Design principles of cell circuits with paradoxical components

Trade-off between the antiviral and vaccinal effects of antibody therapy in the humoral response to HIV

Antibody therapy for HIV-1 infection exerts two broad effects: a drug-like, antiviral effect, which rapidly lowers the viral load, and a vaccinal effect, which may control the viral load long-term by improving the immune response. Here, we elucidate a trade-off between these two effects as they pertain to the humoral response, which may compromise antibody therapy aimed at eliciting long-term HIV-1 remission. We developed a multi-scale computational model that combined within-host viral dynamics and stochastic simulations of the germinal centre (GC) reaction, enabling simultaneous quantification of the antiviral and vaccinal effects of antibody therapy. The model predicted that increasing antibody dosage or antibody-antigen affinity increased immune complex formation and enhanced GC output. Beyond a point, however, a strong antiviral effect reduced antigen levels substantially, extinguishing GCs and limiting the humoral response. We found signatures of this trade-off in clinical studies. Accounting for the trade-off could be important in optimizing antibody therapy for HIV-1 remission.

CaMKK2: bridging the gap between Ca2+ signaling and energy-sensing

Calcium (Ca2+) ions are ubiquitous and indispensable signaling messengers that regulate virtually every cell function. The unique ability of Ca2+ to regulate so many different processes yet cause stimulus specific changes in cell function requires sensing and decoding of Ca2+ signals. Ca2+-sensing proteins, such as calmodulin, decode Ca2+ signals by binding and modifying the function of a diverse range of effector proteins. These effectors include the Ca2+-calmodulin dependent protein kinase kinase-2 (CaMKK2) enzyme, which is the core component of a signaling cascade that plays a key role in important physiological and pathophysiological processes, including brain function and cancer. In addition to its role as a Ca2+ signal decoder, CaMKK2 also serves as an important junction point that connects Ca2+ signaling with energy metabolism. By activating the metabolic regulator AMP-activated protein kinase (AMPK), CaMKK2 integrates Ca2+ signals with cellular energy status, enabling the synchronization of cellular activities regulated by Ca2+ with energy availability. Here, we review the structure, regulation, and function of CaMKK2 and discuss its potential as a treatment target for neurological disorders, metabolic disease, and cancer.

Context-dependent regulation of lipid accumulation in adipocytes by a HIF1α-PPARγ feedback network

Hypoxia-induced upregulation of HIF1α triggers adipose tissue dysfunction and insulin resistance in obese patients. HIF1α closely interacts with PPARγ, the master regulator of adipocyte differentiation and lipid accumulation, but there are conflicting results regarding how this interaction controls the excessive lipid accumulation that drives adipocyte dysfunction. To directly address these conflicts, we established a differentiation system that recapitulated prior seemingly opposing observations made across different experimental settings. Using single-cell imaging and coarse-grained mathematical modeling, we show how HIF1α can both promote and repress lipid accumulation during adipogenesis. Our model predicted and our experiments confirmed that the opposing roles of HIF1α are isolated from each other by the positive-feedback-mediated upregulation of PPARγ that drives adipocyte differentiation. Finally, we identify three factors: strength of the differentiation cue, timing of hypoxic perturbation, and strength of HIF1α expression changes that, when considered together, provide an explanation for many of the previous conflicting reports.

Competition and evolutionary selection among core regulatory motifs in gene expression control

Gene products that are beneficial in one environment may become burdensome in another, prompting the emergence of diverse regulatory schemes that carry their own bioenergetic cost. By ensuring that regulators are only expressed when needed, we demonstrate that autoregulation generally offers an advantage in an environment combining mutation and time-varying selection. Whether positive or negative feedback emerges as dominant depends primarily on the demand for the target gene product, typically to ensure that the detrimental impact of inevitable mutations is minimized. While self-repression of the regulator curbs the spread of these loss-of-function mutations, self-activation instead facilitates their propagation. By analyzing the transcription network of multiple model organisms, we reveal that reduced bioenergetic cost may contribute to the preferential selection of autoregulation among transcription factors. Our results not only uncover how seemingly equivalent regulatory motifs have fundamentally different impact on population structure, growth dynamics, and evolutionary outcomes, but they can also be leveraged to promote the design of evolutionarily robust synthetic gene circuits.

Matrix stability and bifurcation analysis by a network-based approach

In this paper, we develop a network-based methodology to investigate the problems related to matrix stability and bifurcations in nonlinear dynamical systems. By matching a matrix with a network, i.e., interaction graph, we propose a new network-based matrix analysis method by proving a theorem about matrix determinant under which matrix stability can be considered in terms of feedback loops. Especially, the approach can tell us how a node, a path, or a feedback loop in the interaction graph affects matrix stability. In addition, the roles played by a node, a path, or a feedback loop in determining bifurcations in nonlinear dynamical systems can also be revealed. Therefore, the approach can help us to screen optimal node or node combinations. By perturbing them, unstable matrices can be stabilized more efficiently or bifurcations can be induced more easily to realize desired state transitions. To illustrate feasibility and efficiency of the approach, some simple matrices are used to show how single or combinatorial perturbations affect matrix stability and induce bifurcations. In addition, the main idea is also illustrated through a biological problem related to T cell development with three nodes: TCF-1, GATA3, and PU.1, which can be considered to be a three-variable nonlinear dynamical system. The approach is especially helpful in understanding crucial roles of single or molecule combinations in biomolecular networks. The approach presented here can be expected to analyze other biological networks related to cell fate transitions and systematic perturbation strategy selection.

Redox-Mediated Rewiring of Signalling Pathways: The Role of a Cellular Clock in Brain Health and Disease

Metazoan signalling pathways can be rewired to dampen or amplify the rate of events, such as those that occur in development and aging. Given that a linear network topology restricts the capacity to rewire signalling pathways, such scalability of the pace of biological events suggests the existence of programmable non-linear elements in the underlying signalling pathways. Here, we review the network topology of key signalling pathways with a focus on redox-sensitive proteins, including PTEN and Ras GTPase, that reshape the connectivity profile of signalling pathways in response to an altered redox state. While this network-level impact of redox is achieved by the modulation of individual redox-sensitive proteins, it is the population by these proteins of critical nodes in a network topology of signal transduction pathways that amplifies the impact of redox-mediated reprogramming. We propose that redox-mediated rewiring is essential to regulate the rate of transmission of biological signals, giving rise to a programmable cellular clock that orchestrates the pace of biological phenomena such as development and aging. We further review the evidence that an aberrant redox-mediated modulation of output of the cellular clock contributes to the emergence of pathological conditions affecting the human brain.

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.

