Crosstalk and the evolvability of intracellular communication

Targeting CD44 and other pleiotropic co-receptors as a means for broad inhibition of tumor growth and metastasis

Although progress has been made in the treatment of cancer, particularly for the four major types of cancers affecting the lungs, colon, breast and prostate, resistance to cancer treatment often emerges upon inhibition of major signaling pathways, which leads to the activation of additional pathways as a last-resort survival mechanism by the cancer cells. This signaling plasticity provides cancer cells with a level of operational freedom, reducing treatment efficacy. Plasticity is a characteristic of cancer cells that are not only able to switch signaling pathways but also from one cellular state (differentiated cells to stem cells or vice versa) to another. It seems implausible that the inhibition of one or a few signaling pathways of heterogeneous and plastic tumors can sustain a durable effect. We propose that inhibiting molecules with pleiotropic functions such as cell surface co-receptors can be a key to preventing therapy escape instead of targeting bona fide receptors. Therefore, we ask the question whether co-receptors often considered as "accessory molecules" are an overlooked key to control cancer cell behavior.

Cell Adaptive Fitness and Cancer Evolutionary Dynamics

Genome instability of cancer cells translates into increased entropy and lower information processing capacity, leading to metabolic reprograming toward higher energy states, presumed to be aligned with a cancer growth imperative. Dubbed as the cell adaptive fitness, the proposition postulates that the coupling between cell signaling and metabolism constrains cancer evolutionary dynamics along trajectories privileged by the maintenance of metabolic sufficiency for survival. In particular, the conjecture postulates that clonal expansion becomes restricted when genetic alterations induce a sufficiently high level of disorder, that is, high entropy, in the regulatory signaling network, abrogating as a result the ability of cancer cells to successfully replicate, leading to a stage of clonal stagnation. The proposition is analyzed in the context of an in-silico model of tumor evolutionary dynamics to illustrate how cell-inherent adaptive fitness may predictably constrain clonal evolution of tumors, which would have significant implications for the design of adaptive cancer therapies.

How cells sense and integrate information from different sources

Cell signaling is a fundamental cellular process that enables cells to sense and respond to information in their surroundings. At the molecular level, signaling is primarily carried out by transmembrane protein receptors that can initiate complex downstream signal transduction cascades to alter cellular behavior. In the human body, different cells can be exposed to a wide variety of environmental conditions, and cells express diverse classes of receptors capable of sensing and integrating different signals. Furthermore, different receptors and signaling pathways can crosstalk with each other to calibrate the cellular response. Crosstalk occurs through multiple mechanisms at different levels of signaling pathways. In this review, we discuss how cells sense and integrate different chemical, mechanical, and spatial signals as well as the mechanisms of crosstalk between pathways. To illustrate these concepts, we use a few well-studied signaling pathways, including receptor tyrosine kinases and integrin receptors. Finally, we discuss the implications of dysregulated cellular sensing on driving diseases such as cancer. This article is categorized under: Cancer > Molecular and Cellular Physiology Metabolic Diseases > Molecular and Cellular Physiology.

Signaling pathways and their potential therapeutic utility in esophageal squamous cell carcinoma

Esophageal cancer is a complex gastrointestinal malignancy with an extremely poor outcome. Approximately 80% of cases of this malignancy in Asian countries including India are of squamous cell origin, termed Esophageal Squamous Cell Carcinoma (ESCC).The five-year survival rate in ESCC patients is less than 20%. Neo-adjuvant chemo-radiotherapy (NACRT) followed by surgical resection remains the major therapeutic strategy for patients with operable ESCC. However, resistance to NACRT and local recurrence after initial treatment are the leading cause of dismal outcomes in these patients. Therefore, an alternative strategy to promote response to the therapy and reduce the post-operative disease recurrence is highly needed. At the molecular level, wide variations have been observed in tumor characteristics among different populations, nevertheless, several common molecular features have been identified which orchestrate disease progression and clinical outcome in the malignancy. Therefore, determination of candidate molecular pathways for targeted therapy remains the mainstream idea of focus in ESCC research. In this review, we have discussed the key signaling pathways associated with ESCC, i.e., Notch, Wnt, and Nrf2 pathways, and their crosstalk during disease progression. We further discuss the recent developments of novel agents to target these pathways in the context of targeted cancer therapy. In-depth research of the signaling pathways, gene signatures, and a combinatorial approach may help in discovering targeted therapy for ESCC.

