GENERALIST: A latent space based generative model for protein sequence families.

Generative models of protein sequence families are an important tool in the repertoire of protein scientists and engineers alike. However, state-of-the-art generative approaches face inference, accuracy, and overfitting- related obstacles when modeling moderately sized to large proteins and/or protein families with low sequence coverage. Here, we present a simple to learn, tunable, and accurate generative model, GENERALIST: GENERAtive nonLInear tenSor-factorizaTion for protein sequences. GENERALIST accurately captures several high order summary statistics of amino acid covariation. GENERALIST also predicts conservative local optimal sequences which are likely to fold in stable 3D structure. Importantly, unlike current methods, the density of sequences in GENERALIST-modeled sequence ensembles closely resembles the corresponding natural ensembles. Finally, GENERALIST embeds protein sequences in an informative latent space. GENERALIST will be an important tool to study protein sequence variability.

Reverse Plasticity Underlies Rapid Evolution by Clonal Selection within Populations of Fibroblasts Propagated on a Novel Soft Substrate.

Mechanical properties such as substrate stiffness are a ubiquitous feature of a cell's environment. Many types of animal cells exhibit canonical phenotypic plasticity when grown on substrates of differing stiffness, in vitro and in vivo. Whether such plasticity is a multivariate optimum due to hundreds of millions of years of animal evolution, or instead is a compromise between conflicting selective demands, is unknown. We addressed these questions by means of experimental evolution of populations of mouse fibroblasts propagated for approximately 90 cell generations on soft or stiff substrates. The ancestral cells grow twice as fast on stiff substrate as on soft substrate and exhibit the canonical phenotypic plasticity. Soft-selected lines derived from a genetically diverse ancestral population increased growth rate on soft substrate to the ancestral level on stiff substrate and evolved the same multivariate phenotype. The pattern of plasticity in the soft-selected lines was opposite of the ancestral pattern, suggesting that reverse plasticity underlies the observed rapid evolution. Conversely, growth rate and phenotypes did not change in selected lines derived from clonal cells. Overall, our results suggest that the changes were the result of genetic evolution and not phenotypic plasticity per se. Whole-transcriptome analysis revealed consistent differentiation between ancestral and soft-selected populations, and that both emergent phenotypes and gene expression tended to revert in the soft-selected lines. However, the selected populations appear to have achieved the same phenotypic outcome by means of at least two distinct transcriptional architectures related to mechanotransduction and proliferation.

Quantifying spatiotemporal variability and noise in absolute microbiota abundances using replicate sampling.

Metagenomic sequencing has enabled detailed investigation of diverse microbial communities, but understanding their spatiotemporal variability remains an important challenge. Here, we present decomposition of variance using replicate sampling (DIVERS), a method based on replicate sampling and spike-in sequencing. The method quantifies the contributions of temporal dynamics, spatial sampling variability, and technical noise to the variances and covariances of absolute bacterial abundances. We applied DIVERS to investigate a high-resolution time series of the human gut microbiome and a spatial survey of a soil bacterial community in Manhattan's Central Park. Our analysis showed that in the gut, technical noise dominated the abundance variability for nearly half of the detected taxa. DIVERS also revealed substantial spatial heterogeneity of gut microbiota, and high temporal covariances of taxa within the Bacteroidetes phylum. In the soil community, spatial variability primarily contributed to abundance fluctuations at short time scales (weeks), while temporal variability dominated at longer time scales (several months).

SiGMoiD: A super-statistical generative model for binary data.

In modern computational biology, there is great interest in building probabilistic models to describe collections of a large number of co-varying binary variables. However, current approaches to build generative models rely on modelers' identification of constraints and are computationally expensive to infer when the number of variables is large (N~100). Here, we address both these issues with Super-statistical Generative Model for binary Data (SiGMoiD). SiGMoiD is a maximum entropy-based framework where we imagine the data as arising from super-statistical system; individual binary variables in a given sample are coupled to the same 'bath' whose intensive variables vary from sample to sample. Importantly, unlike standard maximum entropy approaches where modeler specifies the constraints, the SiGMoiD algorithm infers them directly from the data. Due to this optimal choice of constraints, SiGMoiD allows us to model collections of a very large number (N>1000) of binary variables. Finally, SiGMoiD offers a reduced dimensional description of the data, allowing us to identify clusters of similar data points as well as binary variables. We illustrate the versatility of SiGMoiD using multiple datasets spanning several time- and length-scales.

