Identifying feasible operating regimes for early T-cell recognition: The speed, energy, accuracy trade-off in kinetic proofreading and adaptive sorting

In the immune system, T cells can quickly discriminate between foreign and self ligands with high accuracy. There is evidence that T-cells achieve this remarkable performance utilizing a network architecture based on a generalization of kinetic proofreading (KPR). KPR-based mechanisms actively consume energy to increase the specificity beyond what is possible in equilibrium.An important theoretical question that arises is to understand the trade-offs and fundamental limits on accuracy, speed, and dissipation (energy consumption) in KPR and its generalization. Here, we revisit this question through numerical simulations where we simultaneously measure the speed, accuracy, and energy consumption of the KPR and adaptive sorting networks for different parameter choices. Our simulations highlight the existence of a 'feasible operating regime' in the speed-energy-accuracy plane where T-cells can quickly differentiate between foreign and self ligands at reasonable energy expenditure. We give general arguments for why we expect this feasible operating regime to be a generic property of all KPR-based biochemical networks and discuss implications for our understanding of the T cell receptor circuit.Comment: 14 pages, 8 figure

The Community Simulator: A Python package for microbial ecology

Natural microbial communities contain hundreds to thousands of interacting species. For this reason, computational simulations are playing an increasingly important role in microbial ecology. In this manuscript, we present a new open-source, freely available Python package called Community Simulator for simulating microbial population dynamics in a reproducible, transparent and scalable way. The Community Simulator includes five major elements: tools for preparing the initial states and environmental conditions for a set of samples, automatic generation of dynamical equations based on a dictionary of modeling assumptions, random parameter sampling with tunable levels of metabolic and taxonomic structure, parallel integration of the dynamical equations, and support for metacommunity dynamics with migration between samples. To significantly speed up simulations using Community Simulator, our Python package implements a new Expectation-Maximization (EM) algorithm for finding equilibrium states of community dynamics that exploits a recently discovered duality between ecological dynamics and convex optimization. We present data showing that this EM algorithm improves performance by between one and two orders compared to direct numerical integration of the corresponding ordinary differential equations. We conclude by listing several recent applications of the Community Simulator to problems in microbial ecology, and discussing possible extensions of the package for directly analyzing microbiome compositional data.Comment: 14 pages, 6 figure

Statistical Mechanics of Microbiomes:

Thesis advisor: Pankaj MehtaThesis advisor: Ziqiang WangNature has revealed an astounding degree of phylogenetic and physiological diversity in natural environments -- especially in the microbial world. Microbial communities are incredibly diverse, ranging from 500-1000 species in human guts to over 1000 species in marine ecosystems. Historically, theoretical ecologists have devoted considerable effort to analyzing ecosystems consisting of a few species. However, analytical approaches and theoretical insights derived from small ecosystems consisting of a few species may not scale up to diverse ecosystems. Understanding such large complex ecosystems poses fundamental challenges to current theories and analytical approaches for modeling and understanding the microbial world. One promising approach for tackling this challenge that I develop in my thesis is to adapt and expand ideas from statistical mechanics to theoretical ecology. Statistical mechanics has helped us to understand how collective behaviors emerge from the interaction of many individual components. In this thesis, I present a unified theoretical framework for understanding complex ecosystems based on statistical mechanics, random matrix theories, and convex optimization. My thesis work has three key aspects: modeling, simulations, and theories. Modeling: Classical ecological models often focus on predator-prey relationships. However, this is not the norm in the microbial world. Unlike most macroscopic organisms, microbes relie on consuming and producing small organic molecules for energy and reproduction. In this thesis, we develop a new Microbial Consumer Resource Model that takes into account these types of metabolic cross-feeding interactions. We demonstrate that this model can qualitatively reproduce and explain statistical patterns observed in large survey data, including Earth Microbiome Project and the Human Microbiome Project. Simulations: Computational simulations are essential in theoretical ecology. Complex ecological models often involve ordinary differential equations (ODE) containing hundreds to thousands of interacting variables. Typical ODE solvers are based on numerical integration methods, which are both time and resource intensive. To overcome this bottleneck, we derived a surprising duality between constrained convex optimization and generalized consumer-resource models describing ecological dynamics. This allows us to develop a fast algorithm to solve the steady-state of complex ecological models. This improves computational performance by between 2-3 orders of magnitude compared to direct numerical integration of the corresponding ODEs. Theories:Few theoretical approaches allow for the analytic study of communities containing a large number of species. Recently, there has been considerable interest in the idea that ecosystems can be thought of as a type of disordered systems. This mapping suggests that understanding community coexistence patterns is actually a problem in "spin-glass'' physics. This has motivated physicists to use insights from spin glass theory to uncover the universal features of complex ecosystems. In this thesis, I use and extend the cavity method, originally developed in spin glass theories, to answer fundamental ecological questions regarding the stability, diversity, and robustness of ecosystems. I use the cavity method to derive new species backing bounds and uncover novel phase transitions to typicality.Thesis (PhD) — Boston College, 2021.Submitted to: Boston College. Graduate School of Arts and Sciences.Discipline: Physics

ToothInpaintor: Tooth Inpainting from Partial 3D Dental Model and 2D Panoramic Image

In orthodontic treatment, a full tooth model consisting of both the crown and root is indispensable in making the treatment plan. However, acquiring tooth root information to obtain the full tooth model from CBCT images is sometimes restricted due to the massive radiation of CBCT scanning. Thus, reconstructing the full tooth shape from the ready-to-use input, e.g., the partial intra-oral scan and the 2D panoramic image, is an applicable and valuable solution. In this paper, we propose a neural network, called ToothInpaintor, that takes as input a partial 3D dental model and a 2D panoramic image and reconstructs the full tooth model with high-quality root(s). Technically, we utilize the implicit representation for both the 3D and 2D inputs, and learn a latent space of the full tooth shapes. At test time, given an input, we successfully project it to the learned latent space via neural optimization to obtain the full tooth model conditioned on the input. To help find the robust projection, a novel adversarial learning module is exploited in our pipeline. We extensively evaluate our method on a dataset collected from real-world clinics. The evaluation, comparison, and comprehensive ablation studies demonstrate that our approach produces accurate complete tooth models robustly and outperforms the state-of-the-art methods