Making human immune systems more interpretable through systems immunology

The human immune system is a distributed system of specialized cell populations with unique functions that collectively give rise to immune responses to infections and during immune-mediated diseases. Cell composition, plasma proteins, and functional responses vary among individuals, making the system difficult to interpret, but this variation is nonrandom. With careful analyses using novel experimental and computational tools, human immune system composition and function carry interpretable information. Here, we propose that systems-level analyses offer an opportunity to make human immune responses more interpretable in the future, and we discuss herein important considerations and lessons learned to this end. Predictable human immunology holds implications for better diagnostic and curative precision in patients with infectious and immune-associated diseases.

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

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

A multifarious exploration of synaptic tagging and capture hypothesis in synaptic plasticity: Development of an integrated mathematical model and computational experiments

The synaptic tagging and capture (STC) hypothesis not only explain the integration and association of synaptic activities, but also the formation of learning and memory. The synaptic pathways involved in the synaptic tagging and capture phenomenon are called STC pathways. The STC hypothesis provides a potential explanation of the neuronal and synaptic processes underlying the synaptic consolidation of memories. Several mechanisms and molecules have been proposed to explain the process of memory allocation and synaptic tags, respectively. However, a clear link between the STC hypothesis and memory allocation is still missing because the encoding of memories in neural circuits is mainly associated with strongly recurrently connected groups of neurons. To explore the mechanisms of potential synaptic tagging candidates and their involvement in the process of memory allocation, we develop a mathematical model for a single dendritic spine based on five essential criteria of a synaptic tag. By developing a mathematical model, we attempt to understand the roles of the potentially critical molecular networks underlying the STC and the essential attributes of a synaptic tag. We include essential memory molecules in the STC model that have been identified in earlier studies as crucial for STC pathways. CaMKII activation is critical for the setting of the initial tag; however, coordinated activities with other kinases and the biochemical pathways are necessary for the tag to be stable. PKA modulates NMDAR-mediated Ca²⁺ signalling. Similarly, PKA and ERK crosstalk is essential for Ca²⁺ - mediated protein synthesis during l-LTP. Our theoretical model explains the quantitative contribution of Tags and protein synthesis during l-LTP in synaptic strength.

Microenvironmental sensing by fibroblasts controls macrophage population size

Animal tissues comprise diverse cell types. However, the mechanisms controlling the number of each cell type within tissue compartments remain poorly understood. Here, we report that different cell types utilize distinct strategies to control population numbers. Proliferation of fibroblasts, stromal cells important for tissue integrity, is limited by space availability. In contrast, proliferation of macrophages, innate immune cells involved in defense, repair, and homeostasis, is constrained by growth factor availability. Examination of density-dependent gene expression in fibroblasts revealed that Hippo and TGF-β target genes are both regulated by cell density. We found YAP1, the transcriptional coactivator of the Hippo signaling pathway, directly regulates expression of Csf1, the lineage-specific growth factor for macrophages, through an enhancer of Csf1 that is specifically active in fibroblasts. Activation of YAP1 in fibroblasts elevates Csf1 expression and is sufficient to increase the number of macrophages at steady state. Our data also suggest that expression programs in fibroblasts that change with density may result from sensing of mechanical force through actin-dependent mechanisms. Altogether, we demonstrate that two different modes of population control are connected and coordinated to regulate cell numbers of distinct cell types. Sensing of the tissue environment may serve as a general strategy to control tissue composition.

A probabilistic Boolean model on hair follicle cell fate regulation by TGF-β

Hair follicles (HFs) are mini skin organs that undergo cyclic growth. Various signals regulate HF cell fate decisions jointly. Recent experimental results suggest that transforming growth factor beta (TGF-β) exhibits a dual role in HF cell fate regulation that can be either anti- or pro-apoptosis. To understand the underlying mechanisms of HF cell fate control, we develop a novel probabilistic Boolean network (pBN) model on the HF epithelial cell gene regulation dynamics. First, the model is derived from literature, then refined using single-cell RNA sequencing data. Using the model, we both explore the mechanisms underlying HF cell fate decisions and make predictions that could potentially guide future experiments: 1) we propose that a threshold-like switch in the TGF-β strength may necessitate the dual roles of TGF-β in either activating apoptosis or cell proliferation, in cooperation with bone morphogenetic protein (BMP) and tumor necrosis factor (TNF) and at different stages of a follicle growth cycle; 2) our model shows concordance with the high-activator-low-inhibitor theory of anagen initiation; 3) we predict that TNF may be more effective in catagen initiation than TGF-β, and they may cooperate in a two-step fashion; 4) finally, predictions of gene knockout and overexpression reveal the roles in HF cell fate regulations of each gene. Attractor and motif analysis from the associated Boolean networks reveal the relations between the topological structure of the gene regulation network and the cell fate regulation mechanism. A discrete spatial model equipped with the pBN illustrates how TGF-β and TNF cooperate in initiating and driving the apoptosis wave during catagen.

Principles of Endocrine Regulation: Reconciling Tensions Between Robustness in Performance and Adaptation to Change

Endocrine regulation in the hypothalamic-pituitary-thyroid (HPT) axis is orchestrated by physiological circuits which integrate multiple internal and external influences. Essentially, it provides either of the two responses to overt biological challenges: to defend the homeostatic range of a target hormone or adapt it to changing environmental conditions. Under certain conditions, such flexibility may exceed the capability of a simple feedback control loop, rather requiring more intricate networks of communication between the system's components. A new minimal mathematical model, in the form of a parametrized nonlinear dynamical system, is here formulated as a proof-of-concept to elucidate the principles of the HPT axis regulation. In particular, it allows uncovering mechanisms for the homeostasis of the key biologically active hormone free triiodothyronine (FT3). One mechanism supports the preservation of FT3 homeostasis, whilst the other is responsible for the adaptation of the homeostatic state to a new level. Together these allow optimum resilience in stressful situations. Preservation of FT3 homeostasis, despite changes in FT4 and TSH levels, is found to be an achievable system goal by joining elements of top-down and bottom-up regulation in a cascade of targeted feedforward and feedback loops. Simultaneously, the model accounts for the combination of properties regarded as essential to endocrine regulation, namely sensitivity, the anticipation of an adverse event, robustness, and adaptation. The model therefore offers fundamental theoretical insights into the effective system control of the HPT axis.

The inverse correlation between robustness and sensitivity to autoregulation in two-component systems

Two-component systems (TCS) are signal transduction systems in bacteria and many other organisms that relay the sensory signal to genetic components. TCS consist of two proteins: a histidine kinase and a response regulator that the histidine kinase activates. This seemingly simple machinery can generate complex regulatory dynamics that enables the level of gene expression that matches the input signal: many TCS response regulators act on their own genes as transcription factors, resulting in a positive autoregulation mechanism. This regulation, in return, modulates the transcription factor activity as a function of the input signal. Positive autoregulation does not necessarily result in positive feedback. Sensitivity to autoregulation is quantified as the output level amplification resulting from the positive autoregulation mechanism. Another structural property of these systems is formally characterized as "robustness": in a robust TCS, the output of the system is solely a function of the input signal. Thus, a robust TCS remains insensitive to fluctuations in the concentrations of its protein components and, this way, maintains the precision in the output transcription factor activity in response to input stimulus. In this paper, we show with a formal model that TCS operate on a spectrum of inverse correlation between robustness and sensitivity to autoregulation. Our model predicts that the modulation by positive autoregulation is a function of loss in TCS robustness, for example, by spontaneous dephosphorylation of the histidine kinase. Consequently, the loss in robustness provides a proportional modulation by positive autoregulation to widen the response range with a scaled amplification of the output. At the other end of the spectrum, in the presence of a strictly robust TCS machinery, amplification of the transcription factor activity by autoregulation is diminished. We show that our results are in agreement with published experimental results. Our results suggest that these TCS evolve to converge at a trade-off between robustness and positive autoregulation.