Making sense of BMP signaling complexity

Cellular signaling systems are immensely complex. Dedicated experimental and theoretical approaches are therefore required to decipher how they function. In this issue of Cell Systems, two studies systematically interrogate the Bone Morphogenetic Protein (BMP) pathway, uncovering mechanisms and consequences of distinct responses to combinations of BMP ligands.

Reconstruction and analysis of a large-scale binary Ras-effector signaling network

BACKGROUND

Ras is a key cellular signaling hub that controls numerous cell fates via multiple downstream effector pathways. While pathways downstream of effectors such as Raf, PI3K and RalGDS are extensively described in the literature, how other effectors signal downstream of Ras is often still enigmatic.

METHODS

A comprehensive and unbiased Ras-effector network was reconstructed downstream of 43 effector proteins (converging onto 12 effector classes) using public pathway and protein-protein interaction (PPI) databases. The output is an oriented graph of pairwise interactions defining a 3-layer signaling network downstream of Ras. The 2290 proteins comprising the network were studied for their implication in signaling crosstalk and feedbacks, their subcellular localizations, and their cellular functions.

RESULTS

The final Ras-effector network consists of 2290 proteins that are connected via 19,080 binary PPIs, increasingly distributed across the downstream layers, with 441 PPIs in layer 1, 1660 in layer 2, and 16,979 in layer 3. We identified a high level of crosstalk among proteins of the 12 effector classes. A class-specific Ras sub-network was generated in CellDesigner (.xml file) and a functional enrichment analysis thereof shows that 58% of the processes have previously been associated to a respective effector pathway, with the remaining providing insights into novel and unexplored functions of specific effector pathways.

CONCLUSIONS

Our large-scale and cell general Ras-effector network is a crucial steppingstone towards defining the network boundaries. It constitutes a 'reference interactome' and can be contextualized for specific conditions, e.g. different cell types or biopsy material obtained from cancer patients. Further, it can serve as a basis for elucidating systems properties, such as input-output relationships, crosstalk, and pathway redundancy. Video Abstract.

Dynamic Self-Rectifying Liquid Metal-Semiconductor Heterointerfaces: A Platform for Development of Bioinspired Afferent Systems

The assembly of geometrically complex and dynamically active liquid metal/semiconductor heterointerfaces has drawn extensive attention in multidimensional electronic systems. In this study the chemovoltaic driven reactions have enabled the microfluidity of hydrophobic galinstan into a three-dimensional (3D) semiconductor matrix. A dynamic heterointerface is developed between the atomically thin surface oxide of galinstan and the TiO₂-Ni interface. Upon the growth of Ga₂O₃ film at the Ga₂O₃-TiO₂ heterointerface, the partial reduction of the TiO₂ film was confirmed by material characterization techniques. The conductance imaging spectroscopy and electrical measurements are used to investigate the charge transfer at heterointerfaces. Concurrently, the dynamic conductance in artificial synaptic junctions is modulated to mimic the biofunctional communication characteristics of multipolar neurons, including slow and fast inhibitory and excitatory postsynaptic responses. The self-rectifying characteristics, femtojoule energy processing, tunable synaptic events, and notably the coordinated signal recognition are the main characteristics of this multisynaptic device. This novel 3D design of liquid metal-semiconductor structure opens up new opportunities for the development of bioinspired afferent systems. It further facilitates the realization of physical phenomena at liquid metal-semiconductor heterointerfaces.

