Strategies for calibrating models of biology

Computational and mathematical modelling has become a valuable tool for investigating biological systems. Modelling enables prediction of how biological components interact to deliver system-level properties and extrapolation of biological system performance to contexts and experimental conditions where this is unknown. A model's value hinges on knowing that it faithfully represents the biology under the contexts of use, or clearly ascertaining otherwise and thus motivating further model refinement. These qualities are evaluated through calibration, typically formulated as identifying model parameter values that align model and biological behaviours as measured through a metric applied to both. Calibration is critical to modelling but is often underappreciated. A failure to appropriately calibrate risks unrepresentative models that generate erroneous insights. Here, we review a suite of strategies to more rigorously challenge a model's representation of a biological system. All are motivated by features of biological systems, and illustrative examples are drawn from the modelling literature. We examine the calibration of a model against distributions of biological behaviours or outcomes, not only average values. We argue for calibration even where model parameter values are experimentally ascertained. We explore how single metrics can be non-distinguishing for complex systems, with multiple-component dynamic and interaction configurations giving rise to the same metric output. Under these conditions, calibration is insufficiently constraining and the model non-identifiable: multiple solutions to the calibration problem exist. We draw an analogy to curve fitting and argue that calibrating a biological model against a single experiment or context is akin to curve fitting against a single data point. Though useful for communicating model results, we explore how metrics that quantify heavily emergent properties may not be suitable for use in calibration. Lastly, we consider the role of sensitivity and uncertainty analysis in calibration and the interpretation of model results. Our goal in this manuscript is to encourage a deeper consideration of calibration, and how to increase its capacity to either deliver faithful models or demonstrate them otherwise

Calibration of ionic and cellular cardiac electrophysiology models

© 2020 The Authors. WIREs Systems Biology and Medicine published by Wiley Periodicals, Inc. Cardiac electrophysiology models are among the most mature and well-studied mathematical models of biological systems. This maturity is bringing new challenges as models are being used increasingly to make quantitative rather than qualitative predictions. As such, calibrating the parameters within ion current and action potential (AP) models to experimental data sets is a crucial step in constructing a predictive model. This review highlights some of the fundamental concepts in cardiac model calibration and is intended to be readily understood by computational and mathematical modelers working in other fields of biology. We discuss the classic and latest approaches to calibration in the electrophysiology field, at both the ion channel and cellular AP scales. We end with a discussion of the many challenges that work to date has raised and the need for reproducible descriptions of the calibration process to enable models to be recalibrated to new data sets and built upon for new studies. This article is categorized under: Analytical and Computational Methods > Computational Methods Physiology > Mammalian Physiology in Health and Disease Models of Systems Properties and Processes > Cellular Models

Enabling rational gut microbiome manipulations by understanding gut ecology through experimentally-evidenced in silico models

© 2021 The Author(s). The gut microbiome has emerged as a contributing factor in non-communicable disease, rendering it a target of health-promoting interventions. Yet current understanding of the host-microbiome dynamic is insufficient to predict the variation in intervention outcomes across individuals. We explore the mechanisms that underpin the gut bacterial ecosystem and highlight how a more complete understanding of this ecology will enable improved intervention outcomes. This ecology varies within the gut over space and time. Interventions disrupt these processes, with cascading consequences throughout the ecosystem. In vivo studies cannot isolate and probe these processes at the required spatiotemporal resolutions, and in vitro studies lack the representative complexity required. However, we highlight that, together, both approaches can inform in silico models that integrate cellular-level dynamics, can extrapolate to explain bacterial community outcomes, permit experimentation and observation over ecological processes at high spatiotemporal resolution, and can serve as predictive platforms on which to prototype interventions. Thus, it is a concerted integration of these techniques that will enable rational targeted manipulations of the gut ecosystem.University of Sydney’s Centre for Advanced Food and Engineering; JPMO acknowledges a PhD scholarship from the Faculty of Engineering at the University of Sydney. ERS acknowledges the financial support from the à Beckett Cancer Research Trust (University of Sydney Fellowship)

