An integrated analysis of high-density cell lineage trees (CLTs) and single-cell transcriptomes of differentiating colonies enabled the resolution of known cell types and developmental hierarchies. This dataset showed little support for the contribution of transcriptional memory to developmental robustness. Nevertheless, stable terminal cell type compositions are observed among many subclones, which enhances developmental robustness because the colony can retain a relatively stable composition even if some subclones are abolished by cell death. Furthermore, it is found that many subclones are formed by sub-CLTs resembling each other in terms of both terminal cell type compositions and topological structures. The presence of stereotyped sub-CLTs constitutes a novel basis for developmental robustness. Moreover, these results suggest a unique perspective on individual cells' function in the context of stereotyped sub-CLTs, which can bridge the knowledge of the atlas of cell types and how they are organized into functional tissues.

Fundamental Trade-Offs in the Robustness of Biological Systems with Feedback Regulation

Natural biological systems use feedback regulation to effectively respond and adapt to their changing environment. Even though in engineered systems we understand how accurate feedback can be depending on the electronic or mechanical parts that it is implemented with, we largely lack a similar theoretical framework to study feedback regulation in biological systems. Specifically, it is not fully understood or quantified how accurate or robust the implementation of biological feedback actually is. In this paper, we study the sensitivity of biological feedback to variations in biochemical parameters using five example circuits: positive autoregulation, negative autoregulation, double-positive feedback, positive-negative feedback, and double-negative feedback (the toggle switch). We find that some of these examples of biological feedback are subjected to fundamental performance trade-offs, and we propose multi-objective optimization as a framework to study their properties. The impact of this work is to improve robust circuit design for synthetic biology and to improve our understanding of feedback for systems biology.

Urease-coupled systems and materials: design strategies, scope and applications

Synthetic systems have co-opted urease, a crucial enzyme serving many biological functions, to recapitulate complex biological features. Therefore, the urease-urea feedback reaction network (FCRN) is reciprocated with soft materials to induce various animate-like features, including self-regulation, error correction, and decision-making capabilities, that are processed through a variety of non-linear functions. Although free-urease-based homogeneous systems are capable of adhering to many non-linear characteristics, they lack the ability to showcase the diffusion-controlled spatiotemporal phenomena. Therefore, it demands urease immobilization, whereby a compartmentalized reaction hub can facilitate the interplay of FCRN with reaction diffusion to regulate the system's operation, allowing various non-linear responses and spatiotemporal self-organization. Indeed, the beneficial framework of urease-based commercial systems in modern technology necessitates the accessibility, reusability, and long-term stability of urease. Consequently, several techniques for urease immobilization merit attention. This review highlights the diverse covalent and non-covalent approaches for urease immobilization on different substrates and illustrates several chemical reactions and non-covalent interactions as tools for creating targeted systems and soft materials to realize many on-demand functions. We also emphasize how the advancement of systems chemistry has propelled research in soft materials to comprehend system-level applications by demonstrating several emerging non-linear functions with potent applications in many directions, including sensing, soft robotics, regulation of material properties and many more.

Diet induced insulin resistance is due to induction of PTEN expression

Insulin resistance is a condition associated with obesity, type 2 diabetes(T2D), hyperinsulinemia, hyperglycemia and defined by reduced sensitivity to insulin signaling. Molecular causes and early signaling events underlying insulin resistance are not well understood. Here we show that insulin activation of PI3K/AKT/mTOR signaling in insulin target tissues, causes mTORC1 induction of PTEN translation, a negative regulator of PI3K signaling. We hypothesized that insulin resistance is due to insulin dependent induction of PTEN that prevents further increases in PI3K signaling. In a diet induced animal model of obesity and insulin resistance, we show that PTEN levels are increased in fat, muscle, and liver. Hyperinsulinemia and PTEN induction are followed by hyperglycemia, severe glucose intolerance, and hepatic steatosis. In response to chronic hyperinsulinemia, PTEN remains increased, while AKT activity is induced transiently before settling down to a PTEN-high and AKT-low state in the tissues, predicted by computational modeling of the PTEN-AKT feedback loop. Treatment with PTEN and mTORC1 inhibitors prevent and reverse the effect of PTEN induction, rescue insulin resistance and increase PI3K/AKT signaling. Thus, we show that PTEN induction by increased insulin levels elevates feedback inhibition of the pathway causing insulin resistance, its associated phenotypes, and is a potential therapeutic target.

Reprogramming feedback strength in gibberellin biosynthesis highlights conditional regulation by the circadian clock and carbon dioxide

The phytohormone gibberellin (GA) is an important regulator of plant morphology and reproduction, and the biosynthesis and distribution of GA in planta is agriculturally relevant to past and current breeding efforts. Tools like biosensors, extensive molecular genetic resources in reference plants and mathematical models have greatly contributed to current understanding of GA homeostasis; however, these tools are difficult to tune or repurpose for engineering crop plants. Previously, we showed that a GA-regulated Hormone Activated CAS9-based Repressor (GAHACR) functions in planta. Here, we use GAHACRs to modulate the strength of feedback on endemic GA regulated genes, and to directly test the importance of transcriptional feedback in GA signaling. We first adapted existing mathematical models to predict the impact of targeting a GAHACR to different nodes in the GA biosynthesis pathway, and then implemented a perturbation predicted by the model to lower GA levels. Specifically, we individually targeted either the biosynthetic gene GA20 oxidase (GA20ox) or the GA receptor GID1, and characterized primary root length, flowering time and the transcriptome of these transgenic lines. Using this approach, we identified a strong connection between GA signaling status and the circadian clock, which can be largely attenuated by elevated carbon dioxide levels. Our results identify a node in the GA signaling pathway that can be engineered to modulate plant size and flowering time. Our results also raise concerns that rising atmospheric CO2 concentration are likely to reverse many of the gains of Green Revolution crops.

