Principles of Cell Circuits for Tissue Repair and Fibrosis

Tissue repair is a protective response after injury, but repetitive or prolonged injury can lead to fibrosis, a pathological state of excessive scarring. To pinpoint the dynamic mechanisms underlying fibrosis, it is important to understand the principles of the cell circuits that carry out tissue repair. In this study, we establish a cell-circuit framework for the myofibroblast-macrophage circuit in wound healing, including the accumulation of scar-forming extracellular matrix. We find that fibrosis results from multistability between three outcomes, which we term "hot fibrosis" characterized by many macrophages, "cold fibrosis" lacking macrophages, and normal wound healing. This framework clarifies several unexplained phenomena including the paradoxical effect of macrophage depletion, the limited time-window in which removing inflammation leads to healing, and why scar maturation takes months. We define key parameters that control the transition from healing to fibrosis, which may serve as potential targets for therapeutic reduction of fibrosis.

Testing the Anna Karenina Principle in Human Microbiome-Associated Diseases

The AKP (Anna Karenina principle), which refers to observations inspired by the opening line of Leo Tolstoy's Anna Karenina, “all happy families are all alike; each unhappy family is unhappy in its own way,” predicts that all “healthy” microbiomes are alike and each disease-associated microbiome is “sick” in its own way in human microbiome-associated diseases (MADs). The AKP hypothesis predicts the rise of heterogeneity/stochasticity in human microbiomes associated with dysbiosis due to MADs. We used the beta-diversity in Hill numbers and stochasticity analysis to detect AKP and anti-AKP effects. We tested the AKP with 27 human MAD studies and discovered that the AKP, anti-AKP, and non-AKP effects were exhibited in approximately 50%, 25%, and 25% of the MAD cases, respectively. Mechanistically, AKP effects are primarily influenced by highly dominant microbial species and less influenced by rare species. In contrast, all species appear to play equal roles in influencing anti-AKP effects.

•

About a half of microbiome-associated diseases follow AKP (Anna Karenina principle)

•

AKP effects are primarily influenced by highly dominant microbial species

•

About one-fourth of microbiome-associated diseases follow the anti-AKP

•

All species appear to play equal roles in influencing the anti-AKP effects

Cell Shape and Durotaxis Explained from Cell-Extracellular Matrix Forces and Focal Adhesion Dynamics

Many cells are small and rounded on soft extracellular matrices (ECM), elongated on stiffer ECMs, and flattened on hard ECMs. Cells also migrate up stiffness gradients (durotaxis). Using a hybrid cellular Potts and finite-element model extended with ODE-based models of focal adhesion (FA) turnover, we show that the full range of cell shape and durotaxis can be explained in unison from dynamics of FAs, in contrast to previous mathematical models. In our 2D cell-shape model, FAs grow due to cell traction forces. Forces develop faster on stiff ECMs, causing FAs to stabilize and, consequently, cells to spread on stiff ECMs. If ECM stress further stabilizes FAs, cells elongate on substrates of intermediate stiffness. We show that durotaxis follows from the same set of assumptions. Our model contributes to the understanding of the basic responses of cells to ECM stiffness, paving the way for future modeling of more complex cell-ECM interactions.

The mechanism of full activation of tumor suppressor PTEN at the phosphoinositide-enriched membrane

Tumor suppressor PTEN, the second most highly mutated protein in cancer, dephosphorylates signaling lipid PIP₃ produced by PI3Ks. Excess PIP₃ promotes cell proliferation. The mechanism at the membrane of this pivotal phosphatase is unknown hindering drug discovery. Exploiting explicit solvent simulations, we tracked full-length PTEN trafficking from the cytosol to the membrane. We observed its interaction with membranes composed of zwitterionic phosphatidylcholine, anionic phosphatidylserine, and phosphoinositides, including signaling lipids PIP₂ and PIP₃. We tracked its moving away from the zwitterionic and getting absorbed onto anionic membrane that harbors PIP₃. We followed it localizing on microdomains enriched in signaling lipids, as PI3K does, and observed PIP₃ allosterically unfolding the N-terminal PIP₂ binding domain, positioning it favorably for the polybasic motif interaction with PIP₂. Finally, we determined PTEN catalytic action at the membrane, all in line with experimental observations, deciphering the mechanisms of how PTEN anchors to the membrane and restrains cancer.

