Biology subjects

Einav, T.

Publications and source records attributed to Einav, T..

17 recordsLinked to original sources

bioRxiv2026

An Open Benchmark for Systems Vaccinology: Insights from the CMI-PB Challenges

Systems vaccinology approaches have identified factors affecting vaccine responses in multiple studies, but the ability of computational models to generalize these findings to unseen data remains unclear. We established a community resource to create and compare models predicting B. pertussis booster vaccination responses and put such modeling approaches to the test. We compiled multi-modal experimental training data from three independent cohorts (n=117 individuals), and asked investigators to predict vaccine responses in a cohort of 54 newly recruited individuals using only their pre-booster vaccination data. We benchmarked a total of 107 computational models. Top-performing models were characterized by workflows that prioritized rigorous data preprocessing, robust imputation of missing data, and the use of multi-omics integration or non-linear machine learning. We identified pre-existing antigen-specific antibody titers and baseline monocyte frequencies as the most consistent predictors of post-vaccination immunity, highlighting the dominant role of individual immune setpoints. We established the resulting datasets and evaluation framework as a community resource to advance predictive immunology and facilitate personalized vaccination strategies.

immunology↗

bioRxiv2024

Putting computational models of immunity to the test - an invited challenge to predict B. pertussis vaccination outcomes

Systems vaccinology studies have been used to build computational models that predict individual vaccine responses and identify the factors contributing to differences in outcome. Comparing such models is challenging due to variability in study designs. To address this, we established a community resource to compare models predicting B. pertussis booster responses and generate experimental data for the explicit purpose of model evaluation. We here describe our second computational prediction challenge using this resource, where we benchmarked 49 algorithms from 53 scientists. We found that the most successful models stood out in their handling of nonlinearities, reducing large feature sets to representative subsets, and advanced data preprocessing. In contrast, we found that models adopted from literature that were developed to predict vaccine antibody responses in other settings performed poorly, reinforcing the need for purpose-built models. Overall, this demonstrates the value of purpose-generated datasets for rigorous and open model evaluations to identify features that improve the reliability and applicability of computational models in vaccine response prediction.

immunology↗

bioRxiv2024

Pre-existing Immunity to Influenza Aids Ferrets in Developing Stronger and Broader Vaccine-induced Antibody Responses

Influenza seasons occur annually, building immune history for individuals, but the influence of this history on subsequent influenza vaccine protection remains unclear. We extracted data from an animal trial to study its potential impact. The trial involved 80 ferrets, each receiving either one type of infection or a placebo before vaccination. We quantified the vaccine protection by evaluating hemagglutination inhibition (HAI) antibody titer responses. We tested whether hosts with different infection histories exhibited similar level of responses when receiving the same vaccine for all homologous and heterologous outcomes. We observed that different pre-existing immunities were generally beneficial to vaccine induced responses, but varied in magnitude. Without pre-immunity, post-vaccination HAI titers after the 1st dose of the vaccine were less likely to be above 1:40, and a booster shot was needed. Our study suggests that pre-existing immunity may strengthen and extend the homologous and heterologous vaccine protection.

immunology↗

bioRxiv2022

Data-driven model of glycolysis identifies the role of allostery in maintaining ATP homeostasis

Glycolysis is a conserved metabolic pathway that produces ATP and biosynthetic precursors. Here, we use mathematical modeling to investigate how the control of mammalian glycolytic enzymes through allostery and mass action accomplishes various tasks of ATP homeostasis, such as controlling the rate of ATP production, maintaining high and stable ATP levels, and ensuring that ATP hydrolysis generates a net excess of energy. Our model uses data-derived enzyme rate equations, recapitulates the key tasks of glycolytic ATP homeostasis, and accurately predicts absolute concentrations of glycolytic intermediates and isotope tracing kinetics in live cells. We find that allosteric regulation of hexokinase (HK) and phosphofructokinase (PFK) by ATP, ADP, inorganic phosphate and glucose-6-phosphate (G6P), the surplus of lower glycolysis enzymes, and a large non-adenine phosphate pool are essential to robustly maintain high ATP levels and to prevent uncontrolled accumulation of phosphorylated intermediates of upper glycolysis. Meanwhile, mass action alone is sufficient to control ATP production rate and maintain high energy of ATP hydrolysis. Our results suggest a revision of the textbook view that the function of allosteric regulation of HK, PFK and PK is to control the net flux through glycolysis in response to variable ATP demand.

