Alys Clark (University of Auckland), Sara Loo (Johns Hopkins University), Burcu Gürbüz (Johannes Gutenberg-University Mainz), Thomas Woolley (Cardiff University), and Olivia Chu (Dartmouth College). 

1. News – updates from: 
   • SMB Subgroups and DEI Committee
   • Upcoming Conferences and Workshops
   • Updates from Royal Society Publishing
2. People – New editor Burcu Gürbüz.
3. Editorial – on Big Moves during academic careers.
4. Featured Figure – Highlighting the research by early career researcher Veronica Ciocanel, Duke University.

To see the articles in this issue, click the links at the above items.

Contributing Content

Issues of the newsletter are released four times per year in Spring, Summer, Autumn, and Winter. The newsletter serves the SMB community with news and updates, so please share it with your colleagues and contribute content to future issues.

If you have any suggestions for content or on how to improve the newsletter, please contact us at any time. We appreciate and welcome feedback and ideas from the community. The editors can be reached at newsletter@smb.org.

We hope you enjoy this issue of the newsletter!

Alys, Sara, Thomas, Burcu, and Olivia

News Section

By Sara Loo and Olivia Chu

SMB Subgroups Update

Cell and Developmental Biology Subgroup

The SMB Cell and Developmental Biology (CDEV) subgroup held its first virtual mini-conference in March 2024 (picture attached), featuring about 25 speakers and panelists, with participants registering from across 5 continents! Our virtual mini-conference “Cell and Development Festival Week” consisted of 5 sessions across 4 days, each with about two hours of programming. Thank you to all who presented and participated. Our subgroup has also continued to post interviews highlighting scientists in mathematical cell and developmental biology. In our three most recent blog posts, we hear from Evan Curcio, Duncan Martinson, and Hannah Scanlon; see https://smb-celldevbio.github.io/blog/ for their interviews, as well as past interviews of other members of the CDEV community.

Immunobiology and Infection Subgroup

As a follow-up to the NIAID/SMB Workshop on Multiscale Modeling of Infectious and Immune-Mediated Diseases held at last summer’s SMB Annual Meeting, the Immunobiology and Infection Subgroup would like to highlight a paper summarizing the event that was recently published in the Bulletin of Mathematical Biology: https://doi.org/10.1007/s11538-024-01276-2 We look forward to organizing similar events/workshops in coordination with other SMB subgroups!

MathOnco Subgroup

Jason George and Harsh Jain’s terms as co-chairs are coming to an end. We are in the process of recruiting 2 co-chairs. Nominations have closed.

The subgroup is also organizing a mini-symposium at the SMB 2024 Annual Meeting in Korea - ‘Emerging Researchers in Mathematical Oncology: The ONCO Group Minisymposium’. This will feature 10 talks by exciting early-career researchers from around the world.

SMB DEI Committee

1. The DEI Committee is pleased to share a recent publication in the Bulletin of Mathematical Biology entitled “Integrating Diversity, Equity, and Inclusion into Preclinical, Clinical, and Public Health Mathematical Models”. The paper is a follow-up from the DEI-focused session and discussion at the 2023 SMB Annual Meeting held in Columbus, Ohio last year. The article presents key issues for the increased integration of DEI in mathematical modelling in biology. Such integration ensures the applicability and relevancy of mathematical models and their predictions to all.

Justin Sheen, Lee Curtin, Stacey Finley, Anna Konstorum, Reginald McGee, and Morgan Craig. “Integrating Diversity, Equity, and Inclusion into Preclinical, Clinical, and Public Health Mathematical Models”. Bull Math Biol 86, 56 (2024). https://doi.org/10.1007/s11538-024-01282-42.

The 2nd Diversity of Math Bio Summer Virtual Seminar Series starts June 4! This series aims to highlight the diversity of mathematical biology research and the diversity of researchers in the field. The talks will be held on Tuesdays at 8:00 PDT / 11:00 EDT / 17:00 CEST via zoom. See the attached flyer for more details and register at https://tinyurl.com/SMB-Diversity-Summer2024 to receive the zoom information. Please join in for an exciting summer of math bio talks!