Beyond the heat shock pathway: Heat stress responses in Drosophila development

Heat stress has broad effects on an organism and is an inevitable part of life. Embryos face a particular challenge when faced with heat stress - the intricate molecular processes that pattern the embryo can all be affected by heat, and the embryo lacks some of the strategies that adults can use to manage or avoid heat stress. We use Drosophila melanogaster as a model, as insects are capable of developing normally under a wide range of temperatures and are exposed to daily temperature swings as they develop. Research has focused on the heat shock pathway and the transcription of heat shock proteins as the main response to heat and heat damage. This review explores embryonic heat responses beyond the heat shock pathway. We examine the effects of heat from a biochemical standpoint, as well as highlighting other mechanisms of heat stress regulation, such as miRNA activity or other signaling pathways. We discuss how different elements of the heat stress response must be coordinated across the embryo to enable development under a wide range of temperatures. Studying heat stress in Drosophila melanogaster can be a powerful lens into how developmental systems ensure robustness to environmental factors.

Restoring Homeostasis: Treating Amyotrophic Lateral Sclerosis by Resolving Dynamic Regulatory Instability

Amyotrophic lateral sclerosis (ALS) has an interactive, multifactorial etiology that makes treatment success elusive. This study evaluates how regulatory dynamics impact disease progression and treatment. Computational models of wild-type (WT) and transgenic SOD1-G93A mouse physiology dynamics were built using the first-principles-based first-order feedback framework of dynamic meta-analysis with parameter optimization. Two in silico models were developed: a WT mouse model to simulate normal homeostasis and a SOD1-G93A ALS model to simulate ALS pathology dynamics and their response to in silico treatments. The model simulates functional molecular mechanisms for apoptosis, metal chelation, energetics, excitotoxicity, inflammation, oxidative stress, and proteomics using curated data from published SOD1-G93A mouse experiments. Temporal disease progression measures (rotarod, grip strength, body weight) were used for validation. Results illustrate that untreated SOD1-G93A ALS dynamics cannot maintain homeostasis due to a mathematical oscillating instability as determined by eigenvalue analysis. The onset and magnitude of homeostatic instability corresponded to disease onset and progression. Oscillations were associated with high feedback gain due to hypervigilant regulation. Multiple combination treatments stabilized the SOD1-G93A ALS mouse dynamics to near-normal WT homeostasis. However, treatment timing and effect size were critical to stabilization corresponding to therapeutic success. The dynamics-based approach redefines therapeutic strategies by emphasizing the restoration of homeostasis through precisely timed and stabilizing combination therapies, presenting a promising framework for application to other multifactorial neurodegenerative diseases.

Redefining Roles: A Paradigm Shift in Tryptophan-Kynurenine Metabolism for Innovative Clinical Applications

The tryptophan-kynurenine (KYN) pathway has long been recognized for its essential role in generating metabolites that influence various physiological processes. Traditionally, these metabolites have been categorized into distinct, often opposing groups, such as pro-oxidant versus antioxidant, excitotoxic/neurotoxic versus neuroprotective. This dichotomous framework has shaped much of the research on conditions like neurodegenerative and neuropsychiatric disorders, as well as cancer, where metabolic imbalances are a key feature. The effects are significantly influenced by various factors, including the concentration of metabolites and the particular cellular milieu in which they are generated. A molecule that acts as neuroprotective at low concentrations may exhibit neurotoxic effects at elevated levels. The oxidative equilibrium of the surrounding environment can alter the function of KYN from an antioxidant to a pro-oxidant. This narrative review offers a comprehensive examination and analysis of the contemporary understanding of KYN metabolites, emphasizing their multifaceted biological functions and their relevance in numerous physiological and pathological processes. This underscores the pressing necessity for a paradigm shift in the comprehension of KYN metabolism. Understanding the context-dependent roles of KYN metabolites is vital for novel therapies in conditions like Alzheimer's disease, multiple sclerosis, and cancer. Comprehensive pathway modulation, including balancing inflammatory signals and enzyme regulation, offers promising avenues for targeted, effective treatments.

The role of uncertainty and negative feedback loops in the evolution of induced immune defenses

Organisms use constitutive or induced defenses against pathogens and other external threats. Constitutive defenses are constantly on, whereas induced defenses are activated when needed. Each of these strategies has costs and benefits, which can affect the type of defense that evolves in response to pathogens. In addition, induced defenses are usually regulated by multiple negative feedback mechanisms that prevent overactivation of the immune response. However, it is unclear how negative feedback affects the costs, benefits, and evolution of induced responses. To address this gap, we developed a mechanistic model of the well-characterized Drosophila melanogaster immune signaling network that includes 3 separate mechanisms of negative feedback as a representative of the widespread phenomenon of multilevel regulation of induced responses. We show that, under stochastic fly-bacteria encounters, an induced defense is favored when bacterial encounters are rare or uncertain, but in ways that depend on the bacterial proliferation rate. Our model also predicts that the specific negative regulators that optimize the induced response depend on the bacterial proliferation rate, linking negative feedback mechanisms to the factors that favor induction.