A simple regulatory architecture allows learning the statistical structure of a changing environment

Bacteria live in environments that are continuously fluctuating and changing. Exploiting any predictability of such fluctuations can lead to an increased fitness. On longer timescales, bacteria can 'learn' the structure of these fluctuations through evolution. However, on shorter timescales, inferring the statistics of the environment and acting upon this information would need to be accomplished by physiological mechanisms. Here, we use a model of metabolism to show that a simple generalization of a common regulatory motif (end-product inhibition) is sufficient both for learning continuous-valued features of the statistical structure of the environment and for translating this information into predictive behavior; moreover, it accomplishes these tasks near-optimally. We discuss plausible genetic circuits that could instantiate the mechanism we describe, including one similar to the architecture of two-component signaling, and argue that the key ingredients required for such predictive behavior are readily accessible to bacteria.

Molecular switch architecture determines response properties of signaling pathways

Many intracellular signaling pathways are composed of molecular switches, proteins that transition between two states-on and off Typically, signaling is initiated when an external stimulus activates its cognate receptor that, in turn, causes downstream switches to transition from off to on using one of the following mechanisms: activation, in which the transition rate from the off state to the on state increases; derepression, in which the transition rate from the on state to the off state decreases; and concerted, in which activation and derepression operate simultaneously. We use mathematical modeling to compare these signaling mechanisms in terms of their dose-response curves, response times, and abilities to process upstream fluctuations. Our analysis elucidates several operating principles for molecular switches. First, activation increases the sensitivity of the pathway, whereas derepression decreases sensitivity. Second, activation generates response times that decrease with signal strength, whereas derepression causes response times to increase with signal strength. These opposing features allow the concerted mechanism to not only show dose-response alignment, but also to decouple the response time from stimulus strength. However, these potentially beneficial properties come at the expense of increased susceptibility to upstream fluctuations. We demonstrate that these operating principles also hold when the models are extended to include additional features, such as receptor removal, kinetic proofreading, and cascades of switches. In total, we show how the architecture of molecular switches govern their response properties. We also discuss the biological implications of our findings.

Expression pattern determines regulatory logic

Large amounts of effort have been invested in trying to understand how a single genome is able to specify the identity of hundreds of cell types. Inspired by some aspects of Caenorhabditis elegans biology, we implemented an in silico evolutionary strategy to produce gene regulatory networks (GRNs) that drive cell-specific gene expression patterns, mimicking the process of terminal cell differentiation. Dynamics of the gene regulatory networks are governed by a thermodynamic model of gene expression, which uses DNA sequences and transcription factor degenerate position weight matrixes as input. In a version of the model, we included chromatin accessibility. Experimentally, it has been determined that cell-specific and broadly expressed genes are regulated differently. In our in silico evolved GRNs, broadly expressed genes are regulated very redundantly and the architecture of their cis-regulatory modules is different, in accordance to what has been found in C. elegans and also in other systems. Finally, we found differences in topological positions in GRNs between these two classes of genes, which help to explain why broadly expressed genes are so resilient to mutations. Overall, our results offer an explanatory hypothesis on why broadly expressed genes are regulated so redundantly compared to cell-specific genes, which can be extrapolated to phenomena such as ChIP-seq HOT regions.

From protocells to prototissues: a materials chemistry approach

Prototissues comprise free-standing 3D networks of interconnected protocell consortia that communicate and display synergistic functions. Significantly, they can be constructed from functional molecules and materials, providing unprecedented opportunities to design tissue-like architectures that can do more than simply mimic living tissues. They could function under extreme conditions and exhibit a wide range of mechanical properties and bio-inspired metabolic functions. In this perspective, I will start by describing recent advancements in the design and synthetic construction of prototissues. I will then discuss the next challenges and the future impact of this emerging research field, which is destined to find applications in the most diverse areas of science and technology, from biomedical science to environmental science, and soft robotics.

Network structure of cascading neural systems predicts stimulus propagation and recovery

OBJECTIVE

Many neural systems display spontaneous, spatiotemporal patterns of neural activity that are crucial for information processing. While these cascading patterns presumably arise from the underlying network of synaptic connections between neurons, the precise contribution of the network's local and global connectivity to these patterns and information processing remains largely unknown.

APPROACH

Here, we demonstrate how network structure supports information processing through network dynamics in empirical and simulated spiking neurons using mathematical tools from linear systems theory, network control theory, and information theory.

MAIN RESULTS

In particular, we show that activity, and the information that it contains, travels through cycles in real and simulated networks.

SIGNIFICANCE

Broadly, our results demonstrate how cascading neural networks could contribute to cognitive faculties that require lasting activation of neuronal patterns, such as working memory or attention.

Synthetic metabolic computation in a bioluminescence-sensing system

Bioluminescence is visible light produced and emitted by living cells using various biological systems (e.g. luxCDABE cassette). Today, this phenomenon is widely exploited in biological research, biotechnology and medical applications as a quantitative technique for the detection of biological signals. However, this technique has mostly been used to detect a single input only. In this work, we re-engineered the complex genetic structure of luxCDABE cassette to build a biological unit that can detect multi-inputs, process the cellular information and report the computation results. We first split the luxCDABE operon into several parts to create a genetic circuit that can compute a soft minimum in living cells. Then, we used the new design to implement an AND logic function with better performance as compared to AND logic functions based on protein-protein interactions. Furthermore, by controlling the reverse reaction of the luxCDABE cassette independently from the forward reaction, we built a comparator with a programmable detection threshold. Finally, we applied the redesigned cassette to build an incoherent feedforward loop that reduced the unwanted crosstalk between stress-responsive promoters (recA, katG). This work demonstrates the construction of genetic circuits that combine regulations of gene expression with metabolic pathways, for sensing and computing in living cells.

Modeling cell-cell communication for immune systems across space and time

Communicating is crucial for cells to coordinate their behaviors. Immunological processes, involving diverse cytokines and cell types, are ideal for developing frameworks for modeling coordinated behaviors of cells. Here, we review recent studies that combine modeling and experiments to reveal how immune systems use autocrine, paracrine, and juxtacrine signals to achieve behaviors such as controlling population densities and hair regenerations. We explain that models are useful because one can computationally vary numerous parameters, in experimentally infeasible ways, to evaluate alternate immunological responses. For each model, we focus on the length-scales and time-scales involved and explain why integrating multiple length-scales and time-scales in a model remain challenging. We suggest promising modeling strategies for meeting this challenge and their practical consequences.