•

PTEN localizes on membrane microdomains enriched in phosphoinositides, as PI3K does

•

Full PTEN activation requires both signaling lipids, PIP₂ and PIP₃

•

Strong salt bridge interactions sustain stable PTEN membrane localization

•

Substrate-induced P loop conformational change implicates PTEN catalytic activity

Autoamplification and Competition Drive Symmetry Breaking: Initiation of Centriole Duplication by the PLK4-STIL Network

Centrioles, the cores of centrosomes and cilia, duplicate every cell cycle to ensure their faithful inheritance. How only a single procentriole is produced on each mother centriole remains enigmatic. We propose the first mechanistic biophysical model for procentriole initiation which posits that interactions between kinase PLK4 and its activator-substrate STIL are central for procentriole initiation. The model recapitulates the transition from a uniform "ring" of PLK4 surrounding the mother centriole to a single PLK4 "spot" that initiates procentriole assembly. This symmetry breaking requires autocatalytic activation of PLK4 and enhanced centriolar anchoring of PLK4 by phosphorylated STIL. We find that in situ degradation of active PLK4 cannot break symmetry. The model predicts that competition between transient PLK4 activity maxima for PLK4-STIL complexes destabilizes the PLK4 ring and produces instead a single PLK4 spot. Weakening of competition by overexpression of PLK4 and STIL causes progressive addition of supernumerary procentrioles, as observed experimentally.

A Hybrid Flux Balance Analysis and Machine Learning Pipeline Elucidates Metabolic Adaptation in Cyanobacteria

Machine learning has recently emerged as a promising tool for inferring multi-omic relationships in biological systems. At the same time, genome-scale metabolic models (GSMMs) can be integrated with such multi-omic data to refine phenotypic predictions. In this work, we use a multi-omic machine learning pipeline to analyze a GSMM of Synechococcus sp. PCC 7002, a cyanobacterium with large potential to produce renewable biofuels. We use regularized flux balance analysis to observe flux response between conditions across photosynthesis and energy metabolism. We then incorporate principal-component analysis, k-means clustering, and LASSO regularization to reduce dimensionality and extract key cross-omic features. Our results suggest that combining metabolic modeling with machine learning elucidates mechanisms used by cyanobacteria to cope with fluctuations in light intensity and salinity that cannot be detected using transcriptomics alone. Furthermore, GSMMs introduce critical mechanistic details that improve the performance of omic-based machine learning methods.

Integrating Quantitative Assays with Biologically Based Mathematical Modeling for Predictive Oncology

We provide an overview on the use of biological assays to calibrate and initialize mechanism-based models of cancer phenomena. Although artificial intelligence methods currently dominate the landscape in computational oncology, mathematical models that seek to explicitly incorporate biological mechanisms into their formalism are of increasing interest. These models can guide experimental design and provide insights into the underlying mechanisms of cancer progression. Historically, these models have included a myriad of parameters that have been difficult to quantify in biologically relevant systems, limiting their practical insights. Recently, however, there has been much interest calibrating biologically based models with the quantitative measurements available from (for example) RNA sequencing, time-resolved microscopy, and in vivo imaging. In this contribution, we summarize how a variety of experimental methods quantify tumor characteristics from the molecular to tissue scales and describe how such data can be directly integrated with mechanism-based models to improve predictions of tumor growth and treatment response.

Cell size distribution of lineage data: analytic results and parameter inference

Recent advances in single-cell technologies have enabled time-resolved measurements of the cell size over several cell cycles. These data encode information on how cells correct size aberrations so that they do not grow abnormally large or small. Here, we formulate a piecewise deterministic Markov model describing the evolution of the cell size over many generations, for all three cell size homeostasis strategies (timer, sizer, and adder). The model is solved to obtain an analytical expression for the non-Gaussian cell size distribution in a cell lineage; the theory is used to understand how the shape of the distribution is influenced by the parameters controlling the dynamics of the cell cycle and by the choice of cell tracking protocol. The theoretical cell size distribution is found to provide an excellent match to the experimental cell size distribution of E. coli lineage data collected under various growth conditions.

Mathematical Modeling Highlights the Complex Role of AKT in TRAIL-Induced Apoptosis of Colorectal Carcinoma Cells

Protein kinase B/AKT is a highly connected protein involved in a range of signaling pathways. Although it is known to regulate several proteins in the apoptotic pathway, its system-level effects remain poorly understood. We investigated the dynamic interactions between AKT and key apoptotic proteins and constructed a deterministic ordinary differential equation protein interaction model of extrinsic apoptosis. Incorporating AKT and its indirect inhibitor, phosphatase and tensin homolog (PTEN), this was used to generate predictions of system dynamics. Using eigen analysis, we identified AKT and cytochrome c as the protein species most sensitive to perturbations. Cell death assays in Type II HCT116 colorectal carcinoma cells revealed a tendency toward Type I cell death behavior in the XIAP^−/− background, with cells displaying accelerated TRAIL-induced apoptosis. Finally, AKT inhibition experiments implicated AKT and not PTEN in influencing apoptotic proteins during early phases of TRAIL-induced apoptosis.