The Maximum Caliber Variational Principle for Nonequilibria.

Ever since Clausius in 1865 and Boltzmann in 1877, the concepts of entropy and of its maximization have been the foundations for predicting how material equilibria derive from microscopic properties. But, despite much work, there has been no equally satisfactory general variational principle for nonequilibrium situations. However, in 1980, a new avenue was opened by E.T. Jaynes and by Shore and Johnson. We review here maximum caliber, which is a maximum-entropy-like principle that can infer distributions of flows over pathways, given dynamical constraints. This approach is providing new insights, particularly into few-particle complex systems, such as gene circuits, protein conformational reaction coordinates, network traffic, bird flocking, cell motility, and neuronal firing.

Introducing User-Prescribed Constraints in Markov Chains for Nonlinear Dimensionality Reduction.

Stochastic kernel-based dimensionality-reduction approaches have become popular in the past decade. The central component of many of these methods is a symmetric kernel that quantifies the vicinity between pairs of data points and a kernel-induced Markov chain on the data. Typically, the Markov chain is fully specified by the kernel through row normalization. However, in many cases, it is desirable to impose user-specified stationary-state and dynamical constraints on the Markov chain. Unfortunately, no systematic framework exists to impose such user-defined constraints. Here, based on our previous work on inference of Markov models, we introduce a path entropy maximization based approach to derive the transition probabilities of Markov chains using a kernel and additional user-specified constraints. We illustrate the usefulness of these Markov chains with examples.

Building Markov state models using optimal transport theory.

Markov State Models (MSMs) describe the rates and routes in conformational dynamics of biomolecules. Computational estimation of MSMs can be expensive because molecular simulations are slow to find and sample the rare transient events. We describe here an efficient approximate way to determine MSM rate matrices by combining maximum caliber (maximizing path entropies) with optimal transport theory (minimizing some path cost function, as when routing trucks on transportation networks) to patch together transient dynamical information from multiple non-equilibrium simulations. We give toy examples.

Perspective: Maximum caliber is a general variational principle for dynamical systems.

We review here Maximum Caliber (Max Cal), a general variational principle for inferring distributions of paths in dynamical processes and networks. Max Cal is to dynamical trajectories what the principle of maximum entropy is to equilibrium states or stationary populations. In Max Cal, you maximize a path entropy over all possible pathways, subject to dynamical constraints, in order to predict relative path weights. Many well-known relationships of non-equilibrium statistical physics-such as the Green-Kubo fluctuation-dissipation relations, Onsager's reciprocal relations, and Prigogine's minimum entropy production-are limited to near-equilibrium processes. Max Cal is more general. While it can readily derive these results under those limits, Max Cal is also applicable far from equilibrium. We give examples of Max Cal as a method of inference about trajectory distributions from limited data, finding reaction coordinates in bio-molecular simulations, and modeling the complex dynamics of non-thermal systems such as gene regulatory networks or the collective firing of neurons. We also survey its basis in principle and some limitations.

Recombinant transfer in the basic genome of Escherichia coli.

An approximation to the ∼4-Mbp basic genome shared by 32 strains of Escherichia coli representing six evolutionary groups has been derived and analyzed computationally. A multiple alignment of the 32 complete genome sequences was filtered to remove mobile elements and identify the most reliable ∼90% of the aligned length of each of the resulting 496 basic-genome pairs. Patterns of single base-pair mutations (SNPs) in aligned pairs distinguish clonally inherited regions from regions where either genome has acquired DNA fragments from diverged genomes by homologous recombination since their last common ancestor. Such recombinant transfer is pervasive across the basic genome, mostly between genomes in the same evolutionary group, and generates many unique mosaic patterns. The six least-diverged genome pairs have one or two recombinant transfers of length ∼40-115 kbp (and few if any other transfers), each containing one or more gene clusters known to confer strong selective advantage in some environments. Moderately diverged genome pairs (0.4-1% SNPs) show mosaic patterns of interspersed clonal and recombinant regions of varying lengths throughout the basic genome, whereas more highly diverged pairs within an evolutionary group or pairs between evolutionary groups having >1.3% SNPs have few clonal matches longer than a few kilobase pairs. Many recombinant transfers appear to incorporate fragments of the entering DNA produced by restriction systems of the recipient cell. A simple computational model can closely fit the data. Most recombinant transfers seem likely to be due to generalized transduction by coevolving populations of phages, which could efficiently distribute variability throughout bacterial genomes.