A dynamical motif comprising the interactions between antigens and CD8 T cells may underlie the outcomes of viral infections

Some viral infections culminate in very different outcomes in different individuals. They can be rapidly cleared in some, cause persistent infection in others, and cause mortality from immunopathology in yet others. The conventional view is that the different outcomes arise as a consequence of the complex interactions between a large number of different factors (virus, different immune cells, and cytokines). Here, we identify a simple dynamical motif comprising the essential interactions between antigens and CD8 T cells and posit it as predominantly determining the outcomes. Viral antigen can activate CD8 T cells, which in turn, can kill infected cells. Sustained antigen stimulation, however, can cause CD8 T-cell exhaustion, compromising effector function. Using mathematical modeling, we show that the motif comprising these interactions recapitulates all of the outcomes observed. The motif presents a conceptual framework to understand the variable outcomes of infection. It also explains a number of confounding experimental observations, including the variation in outcomes with the viral inoculum size, the evolutionary advantage of exhaustion in preventing lethal pathology, the ability of natural killer (NK) cells to act as rheostats tuning outcomes, and the role of the innate immune response in the spontaneous clearance of hepatitis C. Interventions that modulate the interactions in the motif may present routes to clear persistent infections or limit immunopathology.

DNA-based communication in populations of synthetic protocells

Developing molecular communication platforms based on orthogonal communication channels is a crucial step towards engineering artificial multicellular systems. Here, we present a general and scalable platform entitled 'biomolecular implementation of protocellular communication' (BIO-PC) to engineer distributed multichannel molecular communication between populations of non-lipid semipermeable microcapsules. Our method leverages the modularity and scalability of enzyme-free DNA strand-displacement circuits to develop protocellular consortia that can sense, process and respond to DNA-based messages. We engineer a rich variety of biochemical communication devices capable of cascaded amplification, bidirectional communication and distributed computational operations. Encapsulating DNA strand-displacement circuits further allows their use in concentrated serum where non-compartmentalized DNA circuits cannot operate. BIO-PC enables reliable execution of distributed DNA-based molecular programs in biologically relevant environments and opens new directions in DNA computing and minimal cell technology.

Creative exploration as a scale-invariant search on a meaning landscape

Can knowledge accumulated in systems biology on mechanisms governing cell behavior help us to elucidate cognitive processes, such as human creative search? To address this, we focus on the property of scale invariance, which allows sensory systems to adapt to environmental signals spanning orders of magnitude. For example, bacteria search for nutrients, by responding to relative changes in nutrient concentration rather than absolute levels, via a sensory mechanism termed fold-change detection (FCD). Scale invariance is prevalent in cognition, yet the specific mechanisms are mostly unknown. Here, we screen many possible dynamic equation topologies, to find that an FCD model best describes creative search dynamics. The model further predicts robustness to variations in meaning perception, in agreement with behavioral data. We thus suggest FCD as a specific mechanism for scale invariant search, connecting sensory processes of cells and cognitive processes in human.

Modeling Cell-to-Cell Communication Networks Using Response-Time Distributions

Cell-to-cell communication networks have critical roles in coordinating diverse organismal processes, such as tissue development or immune cell response. However, compared with intracellular signal transduction networks, the function and engineering principles of cell-to-cell communication networks are far less understood. Major complications include: cells are themselves regulated by complex intracellular signaling networks; individual cells are heterogeneous; and output of any one cell can recursively become an additional input signal to other cells. Here, we make use of a framework that treats intracellular signal transduction networks as "black boxes" with characterized input-to-output response relationships. We study simple cell-to-cell communication circuit motifs and find conditions that generate bimodal responses in time, as well as mechanisms for independently controlling synchronization and delay of cell-population responses. We apply our modeling approach to explain otherwise puzzling data on cytokine secretion onset times in T cells. Our approach can be used to predict communication network structure using experimentally accessible input-to-output measurements and without detailed knowledge of intermediate steps.

Reconstructing a metazoan genetic pathway with transcriptome-wide epistasis measurements

RNA-sequencing (RNA-seq) is commonly used to identify genetic modules that respond to perturbations. In single cells, transcriptomes have been used as phenotypes, but this concept has not been applied to whole-organism RNA-seq. Also, quantifying and interpreting epistatic effects using expression profiles remains a challenge. We developed a single coefficient to quantify transcriptome-wide epistasis that reflects the underlying interactions and which can be interpreted intuitively. To demonstrate our approach, we sequenced four single and two double mutants of Caenorhabditis elegans From these mutants, we reconstructed the known hypoxia pathway. In addition, we uncovered a class of 56 genes with HIF-1-dependent expression that have opposite changes in expression in mutants of two genes that cooperate to negatively regulate HIF-1 abundance; however, the double mutant of these genes exhibits suppression epistasis. This class violates the classical model of HIF-1 regulation but can be explained by postulating a role of hydroxylated HIF-1 in transcriptional control.

Endocytosis as a stabilizing mechanism for tissue homeostasis

Cells in tissues communicate by secreted growth factors (GF) and other signals. An important function of cell circuits is tissue homeostasis: maintaining proper balance between the amounts of different cell types. Homeostasis requires negative feedback on the GFs, to avoid a runaway situation in which cells stimulate each other and grow without control. Feedback can be obtained in at least two ways: endocytosis in which a cell removes its cognate GF by internalization and cross-inhibition in which a GF down-regulates the production of another GF. Here we ask whether there are design principles for cell circuits to achieve tissue homeostasis. We develop an analytically solvable framework for circuits with multiple cell types and find that feedback by endocytosis is far more robust to parameter variation and has faster responses than cross-inhibition. Endocytosis, which is found ubiquitously across tissues, can even provide homeostasis to three and four communicating cell types. These design principles form a conceptual basis for how tissues maintain a healthy balance of cell types and how balance may be disrupted in diseases such as degeneration and fibrosis.

Circuit Design Features of a Stable Two-Cell System

Cell communication within tissues is mediated by multiple paracrine signals including growth factors, which control cell survival and proliferation. Cells and the growth factors they produce and receive constitute a circuit with specific properties that ensure homeostasis. Here, we used computational and experimental approaches to characterize the features of cell circuits based on growth factor exchange between macrophages and fibroblasts, two cell types found in most mammalian tissues. We found that the macrophage-fibroblast cell circuit is stable and robust to perturbations. Analytical screening of all possible two-cell circuit topologies revealed the circuit features sufficient for stability, including environmental constraint and negative-feedback regulation. Moreover, we found that cell-cell contact is essential for the stability of the macrophage-fibroblast circuit. These findings illustrate principles of cell circuit design and provide a quantitative perspective on cell interactions.