•

TRAIL-induced apoptosis model describes AKT protein interaction dynamics

•

AKT and cytochrome c identified as the proteins most sensitive to perturbations

•

HCT116 cells shift from Type II to Type I cell death behavior in XIAP^−/− background

•

AKT and not PTEN influences early phases of TRAIL-induced apoptosis

Architectural Principles for Characterizing the Performance of Antithetic Integral Feedback Networks

As we begin to design increasingly complex synthetic biomolecular systems, it is essential to develop rational design methodologies that yield predictable circuit performance. Here we apply mathematical tools from the theory of control and dynamical systems to yield practical insights into the architecture and function of a particular class of biological feedback circuit. Specifically, we show that it is possible to analytically characterize both the operating regime and performance tradeoffs of an antithetic integral feedback circuit architecture. Furthermore, we demonstrate how these principles can be applied to inform the design process of a particular synthetic feedback circuit.

Learning from HIV-1 to predict the immunogenicity of T cell epitopes in SARS-CoV-2

We describe a physics-based learning model for predicting the immunogenicity of cytotoxic T lymphocyte (CTL) epitopes derived from diverse pathogens including SARS-CoV-2. The model was trained and optimized on the relative immunodominance of CTL epitopes in human immunodeficiency virus infection. Its accuracy was tested against experimental data from patients with COVID-19. Our model predicts that only some SARS-CoV-2 epitopes predicted to bind to HLA molecules are immunogenic. The immunogenic CTL epitopes across all SARS-CoV-2 proteins are predicted to provide broad population coverage, but those from the SARS-CoV-2 spike protein alone are unlikely to do so. Our model also predicts that several immunogenic SARS-CoV-2 CTL epitopes are identical to seasonal coronaviruses circulating in the population and such cross-reactive CD8+ T cells can indeed be detected in prepandemic blood donors, suggesting that some level of CTL immunity against COVID-19 may be present in some individuals before SARS-CoV-2 infection.

Random Parametric Perturbations of Gene Regulatory Circuit Uncover State Transitions in Cell Cycle

Many biological processes involve precise cellular state transitions controlled by complex gene regulation. Here, we use budding yeast cell cycle as a model system and explore how a gene regulatory circuit encodes essential information of state transitions. We present a generalized random circuit perturbation method for circuits containing heterogeneous regulation types and its usage to analyze both steady and oscillatory states from an ensemble of circuit models with random kinetic parameters. The stable steady states form robust clusters with a circular structure that are associated with cell cycle phases. This circular structure in the clusters is consistent with single-cell RNA sequencing data. The oscillatory states specify the irreversible state transitions along cell cycle progression. Furthermore, we identify possible mechanisms to understand the irreversible state transitions from the steady states. We expect this approach to be robust and generally applicable to unbiasedly predict dynamical transitions of a gene regulatory circuit.

Metagenomic discovery and functional validation of L-asparaginases with anti-leukemic effect from the Caspian Sea

By screening 27,000 publicly available prokaryotic genomes, we recovered ca. 6300 type I and ca. 5200 type II putative L-asparaginase highlighting the vast potential of prokaryotes. Caspian water with similar salt composition to the human serum was targeted for in silico L-asparaginase screening. We screened ca. three million predicted genes of its assembled metagenomes that resulted in annotation of 87 putative L-asparaginase genes. The L-asparagine hydrolysis was experimentally confirmed by synthesizing and cloning three selected genes in E. coli. Catalytic parameters of the purified enzymes were determined to be among the most desirable reported values. Two recombinant enzymes represented remarkable anti-proliferative activity (IC50 <1IU/ml) against leukemia cell line Jurkat while no cytotoxic effect on human erythrocytes or human umbilical vein endothelial cells was detected. Similar salinity and ionic concentration of the Caspian water to the human serum highlights the potential of secretory L-asparaginases recovered from these metagenomes as potential treatment agents.

Theoretical Models of Neural Development

Constructing a functioning nervous system requires the precise orchestration of a vast array of mechanical, molecular, and neural-activity-dependent cues. Theoretical models can play a vital role in helping to frame quantitative issues, reveal mathematical commonalities between apparently diverse systems, identify what is and what is not possible in principle, and test the abilities of specific mechanisms to explain the data. This review focuses on the progress that has been made over the last decade in our theoretical understanding of neural development.