How Does Escherichia coli Allocate Proteome?

Microorganisms are shown to actively partition their intracellular resources, such as proteins, for growth optimization. Recent experiments have begun to reveal molecular components unpinning the partition; however, quantitatively, it remains unclear how individual parts orchestrate to yield precise resource allocation that is both robust and dynamic. Here, we developed a coarse-grained mathematical framework that centers on guanosine pentaphosphate (ppGpp)-mediated regulation and used it to systematically uncover the design principles of proteome allocation in Escherichia coli. Our results showed that the cellular ability of resource partition lies in an ultrasensitive, negative feedback-controlling topology with the ultrasensitivity arising from zero-order amino acid kinetics and the negative feedback from ppGpp-controlled ribosome synthesis. In addition, together with the time-scale separation between slow ribosome kinetics and fast turnovers of ppGpp and amino acids, the network topology confers the organism an optimization mechanism that mimics sliding mode control, a nonlinear optimization strategy that is widely used in man-made systems. We further showed that such a controlling mechanism is robust against parameter variations and molecular fluctuations and is also efficient for biomass production over time. This work elucidates the fundamental controlling mechanism of E. coli proteome allocation, thereby providing insights into quantitative microbial physiology as well as the design of synthetic gene networks.

The value of protein allostery in rational anticancer drug design: an update

INTRODUCTION

Allosteric drugs are advantageous. However, they still face hurdles, including identification of allosteric sites that will effectively alter the active site. Current strategies largely focus on identifying pockets away from the active sites into which the allosteric ligand will dock and do not account for exactly how the active site is altered. Favorable allosteric inhibitors dock into sites that are nearby the active sites and follow nature, mimicking diverse allosteric regulation strategies.

AREAS COVERED

The following article underscores the immense significance of allostery in drug design, describes current allosteric strategies, and especially offers a direction going forward. The article concludes with the authors' expert perspectives on the subject.

EXPERT OPINION

To select a productive venue in allosteric inhibitor development, we should learn from nature. Currently, useful strategies follow this route. Consider, for example, the mechanisms exploited in relieving autoinhibition and in harnessing allosteric degraders. Mimicking compensatory, or rescue mutations may also fall into such a thesis, as can molecular glues that capture features of scaffolding proteins. Capturing nature and creatively tailoring its mimicry can continue to innovate allosteric drug discovery.

Fibromyalgia pathogenesis explained by a neuroendocrine multistable model

Fibromyalgia (FM) is a central disorder characterized by chronic pain, fatigue, insomnia, depression, and other minor symptoms. Knowledge about pathogenesis is lacking, diagnosis difficult, clinical approach puzzling, and patient management disappointing. We conducted a theoretical study based on literature data and computational analysis, aimed at developing a comprehensive model of FM pathogenesis and addressing suitable therapeutic targets. We started from the evidence that FM must involve a dysregulation of central pain processing, is female prevalent, suggesting a role for the hypothalamus-pituitary-gonadal (HPG) axis, and is stress-related, suggesting a role for the HP-adrenocortical (HPA) axis. Central pathogenesis was supposed to involve a pain processing loop system including the thalamic ventroposterolateral nucleus (VPL), the primary somatosensory cortex (SSC), and the thalamic reticular nucleus (TRN). For decreasing GABAergic and/or increasing glutamatergic transmission, the loop system crosses a bifurcation point, switching from monostable to bistable, and converging on a high-firing-rate steady state supposed to be the pathogenic condition. Thereafter, we showed that GABAergic transmission is positively correlated with gonadal-hormone-derived neurosteroids, notably allopregnanolone, whereas glutamatergic transmission is positively correlated with stress-induced glucocorticoids, notably cortisol. Finally, we built a dynamic model describing a multistable, double-inhibitory loop between HPG and HPA axes. This system has a high-HPA/low-HPG steady state, allegedly reached in females under combined premenstrual/postpartum brain allopregnanolone withdrawal and stress condition, driving the thalamocortical loop to the high-firing-rate steady state, and explaining the connection between endocrine and neural mechanisms in FM pathogenesis. Our model accounts for FM female prevalence and stress correlation, suggesting the use of neurosteroid drugs as a possible solution to currently unsolved problems in the clinical treatment of the disease.

An emerging role for tissue plasticity in developmental precision

Reproducible tissue morphology is a fundamental feature of embryonic development. To ensure such robustness during tissue morphogenesis, inherent noise in biological processes must be buffered. While redundant genes, parallel signaling pathways and intricate network topologies are known to reduce noise, over the last few years, mechanical properties of tissues have been shown to play a vital role. Here, taking the example of somite shape changes, I will discuss how tissues are highly plastic in their ability to change shapes leading to increased precision and reproducibility.

A Lifelike guided journey through the pathophysiology of pulmonary hypertension-from measured metabolites to the mechanism of action of drugs

Introduction

Pulmonary hypertension (PH) is a pathological condition that affects approximately 1% of the population. The prognosis for many patients is poor, even after treatment. Our knowledge about the pathophysiological mechanisms that cause or are involved in the progression of PH is incomplete. Additionally, the mechanism of action of many drugs used to treat pulmonary hypertension, including sotatercept, requires elucidation.