Several Indicators of Critical Transitions for Complex Diseases Based on Stochastic Analysis

Many complex diseases (chronic disease onset, development and differentiation, self-assembly, etc.) are reminiscent of phase transitions in a dynamical system: quantitative changes accumulate largely unnoticed until a critical threshold is reached, which causes abrupt qualitative changes of the system. Understanding such nonlinear behaviors is critical to dissect the multiple genetic/environmental factors that together shape the genetic and physiological landscape underlying basic biological functions and to identify the key driving molecules. Based on stochastic differential equation (SDE) model, we theoretically derive three statistical indicators, that is, coefficient of variation (CV), transformed Pearson's correlation coefficient (TPC), and transformed probability distribution (TPD), to identify critical transitions and detect the early-warning signals of the phase transition in complex diseases. To verify the effectiveness of these early-warning indexes, we use high-throughput data for three complex diseases, including influenza caused by either H3N2 or H1N1 and acute lung injury, to extract the dynamical network biomarkers (DNBs) responsible for catastrophic transition into the disease state from predisease state. The numerical results indicate that the derived indicators provide a data-based quantitative analysis for early-warning signals for critical transitions in complex diseases or other dynamical systems.

Two-Element Transcriptional Regulation in the Canonical Wnt Pathway

The canonical Wnt pathway regulates numerous fundamental processes throughout development and adult physiology and is often disrupted in diseases [1-4]. Signal in the pathway is transduced by β-catenin, which in complex with Tcf/Lef regulates transcription. Despite the many processes that the Wnt pathway governs, β-catenin acts primarily on a single cis element in the DNA, the Wnt-responsive element (WRE), at times potentiated by a nearby Helper site. In this study, working with Xenopus, mouse, and human systems, we identified a cis element, distinct from WRE, upon which β-catenin and Tcf act. The element is 11 bp long, hundreds of bases apart from the WRE, and exhibits a suppressive effect. In Xenopus patterning, loss of the 11-bp negative regulatory elements (11-bp NREs) broadened dorsal expression of siamois. In mouse embryonic stem cells, genomic deletion of the 11-bp NREs in the promoter elevated Brachyury expression. This reveals a previously unappreciated mechanism within the Wnt pathway, where gene response is not only driven by WREs but also tuned by 11-bp NREs. Using electrophoretic mobility shift assay (EMSA) and chromatin immunoprecipitation (ChIP), we found evidence for the NREs binding to β-catenin and Tcf-suggesting a dual action by β-catenin as a signal and a feedforward sensor. Analyzing β-catenin ChIP sequencing in human cells, we found the 11-bp NREs co-localizing with the WRE in 45%-71% of the peaks, suggesting a widespread role for the mechanism. This study presents an example of a more complex cis regulation by a signaling pathway, where a signal is processed through two distinct cis elements in a gene circuitry.

Mechanical stretch triggers rapid epithelial cell division through Piezo1

Despite acting as a barrier for the organs they encase, epithelial cells turnover at some of the fastest rates in the body. Yet, epithelial cell division must be tightly linked to cell death to preserve barrier function and prevent tumour formation. How do the number of dying cells match those dividing to maintain constant numbers? We previously found that when epithelial cells become too crowded, they activate the stretch-activated channel Piezo1 to trigger extrusion of cells that later die¹. Conversely, what controls epithelial cell division to balance cell death at steady state? Here, we find that cell division occurs in regions of low cell density, where epithelial cells are stretched. By experimentally stretching epithelia, we find that mechanical stretch itself rapidly stimulates cell division through activation of the same Piezo1 channel. To do so, stretch triggers cells paused in early G2 to activate calcium-dependent ERK1/2 phosphorylation that activates cyclin B transcription necessary to drive cells into mitosis. Although both epithelial cell division and cell extrusion require Piezo1 at steady state, the type of mechanical force controls the outcome: stretch induces cell division whereas crowding induces extrusion. How Piezo1-dependent calcium transients activate two opposing processes may depend on where and how Piezo1 is activated since it accumulates in different subcellular sites with increasing cell density. In sparse epithelial regions where cells divide, Piezo1 localizes to the plasma membrane and cytoplasm whereas in dense regions where cells extrude, it forms large cytoplasmic aggregates. Because Piezo1 senses both mechanical crowding and stretch, it may act as a homeostatic sensor to control epithelial cell numbers, triggering extrusion/apoptosis in crowded regions and cell division in sparse regions.

Early commitment and robust differentiation in colonic crypts

Tissue stem cells produce a constant flux of differentiated cells with distinct proportions. Here, we show that stem cells in colonic crypts differentiate early to form precisely 1:3 ratio of secretory to absorptive cells. This precision is surprising, as there are only eight stem cells making irreversible fate decisions, and so large stochastic effects of this small pool should have yielded much larger noise in cell proportions. We use single molecule FISH, lineage‐tracing mice and simulations to identify the homeostatic mechanisms facilitating robust proportions. We find that Delta‐Notch lateral inhibition operates in a restricted spatial zone to reduce initial noise in cell proportions. Increased dwell time and dispersive migration of secretory cells further averages additional variability added during progenitor divisions and breaks up continuous patches of same‐fate cells. These noise‐reducing mechanisms resolve the trade‐off between early commitment and robust differentiation and ensure spatially uniform spread of secretory cells. Our findings may apply to other cases where small progenitor pools expand to give rise to precise tissue cell proportions.

Extracellular matrix stiffness causes systematic variations in proliferation and chemosensitivity in myeloid leukemias

Extracellular matrix stiffness influences biological functions of some tumors. However, it remains unclear how cancer subtypes with different oncogenic mutations respond to matrix stiffness. In addition, the relevance of matrix stiffness to in vivo tumor growth kinetics and drug efficacy remains elusive. Here, we designed 3D hydrogels with physical parameters relevant to hematopoietic tissues and adapted them to a quantitative high-throughput screening format to facilitate mechanistic investigations into the role of matrix stiffness on myeloid leukemias. Matrix stiffness regulates proliferation of some acute myeloid leukemia types, including MLL-AF9⁺ MOLM-14 cells, in a biphasic manner by autocrine regulation, whereas it decreases that of chronic myeloid leukemia BCR-ABL⁺ K-562 cells. Although Arg-Gly-Asp (RGD) integrin ligand and matrix softening confer resistance to a number of drugs, cells become sensitive to drugs against protein kinase B (PKB or AKT) and rapidly accelerated fibrosarcoma (RAF) proteins regardless of matrix stiffness when MLL-AF9 and BCR-ABL are overexpressed in K-562 and MOLM-14 cells, respectively. By adapting the same hydrogels to a xenograft model of extramedullary leukemias, we confirm the pathological relevance of matrix stiffness in growth kinetics and drug sensitivity against standard chemotherapy in vivo. The results thus demonstrate the importance of incorporating 3D mechanical cues into screening for anticancer drugs.