Exploration of Cell Development Pathways through High-Dimensional Single Cell Analysis in Trajectory Space

High-dimensional single cell profiling coupled with computational modeling is emerging as a powerful tool to elucidate developmental programs directing cell lineages. We introduce tSpace, an algorithm based on the concept of "trajectory space", in which cells are defined by their distance along nearest neighbor pathways to every other cell in a population. Graphical mapping of cells in trajectory space allows unsupervised reconstruction and exploration of complex developmental sequences. Applied to flow and mass cytometry data, the method faithfully reconstructs thymic T cell development and reveals development and trafficking regulation of tonsillar B cells. Applied to the single cell transcriptome of mouse intestine and C. elegans, the method recapitulates development from intestinal stem cells to specialized epithelial phenotypes more faithfully than existing algorithms and orders C. elegans cells concordantly to the associated embryonic time. tSpace profiling of complex populations is well suited for hypothesis generation in developing cell systems.

Molecular Crowding and Diffusion-Capture in Synapses

Cell membranes often contain domains with important physiological functions. A typical example are neuronal synapses, whose capacity to capture receptors for neurotransmitters is central to neuronal functions. Receptors diffuse in the membrane until they are stabilized by interactions with stable elements, the scaffold. Single particle tracking experiments demonstrated that these interactions are rather weak and that lateral diffusion is strongly impaired in the post-synaptic membrane due to molecular crowding. We investigated how the distribution of scaffolding molecules and molecular crowding affect the capture of receptors. In particle-based Monte Carlo simulations, based on experimental data of molecular diffusion and organization, crowding enhanced the receptor-scaffold interaction but reduced the capture of new molecules. The distribution of scaffolding sites in several clusters reduced crowding and fostered the exchange of molecules accelerating synaptic plasticity. Synapses could switch between two regimes, becoming more stable or more plastic depending on the internal distribution of molecules.

•

The good: molecular crowding enhances the interaction receptors-scaffold

•

The bad: the exchange of molecules with extrasynaptic areas is reduced by crowding

•

Molecular crowding helps synapses to be stable

•

Nanoclusters of scaffold sites reduce crowding effects and favor synaptic plasticity

Numerical Cognition Based on Precise Counting with a Single Spiking Neuron

Insects are able to solve basic numerical cognition tasks. We show that estimation of numerosity can be realized and learned by a single spiking neuron with an appropriate synaptic plasticity rule. This model can be efficiently trained to detect arbitrary spatiotemporal spike patterns on a noisy and dynamic background with high precision and low variance. When put to test in a task that requires counting of visual concepts in a static image it required considerably less training epochs than a convolutional neural network to achieve equal performance. When mimicking a behavioral task in free-flying bees that requires numerical cognition, the model reaches a similar success rate in making correct decisions. We propose that using action potentials to represent basic numerical concepts with a single spiking neuron is beneficial for organisms with small brains and limited neuronal resources.

SkinBug: an artificial intelligence approach to predict human skin microbiome-mediated metabolism of biotics and xenobiotics

In addition to being pivotal for the host health, the skin microbiome possesses a large reservoir of metabolic enzymes, which can metabolize molecules (cosmetics, medicines, pollutants, etc.) that form a major part of the skin exposome. Therefore, to predict the complete metabolism of any molecule by skin microbiome, a curated database of metabolic enzymes (1,094,153), reactions, and substrates from ∼900 bacterial species from 19 different skin sites were used to develop “SkinBug.” It integrates machine learning, neural networks, and chemoinformatics methods, and displays a multiclass multilabel accuracy of up to 82.4% and binary accuracy of up to 90.0%. SkinBug predicts all possible metabolic reactions and associated enzymes, reaction centers, skin microbiome species harboring the enzyme, and the respective skin sites. Thus, SkinBug will be an indispensable tool to predict xenobiotic/biotic metabolism by skin microbiome and will find applications in exposome and microbiome studies, dermatology, and skin cancer research.

•

SkinBug is AI/ML-based tool to predict metabolism of molecules by Skin microbiome

•

Database of 1,094,153 metabolic enzymes from 897 pangenomes of skin microbiome

•

Predicts enzymes, bacterial species, and skin sites for the predicted reactions

•

82.4% multilabel and 90.0% binary accuracy, and validated on 28 diverse real cases

Overcome Competitive Exclusion in Ecosystems

Explaining biodiversity in nature is a fundamental problem in ecology. An outstanding challenge is embodied in the so-called Competitive Exclusion Principle: two species competing for one limiting resource cannot coexist at constant population densities, or more generally, the number of consumer species in steady coexistence cannot exceed that of resources. The fact that competitive exclusion is rarely observed in natural ecosystems has not been fully understood. Here we show that, by forming chasing pairs and chasing triplets among the consumers and resources in the consumption process, the Competitive Exclusion Principle can be naturally violated. The modeling framework developed here is broadly applicable and can be used to explain the biodiversity of many consumer-resource ecosystems and hence deepens our understanding of biodiversity in nature.