Methods

Using our graph-powered knowledge mining software Lifelike in combination with a very small patient metabolite data set, we demonstrate how we derive detailed mechanistic hypotheses on the mechanisms of PH pathophysiology and clinical drugs.

Results

In PH patients, the concentration of hypoxanthine, 12(S)-HETE, glutamic acid, and sphingosine 1 phosphate is significantly higher, while the concentration of L-arginine and L-histidine is lower than in healthy controls. Using the graph-based data analysis, gene ontology, and semantic association capabilities of Lifelike, led us to connect the differentially expressed metabolites with G-protein signaling and SRC. Then, we associated SRC with IL6 signaling. Subsequently, we found associations that connect SRC, and IL6 to activin and BMP signaling. Lastly, we analyzed the mechanisms of action of several existing and novel pharmacological treatments for PH. Lifelike elucidated the interplay between G-protein, IL6, activin, and BMP signaling. Those pathways regulate hallmark pathophysiological processes of PH, including vasoconstriction, endothelial barrier function, cell proliferation, and apoptosis.

Discussion

The results highlight the importance of SRC, ERK1, AKT, and MLC activity in PH. The molecular pathways affected by existing and novel treatments for PH also converge on these molecules. Importantly, sotatercept affects SRC, ERK1, AKT, and MLC simultaneously. The present study shows the power of mining knowledge graphs using Lifelike's diverse set of data analytics functionalities for developing knowledge-driven hypotheses on PH pathophysiological and drug mechanisms and their interactions. We believe that Lifelike and our presented approach will be valuable for future mechanistic studies of PH, other diseases, and drugs.

Artificial Homeostasis Systems Based on Feedback Reaction Networks: Design Principles and Future Promises

Feedback-controlled chemical reaction networks (FCRNs) are indispensable for various biological processes, such as cellular mechanisms, patterns, and signaling pathways. Through the intricate interplay of many feedback loops (FLs), FCRNs maintain a stable internal cellular environment. Currently, creating minimalistic synthetic cells is the long-term objective of systems chemistry, which is motivated by such natural integrity. The design, kinetic optimization, and analysis of FCRNs to exhibit functions akin to those of a cell still pose significant challenges. Indeed, reaching synthetic homeostasis is essential for engineering synthetic cell components. However, maintaining homeostasis in artificial systems against various agitations is a difficult task. Several biological events can provide us with guidelines for a conceptual understanding of homeostasis, which can be further applicable in designing artificial synthetic systems. In this regard, we organize our review with artificial homeostasis systems driven by FCRNs at different length scales, including homogeneous, compartmentalized, and soft material systems. First, we stretch a quick overview of FCRNs in different molecular and supramolecular systems, which are the essential toolbox for engineering different nonlinear functions and homeostatic systems. Moreover, the existing history of synthetic homeostasis in chemical and material systems and their advanced functions with self-correcting, and regulating properties are also emphasized.

Nonlinear Growth Dynamics of Neuronal Cells Cultured on Directional Surfaces

During the development of the nervous system, neuronal cells extend axons and dendrites that form complex neuronal networks, which are essential for transmitting and processing information. Understanding the physical processes that underlie the formation of neuronal networks is essential for gaining a deeper insight into higher-order brain functions such as sensory processing, learning, and memory. In the process of creating networks, axons travel towards other recipient neurons, directed by a combination of internal and external cues that include genetic instructions, biochemical signals, as well as external mechanical and geometrical stimuli. Although there have been significant recent advances, the basic principles governing axonal growth, collective dynamics, and the development of neuronal networks remain poorly understood. In this paper, we present a detailed analysis of nonlinear dynamics for axonal growth on surfaces with periodic geometrical patterns. We show that axonal growth on these surfaces is described by nonlinear Langevin equations with speed-dependent deterministic terms and gaussian stochastic noise. This theoretical model yields a comprehensive description of axonal growth at both intermediate and long time scales (tens of hours after cell plating), and predicts key dynamical parameters, such as speed and angular correlation functions, axonal mean squared lengths, and diffusion (cell motility) coefficients. We use this model to perform simulations of axonal trajectories on the growth surfaces, in turn demonstrating very good agreement between simulated growth and the experimental results. These results provide important insights into the current understanding of the dynamical behavior of neurons, the self-wiring of the nervous system, as well as for designing innovative biomimetic neural network models.

Alternative splicing and related RNA binding proteins in human health and disease

Alternative splicing (AS) serves as a pivotal mechanism in transcriptional regulation, engendering transcript diversity, and modifications in protein structure and functionality. Across varying tissues, developmental stages, or under specific conditions, AS gives rise to distinct splice isoforms. This implies that these isoforms possess unique temporal and spatial roles, thereby associating AS with standard biological activities and diseases. Among these, AS-related RNA-binding proteins (RBPs) play an instrumental role in regulating alternative splicing events. Under physiological conditions, the diversity of proteins mediated by AS influences the structure, function, interaction, and localization of proteins, thereby participating in the differentiation and development of an array of tissues and organs. Under pathological conditions, alterations in AS are linked with various diseases, particularly cancer. These changes can lead to modifications in gene splicing patterns, culminating in changes or loss of protein functionality. For instance, in cancer, abnormalities in AS and RBPs may result in aberrant expression of cancer-associated genes, thereby promoting the onset and progression of tumors. AS and RBPs are also associated with numerous neurodegenerative diseases and autoimmune diseases. Consequently, the study of AS across different tissues holds significant value. This review provides a detailed account of the recent advancements in the study of alternative splicing and AS-related RNA-binding proteins in tissue development and diseases, which aids in deepening the understanding of gene expression complexity and offers new insights and methodologies for precision medicine.