A distributed residue network permits conformational binding specificity in a conserved family of actin remodelers

Metazoan proteomes contain many paralogous proteins that have evolved distinct functions. The Ena/VASP family of actin regulators consists of three members that share an EVH1 interaction domain with a 100 % conserved binding site. A proteome-wide screen revealed photoreceptor cilium actin regulator (PCARE) as a high-affinity ligand for ENAH EVH1. Here, we report the surprising observation that PCARE is ~100-fold specific for ENAH over paralogs VASP and EVL and can selectively bind ENAH and inhibit ENAH-dependent adhesion in cells. Specificity arises from a mechanism whereby PCARE stabilizes a conformation of the ENAH EVH1 domain that is inaccessible to family members VASP and EVL. Structure-based modeling rapidly identified seven residues distributed throughout EVL that are sufficient to differentiate binding by ENAH vs. EVL. By exploiting the ENAH-specific conformation, we rationally designed the tightest and most selective ENAH binder to date. Our work uncovers a conformational mechanism of interaction specificity that distinguishes highly similar paralogs and establishes tools for dissecting specific Ena/VASP functions in processes including cancer cell invasion.

Corona Virus Disease 2019 (COVID-19) as a System-Level Infectious Disease With Distinct Sex Disparities

The current global pandemic of the Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) causing COVID-19, has infected millions of people and continues to pose a threat to many more. Angiotensin-Converting Enzyme 2 (ACE2) is an important player of the Renin-Angiotensin System (RAS) expressed on the surface of the lung, heart, kidney, neurons, and endothelial cells, which mediates SARS-CoV-2 entry into the host cells. The cytokine storms of COVID-19 arise from the large recruitment of immune cells because of the dis-synchronized hyperactive immune system, lead to many abnormalities including hyper-inflammation, endotheliopathy, and hypercoagulability that produce multi-organ dysfunction and increased the risk of arterial and venous thrombosis resulting in more severe illness and mortality. We discuss the aberrated interconnectedness and forthcoming crosstalks between immunity, the endothelium, and coagulation, as well as how sex disparities affect the severity and outcome of COVID-19 and harm men especially. Further, our conceptual framework may help to explain why persistent symptoms, such as reduced physical fitness and fatigue during long COVID, may be rooted in the clotting system.

Adaptive gene misregulation

Because gene expression is important for evolutionary adaptation, its misregulation is an important cause of maladaptation. A misregulated gene can be incorrectly silent ("off") when a transcription factor (TF) that is required for its activation does not binds its regulatory region. Conversely, a misregulated gene can be incorrectly active ("on") when a TF not normally involved in its activation binds its regulatory region, a phenomenon also known as regulatory crosstalk. DNA mutations that destroy or create TF binding sites on DNA are an important source of misregulation and crosstalk. Although misregulation reduces fitness in an environment to which an organism is well-adapted, it may become adaptive in a new environment. Here, I derive simple yet general mathematical expressions that delimit the conditions under which misregulation can be adaptive. These expressions depend on the strength of selection against misregulation, on the fraction of DNA sequence space filled with TF binding sites, and on the fraction of genes that must be expressed for optimal adaptation. I then use empirical data from RNA sequencing, protein-binding microarrays, and genome evolution, together with population genetic simulations to ask when these conditions are likely to be met. I show that they can be met under realistic circumstances, but these circumstances may vary among organisms and environments. My analysis provides a framework in which improved theory and data collection can help us demonstrate the role of misregulation in adaptation. It also shows that misregulation, like DNA mutation, is one of life's many imperfections that can help propel Darwinian evolution.