systems biology↗

bioRxiv2022

Massively Increasing the number of Antibody-Virus Interactions across Studies

A central challenge in every field of biology is to use existing measurements to predict the outcomes of future experiments. In this work, we consider the wealth of antibody inhibition data against variants of the influenza virus. Due to this viruss genetic diversity and evolvability, the variants examined in one study will often have little-to-no overlap with other studies, making it difficult to discern common patterns or unify datasets for further analysis. To that end, we develop a computational framework that predicts how an antibody or serum would inhibit any variant from any other study. We use this framework to greatly expand seven influenza datasets utilizing hemagglutination inhibition, validating our method upon 200,000 existing measurements and predicting 2,000,000 new values {+/-} uncertainties. With these new values, we quantify the transferability between seven vaccination and infection studies in humans and ferrets, show that the serum potency is negatively correlated with breadth, and present a tool for pandemic preparedness. This data-driven approach does not require any information beyond each viruss name and measurements, and even datasets with as few as 5 viruses can be expanded, making this approach widely applicable. Future influenza studies using hemagglutination inhibition can directly utilize our curated datasets to predict newly measured antibody responses against {approx}80 H3N2 influenza viruses from 1968-2011, whereas immunological studies utilizing other viruses or a different assay only need a single partially-overlapping dataset to extend their work. In essence, this approach enables a shift in perspective when analyzing data from "what you see is what you get" into "what anyone sees is what everyone gets."

immunology↗

bioRxiv2021

Harnessing Matrix Completion to Unify and Extend Viral Serology Studies

The development of new vaccine strategies, as well as our understanding of key processes that shape viral evolution and host antibody repertoires, rely on measuring multiple antibody responses against large panels of viruses. Given the enormous diversity of circulating virus strains and antibody responses, exhaustive testing of all antibody-virus interactions is infeasible. Even within individual studies with limited panels, exhaustive testing is not always done, and there is no common framework for combining information across studies with partially overlapping panels, especially when the assay type or host species differ. Results from prior studies have demonstrated that virus-antibody interactions can be characterized in a vastly simpler and lower-dimensional space, suggesting that relatively few measurements could accurately predict unmeasured antibody-virus interactions. Here, we apply matrix completion to several of the largest-scale studies for both influenza and HIV-1. We explore how prediction accuracy evolves as the number of available measurements changes and approximate the number of additional measurements necessary in several highly incomplete datasets (suggesting [~]250,000 measurements could be saved). In addition, we show how the method can combine disparate datasets, even when the number of available measurements is below the theoretical limit that guarantees successful prediction. This approach can be readily generalized to other viruses or more broadly to other low-dimensional biological datasets. SignificanceOne of the central problems in immunology is to characterize how our vast array of antibodies inhibits the diverse pathogens we encounter in our lives. In this work, we apply a well-studied mathematical technique called matrix completion that leverages patterns in partially-observed antibody-virus inhibition data to infer unmeasured interactions. We predict the results of tens of thousands of missing experiments in influenza and HIV-1 studies and quantify the expected error of our estimates. By harnessing matrix completion, future experiments could be designed that only collect a fraction of measurements, saving time and resources while maximizing the information gained.

immunology↗

bioRxiv2020

Mapping the Antibody Repertoires in Ferrets with Repeated Influenza A/H3 Infections: Is Original Antigenic Sin Really "Sinful"?

The influenza-specific antibody repertoire is continuously reshaped by infection and vaccination. The host immune response to contemporary viruses can be redirected to preferentially boost antibodies specific for viruses encountered early in life, a phenomenon called original antigenic sin (OAS) that is suggested to be responsible for diminished vaccine effectiveness after repeated vaccination. In this study, we used a new computational tool called Neutralization Map to determine the hemagglutination inhibition profiles of individual antibodies within ferret antisera elicited by repeated influenza A/H3 infections. Our results suggest that repeated infections continuously reshape the ferret antibody repertoire, but that a broadly neutralizing antibody signature can nevertheless be induced irrespective of OAS. Overall, our study offers a new way to visualize how immune history shapes individual antibodies within a repertoire, which may help inform future vaccine design.