Dates: June 4, June 18, July 16, July 30, August 30

• Confirmed Speakers:
  
• 1. June 4: Paola Vera-Licona, University of Connecticut Health Center; Omar Saucedo, Virginia Tech
  2. June 18: Punit Gandhi, Virginia Commonwealth University; Maisha Marzan, North Central College
  3. July 16: Kristina Wicke, New Jersey Institute of Technology; Celeste Vallejo, Simulations Plus
  4. July 30: Van Pham, University of South Florida; Alex Ochoa, Duke University
  5. August 13: Malena Español, Arizona State University

Upcoming Conferences and Workshops

Society for Mathematical Biology Annual Meeting

From 30^th June - 5^th July Friday 2024, the joint annual meeting of the Korean Society for Mathematical Biology and the Society for Mathematical Biology will be held at KonKuk University, Seoul, Republic of Korea. For more details check the conference website: https://smb2024.org/

Royal Society Publishing

The Royal Society's journal Proceedings of the Royal Society A welcomes submissions of research and review articles in Mathematical Biology. With a broad, international readership and a thorough, constructive review process, authors can be confident that their work published with us will have an impact.

Browse recent content including articles such as Structural identifiability analysis of linear reaction–advection–diffusion processes in mathematical biology on the website at https://royalsocietypublishing.org/journal/rspa

Proceedings A publishes review articles of interest to a wide range of scientists and the Reviews Editors welcome proposals for new reviews. All review articles are made immediately open access at no cost to the author. See https://royalsocietypublishing.org/rspa/reviews for information on proposing a Review and see some recent review articles at https://royalsocietypublishing.org/toc/rspa/2024/480/2285

People

By Thomas Woolley

We interviewed one of our two new editors Burcu Gürbüz (Johannes Gutenberg-University Mainz). Find out more here.

Editorial

By Alys Clark

Big moves

Moving institutions, or moving countries, is often talked about from the perspective of furthering an academic career. There are benefits from learning a new way of working, or from seeking a career pathway that simply isn’t available at your home institution. Across the field of mathematical biology most of us will have come across students and academics who have moved for one reason or another, and throughout an academic career opportunities may arise to further our careers further afield.

We often discuss the academic considerations around these big moves - are we going to be studying or working at a good institution, what is the reputation of our new supervisor or mentor, and are we going to be moving to a research environment where we are going to be intellectually stimulated and supported? It is really important to talk to those that you will be working with or for, and making sure that the culture of the research team and/or department you will be working for suits you and your background. Perhaps less discussed though are the everyday practicalities of such a move and these may be equally important determinants of success at your new home (be it a short or a long term stay).

I recall on my own (second) international move, for a postdoc position, I found myself in the situation of being unable to open a bank account to pay my salary into without proof of address, and being unable to move into an apartment without proof of finances (i.e. a salary). It is really stressful in your first days or weeks in a new country to be navigating these kind of situations (which are ultimately resolvable) so planning where you might live and the administrative steps you will need to take to set yourself up in advance is desirable. Luckily, there will likely be many that have been in the same situation before you, so your new workplace may be able to put you in touch with people who have recently been through the same process. For those who are moving for employment, working out whether you be eligible for retirement plans (or whether they are transferable if you move again) is also a good idea. Making friends takes time, and joining clubs and community groups can help some to establish networks, but time of life and the community to which you move can influence how long it takes to feel at home. We are all now much more used to online communication which can help you to stay connected with old friends and family. But with patience these connections in your new home build too, so it is worthwhile building those connections even if the move is short term.

There are also international contexts to research priorities and funding. Post-PhD access to research funding may be a priority for establishing a career, but in many countries funding is limited to people with citizenship or residence of the country in question. This may be worth checking before you move to avoid any surprises. In mathematical biology, local priorities may also drive research direction. Will you be able to establish good links to experimental scientists, and are there requirements for ethical approval or consultation specific to your new institution that you should be aware of?