Multiscale Modeling of T Cells in Mycobacterium Tuberculosis Infection

Tuberculosis (TB), caused by infection with Mycobacterium tuberculosis (Mtb), is one of the deadliest infectious diseases in the world and remains a significant global health burden. Central to the immune response against Mtb are T cells, a type of adaptive immune cell that can kill infected cells, secrete cytokines to activate other immune cells, and orchestrate the broader immune response. Over the past few decades, experimental studies have significantly furthered our understanding of T-cell biology and function during Mtb infection. However, these findings have yet to translate to a clinically effective TB vaccine. As a complementary approach to experimental studies, systems biology and computational modeling can provide context to T-cell function by describing T-cell interactions with other immune cells across multiple scales. In this thesis we utilize a systems biology approach to characterize T-cell behavior, function, and movement across multiple physiological and temporal scales during Mtb infection. In addition, we develop a whole-host model of the immune response to Mtb. Following infection with Mtb, the immune response leads to the development of multiple lung granulomas – organized structures composed of immune cells that surround bacteria. Using a previously developed agent-based model of granuloma formation and function, we explore the role of T cells within the granuloma and predict that T-cell exhaustion, a type of T-cell dysfunction, is prevented from occurring by the physical structure of the granuloma. Next, we develop a novel whole lung model that tracks the formation of multiple granulomas. Using this model, we predict that a special type of T-cell, called a multi-functional CD8+ T cell, is key in preventing dissemination events - when bacteria escape one granuloma and seed the formation of a new one elsewhere in the lung. We also present a model of T-cell priming, proliferation, and differentiation within the lymph nodes and blood following TB vaccination and illustrate that non-human primates and humans respond similarly when receiving TB vaccination. We mathematically link the whole lung model and lymph node and blood model to create a whole-host model of the immune response following Mtb infection. We show that this model can capture various aspects of human and non-human primate TB disease and predict that biomarkers in the blood may only faithfully represent events in the lung at early time points after infection. Using this model, we predict that resident memory T cells are important mediators of protection against reinfection with Mtb and additionally predict the lifespan of these crucial cells in humans. Finally, we develop a protocol for calibrating mathematical and computational models to experimental datasets. Overall, this dissertation builds on our knowledge of the various roles T cells play in responding to Mtb infection, presents a set of computational models for evaluating the T-cell response to either infection or vaccination, and identifies mechanisms that control different outcomes across multiple scales following Mtb infection, reinfection, or vaccination.PHDBioinformaticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/167940/1/louisjos_1.pd

The IDEA of Us : An Identity-Aware Architecture for Autonomous Systems

Autonomous systems, such as drones and rescue robots, are increasingly used during emergencies. They deliver services and provide situational awareness that facilitate emergency management and response. To do so, they need to interact and cooperate with humans in their environment. Human behaviour is uncertain and complex, so it can be difficult to reason about it formally. In this paper, we propose IDEA: an adaptive software architecture that enables cooperation between humans and autonomous systems, by leveraging in the social identity approach. This approach establishes that group membership drives human behaviour. Identity and group membership are crucial during emergencies, as they influence cooperation among survivors. IDEA systems infer the social identity of surrounding humans, thereby establishing their group membership. By reasoning about groups, we limit the number of cooperation strategies the system needs to explore. IDEA systems select a strategy from the equilibrium analysis of game-theoretic models, that represent interactions between group members and the IDEA system. We demonstrate our approach using a search-and-rescue scenario, in which an IDEA rescue robot optimises evacuation by collaborating with survivors. Using an empirically validated agent-based model, we show that the deployment of the IDEA system can reduce median evacuation time by $13.6\%$

The IDEA of Us: An Identity-Aware Architecture for Autonomous Systems

Autonomous systems, such as drones and rescue robots, are increasingly used during emergencies. They deliver services and provide situational awareness that facilitate emergency management and response. To do so, they need to interact and cooperate with humans in their environment. Human behaviour is uncertain and complex, so it can be difficult to reason about it formally. In this paper, we propose IDEA: an adaptive software architecture that enables cooperation between humans and autonomous systems, by leveraging in the social identity approach. This approach establishes that group membership drives human behaviour. Identity and group membership are crucial during emergencies, as they influence cooperation among survivors. IDEA systems infer the social identity of surrounding humans, thereby establishing their group membership. By reasoning about groups, we limit the number of cooperation strategies the system needs to explore. IDEA systems select a strategy from the equilibrium analysis of game-theoretic models, that represent interactions between group members and the IDEA system. We demonstrate our approach using a search-and-rescue scenario, in which an IDEA rescue robot optimises evacuation by collaborating with survivors. Using an empirically validated agent-based model, we show that the deployment of the IDEA system can reduce median evacuation time by 13.6

Integrating mathematical models with experimental data from in vivo studies to investigate the within-host dynamics of Salmonella Typhimurium

Bacterial diseases have historically accounted for a high burden of morbidity and mortality worldwide. While antibiotic therapy and immunisations have significantly improved clinical outcomes in the last couple of centuries, the rate at which new treatments and vaccines are being developed has slowed down. One way to accelerate innovation in the field is by understanding the host-bacteria interactions that take place in vivo and affect the clinical outcome of the disease, the response to treatment and the immunological response following vaccination. Mechanistic mathematical models are being increasingly used to advance our understanding about unobservable, host-pathogen interactions in vivo. These models allow us to develop hypotheses about the underlying biological mechanisms responsible for an observable and measurable outcome (e.g. the number of bacteria in a tissue of interest) and use them to try to match what has been experimentally or clinically observed. In this thesis, I have developed mechanistic models and used them in conjunction with experimental data on systemic murine Salmonella Typhimurium infections to: 1. characterise the effects of different vaccine preparations on the immune system of the host and its response to a secondary bacterial challenge, 2. compare the effects of different antibiotic classes on bacterial populations residing in different organs, and predict whether the residual bacteria following antibiotic treatment differ in their proliferative capacity, 3. develop a new, flexible methodological tool for a priori experimental design in studies of bacterial dynamics at the level of the population within the host.Newnham College Major Studentship Cambridge Trust Studentshi