Fractional response analysis reveals logarithmic cytokine responses in cellular populations

Although we can now measure single-cell signaling responses with multivariate, high-throughput techniques our ability to interpret such measurements is still limited. Even interpretation of dose–response based on single-cell data is not straightforward: signaling responses can differ significantly between cells, encompass multiple signaling effectors, and have dynamic character. Here, we use probabilistic modeling and information-theory to introduce fractional response analysis (FRA), which quantifies changes in fractions of cells with given response levels. FRA can be universally performed for heterogeneous, multivariate, and dynamic measurements and, as we demonstrate, quantifies otherwise hidden patterns in single-cell data. In particular, we show that fractional responses to type I interferon in human peripheral blood mononuclear cells are very similar across different cell types, despite significant differences in mean or median responses and degrees of cell-to-cell heterogeneity. Further, we demonstrate that fractional responses to cytokines scale linearly with the log of the cytokine dose, which uncovers that heterogeneous cellular populations are sensitive to fold-changes in the dose, as opposed to additive changes.

Our ability to interpret single-cell multivariate signaling responses is still limited. Here the authors introduce fractional response analysis (FRA), involving fractional cell counting, capable of deconvoluting heterogeneous multivariate responses of cellular populations.

Crosstalk between 17β-Estradiol and TGF-β Signaling Modulates Glioblastoma Progression

Epithelial–mesenchymal transition (EMT) is an essential mechanism contributing to glioblastoma multiforme (GBM) progression, the most common and malignant brain tumor. EMT is induced by signaling pathways that crosstalk and regulate an intricate regulatory network of transcription factors. It has been shown that downstream components of 17β-estradiol (E2) and transforming growth factor β (TGF-β) signaling pathways crosstalk in estrogen-sensitive tumors. However, little is known about the interaction between the E2 and TGF-β signaling components in brain tumors. We have investigated the relationship between E2 and TGF-β signaling pathways and their effects on EMT induction in human GBM-derived cells. Here, we showed that E2 and TGF-β negatively regulated the expression of estrogen receptor α (ER-α) and Smad2/3. TGF-β induced Smad2 phosphorylation and its subsequent nuclear translocation, which E2 inhibited. Both TGF-β and E2 induced cellular processes related to EMT, such as morphological changes, actin filament reorganization, and mesenchymal markers (N-cadherin and vimentin) expression. Interestingly, we found that the co-treatment of E2 and TGF-β blocked EMT activation. Our results suggest that E2 and TGF-β signaling pathways interact through ER-α and Smad2/3 mediators in cells derived from human GBM and inhibit EMT activation induced by both factors alone.

The Crosstalk between FAK and Wnt Signaling Pathways in Cancer and Its Therapeutic Implication

Focal adhesion kinase (FAK) and Wnt signaling pathways are important contributors to tumorigenesis in several cancers. While most results come from studies investigating these pathways individually, there is increasing evidence of a functional crosstalk between both signaling pathways during development and tumor progression. A number of FAK-Wnt interactions are described, suggesting an intricate, context-specific, and cell type-dependent relationship. During development for instance, FAK acts mainly upstream of Wnt signaling; and although in intestinal homeostasis and mucosal regeneration Wnt seems to function upstream of FAK signaling, FAK activates the Wnt/β-catenin signaling pathway during APC-driven intestinal tumorigenesis. In breast, lung, and pancreatic cancers, FAK is reported to modulate the Wnt signaling pathway, while in prostate cancer, FAK is downstream of Wnt. In malignant mesothelioma, FAK and Wnt show an antagonistic relationship: Inhibiting FAK signaling activates the Wnt pathway and vice versa. As the identification of effective Wnt inhibitors to translate in the clinical setting remains an outstanding challenge, further understanding of the functional interaction between Wnt and FAK could reveal new therapeutic opportunities and approaches greatly needed in clinical oncology. In this review, we summarize some of the most relevant interactions between FAK and Wnt in different cancers, address the current landscape of Wnt- and FAK-targeted therapies in different clinical trials, and discuss the rationale for targeting the FAK-Wnt crosstalk, along with the possible translational implications.