Macroscopic waves, biological clocks and morphogenesis driven by light in a giant unicellular green alga

A hallmark of self-organisation in living systems is their capacity to stabilise their own dynamics, often appearing to anticipate and act upon potential outcomes. Caulerpa brachypus is a marine green alga consisting of differentiated organs resembling leaves, stems and roots. While an individual can exceed a metre in size, it is a single multinucleated giant cell. Thus Caulerpa presents the mystery of morphogenesis on macroscopic scales in the absence of cellularization. The experiments reported here reveal self-organised waves of greenness - chloroplasts - that propagate throughout the alga in anticipation of the day-night light cycle. Using dynamical systems analysis we show that these waves are coupled to a self-sustained oscillator, and demonstrate their entrainment to light. Under constant conditions light intensity affects the natural period and drives transition to temporal disorder. Moreover, we find distinct morphologies depending on light temporal patterns, suggesting waves of chlorophyll could link biological oscillators to metabolism and morphogenesis in this giant single-celled organism.

Modulating Biological Rhythms: A Noncomputational Strategy Harnessing Nonlinearity and Decoupling Frequency and Amplitude

Understanding and achieving concurrent modulation of amplitude and frequency, particularly adjusting one quantity and simultaneously sustaining the other at an invariant level, are of paramount importance for complex biophysical systems, including the signal pathway where different frequency indicates different upstream signal yielding a certain downstream physiological function while different amplitude further determines different efficacy of a downstream output. However, such modulators with clearly described and universally valid mechanisms are still lacking. Here, we rigorously propose an easy-to-use control strategy containing only one or two steps, leveraging the nonlinearity in the modulated systems to decouple frequency and amplitude in a noncomputational manner. The strategy's efficacy is demonstrated using representative biochemical systems and, thus, it could be potentially applicable to modulating rhythms in experiments of biochemistry and synthetic biology.

Distribution modeling quantifies collective TH cell decision circuits in chronic inflammation

Immune responses are tightly regulated by a diverse set of interacting immune cell populations. Alongside decision-making processes such as differentiation into specific effector cell types, immune cells initiate proliferation at the beginning of an inflammation, forming two layers of complexity. Here, we developed a general mathematical framework for the data-driven analysis of collective immune cell dynamics. We identified qualitative and quantitative properties of generic network motifs, and we specified differentiation dynamics by analysis of kinetic transcriptome data. Furthermore, we derived a specific, data-driven mathematical model for T helper 1 versus T follicular helper cell-fate decision dynamics in acute and chronic lymphocytic choriomeningitis virus infections in mice. The model recapitulates important dynamical properties without model fitting and solely by using measured response-time distributions. Model simulations predict different windows of opportunity for perturbation in acute and chronic infection scenarios, with potential implications for optimization of targeted immunotherapy.

Are physiological oscillations physiological?

Despite widespread and striking examples of physiological oscillations, their functional role is often unclear. Even glycolysis, the paradigm example of oscillatory biochemistry, has seen questions about its oscillatory function. Here, we take a systems approach to argue that oscillations play critical physiological roles, such as enabling systems to avoid desensitization, to avoid chronically high and therefore toxic levels of chemicals, and to become more resistant to noise. Oscillation also enables complex physiological systems to reconcile incompatible conditions such as oxidation and reduction, by cycling between them, and to synchronize the oscillations of many small units into one large effect. In pancreatic β-cells, glycolytic oscillations synchronize with calcium and mitochondrial oscillations to drive pulsatile insulin release, critical for liver regulation of glucose. In addition, oscillation can keep biological time, essential for embryonic development in promoting cell diversity and pattern formation. The functional importance of oscillatory processes requires a re-thinking of the traditional doctrine of homeostasis, holding that physiological quantities are maintained at constant equilibrium values, a view that has largely failed in the clinic. A more dynamic approach will initiate a paradigm shift in our view of health and disease. A deeper look into the mechanisms that create, sustain and abolish oscillatory processes requires the language of nonlinear dynamics, well beyond the linearization techniques of equilibrium control theory. Nonlinear dynamics enables us to identify oscillatory ('pacemaking') mechanisms at the cellular, tissue and system levels.

Regulation strategies for two-output biomolecular networks

Feedback control theory facilitates the development of self-regulating systems with desired performance which are predictable and insensitive to disturbances. Feedback regulatory topologies are found in many natural systems and have been of key importance in the design of reliable synthetic bio-devices operating in complex biological environments. Here, we study control schemes for biomolecular processes with two outputs of interest, expanding previously described concepts based on single-output systems. Regulation of such processes may unlock new design possibilities but can be challenging due to coupling interactions; also potential disturbances applied on one of the outputs may affect both. We therefore propose architectures for robustly manipulating the ratio/product and linear combinations of the outputs as well as each of the outputs independently. To demonstrate their characteristics, we apply these architectures to a simple process of two mutually activated biomolecular species. We also highlight the potential for experimental implementation by exploring synthetic realizations both in vivo and in vitro. This work presents an important step forward in building bio-devices capable of sophisticated functions.