Mini-grand canonical ensemble: Chemical potential in the solvation shell.

Quantifying the statistics of occupancy of solvent molecules in the vicinity of solutes is central to our understanding of solvation phenomena. Number fluctuations in small solvation shells around solutes cannot be described within the macroscopic grand canonical framework using a single chemical potential that represents the solvent bath. In this communication, we hypothesize that molecular-sized observation volumes such as solvation shells are best described by coupling the solvation shell with a mixture of particle baths each with its own chemical potential. We confirm our hypotheses by studying the enhanced fluctuations in the occupancy statistics of hard sphere solvent particles around a distinguished hard sphere solute particle. Connections with established theories of solvation are also discussed.

A maximum entropy framework for nonexponential distributions.

Probability distributions having power-law tails are observed in a broad range of social, economic, and biological systems. We describe here a potentially useful common framework. We derive distribution functions for situations in which a "joiner particle" k pays some form of price to enter a community of size , where costs are subject to economies of scale. Maximizing the Boltzmann-Gibbs-Shannon entropy subject to this energy-like constraint predicts a distribution having a power-law tail; it reduces to the Boltzmann distribution in the absence of economies of scale. We show that the predicted function gives excellent fits to 13 different distribution functions, ranging from friendship links in social networks, to protein-protein interactions, to the severity of terrorist attacks. This approach may give useful insights into when to expect power-law distributions in the natural and social sciences.

Endovenous Laser Ablation of Perforating Veins: Feasibility, Safety, and Occlusion Rate Using a 1,470-nm Laser and Bare-Tip Fiber.

PURPOSE: To describe technical feasibility and safety of perforating vein ablation with the use of a 1,470-nm laser and bare-tip fiber in the management of chronic venous insufficiency (CVI). MATERIALS AND METHODS: A total of 171 perforating veins were ablated in 101 limbs of 87 patients (mean age, 54.4 y; 79% female). Outcomes included sonographic occlusion of ablated perforator, subjective changes of insufficiency symptoms, incidence of procedure-related side effects (pain, hyperpigmentation), and complications (burn, infection, deep vein thrombosis, paresthesia). Correlation between perforator closure and patient symptoms was assessed by Pearson χ(2) test. Factors influencing failure of perforator closure were analyzed by analysis of variance. RESULTS: Forty-nine perforating veins had previous great saphenous vein (GSV) interruption, 25 had previous small saphenous vein (SSV) interruption, 88 had previous GSV and SSV interruption, and 9 had competent saphenous systems. Ninety-one ablations were combined with microphlebectomy, 55 were combined with sclerotherapy, and 25 were performed alone. At 1 and 3 months' follow-up, 94% and 98% of ablated perforators were sonographically occluded, and 82% and 96% of patients noted complete symptom resolution, respectively. Complications included 5 cases of new-onset paresthesia and 1 case of nonocclusive deep vein thrombosis. Ablation failed in 10 perforators, and treatment failure showed significant correlations with higher clinical, etiology, anatomy, and physiology score (P = .002) and history of GSV/SSV interruption (P = .042). CONCLUSIONS: Three-month closure of perforating veins is achievable by using a 1,470-nm laser and bare-tip fiber and can be safely performed alone or in combination with microphlebectomy or sclerotherapy at all stages of CVI severity.

Detecting temperature fluctuations at equilibrium.

The Gibbs and the Boltzmann definition of temperature agree only in the macroscopic limit. The ambiguity in identifying the equilibrium temperature of a finite-sized 'small' system exchanging energy with a bath is usually understood as a limitation of conventional statistical mechanics. We interpret this ambiguity as resulting from a stochastically fluctuating temperature coupled with the phase space variables giving rise to a broad temperature distribution. With this ansatz, we develop the equilibrium statistics and dynamics of small systems. Numerical evidence using an analytically tractable model shows that the effects of temperature fluctuations can be detected in the equilibrium and dynamical properties of the phase space of the small system. Our theory generalizes statistical mechanics to small systems relevant in biophysics and nanotechnology.

Communication: Maximum caliber is a general variational principle for nonequilibrium statistical mechanics.