Information transmission from NFkB signaling dynamics to gene expression

The dynamic signal encoding paradigm suggests that information flows from the extracellular environment into specific signaling patterns (encoding) that are then read by downstream effectors to control cellular behavior. Previous work empirically quantified the information content of dynamic signaling patterns. However, whether this information can be faithfully transmitted to the gene expression level is unclear. Here we used NFkB signaling as a model to understand the accuracy of information transmission from signaling dynamics into gene expression. Using a detailed mathematical model, we simulated realistic NFkB signaling patterns with different degrees of variability. The NFkB patterns were used as an input to a simple gene expression model. Analysis of information transmission between ligand and NFkB and ligand and gene expression allows us to determine information loss in transmission between receptors to dynamic signaling patterns and between signaling dynamics to gene expression. Information loss could occur due to biochemical noise or due to a lack of specificity. We found that noise-free gene expression has very little information loss suggesting that gene expression can preserve specificity in NFkB patterns. As expected, the addition of noise to the gene expression model results in information loss. Interestingly, this effect can be mitigated by a specific choice of parameters that can substantially reduce information loss due to biochemical noise during gene expression. Overall our results show that the cellular capacity for information transmission from dynamic signaling patterns to gene expression can be high enough to preserve ligand specificity and thereby the accuracy of cellular response to environmental cues.

Discussion of some 'knowns' and some 'unknowns' about the tumour suppressor p53

Activation of the tumour suppressor p53 upon cellular stress can induce a number of different cellular processes. The diverse actions of these processes are critical for the protective function of p53 in preventing the development of cancer. However, it is still not fully understood which process(es) activated by p53 is/are critical for tumour suppression and how this might differ depending on the type of cells undergoing neoplastic transformation and the nature of the drivers of oncogenesis. Moreover, it is not clear why upon activation of p53 some cells undergo cell cycle arrest and senescence whereas others die by apoptosis. Here we discuss some of the cellular processes that are crucial for p53-mediated tumour suppression and the factors that could impact cell fate upon p53 activation. Finally, we describe therapies aimed either at activating wild-type p53 or at changing the behaviour of mutant p53 to unleash tumour growth suppressive processes for therapeutic benefit in malignant disease.

The relation between crosstalk and gene regulation form revisited

Genes differ in the frequency at which they are expressed and in the form of regulation used to control their activity. In particular, positive or negative regulation can lead to activation of a gene in response to an external signal. Previous works proposed that the form of regulation of a gene correlates with its frequency of usage: positive regulation when the gene is frequently expressed and negative regulation when infrequently expressed. Such network design means that, in the absence of their regulators, the genes are found in their least required activity state, hence regulatory intervention is often necessary. Due to the multitude of genes and regulators, spurious binding and unbinding events, called “crosstalk”, could occur. To determine how the form of regulation affects the global crosstalk in the network, we used a mathematical model that includes multiple regulators and multiple target genes. We found that crosstalk depends non-monotonically on the availability of regulators. Our analysis showed that excess use of regulation entailed by the formerly suggested network design caused high crosstalk levels in a large part of the parameter space. We therefore considered the opposite ‘idle’ design, where the default unregulated state of genes is their frequently required activity state. We found, that ‘idle’ design minimized the use of regulation and thus minimized crosstalk. In addition, we estimated global crosstalk of S. cerevisiae using transcription factors binding data. We demonstrated that even partial network data could suffice to estimate its global crosstalk, suggesting its applicability to additional organisms. We found that S. cerevisiae estimated crosstalk is lower than that of a random network, suggesting that natural selection reduces crosstalk. In summary, our study highlights a new type of protein production cost which is typically overlooked: that of regulatory interference caused by the presence of excess regulators in the cell. It demonstrates the importance of whole-network descriptions, which could show effects missed by single-gene models.