On biological networks capable of robust adaptation in the presence of uncertainties: A linear systems-theoretic approach

Biological adaptation, the tendency of every living organism to regulate its essential activities in environmental fluctuations, is a well-studied functionality in systems and synthetic biology. In this work, we present a generic methodology inspired by systems theory to discover the design principles for robust adaptation, perfect and imperfect, in two different contexts: (1) in the presence of deterministic external and parametric disturbances and (2) in a stochastic setting. In all the cases, firstly, we translate the necessary qualitative conditions for adaptation to mathematical constraints using the language of systems theory, which we then map back as design requirements for the underlying networks. Thus, contrary to the existing approaches, the proposed methodologies provide an exhaustive set of admissible network structures without resorting to computationally burdensome brute-force techniques. Further, the proposed frameworks do not assume prior knowledge about the particular rate kinetics, thereby validating the conclusions for a large class of biological networks. In the deterministic setting, we show that unlike the incoherent feed-forward network structures (IFFLP or opposer modules), the modules containing negative feedback with buffer action (NFBLB or balancer modules) are robust to parametric fluctuations when a specific part of the network is assumed to remain unaffected. To this end, we propose a sufficient condition for imperfect adaptation and show that adding negative feedback in an IFFLP topology improves the robustness concerning parametric fluctuations. Further, we propose a stricter set of necessary conditions for imperfect adaptation. Turning to the stochastic scenario, we adopt a Wiener-Kolmogorov filter strategy to tune the parameters of a given network structure towards minimum output variance. We show that both NFBLB and IFFLP can be used as a reduced-order W-K filter. Further, we define the notion of nearest neighboring motifs to compare the output variances across different network structures. We argue that the NFBLB achieves adaptation at the cost of a variance higher than its nearest neighboring motifs whereas the IFFLP topology produces locally minimum variance while compared with its nearest neighboring motifs. We present numerical simulations to support the theoretical results. Overall, our results present a generic, systematic, and robust framework for advancing the understanding of complex biological networks.

Navigating Environmental Transitions: the Role of Phenotypic Variation in Bacterial Responses

The ability of bacteria to respond to changes in their environment is critical to their survival, allowing them to withstand stress, form complex communities, and induce virulence responses during host infection. A remarkable feature of many of these bacterial responses is that they are often variable across individual cells, despite occurring in an isogenic population exposed to a homogeneous environmental change, a phenomenon known as phenotypic heterogeneity. Phenotypic heterogeneity can enable bet-hedging or division of labor strategies that allow bacteria to survive fluctuating conditions. Investigating the significance of phenotypic heterogeneity in environmental transitions requires dynamic, single-cell data. Technical advances in quantitative single-cell measurements, imaging, and microfluidics have led to a surge of publications on this topic. Here, we review recent discoveries on single-cell bacterial responses to environmental transitions of various origins and complexities, from simple diauxic shifts to community behaviors in biofilm formation to virulence regulation during infection. We describe how these studies firmly establish that this form of heterogeneity is prevalent and a conserved mechanism by which bacteria cope with fluctuating conditions. We end with an outline of current challenges and future directions for the field. While it remains challenging to predict how an individual bacterium will respond to a given environmental input, we anticipate that capturing the dynamics of the process will begin to resolve this and facilitate rational perturbation of environmental responses for therapeutic and bioengineering purposes.

Biomarker-driven feedback control of synthetic biology systems for next-generation personalized medicine

Many current clinical therapies for chronic diseases involve administration of drugs using dosage and bioavailability parameters estimated for a generalized population. This standard approach carries the risk of under dosing, which may result in ineffective treatment, or overdosing, which may cause undesirable side effects. Consequently, maintaining a drug concentration in the therapeutic window often requires frequent monitoring, adversely affecting the patient’s quality of life. In contrast, endogenous biosystems have evolved finely tuned feedback control loops that govern the physiological functions of the body based on multiple input parameters. To provide personalized treatment for chronic diseases, therefore, we require synthetic systems that can similarly generate a calibrated therapeutic response. Such engineered autonomous closed-loop devices should incorporate a sensor that actively tracks and evaluates the disease severity based on one or more biomarkers, as well as components that utilize these molecular inputs to bio compute and deliver the appropriate level of therapeutic output. Here, we review recent advances in applications of the closed-loop design principle in biomedical implants for treating severe and chronic diseases, highlighting translational studies of cellular therapies. We describe the engineering principles and components of closed-loop therapeutic devices, and discuss their potential to become a key pillar of personalized medicine.