A Regulatory Circuit Composed of a Transcription Factor, IscR, and a Regulatory RNA, RyhB, Controls Fe-S Cluster Delivery

Fe-S clusters are cofactors conserved through all domains of life. Once assembled by dedicated ISC and/or SUF scaffolds, Fe-S clusters are conveyed to their apo-targets via A-type carrier proteins (ATCs). Escherichia coli possesses four such ATCs. ErpA is the only ATC essential under aerobiosis. Recent studies reported a possible regulation of the erpA mRNA by the small RNA (sRNA) RyhB, which controls the expression of many genes under iron starvation. Surprisingly, erpA has not been identified in recent transcriptomic analysis of the iron starvation response, thus bringing into question the actual physiological significance of the putative regulation of erpA by RyhB. Using an sRNA library, we show that among 26 sRNAs, only RyhB represses the expression of an erpA-lacZ translational fusion. We further demonstrate that this repression occurs during iron starvation. Using mutational analysis, we show that RyhB base pairs to the erpA mRNA, inducing its disappearance. In addition, IscR, the master regulator of Fe-S homeostasis, represses expression of erpA at the transcriptional level when iron is abundant, but depleting iron from the medium alleviates this repression. The conjunction of transcriptional derepression by IscR and posttranscriptional repression by RyhB under Fe-limiting conditions is best described as an incoherent regulatory circuit. This double regulation allows full expression of erpA at iron concentrations for which Fe-S biogenesis switches from the ISC to the SUF system. We further provide evidence that this regulatory circuit coordinates ATC usage to iron availability.

Regulatory small RNAs (sRNAs) have emerged as major actors in the control of gene expression in the last few decades. Relatively little is known about how these regulators interact with classical transcription factors to coordinate genetic responses. We show here how an sRNA, RyhB, and a transcription factor, IscR, regulate expression of an essential gene, erpA, in the bacterium E. coli. ErpA is involved in the biogenesis of Fe-S clusters, an important class of cofactors involved in a plethora of cellular reactions. Interestingly, we show that RyhB and IscR repress expression of erpA under opposite conditions in regard to iron concentration, forming a regulatory circuit called an “incoherent network.” This incoherent network serves to maximize expression of erpA at iron concentrations where it is most needed. Altogether, our study paves the way for a better understanding of mixed regulatory networks composed of RNAs and transcription factors.

Controlling Stochasticity in Epithelial-Mesenchymal Transition Through Multiple Intermediate Cellular States

Epithelial-mesenchymal transition (EMT) is an instance of cellular plasticity that plays critical roles in development, regeneration and cancer progression. Recent studies indicate that the transition between epithelial and mesenchymal states is a multi-step and reversible process in which several intermediate phenotypes might coexist. These intermediate states correspond to various forms of stem-like cells in the EMT system, but the function of the multi-step transition or the multiple stem cell phenotypes is unclear. Here, we use mathematical models to show that multiple intermediate phenotypes in the EMT system help to attenuate the overall fluctuations of the cell population in terms of phenotypic compositions, thereby stabilizing a heterogeneous cell population in the EMT spectrum. We found that the ability of the system to attenuate noise on the intermediate states depends on the number of intermediate states, indicating the stem-cell population is more stable when it has more sub-states. Our study reveals a novel advantage of multiple intermediate EMT phenotypes in terms of systems design, and it sheds light on the general design principle of heterogeneous stem cell population.

Paradoxical signaling regulates structural plasticity in dendritic spines

Transient spine enlargement (3- to 5-min timescale) is an important event associated with the structural plasticity of dendritic spines. Many of the molecular mechanisms associated with transient spine enlargement have been identified experimentally. Here, we use a systems biology approach to construct a mathematical model of biochemical signaling and actin-mediated transient spine expansion in response to calcium influx caused by NMDA receptor activation. We have identified that a key feature of this signaling network is the paradoxical signaling loop. Paradoxical components act bifunctionally in signaling networks, and their role is to control both the activation and the inhibition of a desired response function (protein activity or spine volume). Using ordinary differential equation (ODE)-based modeling, we show that the dynamics of different regulators of transient spine expansion, including calmodulin-dependent protein kinase II (CaMKII), RhoA, and Cdc42, and the spine volume can be described using paradoxical signaling loops. Our model is able to capture the experimentally observed dynamics of transient spine volume. Furthermore, we show that actin remodeling events provide a robustness to spine volume dynamics. We also generate experimentally testable predictions about the role of different components and parameters of the network on spine dynamics.

Radiation-induced lung damage promotes breast cancer lung-metastasis through CXCR4 signaling

Radiotherapy is a mainstay in the postoperative treatment of breast cancer as it reduces the risks of local recurrence and mortality after both conservative surgery and mastectomy. Despite recent efforts to decrease irradiation volumes through accelerated partial irradiation techniques, late cardiac and pulmonary toxicity still occurs after breast irradiation. The importance of this pulmonary injury towards lung metastasis is unclear. Preirradiation of lung epithelial cells induces DNA damage, p53 activation and a secretome enriched in the chemokines SDF-1/CXCL12 and MIF. Irradiated lung epithelial cells stimulate adhesion, spreading, growth, and (transendothelial) migration of human MDA-MB-231 and murine 4T1 breast cancer cells. These metastasis-associated cellular activities were largely mimicked by recombinant CXCL12 and MIF. Moreover, an allosteric inhibitor of the CXCR4 receptor prevented the metastasis-associated cellular activities stimulated by the secretome of irradiated lung epithelial cells. Furthermore, partial (10%) irradiation of the right lung significantly stimulated breast cancer lung-specific metastasis in the syngeneic, orthotopic 4T1 breast cancer model.

Our results warrant further investigation of the potential pro-metastatic effects of radiation and indicate the need to develop efficient drugs that will be successful in combination with radiotherapy to prevent therapy-induced spread of cancer cells.

Enzyme Sequestration as a Tuning Point in Controlling Response Dynamics of Signalling Networks

Signalling networks result from combinatorial interactions among many enzymes and scaffolding proteins. These complex systems generate response dynamics that are often essential for correct decision-making in cells. Uncovering biochemical design principles that underpin such response dynamics is a prerequisite to understand evolved signalling networks and to design synthetic ones. Here, we use in silico evolution to explore the possible biochemical design space for signalling networks displaying ultrasensitive and adaptive response dynamics. By running evolutionary simulations mimicking different biochemical scenarios, we find that enzyme sequestration emerges as a key mechanism for enabling such dynamics. Inspired by these findings, and to test the role of sequestration, we design a generic, minimalist model of a signalling cycle, featuring two enzymes and a single scaffolding protein. We show that this simple system is capable of displaying both ultrasensitive and adaptive response dynamics. Furthermore, we find that tuning the concentration or kinetics of the sequestering protein can shift system dynamics between these two response types. These empirical results suggest that enzyme sequestration through scaffolding proteins is exploited by evolution to generate diverse response dynamics in signalling networks and could provide an engineering point in synthetic biology applications.