Genes differ in the frequency at which they are expressed and in the form of regulation used to control their activity. The basic level of regulation is mediated by different types of DNA-binding proteins, where each type regulates particular gene(s). We distinguish between two basic forms of regulation: positive—if a gene is activated by the binding of its regulatory protein, and negative—if it is active unless bound by its regulatory protein. Due to the multitude of genes and regulators, spurious binding and unbinding events, called “crosstalk”, could occur. How does the form of regulation, positive or negative, affect the extent of regulatory crosstalk? To address this question, we used a mathematical model integrating many genes and many regulators. As intuition suggests, we found that in most of the parameter space, crosstalk increased with the availability of regulators. We propose, that crosstalk is usually reduced when networks are designed such that minimal regulation is needed, which we call the ‘idle’ design. In other words: a frequently needed gene will use negative regulation and conversely, a scarcely needed gene will employ positive regulation. In both cases, the requirement for the regulators is minimized. In addition, we demonstrate how crosstalk can be calculated from available datasets and discuss the technical challenges in such calculation, specifically data incompleteness.

Advanced Bottom-Up Engineering of Living Architectures

Bottom-up tissue engineering is a promising approach for designing modular biomimetic structures that aim to recapitulate the intricate hierarchy and biofunctionality of native human tissues. In recent years, this field has seen exciting progress driven by an increasing knowledge of biological systems and their rational deconstruction into key core components. Relevant advances in the bottom-up assembly of unitary living blocks toward the creation of higher order bioarchitectures based on multicellular-rich structures or multicomponent cell-biomaterial synergies are described. An up-to-date critical overview of long-term existing and rapidly emerging technologies for integrative bottom-up tissue engineering is provided, including discussion of their practical challenges and required advances. It is envisioned that a combination of cell-biomaterial constructs with bioadaptable features and biospecific 3D designs will contribute to the development of more robust and functional humanized tissues for therapies and disease models, as well as tools for fundamental biological studies.

Signaling Crosstalk Mechanisms That May Fine-Tune Pathogen-Responsive NFκB

Precise control of inflammatory gene expression is critical for effective host defense without excessive tissue damage. The principal regulator of inflammatory gene expression is nuclear factor kappa B (NFκB), a transcription factor. Nuclear NFκB activity is controlled by IκB proteins, whose stimulus-responsive degradation and re-synthesis provide for transient or dynamic regulation. The IκB-NFκB signaling module receives input signals from a variety of pathogen sensors, such as toll-like receptors (TLRs). The molecular components and mechanisms of NFκB signaling are well-understood and have been reviewed elsewhere in detail. Here we review the molecular mechanisms that mediate cross-regulation of TLR-IκB-NFκB signal transduction by signaling pathways that do not activate NFκB themselves, such as interferon signaling pathways. We distinguish between potential regulatory crosstalk mechanisms that (i) occur proximal to TLRs and thus may have stimulus-specific effects, (ii) affect the core IκB-NFκB signaling module to modulate NFκB activation in response to several stimuli. We review some well-documented examples of molecular crosstalk mechanisms and indicate other potential mechanisms whose physiological roles require further study.

Smartphones and Close Relationships: The Case for an Evolutionary Mismatch

This article introduces and outlines the case for an evolutionary mismatch between smartphones and the social behaviors that help form and maintain close social relationships. As psychological adaptations that enhance human survival and inclusive fitness, self-disclosure and responsiveness evolved in the context of small kin networks to facilitate social bonds, promote trust, and enhance cooperation. These adaptations are central to the development of attachment bonds, and attachment theory is a middle-level evolutionary theory that provides a robust account of the ways human bonding provides for reproductive and inclusive fitness. Evolutionary mismatches operate when modern contexts cue ancestral adaptations in a manner that does not provide for their adaptive benefits. We argue that smartphones and their affordances, although highly beneficial in many circumstances, cue humans’ evolved needs for self-disclosure and responsiveness across broad virtual networks and, in turn, have the potential to undermine immediate interpersonal interactions. We review emerging evidence on the topic of technoference, which is defined as the ways in which smartphone use may interfere with or intrude into everyday social interactions. The article concludes with an empirical agenda for advancing the integrative study of smartphones, intimacy processes, and close relationships.