Intrathyroidal feedforward and feedback network regulating thyroid hormone synthesis and secretion

Thyroid hormones (THs), including T4 and T3, are produced and released by the thyroid gland under the stimulation of thyroid-stimulating hormone (TSH). The homeostasis of THs is regulated via the coordination of the hypothalamic-pituitary-thyroid axis, plasma binding proteins, and local metabolism in tissues. TH synthesis and secretion in the thyrocytes-containing thyroid follicles are exquisitely regulated by an elaborate molecular network comprising enzymes, transporters, signal transduction machineries, and transcription factors. In this article, we synthesized the relevant literature, organized and dissected the complex intrathyroidal regulatory network into structures amenable to functional interpretation and systems-level modeling. Multiple intertwined feedforward and feedback motifs were identified and described, centering around the transcriptional and posttranslational regulations involved in TH synthesis and secretion, including those underpinning the Wolff-Chaikoff and Plummer effects and thyroglobulin-mediated feedback regulation. A more thorough characterization of the intrathyroidal network from a systems biology perspective, including its topology, constituent network motifs, and nonlinear quantitative properties, can help us to better understand and predict the thyroidal dynamics in response to physiological signals, therapeutic interventions, and environmental disruptions.

A general hypothesis of multistable systems in pathophysiology

Despite intensive investigations numerous diseases remain etiologically puzzling and recalcitrant to treatments. A hypothesis is proposed here assuming that these difficulties are due to an unsuitable approach to the mechanisms of life, which is subjugated by an apparent complexity and fails to grasp the uniformity that lays behind. The stability of metabolism, despite the enormous complex of chemical reactions, suggests that reciprocal control is a prerequisite of life. Negative feedback loops have been known for a long time to maintain homeostasis, while more recently, different life processes involved in transitions or changes have been modeled by positive loops giving rise to bistable switches, also including various diseases. The present hypothesis makes a generalization, by assuming that any functional element of a biological system is involved in a positive or a negative feedback loop. Consequently, the hypothesis holds that the starting mechanism of any disease that affects a healthy human can be conceptually reduced to a bistable or multistationary loop system, thus providing a unifying model leading to the discovery of critical therapeutic targets.

A general theory of multistable systems in pathophysiology

Despite intensive investigations numerous diseases remain etiologically puzzling and recalcitrant to treatments. A theory is proposed here assuming that these difficulties are due to an unsuitable approach to the mechanisms of life, which is subjugated by an apparent complexity and fails to grasp the uniformity that lays behind. The stability of metabolism, despite the enormous complex of chemical reactions, suggests that reciprocal control is a prerequisite of life. Negative feedback loops have been known for a long time to maintain homeostasis, while more recently, different life processes involved in transitions or changes have been modeled by positive loops giving rise to bistable switches, also including various diseases. The present theory makes a generalization, by assuming that any functional element of a biological system is involved in a positive or a negative feedback loop. Consequently, the theory holds that the starting mechanism of any disease that affects a healthy human can be conceptually reduced to a bistable or multistationary loop system, thus providing a unifying model leading to the discovery of critical therapeutic targets.

A general hypothesis of multistable systems in pathophysiology

Despite intensive investigations numerous diseases remain etiologically puzzling and recalcitrant to treatments. A hypothesis is proposed here assuming that these difficulties are due to an unsuitable approach to the mechanisms of life, which is subjugated by an apparent complexity and fails to grasp the uniformity that lays behind. The stability of metabolism, despite the enormous complex of chemical reactions, suggests that reciprocal control is a prerequisite of life. Negative feedback loops have been known for a long time to maintain homeostasis, while more recently, different life processes involved in transitions or changes have been modeled by positive loops giving rise to bistable switches, also including various diseases. The present hypothesis makes a generalization, by assuming that any functional element of a biological system is involved in a positive or a negative feedback loop. Consequently, the hypothesis holds that the starting mechanism of any disease that affects a healthy human can be conceptually reduced to a bistable or multistationary loop system, thus providing a unifying model leading to the discovery of critical therapeutic targets.

Quantifying the phenotypic information in mRNA abundance

Quantifying the dependency between mRNA abundance and downstream cellular phenotypes is a fundamental open problem in biology. Advances in multimodal single-cell measurement technologies provide an opportunity to apply new computational frameworks to dissect the contribution of individual genes and gene combinations to a given phenotype. Using an information theory approach, we analyzed multimodal data of the expression of 83 genes in the Ca2+ signaling network and the dynamic Ca2+ response in the same cell. We found that the overall expression levels of these 83 genes explain approximately 60% of Ca2+ signal entropy. The average contribution of each single gene was 17%, revealing a large degree of redundancy between genes. Using different heuristics, we estimated the dependency between the size of a gene set and its information content, revealing that on average, a set of 53 genes contains 54% of the information about Ca2+ signaling. Our results provide the first direct quantification of information content about complex cellular phenotype that exists in mRNA abundance measurements.

Thalamocortical bistable switch as a theoretical model of fibromyalgia pathogenesis inferred from a literature survey

Fibromyalgia (FM) is an unsolved central pain processing disturbance. We aim to provide a unifying model for FM pathogenesis based on a loop network involving thalamocortical regions, i.e., the ventroposterior lateral thalamus (VPL), the somatosensory cortex (SC), and the thalamic reticular nucleus (TRN). The dynamics of the loop have been described by three differential equations having neuron mean firing rates as variables and containing Hill functions to model mutual interactions among the loop elements. A computational analysis conducted with MATLAB has shown a transition from monostability to bistability of the loop behavior for a weakening of GABAergic transmission between TRN and VPL. This involves the appearance of a high-firing-rate steady state, which becomes dominant and is assumed to represent pathogenic pain processing giving rise to chronic pain. Our model is consistent with a bulk of literature evidence, such as neuroimaging and pharmacological data collected on FM patients, and with correlations between FM and immunoendocrine conditions, such as stress, perimenopause, chronic inflammation, obesity, and chronic dizziness. The model suggests that critical targets for FM treatment are to be found among immunoendocrine pathways leading to GABA/glutamate imbalance having an impact on the thalamocortical system.