immunology↗

bioRxiv2020

Decomposing Polyclonal Sera using Neutralization Maps

Antibodies constitute a key line of defense against the diverse pathogens we encounter in our lives. While the interactions between a single antibody and a single virus are routinely characterized in exquisite detail, the inherent tradeoffs between attributes such as potency and breadth remain unclear. In addition, there is a wide gap between the discrete interactions of single antibodies and how antibody mixtures collectively repel multiple viruses. Here, we develop a new form of antigenic cartography called a Neutralization Landscape that enumerates the full space of antibody-virus interactions for antibodies targeting the influenza hemagglutinin stem. This reference set of antibody behaviors transforms the potency-breadth tradeoff into a readily solvable geometry problem. Using the Neutralization Landscape, we decompose the collective neutralization from multiple antibodies to characterize the composition and functional properties of the stem antibodies within. Looking forward, this framework can leverage the serological assays routinely performed for influenza surveillance to analyze how an individuals antibody repertoire evolves over time in response to vaccination or infection. One-Sentence SummaryWe describe the full range of behaviors for antibodies targeting the stem of influenza hemagglutinin, enabling us to decompose the collective behavior of antibody mixtures and characterize the individual antibodies within.

immunology↗

bioRxiv2020

Multiplexed characterization of rationally designed promoter architectures deconstructs combinatorial logic for IPTG-inducible systems

A crucial step towards engineering biological systems is the ability to precisely tune the genetic response to environmental stimuli. In the case of Escherichia coli inducible promoters, our incomplete understanding of the relationship between sequence composition and gene expression hinders our ability to predictably control transcriptional responses. Here, we profile the expression dynamics of 8,269 rationally designed IPTG-inducible promoters that collectively explore the individual and combinatorial effects of RNA polymerase and LacI repressor binding site strengths. Using these data, we fit a statistical mechanics model that accurately models gene expression and reveals properties of theoretically optimal inducible promoters. Furthermore, we characterize three novel promoter architectures and show that repositioning binding sites within promoters influences the types of combinatorial effects observed between promoter elements. In total, this approach enables us to deconstruct relationships between inducible promoter elements and discover practical insights for engineering inducible promoters with desirable characteristics.

synthetic biology↗

bioRxiv2019

When Two are Better than One: Modeling the Mechanisms of Antibody Mixtures

It is difficult to predict how antibodies will behave when mixed together, even after each has been independently characterized. Here, we present a statistical mechanical model for the activity of antibody mixtures that accounts for whether pairs of antibodies bind to distinct or overlapping epitopes. This model requires measuring n individual antibodies and their [Formula] pairwise interactions to predict the 2n potential combinations. We apply this model to epidermal growth factor receptor (EGFR) antibodies and find that the activity of antibody mixtures can be predicted without positing synergy at the molecular level. In addition, we demonstrate how the model can be used in reverse, where straightforward experiments measuring the activity of antibody mixtures can be used to infer the molecular interactions between antibodies. Lastly, we generalize this model to analyze engineered multidomain antibodies, where components of different antibodies are tethered together to form novel amalgams, and characterize how well it predicts recently designed influenza antibodies. Author summaryWith the rise of new antibody combinations in therapeutic regimens, it is important to understand how antibodies work together as well as individually. Here, we investigate the specific case of monoclonal antibodies targeting a cancer-causing receptor or the influenza virus and develop a statistical mechanical framework that predicts the effectiveness of a mixture of antibodies. The power of this model lies in its ability to make a large number of predictions based on a limited amount of data. For example, once 10 antibodies have been individually characterized and their epitopes have been mapped, our model can predict how any of the 210 = 1024 combinations will behave. This predictive power can aid therapeutic efforts by assessing which combinations of antibodies will elicit the most effective response.

biophysics↗

bioRxiv2019

The Energetics of Molecular Adaptation in Transcriptional Regulation

Mutation is a critical mechanism by which evolution explores the functional landscape of proteins. Despite our ability to experimentally inflict mutations at will, it remains difficult to link sequence-level perturbations to systems-level responses. Here, we present a framework centered on measuring changes in the free energy of the system to link individual mutations in an allosteric transcriptional repressor to the parameters which govern its response. We find the energetic effects of the mutations can be categorized into several classes which have characteristic curves as a function of the inducer concentration. We experimentally test these diagnostic predictions using the well-characterized LacI repressor of Escherichia coli, probing several mutations in the DNA binding and inducer binding domains. We find that the change in gene expression due to a point mutation can be captured by modifying only a subset of the model parameters that describe the respective domain of the wild-type protein. These parameters appear to be insulated, with mutations in the DNA binding domain altering only the DNA affinity and those in the inducer binding domain altering only the allosteric parameters. Changing these subsets of parameters tunes the free energy of the system in a way that is concordant with theoretical expectations. Finally, we show that the induction profiles and resulting free energies associated with pairwise double mutants can be predicted with quantitative accuracy given knowledge of the single mutants, providing an avenue for identifying and quantifying epistatic interactions.\n\nSummaryWe present a biophysical model of allosteric transcriptional regulation that directly links the location of a mutation within a repressor to the biophysical parameters that describe its behavior. We explore the phenotypic space of a repressor with mutations in either the inducer binding or DNA binding domains. Using the LacI repressor in E. coli, we make sharp, falsifiable predictions and use this framework to generate a null hypothesis for how double mutants behave given knowledge of the single mutants. Linking mutations to the parameters which govern the system allows for quantitative predictions of how the free energy of the system changes as a result, permitting coarse graining of high-dimensional data into a single-parameter description of the mutational consequences.