Computational approaches to macromolecular interactions in the cell

Structural modeling of a cell is an evolving strategic direction in computational structural biology. It takes advantage of new powerful modeling techniques, deeper understanding of fundamental principles of molecular structure and assembly, and rapid growth of the amount of structural data generated by experimental techniques. Key modeling approaches to principal types of macromolecular assemblies in a cell already exist. The main challenge, along with the further development of these modeling approaches, is putting them together in a consistent, unified whole cell model. This opinion piece addresses the fundamental aspects of modeling macromolecular assemblies in a cell, and the state-of-the-art in modeling of the principal types of such assemblies.

Modeling cell line-specific recruitment of signaling proteins to the insulin-like growth factor 1 receptor

Receptor tyrosine kinases (RTKs) typically contain multiple autophosphorylation sites in their cytoplasmic domains. Once activated, these autophosphorylation sites can recruit downstream signaling proteins containing Src homology 2 (SH2) and phosphotyrosine-binding (PTB) domains, which recognize phosphotyrosine-containing short linear motifs (SLiMs). These domains and SLiMs have polyspecific or promiscuous binding activities. Thus, multiple signaling proteins may compete for binding to a common SLiM and vice versa. To investigate the effects of competition on RTK signaling, we used a rule-based modeling approach to develop and analyze models for ligand-induced recruitment of SH2/PTB domain-containing proteins to autophosphorylation sites in the insulin-like growth factor 1 (IGF1) receptor (IGF1R). Models were parameterized using published datasets reporting protein copy numbers and site-specific binding affinities. Simulations were facilitated by a novel application of model restructuration, to reduce redundancy in rule-derived equations. We compare predictions obtained via numerical simulation of the model to those obtained through simple prediction methods, such as through an analytical approximation, or ranking by copy number and/or K_D value, and find that the simple methods are unable to recapitulate the predictions of numerical simulations. We created 45 cell line-specific models that demonstrate how early events in IGF1R signaling depend on the protein abundance profile of a cell. Simulations, facilitated by model restructuration, identified pairs of IGF1R binding partners that are recruited in anti-correlated and correlated fashions, despite no inclusion of cooperativity in our models. This work shows that the outcome of competition depends on the physicochemical parameters that characterize pairwise interactions, as well as network properties, including network connectivity and the relative abundances of competitors.

Cells rely on networks of interacting biomolecules to sense and respond to environmental perturbations and signals. However, it is unclear how information is processed to generate appropriate and specific responses to signals, especially given that these networks tend to share many components. For example, receptors that detect distinct ligands and regulate distinct cellular activities commonly interact with overlapping sets of downstream signaling proteins. Here, to investigate the downstream signaling of a well-studied receptor tyrosine kinase (RTK), the insulin-like growth factor 1 (IGF1) receptor (IGF1R), we formulated and analyzed 45 cell line-specific mathematical models, which account for recruitment of 18 different binding partners to six sites of receptor autophosphorylation in IGF1R. The models were parameterized using available protein copy number and site-specific affinity measurements, and restructured to allow for network generation. We find that recruitment is influenced by the protein abundance profile of a cell, with different patterns of recruitment in different cell lines. Furthermore, in a given cell line, we find that pairs of IGF1R binding partners may be recruited in a correlated or anti-correlated fashion. We demonstrate that the simulations of the model have greater predictive power than protein copy number and/or binding affinity data, and that even a simple analytical model cannot reproduce the predicted recruitment ranking obtained via simulations. These findings represent testable predictions and indicate that the outputs of IGF1R signaling depend on cell line-specific properties in addition to the properties that are intrinsic to the biomolecules involved.