There has been interest in finding a general variational principle for non-equilibrium statistical mechanics. We give evidence that Maximum Caliber (Max Cal) is such a principle. Max Cal, a variant of maximum entropy, predicts dynamical distribution functions by maximizing a path entropy subject to dynamical constraints, such as average fluxes. We first show that Max Cal leads to standard near-equilibrium results­including the Green-Kubo relations, Onsager's reciprocal relations of coupled flows, and Prigogine's principle of minimum entropy production­in a way that is particularly simple. We develop some generalizations of the Onsager and Prigogine results that apply arbitrarily far from equilibrium. Because Max Cal does not require any notion of "local equilibrium," or any notion of entropy dissipation, or temperature, or even any restriction to material physics, it is more general than many traditional approaches. It also applicable to flows and traffic on networks, for example.

Safety and efficacy of angiographic occlusion of duodenal varices as an alternative to TIPS: review of 32 cases.

UNLABELLED: BACKGROUND/RATIONALE OF STUDY: Analyze safety and efficacy of angiographic-occlusion-with-sclerotherapy/embolotherapy-without-transjugular-intrahepatic-portosystemic-shunt (TIPS) for duodenal varices. Although TIPS is considered the best intermediate-to-long term therapy after failed endoscopic therapy for bleeding varices, the options are not well-defined when TIPS is relatively contraindicated, with scant data on alternative therapies due to relative rarity of duodenal varices. Prior cases were identified by computerized literature search, supplemented by one illustrative case. Favorable clinical outcome after angiography defined as no rebleeding during follow-up, without major procedural complications. RESULTS: Thirty-two cases of duodenal varices treated by angiographic-occlusion-with-sclerotherapy/embolotherapy- without-TIPS were analyzed. Patients averaged 59.5 ± 12.2 years old (female = 59%). Patients presented with melena-16, hematemesis & melena-5, large varices-5, growing varices-2, ruptured varices-1, and other- 3. Twenty-nine patients had cirrhosis; etiologies included: alcoholism-11, hepatitis C-11, primary biliary cirrhosis- 3, hepatitis B-2, Budd-Chiari-1, and idiopathic-1. Three patients did not have cirrhosis, including hepatic metastases from rectal cancer-1, Wilson's disease-1, and chronic liver dysfunction-1. Thirty-one patients underwent esophagogastroduodenoscopy before therapeutic angiography, including fifteen undergoing endoscopic variceal therapy. Therapeutic angiographic techniques included balloon-occluded retrograde-transvenous-obliteration (BRTO) with sclerotherapy and/or embolization-21, DBOE (double-balloon-occluded-embolotherapy)-5, and other-6. Twenty-eight patients (87.5%; 95%-confidence interval: 69-100%) had favorable clinical outcomes after therapeutic angiography. Three patients were therapeutic failures: rebleeding at 0, 5, or 10 days after therapy. One major complication (Enterobacter sepsis) and one minor complication occurred. CONCLUSIONS: This work suggests that angiographic-occlusion-with sclerotherapy/ embolotherapy-without-TIPS is relatively effective (~90% hemostasis-rate), and relatively safe (3% major-complication-rate). This therapy may be a useful treatment option for duodenal varices when endoscopic therapy fails and TIPS is relatively contraindicated.

Evolutionary capacitance and control of protein stability in protein-protein interaction networks.

In addition to their biological function, protein complexes reduce the exposure of the constituent proteins to the risk of undesired oligomerization by reducing the concentration of the free monomeric state. We interpret this reduced risk as a stabilization of the functional state of the protein. We estimate that protein-protein interactions can account for ~2-4 k(B)T of additional stabilization; a substantial contribution to intrinsic stability. We hypothesize that proteins in the interaction network act as evolutionary capacitors which allows their binding partners to explore regions of the sequence space which correspond to less stable proteins. In the interaction network of baker's yeast, we find that statistically proteins that receive higher energetic benefits from the interaction network are more likely to misfold. A simplified fitness landscape wherein the fitness of an organism is inversely proportional to the total concentration of unfolded proteins provides an evolutionary justification for the proposed trends. We conclude by outlining clear biophysical experiments to test our predictions.

The ability to sense the environment is heterogeneously distributed in cell populations.

Channel capacity of signaling networks quantifies their fidelity in sensing extracellular inputs. Low estimates of channel capacities for several mammalian signaling networks suggest that cells can barely detect the presence/absence of environmental signals. However, given the extensive heterogeneity and temporal stability of cell state variables, we hypothesize that the sensing ability itself may depend on the state of the cells. In this work, we present an information-theoretic framework to quantify the distribution of sensing abilities from single-cell data. Using data on two mammalian pathways, we show that sensing abilities are widely distributed in the population and most cells achieve better resolution of inputs compared to an 'average cell'. We verify these predictions using live-cell imaging data on the IGFR/FoxO pathway. Importantly, we identify cell state variables that correlate with cells' sensing abilities. This information-theoretic framework will significantly improve our understanding of how cells sense in their environment.