Dynamics and functional roles of splicing factor autoregulation

Non-core spliceosome components are essential, conserved regulators of alternative splicing. They provide concentration-dependent control of diverse pre-mRNAs. Many splicing factors direct unproductive splicing of their own pre-mRNAs through negative autoregulation. However, the impact of such feedback loops on splicing dynamics at the single-cell level remains unclear. Here, we developed a system to quantitatively analyze negative autoregulatory splicing dynamics by splicing factor SRSF1 in response to perturbations in single HEK293 cells. We show that negative autoregulatory splicing provides critical functions for gene regulation, establishing a ceiling of SRSF1 protein concentration, reducing cell-cell heterogeneity in SRSF1 levels, and buffering variation in transcription. Most important, it adapts SRSF1 splicing activity to variations in demand from other pre-mRNA substrates. A minimal mathematical model of autoregulatory splicing explains these experimentally observed features and provides values for effective biochemical parameters. These results reveal the unique functional roles that splicing negative autoregulation plays in homeostatically regulating transcriptional programs.

A feedback control principle common to several biological and engineered systems

Feedback control is used by many distributed systems to optimize behaviour. Traditional feedback control algorithms spend significant resources to constantly sense and stabilize a continuous control variable of interest, such as vehicle speed for implementing cruise control, or body temperature for maintaining homeostasis. By contrast, discrete-event feedback (e.g. a server acknowledging when data are successfully transmitted, or a brief antennal interaction when an ant returns to the nest after successful foraging) can reduce costs associated with monitoring a continuous variable; however, optimizing behaviour in this setting requires alternative strategies. Here, we studied parallels between discrete-event feedback control strategies in biological and engineered systems. We found that two common engineering rules-additive-increase, upon positive feedback, and multiplicative-decrease, upon negative feedback, and multiplicative-increase multiplicative-decrease-are used by diverse biological systems, including for regulating foraging by harvester ant colonies, for maintaining cell-size homeostasis, and for synaptic learning and adaptation in neural circuits. These rules support several goals of these systems, including optimizing efficiency (i.e. using all available resources); splitting resources fairly among cooperating agents, or conversely, acquiring resources quickly among competing agents; and minimizing the latency of responses, especially when conditions change. We hypothesize that theoretical frameworks from distributed computing may offer new ways to analyse adaptation behaviour of biology systems, and in return, biological strategies may inspire new algorithms for discrete-event feedback control in engineering.

The evolution of DUX4 gene regulation and its implication for facioscapulohumeral muscular dystrophy

Double homeobox 4 (DUX4) is an early embryonic transcription factor whose expression in the skeletal muscle causes facioscapulohumeral muscular dystrophy (FSHD). Despite decades of research, our knowledge of FSHD and DUX4 biology is incomplete, and the disease has currently no cures or targeted therapies. The unusual evolutionary origin of DUX4, its extensive epigenetic and post-transcriptional gene regulation, and various feedback regulatory loops that control its expression and function all contribute to the highly complex nature of FSHD pathogenesis. In this minireview, I synthesize the current state of knowledge in DUX4 and FSHD biology to highlight key areas where further research is needed to better understand DUX4 regulation. I also emphasize post-transcriptional regulation of and by DUX4 via changes in RNA and protein stability that might underlie key features of FSHD pathophysiology. Finally, I discuss the various feedback loops involved in DUX4 regulation and the context-specific consequences of its expression, which could be key to developing novel therapeutic approaches to combat FSHD.

Teaching computational systems biology with an eye on quantitative systems pharmacology at the undergraduate level: Why do it, who would take it, and what should we teach?

Computational systems biology (CSB) is a field that emerged primarily as the product of research activities. As such, it grew in several directions in a distributed and uncoordinated manner making the area appealing and fascinating. The idea of not having to follow a specific path but instead creating one fueled innovation. As the field matured, several interdisciplinary graduate programs emerged attempting to educate future generations of computational systems biologists. These educational initiatives coordinated the dissemination of information across student populations that had already decided to specialize in this field. However, we are now entering an era where CSB, having established itself as a valuable research discipline, is attempting the next major step: Entering undergraduate curricula. As interesting as this endeavor may sound, it has several difficulties, mainly because the field is not uniformly defined. In this manuscript, we argue that this diversity is a significant advantage and that several incarnations of an undergraduate-level CSB biology course could, and should, be developed tailored to programmatic needs. In this manuscript, we share our experiences creating a course as part of a Biomedical Engineering program.

Control of mammalian cell-based devices with genetic programming

Synthetic biology increasingly enables the construction of sophisticated functions in mammalian cells. A particularly promising frontier combines concepts drawn from industrial process control engineering-which is used to confer and balance properties such as stability and efficiency-with understanding as to how living systems have evolved to perform similar tasks with biological components. In this review, we first survey the state-of-the-art for both technologies and strategies available for genetic programming in mammalian cells. We then discuss recent progress in implementing programming objectives inspired by engineered and natural control mechanisms. Finally, we consider the transformative role of model-guided design in the present and future construction of customized mammalian cell functions for applications in biotechnology, medicine, and fundamental research.