Biology subjects

Doyle, J. C.

Publications and source records attributed to Doyle, J. C..

9 recordsLinked to original sources

bioRxiv2025

Proliferation to suppress neoplasia: a general model and a first test in the moon jelly

Cell proliferation is necessary to maintain tissue homeostasis, but proliferation carries with it a risk of cancer. Nevertheless, nature abounds with examples of organisms that achieve low rates of cancer while also proliferating to maintain tissue homeostasis. To understand how organisms might achieve both of these goals, we developed a dynamical model describing cell birth, death, and mutation in a population. The model identifies two distinct regimes. In one regime, as expected, decreasing proliferation delays accumulation of neoplastic cells. In another regime, unexpectedly, increasing proliferation suppresses accumulation of neoplastic cells. In this regime, when more cells proliferate, more cells correspondingly die as a consequence of homeostatic feedback. As long as neoplastic cells are detected and killed preferentially, the high flux of cells acts as a proofreader, eliminating neoplastic cells. High-flux proofreading may seem costly, but it can be effective even when the system does not have a precise detector of neoplastic cells. As a first experimental test of whether high-flux proofreading is biologically relevant, we examined the moon jelly, a cnidarian. Neoplasms have rarely been observed in cnidarians, and yet simply inhibiting proliferation is sufficient to promote neoplasms in the moon jelly. Together, the model and experiments show that high-flux proofreading is an effective cancer resistance strategy. Because cell birth, death, and mutation are fundamentally conserved processes, high-flux proofreading may be widespread. The quantitative framework presented in this study offers clear experimentally testable predictions to assess high-flux proofreading in other systems, and its potential utility for cancer prevention and treatment.

systems biology↗

bioRxiv2023

Flux exponent control predicts metabolic dynamics from network structure

Metabolic dynamics such as stability of steady states, oscillations, lags and growth arrests in stress responses are important for microbial communities in human health, ecology, and metabolic engineering. Yet it is hard to model due to sparse data available on trajectories of metabolic fluxes. For this reason, a constraint-based approach called flux control (e.g., flux balance analysis) was invented to split metabolic systems into known stoichiometry (plant) and unknown fluxes (controller), so that data can be incorporated as refined constraints, and optimization can be used to find behaviors in scenarios of interest. However, flux control can only capture steady state fluxes well, limiting its application to scenarios with days or slower timescales. To overcome this limitation and capture dynamic fluxes, this work proposes a novel constraint-based approach, flux exponent control (FEC). FEC uses a different plant-controller split between the activities of catalytic enzymes and their regulation through binding reactions. Since binding reactions effectively regulate fluxes exponents (from previous works), this yields the rule of FEC, that cells regulate fluxes exponents, not the fluxes themselves as in flux control. In FEC, dynamic regulations of metabolic systems are solutions to optimal control problems that are computationally solvable via model predictive control. Glycolysis, which is known to have minute-timescale oscillations, is used as an example to demon-strate FEC can capture metabolism dynamics from network structure. More generally, FEC brings metabolic dynamics to the realm of control system analysis and design.

bioengineering↗

bioRxiv2021

Control-theoretic immune tradeoffs explain SARS-CoV-2 virulence and transmission variation

Dramatic variation in SARS-CoV-2 virulence and transmission between hosts has driven the COVID-19 pandemic. The complexity and dynamics of the immune response present a challenge to understanding variation in SARS-CoV-2 infections. To address this challenge, we apply control theory, a framework used to study complex feedback systems, to establish rigorous mathematical bounds on immune responses. Two mechanisms of SARS-CoV-2 biology are sufficient to create extreme variation between hosts: (1) a sparsely expressed host receptor and (2) potent, but not unique, suppression of interferon. The resulting model unifies disparate and unexplained features of the SARS-CoV-2 pandemic, predicts features of future viruses that threaten to cause pandemics, and identifies potential interventions.

systems biology↗

bioRxiv2020

Stability and Control of Biomolecular Circuits through Structure

Due to omnipresent uncertainties and environmental disturbances, natural and engineered biological organisms face the challenging control problem of achieving robust performance using unreliable parts. The key to overcoming this challenge rests in identifying structures of biomolecular circuits that are largely invariant despite uncertainties, and building feedback control through such structures. In this work, we develop the tool of log derivatives to capture structures in how the production and degradation rates of molecules depend on concentrations of reactants. We show that log derivatives could establish stability of fixed points based on structure, despite large variations in rates and functional forms of models. Furthermore, we demonstrate how control objectives, such as robust perfect adaptation (i.e. step disturbance rejection), could be implemented through the structures captured. Due to the methods simplicity, structural properties for analysis and design of biomolecular circuits can often be determined by a glance at the equations.

systems biology↗

bioRxiv2020

Metabolic multi-stability and hysteresis in a model aerobe-anaerobe microbiome community