•

Foraging with only chasing pairs cannot break the Competitive Exclusion Principle (CEP)

•

A population dynamics model involving both chasing pairs and triplets can break CEP

•

Redundant foraging within the chasing triplets facilitates species coexistence

•

The theoretical framework is testable in ecosystems involving pack hunting

Mechanistic Modeling of Biochemical Systems without A Priori Parameter Values Using the Design Space Toolbox v.3.0

Mechanistic models of biochemical systems provide a rigorous description of biological phenomena. They are indispensable for making predictions and elucidating biological design principles. To date, mathematical analysis and characterization of these models encounter a bottleneck consisting of large numbers of unknown parameter values. Here, we introduce the Design Space Toolbox v.3.0 (DST3), a software implementation of the Design Space formalism enabling mechanistic modeling without requiring previous knowledge of parameter values. This is achieved by using a phenotype-centric modeling approach, in which the system is first decomposed into a series of biochemical phenotypes. Parameter values realizing phenotypes of interest are subsequently predicted. DST3 represents the most generally applicable implementation of the Design Space formalism and offers unique advantages over earlier versions. By expanding the Design Space formalism and streamlining its distribution, DST3 represents a valuable tool for elucidating biological design principles and designing novel synthetic circuits.

•

DST3 extends the Design Space formalism by identifying additional phenotypes

•

Additional phenotypes arise from cycles, conservations, and metabolic imbalances

•

DST3 enables mechanistic modeling without previous knowledge of parameter values

•

It fully unlocks the potential of the novel phenotype-centric modeling strategy

Experimental and Theoretical Evidence for Bidirectional Signaling via Core Planar Polarity Protein Complexes in Drosophila

In developing tissues, sheets of cells become planar polarized, enabling coordination of cell behaviors. It has been suggested that "signaling" of polarity information between cells may occur either bidirectionally or monodirectionally between the molecules Frizzled (Fz) and Van Gogh (Vang). Using computational modeling we find that both bidirectional and monodirectional signaling models reproduce known non-autonomous phenotypes derived from patches of mutant tissue of key molecules but predict different phenotypes from double mutant tissue, which have previously given conflicting experimental results. Furthermore, we re-examine experimental phenotypes in the Drosophila wing, concluding that signaling is most likely bidirectional. Our modeling suggests that bidirectional signaling can be mediated either indirectly via bidirectional feedbacks between asymmetric intercellular protein complexes or directly via different affinities for protein binding in intercellular complexes, suggesting future avenues for investigation. Our findings offer insight into mechanisms of juxtacrine cell signaling and how tissue-scale properties emerge from individual cell behaviors.

Optimum Threshold Minimizes Noise in Timing of Intracellular Events

How the noisy expression of regulatory proteins affects timing of intracellular events is an intriguing fundamental problem that influences diverse cellular processes. Here we use the bacteriophage λ to study event timing in individual cells where cell lysis is the result of expression and accumulation of a single protein (holin) in the Escherichia coli cell membrane up to a critical threshold level. Site-directed mutagenesis of the holin gene generated phage variants that vary in their lysis times from 30 to 190 min. Observation of the lysis times of single cells reveals an intriguing finding-the noise in lysis timing first decreases with increasing lysis time to reach a minimum and then sharply increases at longer lysis times. A mathematical model with stochastic expression of holin together with dilution from cell growth was sufficient to explain the non-monotonic noise profile and identify holin accumulation thresholds that generate precision in lysis timing.

Impact of sex and pathophysiology on optimal drug choice in hypertensive rats: quantitative insights for precision medicine

Less than half of all hypertensive patients receiving treatment are successful in normalizing their blood pressure. Despite the complexity and heterogeneity of hypertension, the current antihypertensive guidelines are not tailored to the individual patient. As a step toward individualized treatment, we develop a quantitative systems pharmacology model of blood pressure regulation in the spontaneously hypertensive rat (SHR) and generate sex-specific virtual populations of SHRs to account for the heterogeneity between the sexes and within the pathophysiology of hypertension. We then used the mechanistic model integrated with machine learning tools to study how variability in these mechanisms leads to differential responses in rodents to the four primary classes of antihypertensive drugs. We found that both the sex and the pathophysiological profile of the individual play a major role in the response to hypertensive treatments. These results provide insight into potential areas to apply precision medicine in human primary hypertension.