biophysics↗

bioRxiv2019

How the Avidity of Polymerase Binding to the -35/-10 Promoter Sites Affects Gene Expression

Although the key promoter elements necessary to drive transcription in Escherichia coli have long been understood, we still cannot predict the behavior of arbitrary novel promoters, hampering our ability to characterize the myriad of sequenced regulatory architectures as well as to design novel synthetic circuits. This work builds on a beautiful recent experiment by Urtecho et al. who measured the gene expression of over 10,000 promoters spanning all possible combinations of a small set of regulatory elements. Using this data, we demonstrate that a central claim in energy matrix models of gene expression - that each promoter element contributes independently and additively to gene expression - contradicts experimental measurements. We propose that a key missing ingredient from such models is the avidity between the -35 and -10 RNA polymerase binding sites and develop what we call a refined energy matrix model that incorporates this effect. We show that this the refined energy matrix model can characterize the full suite of gene expression data and explore several applications of this framework, namely, how multivalent binding at the -35 and -10 sites can buffer RNAP kinetics against mutations and how promoters that bind overly tightly to RNA polymerase can inhibit gene expression. The success of our approach suggests that avidity represents a key physical principle governing the interaction of RNA polymerase to its promoter.\n\nSignificance StatementCellular behavior is ultimately governed by the genetic program encoded in its DNA and through the arsenal of molecular machines that actively transcribe its genes, yet we lack the ability to predict how an arbitrary DNA sequence will perform. To that end, we analyze the performance of over 10,000 regulatory sequences and develop a model that can predict the behavior of any sequence based on its composition. By considering promoters that only vary by one or two elements, we can characterize how different components interact, providing fundamental insights into the mechanisms of transcription.

biophysics↗

bioRxiv2018

Combinatorial Control through Allostery

Many instances of cellular signaling and transcriptional regulation involve switch-like molecular responses to the presence or absence of input ligands. To understand how these responses come about and how they can be harnessed, we develop a statistical mechanical model to characterize the types of Boolean logic that can arise from allosteric molecules following the Monod-Wyman-Changeux (MWC) model. Building upon previous work, we show how an allosteric molecule regulated by two inputs can elicit AND, OR, NAND and NOR responses, but is unable to realize XOR or XNOR gates. Next, we demonstrate the ability of an MWC molecule to perform ratiometric sensing - a response behavior where activity depends monotonically on the ratio of ligand concentrations. We then extend our analysis to more general schemes of combinatorial control involving either additional binding sites for the two ligands or an additional third ligand and show how these additions can cause a switch in the logic behavior of the molecule. Overall, our results demonstrate the wide variety of control schemes that biological systems can implement using simple mechanisms.

biophysics↗

bioRxiv2018

Quantifying the Entropic and Energetic Effects of Linker Length and Rigidity within Synthetic HIV-1 Antibodies designed to Bind Bivalently to Env Spikes