Multi-objective optimization framework to obtain model-based guidelines for tuning biological synthetic devices: an adaptive network case

Background

Model based design plays a fundamental role in synthetic biology. Exploiting modularity, i.e. using biological parts and interconnecting them to build new and more complex biological circuits is one of the key issues. In this context, mathematical models have been used to generate predictions of the behavior of the designed device. Designers not only want the ability to predict the circuit behavior once all its components have been determined, but also to help on the design and selection of its biological parts, i.e. to provide guidelines for the experimental implementation. This is tantamount to obtaining proper values of the model parameters, for the circuit behavior results from the interplay between model structure and parameters tuning. However, determining crisp values for parameters of the involved parts is not a realistic approach. Uncertainty is ubiquitous to biology, and the characterization of biological parts is not exempt from it. Moreover, the desired dynamical behavior for the designed circuit usually results from a trade-off among several goals to be optimized.

Results

We propose the use of a multi-objective optimization tuning framework to get a model-based set of guidelines for the selection of the kinetic parameters required to build a biological device with desired behavior. The design criteria are encoded in the formulation of the objectives and optimization problem itself. As a result, on the one hand the designer obtains qualitative regions/intervals of values of the circuit parameters giving rise to the predefined circuit behavior; on the other hand, he obtains useful information for its guidance in the implementation process. These parameters are chosen so that they can effectively be tuned at the wet-lab, i.e. they are effective biological tuning knobs. To show the proposed approach, the methodology is applied to the design of a well known biological circuit: a genetic incoherent feed-forward circuit showing adaptive behavior.

Conclusion

The proposed multi-objective optimization design framework is able to provide effective guidelines to tune biological parameters so as to achieve a desired circuit behavior. Moreover, it is easy to analyze the impact of the context on the synthetic device to be designed. That is, one can analyze how the presence of a downstream load influences the performance of the designed circuit, and take it into account.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-016-0269-0) contains supplementary material, which is available to authorized users.

Molecular-Level Tuning of Cellular Autonomy Controls the Collective Behaviors of Cell Populations

A rigorous understanding of how multicellular behaviors arise from the actions of single cells requires quantitative frameworks that bridge the gap between genetic circuits, the arrangement of cells in space, and population-level behaviors. Here, we provide such a framework for a ubiquitous class of multicellular systems-namely, "secrete-and-sense cells" that communicate by secreting and sensing a signaling molecule. By using formal, mathematical arguments and introducing the concept of a phenotype diagram, we show how these cells tune their degrees of autonomous and collective behavior to realize distinct single-cell and population-level phenotypes; these phenomena have biological analogs, such as quorum sensing or paracrine signaling. We also define the "entropy of population," a measurement of the number of arrangements that a population of cells can assume, and demonstrate how a decrease in the entropy of population accompanies the formation of ordered spatial patterns. Our conceptual framework ties together diverse systems, including tissues and microbes, with common principles.

Context-based resolution of semantic conflicts in biological pathways

Background

Interactions between biological entities such as genes, proteins and metabolites, so called pathways, are key features to understand molecular mechanisms of life. As pathway information is being accumulated rapidly through various knowledge resources, there are growing interests in maintaining the integrity of the heterogeneous databases.

Methods

Here, we defined conflict as a status where two contradictory pieces of evidence (i.e. 'A increases B' and 'A decreases B') coexist in a same pathway. This conflict damages unity so that inference of simulation on the integrated pathway network might be unreliable. We defined rule and rule group. A rule consists of interaction of two entities, meta-relation (increase or decrease), and contexts terms about tissue specificity or environmental conditions. The rules, which have the same interaction, are grouped into a rule group. If the rules don't have a unanimous meta-relation, the rule group and the rules are judged as being conflicting.

Results

This analysis revealed that almost 20% of known interactions suffer from conflicting information and conflicting information occurred much more frequently in the literature than the public database.

With consideration for dual functions depending on context, we thought it might resolve conflict to consider context. We grouped rules, which have the same context terms as well as interaction. It's revealed that up to 86% of the conflicts could be resolved by considering context.

Subsequent analysis also showed that those contradictory records generally compete each other closely, but some information might be suspicious when their evidence levels are seriously imbalanced.

Conclusions

By identifying and resolving the conflicts, we expect that pathway databases can be cleaned and used for better secondary analyses such as gene/protein annotation, network dynamics and qualitative/quantitative simulation.

Three-Dimensional Gradients of Cytokine Signaling between T Cells

Immune responses are regulated by diffusible mediators, the cytokines, which act at sub-nanomolar concentrations. The spatial range of cytokine communication is a crucial, yet poorly understood, functional property. Both containment of cytokine action in narrow junctions between immune cells (immunological synapses) and global signaling throughout entire lymph nodes have been proposed, but the conditions under which they might occur are not clear. Here we analyze spatially three-dimensional reaction-diffusion models for the dynamics of cytokine signaling at two successive scales: in immunological synapses and in dense multicellular environments. For realistic parameter values, we observe local spatial gradients, with the cytokine concentration around secreting cells decaying sharply across only a few cell diameters. Focusing on the well-characterized T-cell cytokine interleukin-2, we show how cytokine secretion and competitive uptake determine this signaling range. Uptake is shaped locally by the geometry of the immunological synapse. However, even for narrow synapses, which favor intrasynaptic cytokine consumption, escape fluxes into the extrasynaptic space are expected to be substantial (≥20% of secretion). Hence paracrine signaling will generally extend beyond the synapse but can be limited to cellular microenvironments through uptake by target cells or strong competitors, such as regulatory T cells. By contrast, long-range cytokine signaling requires a high density of cytokine producers or weak consumption (e.g., by sparsely distributed target cells). Thus in a physiological setting, cytokine gradients between cells, and not bulk-phase concentrations, are crucial for cell-to-cell communication, emphasizing the need for spatially resolved data on cytokine signaling.

The adaptive immune system fights pathogens through the activation of immune cell clones that specifically recognize a particular pathogen. Tight contacts, so-called immunological synapses, of immune cells with cells that present ‘digested’ pathogen molecules are pivotal for ensuring specificity. The discovery that immune responses are regulated by small diffusible proteins – the cytokines – has been surprising because cytokine diffusion to ‘bystander’ cells might compromise specificity. It has therefore been argued that cytokines are trapped in immunological synapses, whereas other authors have found that cytokines act on a larger scale through entire lymph nodes. Measurements of cytokine concentrations with fine spatial resolution have not been achieved. Here, we study the spatio-temporal dynamics of cytokines through mathematical analysis and three-dimensional numerical simulation and identify key parameters that control signaling range. We predict that even tight immunological synapses leak a substantial portion of the secreted cytokines. Nevertheless, rapid cellular uptake will render cytokine signals short-range and thus incidental activation of bystander cells can be limited. Long-range signals will only occur with multiple secreting cells or/and slow consumption by sparse target cells. Thus our study identifies key determinants of the spatial range of cytokine communication in realistic multicellular geometries.