The Limited Information Capacity of Cross-Reactive Sensors Drives the Evolutionary Expansion of Signaling

Signaling systems expand by duplications of various components, be it receptors or downstream effectors. However, whether and how duplicated components contribute to higher signaling capacity is unclear, especially because in most cases, their specificities overlap. Using information theory, we found that augmentation of capacity by an increase in the copy number is strongly limited by logarithmic diminishing returns. Moreover, counter to conventional biochemical wisdom, refinements of the response mechanism, e.g., by cooperativity or allostery, do not increase the overall signaling capacity. However, signaling capacity nearly doubles when a promiscuous, non-cognate ligand becomes explicitly recognized via duplication and partial divergence of signaling components. Our findings suggest that expansion of signaling components via duplication and enlistment of promiscuously acting cues is virtually the only accessible evolutionary strategy to achieve overall high-signaling capacity despite overlapping specificities and molecular noise. This mode of expansion also explains the highly cross-wired architecture of signaling pathways.

Precision medicine review: rare driver mutations and their biophysical classification

How can biophysical principles help precision medicine identify rare driver mutations? A major tenet of pragmatic approaches to precision oncology and pharmacology is that driver mutations are very frequent. However, frequency is a statistical attribute, not a mechanistic one. Rare mutations can also act through the same mechanism, and as we discuss below, “latent driver” mutations may also follow the same route, with “helper” mutations. Here, we review how biophysics provides mechanistic guidelines that extend precision medicine. We outline principles and strategies, especially focusing on mutations that drive cancer. Biophysics has contributed profoundly to deciphering biological processes. However, driven by data science, precision medicine has skirted some of its major tenets. Data science embodies genomics, tissue- and cell-specific expression levels, making it capable of defining genome- and systems-wide molecular disease signatures. It classifies cancer driver genes/mutations and affected pathways, and its associated protein structural data guide drug discovery. Biophysics complements data science. It considers structures and their heterogeneous ensembles, explains how mutational variants can signal through distinct pathways, and how allo-network drugs can be harnessed. Biophysics clarifies how one mutation—frequent or rare—can affect multiple phenotypic traits by populating conformations that favor interactions with other network modules. It also suggests how to identify such mutations and their signaling consequences. Biophysics offers principles and strategies that can help precision medicine push the boundaries to transform our insight into biological processes and the practice of personalized medicine. By contrast, “phenotypic drug discovery,” which capitalizes on physiological cellular conditions and first-in-class drug discovery, may not capture the proper molecular variant. This is because variants of the same protein can express more than one phenotype, and a phenotype can be encoded by several variants.

Intrinsic limits of information transmission in biochemical signalling motifs

All living things have evolved to sense changes in their environment in order to respond in adaptive ways. At the cellular level, these sensing systems generally involve receptor molecules at the cell surface, which detect changes outside the cell and relay those changes to the appropriate response elements downstream. With the advent of experimental technologies that can track signalling at the single-cell level, it has become clear that many signalling systems exhibit significant levels of 'noise,' manifesting as differential responses of otherwise identical cells to the same environment. This noise has a large impact on the capacity of cell signalling networks to transmit information from the environment. Application of information theory to experimental data has found that all systems studied to date encode less than 2.5 bits of information, with the majority transmitting significantly less than 1 bit. Given the growing interest in applying information theory to biological data, it is crucial to understand whether the low values observed to date represent some sort of intrinsic limit on information flow given the inherently stochastic nature of biochemical signalling events. In this work, we used a series of computational models to explore how much information a variety of common 'signalling motifs' can encode. We found that the majority of these motifs, which serve as the basic building blocks of cell signalling networks, can encode far more information (4-6 bits) than has ever been observed experimentally. In addition to providing a consistent framework for estimating information-theoretic quantities from experimental data, our findings suggest that the low levels of information flow observed so far in living system are not necessarily due to intrinsic limitations. Further experimental work will be needed to understand whether certain cell signalling systems actually can approach the intrinsic limits described here, and to understand the sources and purpose of the variation that reduces information flow in living cells.