Designing host-associated microbiomes using the consumer/resource model.

A key step towards rational microbiome engineering is the in silico sampling of realistic microbial communities that correspond to desired host phenotypes, and vice versa. This remains challenging due to a lack of generative models that simultaneously model compositions of host-associated microbiomes and host phenotypes. To that end, we present a machine learning model based on the consumer/resource (C/R) framework. In the model, variation in microbial ecosystem composition arises due to differences in the availability of effective resources (latent variables) while species' resource preferences remain conserved. Variation in the same latent variables is used to model phenotypic variation across hosts. In silico microbiomes generated by our model accurately reproduce universal and dataset-specific statistics of bacterial communities. The model allows us to address two salient questions in microbiome design: (1) which host phenotypes maximally constrain the composition of the host-associated microbiome? and (2) what are plausible microbiome compositions corresponding to user-specified host phenotypes? Thus, our model aids the design and analysis of microbial communities associated with host phenotypes of interest.

A global view of T cell metabolism in systemic lupus erythematosus.

Impaired metabolism is recognized as an important contributor to pathogenicity of T cells in Systemic Lupus Erythematosus (SLE). Over the last two decades, we have acquired significant knowledge about the signaling and transcriptomic programs related to metabolic rewiring in healthy and SLE T cells. However, our understanding of metabolic network activity derives largely from studying metabolic pathways in isolation. Here, we argue that enzymatic activities are necessarily coupled through mass and energy balance constraints with in-built network-wide dependencies and compensation mechanisms. Therefore, metabolic rewiring of T cells in SLE must be understood in the context of the entire network, including changes in metabolic demands such as shifts in biomass composition and cytokine secretion rates as well as changes in uptake/excretion rates of multiple nutrients and waste products. As a way forward, we suggest cell physiology experiments and integration of orthogonal metabolic measurements through computational modeling towards a comprehensive understanding of T cell metabolism in lupus.

Computational modeling of drug response identifies mutant-specific constraints for dosing panRAF and MEK inhibitors in melanoma.

Purpose: This study explores the potential of preclinical in vitro cell line response data and computational modeling in identifying optimal dosage requirements of pan-RAF (Belvarafenib) and MEK (Cobimetinib) inhibitors in melanoma treatment. Our research is motivated by the critical role of drug combinations in enhancing anti-cancer responses and the need to close the knowledge gap around selecting effective dosing strategies to maximize their potential. Results: In a drug combination screen of 43 melanoma cell lines, we identified unique dosage landscapes of panRAF and MEK inhibitors for NRAS vs BRAF mutant melanomas. Both experienced benefits, but with a notably more synergistic and narrow dosage range for NRAS mutant melanoma. Computational modeling and molecular experiments attributed the difference to a mechanism of adaptive resistance by negative feedback. We validated in vivo translatability of in vitro dose-response maps by accurately predicting tumor growth in xenografts. Then, we analyzed pharmacokinetic and tumor growth data from Phase 1 clinical trials of Belvarafenib with Cobimetinib to show that the synergy requirement imposes stricter precision dose constraints in NRAS mutant melanoma patients. Conclusion: Leveraging pre-clinical data and computational modeling, our approach proposes dosage strategies that can optimize synergy in drug combinations, while also bringing forth the real-world challenges of staying within a precise dose range. Simple Summary: Combining drugs is crucial for enhancing anti-cancer responses. However, the potential of pre-clinical data in identifying suitable combinations and dosage is often underutilized. In this study, we leverage preclinical in vitro cell line drug response data and computational modeling of signal transduction and of pharmacokinetics to elucidate distinct dose requirements for the combination of pan-RAF and MEK inhibitors in melanoma. Our findings reveal a more synergistic, but narrower dosing landscape in NRAS vs BRAF mutant melanoma, which we linked to a mechanism of adaptive resistance through negative feedback. Further, our analysis suggests the importance of drug dosing strategies to optimize synergy based on mutational context, yet highlights the real-world challenges of maintaining a narrow dose range. This approach establishes a framework for translational investigation of drug responses in the refinement of combination therapy, balancing the potential for synergy and practical feasibility in cancer treatment planning.