Paradoxical signaling by a secreted molecule leads to homeostasis of cell levels

A widespread feature of extracellular signaling in cell circuits is paradoxical pleiotropy: the same secreted signaling molecule can induce opposite effects in the responding cells. For example, the cytokine IL-2 can promote proliferation and death of T cells. The role of such paradoxical signaling remains unclear. To address this, we studied CD4(+) T cell expansion in culture. We found that cells with a 30-fold difference in initial concentrations reached a homeostatic concentration nearly independent of initial cell levels. Below an initial threshold, cell density decayed to extinction (OFF-state). We show that these dynamics relate to the paradoxical effect of IL-2, which increases the proliferation rate cooperatively and the death rate linearly. Mathematical modeling explained the observed cell and cytokine dynamics and predicted conditions that shifted cell fate from homeostasis to the OFF-state. We suggest that paradoxical signaling provides cell circuits with specific dynamical features that are robust to environmental perturbations.

Integrating human pluripotent stem cells into drug development

Integration of physiologically relevant in vitro assays at the earliest stages of drug discovery may improve the likelihood of successfully translating preclinical discoveries to the clinic. Assays based on in vitro-differentiated, human pluripotent stem cell (IVD hPSC)-derived cells, which may better model human physiology, are starting to impact the drug discovery process, but their implementation has been slower than originally anticipated. In this Perspective, we discuss imperatives for incorporating IVD hPSCs into drug discovery and the associated challenges.

Top-level dynamics and the regulated gene response of feed-forward loop transcriptional motifs

Feed-forward loops are hierarchical three-node transcriptional subnetworks, wherein a top-level protein regulates the activity of a target gene via two paths: a direct-regulatory path, and an indirect route, whereby the top-level proteins act implicitly through an intermediate transcription factor. Using a transcriptional network of the model bacterium Escherichia coli, we confirmed that nearly all types of feed-forward loop were significantly overrepresented in the bacterial network. We then used mathematical modeling to study their dynamics by manipulating the rise times of the top-level protein concentration, termed the induction time, through alteration of the protein destruction rates. Rise times of the regulated proteins exhibited two qualitatively different regimes, depending on whether top-level inductions were "fast" or "slow." In the fast regime, rise times were nearly independent of rapid top-level inductions, indicative of biological robustness, and occurred when RNA production rate-limits the protein yield. Alternatively, the protein rise times were dependent upon slower top-level inductions, greater than approximately one bacterial cell cycle. An equation is given for this crossover, which depends upon three parameters of the direct-regulatory path: transcriptional cooperation at the DNA-binding site, a protein-DNA dissociation constant, and the relative magnitude of the top-level protien concentration.

Lineage grammars: describing, simulating and analyzing population dynamics

Background

Precise description of the dynamics of biological processes would enable the mathematical analysis and computational simulation of complex biological phenomena. Languages such as Chemical Reaction Networks and Process Algebras cater for the detailed description of interactions among individuals and for the simulation and analysis of ensuing behaviors of populations. However, often knowledge of such interactions is lacking or not available. Yet complete oblivion to the environment would make the description of any biological process vacuous. Here we present a language for describing population dynamics that abstracts away detailed interaction among individuals, yet captures in broad terms the effect of the changing environment, based on environment-dependent Stochastic Tree Grammars (eSTG). It is comprised of a set of stochastic tree grammar transition rules, which are context-free and as such abstract away specific interactions among individuals. Transition rule probabilities and rates, however, can depend on global parameters such as population size, generation count, and elapsed time.

Results

We show that eSTGs conveniently describe population dynamics at multiple levels including cellular dynamics, tissue development and niches of organisms. Notably, we show the utilization of eSTG for cases in which the dynamics is regulated by environmental factors, which affect the fate and rate of decisions of the different species. eSTGs are lineage grammars, in the sense that execution of an eSTG program generates the corresponding lineage trees, which can be used to analyze the evolutionary and developmental history of the biological system under investigation. These lineage trees contain a representation of the entire events history of the system, including the dynamics that led to the existing as well as to the extinct individuals.

Conclusions

We conclude that our suggested formalism can be used to easily specify, simulate and analyze complex biological systems, and supports modular description of local biological dynamics that can be later used as “black boxes” in a larger scope, thus enabling a gradual and hierarchical definition and simulation of complex biological systems. The simple, yet robust formalism enables to target a broad class of stochastic dynamic behaviors, especially those that can be modeled using global environmental feedback regulation rather than direct interaction between individuals.

Bang-bang model for regulation of local blood flow

The classical model of metabolic regulation of blood flow in muscle tissue implies the maintenance of basal tone in arterioles of resting muscle and their dilation in response to exercise and/or tissue hypoxia via the evoked production of vasodilator metabolites by myocytes. A century-long effort to identify specific metabolites responsible for explaining active and reactive hyperemia has not been successful. Furthermore, the metabolic theory is not compatible with new knowledge on the role of physiological radicals (e.g., nitric oxide, NO, and superoxide anion, O2 (-) ) in the regulation of microvascular tone. We propose a model of regulation in which muscle contraction and active hyperemia are considered the physiologically normal state. We employ the "bang-bang" or "on/off" regulatory model which makes use of a threshold and hysteresis; a float valve to control the water level in a tank is a common example of this type of regulation. Active bang-bang regulation comes into effect when the supply of oxygen and glucose exceeds the demand, leading to activation of membrane NADPH oxidase, release of O2 (-) into the interstitial space and subsequent neutralization of the interstitial NO. Switching arterioles on/off when local blood flow crosses the threshold is realized by a local cell circuit with the properties of a bang-bang controller, determined by its threshold, hysteresis, and dead-band. This model provides a clear and unambiguous interpretation of the mechanism to balance tissue demand with a sufficient supply of nutrients and oxygen.

Role of functionality in two-component signal transduction: a stochastic study

We present a stochastic formalism for signal transduction processes in a bacterial two-component system. Using elementary mass action kinetics, the proposed model takes care of signal transduction in terms of a phosphotransfer mechanism between the cognate partners of a two-component system, viz., the sensor kinase and the response regulator. Based on the difference in functionality of the sensor kinase, the noisy phosphotransfer mechanism has been studied for monofunctional and bifunctional two-component systems using the formalism of the linear noise approximation. Steady-state analysis of both models quantifies different physically realizable quantities, e.g., the variance, the Fano factor (variance/mean), and mutual information. The resultant data reveal that both systems reliably transfer information of extracellular environment under low external stimulus and in a high-kinase-and-phosphatase regime. We extend our analysis further by studying the role of the two-component system in downstream gene regulation.