Changes in the composition of the human microbiome are associated with health and disease. Some microbiome states persist in seemingly unfavorable conditions, e.g., the proliferation of aerobe-anaerobe communities in oxygen-exposed environments in wounds or small intestinal bacterial overgrowth. However, it remains unclear how different stable microbiome states can exist under the same conditions, or why some states persist under seemingly unfavorable conditions. Here, using two microbes relevant to the human microbiome, we combine genome-scale mathematical modeling, bioreactor experiments, transcriptomics, and dynamical systems theory, to show that multi-stability and hysteresis (MSH) is a mechanism that can describe the shift from an aerobe-dominated state to a resilient, paradoxically persistent aerobe-anaerobe state. We examine the impact of changing oxygen and nutrient regimes and identify factors, including changes in metabolism and gene expression, that lead to MSH. When analyzing the transitions between the two states in this system, the familiar conceptual connection between causation and correlation is broken and MSH must be used to interpret the dynamics. Using MSH to analyze microbiome dynamics will improve our conceptual understanding of the stability of microbiome states and the transitions among microbiome states. One sentence summaryMulti-stability and hysteresis (MSH) is a potential mechanism to describe shifts to and persistence of aerobe-anaerobe communities in the microbiome.

ecology↗

bioRxiv2018

Coupled Reaction Networks for Noise Suppression

Noise is intrinsic to many important regulatory processes in living cells, and often forms obstacles to be overcome for reliable biological functions. However, due to stochastic birth and death events of all components in biomolecular systems, suppression of noise of one component by another is fundamentally hard and costly. Quantitatively, a widelycited severe lower bound on noise suppression in biomolecular systems was established by Lestas et. al. in 2010, assuming that the plant and the controller have separate birth and death reactions. This makes the precision observed in several biological phenomena, e.g., cell fate decision making and cell cycle time ordering, seem impossible. We demonstrate that coupling, a mechanism widely observed in biology, could suppress noise lower than the bound of Lestas et. al. with moderate energy cost. Furthermore, we systematically investigate the coupling mechanism in all two-node reaction networks, showing that negative feedback suppresses noise better than incoherent feedforward achitectures, coupled systems have less noise than their decoupled version for a large class of networks, and coupling has its own fundamental limitations in noise suppression. Results in this work have implications for noise suppression in biological control and provide insight for a new efficient mechanism of noise suppression in biology.

systems biology↗

bioRxiv2018

Architectural Principles for Characterizing the Performance of Sequestration Feedback Networks

As we begin to design increasingly complex synthetic biomolecular systems, it is essential to develop rational design methodologies that yield predictable circuit performance. Here we apply theoretical tools from the theory of control and dynamical systems to yield practical insights into the architecture and function of a particular class of biological feedback circuit. Specifically, we show that it is possible to analytically characterize both the operating regime and performance tradeoffs of a sequestration feedback circuit architecture. Further, we demonstrate how these principles can be applied to inform the design process of a particular synthetic feedback circuit.

synthetic biology↗

bioRxiv2018

Robust Perfect Adaptation in Biomolecular Reaction Networks

For control in biomolecular systems, the most basic objective of maintaining a small error in a target variable, say the expression level of some protein, is often difficult due to the presence of both large uncertainty of every type and intrinsic limitations on the controllers implementation. This paper explores the limits of biochemically plausible controller design for the problem of robust perfect adaptation (RPA), biologists term for robust steady state tracking. It is well-known that for a large class of nonlinear systems, a system has RPA iff it has integral feedback control (IFC), which has been used extensively in real control systems to achieve RPA. However, we show that due to intrinsic physical limitations on the dynamics of chemical reaction networks (CRNs), cells cannot implement IFC directly in the concentration of a chemical species. This contrasts with electronic implementations, particularly digital, where it is trivial to implement IFC directly in a single state. Therefore, biomolecular systems have to achieve RPA by encoding the integral control variable into the network architecture of a CRN. We describe a general framework to implement RPA in CRNs and show that well-known network motifs that achieve RPA, such as (negative) integral feedback (IFB) and incoherent feedforward (IFF), are examples of such implementations. We also develop methods to solve the problem of designing integral feedback variables for unknown plants. This standard control notion is surprisingly nontrivial and relatively unstudied in biomolecular control. The methods developed here connect different existing fields and approaches on the problem of biomolecular control, and hold promise for systematic chemical reaction controller synthesis as well as analysis.

synthetic biology↗

bioRxiv2016

Novel computational method for predicting polytherapy switching strategies to overcome tumor heterogeneity and evolution

1The success of targeted cancer therapy is limited by drug resistance that can result from tumor genetic heterogeneity. The current approach to address resistance typically involves initiating a new treatment after clinical/radiographic disease progression, ultimately resulting in futility in most patients. Towards a potential alternative solution, we developed a novel computational framework that uses human cancer profiling data to systematically identify dynamic, pre-emptive, and sometimes non-intuitive treatment strategies that can better control tumors in real-time. By studying lung adenocarcinoma clinical specimens and preclinical models, our computational analyses revealed that the best anti-cancer strategies addressed existing resistant subpopulations as they emerged dynamically during treatment. In some cases, the best computed treatment strategy used unconventional therapy switching while the bulk tumor was responding, a prediction we confirmed in vitro. The new framework presented here could guide the principled implementation of dynamic molecular monitoring and treatment strategies to improve cancer control.

cancer biology↗