Due to the low density of envelope (Env) spikes on the surface of HIV-1, neutralizing IgG antibodies rarely bind bivalently using both antigen-binding arms (Fabs) to crosslink between spikes (inter-spike crosslinking), instead resorting to weaker monovalent binding that is more sensitive to Env mutations. Synthetic antibodies designed to bivalently bind a single Env trimer (intra-spike crosslinking) were previously shown to exhibit increased neutralization potencies. In initial work, diFabs joined by varying lengths of rigid double-stranded DNA (dsDNA) were considered. Anticipating future experiments to improve synthetic antibodies, we investigate whether linkers with different rigidities could enhance diFab potency by modeling DNA-Fabs containing different combinations of rigid dsDNA and flexible single-stranded DNA (ssDNA) and characterizing their neutralization potential. Model predictions suggest that while a long flexible polymer may be capable of bivalent binding, it exhibits weak neutralization due to the large loss in entropic degrees of freedom when both Fabs are bound. In contrast, the strongest neutralization potencies are predicted to require a rigid linker that optimally spans the distance between two Fab binding sites on an Env trimer, and avidity can be further boosted by incorporating more Fabs into these constructs. These results inform the design of multivalent anti-HIV-1 therapeutics that utilize avidity effects to remain potent against HIV-1 in the face of the rapid mutation of Env spikes.\n\nSignificanceIgG antibodies utilize avidity to increase their apparent affinities through simultaneous binding of two antigen-binding Fabs - if one Fab dissociates from an antigen, the other Fab can remain attached, allowing rebinding. HIV-1 foils this strategy by having few, and highly-separated, Envelope spike targets for antibodies, forcing most IgGs to bind monovalently. Here we develop a statistical mechanics model of synthetic diFabs joined by DNA linkers of different lengths and flexibilities. This framework enables us to translate the energetic and entropic effects of the linker into the neutralization potency of a diFab. We demonstrate that the avidity of multivalent binding is enhanced by using rigid linkers or including additional Fabs capable of simultaneous binding, providing the means to quantitatively predict the potencies of other antibody designs.

immunology↗

bioRxiv2017

Theoretical Analysis of Inducer and Operator Binding for Cyclic-AMP Receptor Protein Mutants

Allosteric transcription factors undergo binding events both at their inducer binding sites as well as at distinct DNA binding domains, and it is often difficult to disentangle the structural and functional consequences of these two classes of interactions. In this work, we compare the ability of two statistical mechanical models - the Monod-Wyman-Changeux (MWC) and the Koshland-Nemethy-Filmer (KNF) models of protein conformational change - to characterize the multi-step activation mechanism of the broadly acting cyclic-AMP receptor protein (CRP). We first consider the allosteric transition resulting from cyclic-AMP binding to CRP, then analyze how CRP binds to its operator, and finally investigate the ability of CRP to activate gene expression. In light of these models, we examine data from a beautiful recent experiment that created a single-chain version of the CRP homodimer, thereby enabling each subunit to be mutated separately. Using this construct, six mutants were created using all possible combinations of the wild type subunit, a D53H mutant subunit, and an S62F mutant subunit. We demonstrate that both the MWC and KNF models can explain the behavior of all six mutants using a small, self-consistent set of parameters. In comparing the results, we find that the MWC model slightly outperforms the KNF model in the quality of its fits, but more importantly the parameters inferred by the MWC model are more in line with structural knowledge of CRP. In addition, we discuss how the conceptual framework developed here for CRP enables us to not merely analyze data retrospectively, but has the predictive power to determine how combinations of mutations will interact, how double mutants will behave, and how each construct would regulate gene expression.

biophysics↗

bioRxiv2017

An allosteric theory of transcription factor induction

Allosteric regulation is found across all domains of life, yet we still lack simple, predictive theories that directly link the experimentally tunable parameters of a system to its input-output response. To that end, we present a general theory of allosteric transcriptional regulation using the Monod-Wyman-Changeux model. We rigorously test this model using the ubiquitous simple repression motif in bacteria by first predicting the behavior of strains that span a large range of repressor copy numbers and DNA binding strengths and then constructing and measuring their response. Our model not only accurately captures the induction profiles of these strains but also enables us to derive analytic expressions for key properties such as the dynamic range and [EC50]. Finally, we derive an expression for the free energy of allosteric repressors which enables us to collapse our experimental data onto a single master curve that captures the diverse phenomenology of the induction profiles.

biophysics↗

bioRxiv2017

Monod-Wyman-Changeux Analysis of Ligand-Gated Ion Channel Mutants

We present a framework for computing the gating properties of ligand-gated ion channel mutants using the Monod-Wyman-Changeux (MWC) model of allostery. We derive simple analytic formulas for key functional properties such as the leakiness, dynamic range, half-maximal effective concentration ([EC50]), and effective Hill coefficient, and explore the full spectrum of phenotypes that are accessible through mutations. Specifically, we consider mutations in the channel pore of nicotinic acetylcholine receptor (nAChR) and the ligand binding domain of a cyclic nucleotide-gated (CNG) ion channel, demonstrating how each mutation can be characterized as only affecting a subset of the biophysical parameters. In addition, we show how the unifying perspective offered by the MWC model allows us, perhaps surprisingly, to collapse the plethora of dose-response data from different classes of ion channels into a universal family of curves.

biophysics↗