Finally, a move between countries, or even between cities is not for everyone and there are multiple reasons that mean we are best to stay just where we are. Luckily, the thought that we must move to ensure success are fading and there are several opportunities to learn new ways of thinking and doing research from our international colleagues via short term stays or online events. This might include learning from experts in your own field of mathematical biology, or even picking up some experimental skills to complement the theory you are developing. Keep an eye on SMB subgroup news for opportunities, or keep a look out for summer schools or workshops associated with conferences you may be attending online or in person.

Featured Figure

By Sara Loo and Olivia Chu

Early Career Feature - Veronica Ciocanel, Duke University

In this issue, we feature a recent article "Parameter Identifiability in PDE Models of Fluorescence Recovery After Photobleaching", by Veronica Ciocanel, Assistant Professor of Mathematics and Biology at Duke University. We asked Veronica to tell us a bit more about her work here:

Identifying unique parameters for mathematical models describing biological data can be challenging. When studying models of macromolecular dynamics inside cells, spatial movement (characterized by diffusion, transport, and binding dynamics) can be significant and has an impact on the parameters that describe a given model. Therefore, partial differential equations (PDEs) that track the dynamics of proteins as a function of time and space are often an appropriate modeling framework. However, PDEs present challenges when trying to understand identifiability, especially since many established in vivo measurements of protein dynamics average out the spatial information.

In this work, we focus on biological data obtained from a commonly-used and versatile experimental technique for probing protein dynamics in living cells: FRAP (fluorescence recovery after photobleaching). In particular, we would like to understand what insights we can gain from FRAP data about binding protein interactions in RNA localization bodies (biomolecular condensates) in oocytes of the frog Xenopus laevis. We find that known methods of (structural and practical) parameter identifiability have certain limitations for FRAP data and for the reaction-diffusion PDEs describing the binding protein dynamics. We therefore propose a pipeline for assessing parameter identifiability and for learning parameter combinations for this model. This method recovers the protein diffusion coefficient in synthetic datasets and predicts and the relationship between binding and unbinding rates in experimental datasets. Ultimately, we would like to use these insights to understand how various protein components interact and bind with RNA in biomolecular condensates.

Figure Caption:

A) Schematic of a stage II Xenopus frog oocyte with RNA granules localizing at the bottom shown in magenta. The black square region is shown magnified on the right, with a cartoon of a FRAP (fluorescence recovery after photobleaching) experimental bleach spot. B) The amount of fluorescence in the bleach spot over time gives rise to the blue experimental FRAP curve (blue). The fit with simulated FRAP data is equally good with two sets of binding/unbinding rate parameters as indicated in panel C). C) Approximation of the likelihood landscape for the non-identifiable parameters.

You can find out more about this research here: https://link.springer.com/article/10.1007/s11538-024-01266-4 

Dimensional Dependence of Binding Kinetics

by Megan Dixon & James Keener 

Read the paper

Experimentally, the strength of protein-protein interactions is typically measured in solution, and dissociation constants are traditionally reported in units of volume concentration. It is often assumed that these three-dimensional dissociation constants give direct insight into how tightly the same proteins bind when they are membrane-associated. In this article, we explore and counter this notion. We demonstrate mathematically that dissociation constants are highly dependent on dimension. In both discrete and continuous space, we present and analyze stochastic models of binding kinetics in one, two, and three dimensions. Not only do dissociation constants in two dimensions have different units and forms than dissociation constants in three dimensions, the conversion between them is quite complex and requires detailed information. We present a novel formula to convert three-dimensional dissociation constants to two-dimensional dissociation constants. This conversion allows for better understanding of protein interactions on membranes and how to appropriately model them.

Measles Infection Dose Responses: Insights from Mathematical Modeling

by Anet Anelone & Hannah Clapham 

Read the paper

The measles virus (MV) is highly contagious and affects the whole body, including the skin and the immune system. As MV infection dose increases above one infectious particle, the peak of infectious viral load occurs sooner, yet its magnitude remains constant. It is important to improve understanding of measles, in part due to the re-emergence of measles outbreak worldwide, and a lack of research. We investigated mechanisms determining the outcomes of measles infection doses. We evaluated relevant biological hypotheses, and their respective mathematical formulations, to describe and fit data on the time course of measles infectious viral load in the peripheral blood of monkeys, following experimental measles infection with different doses. When MV infection dose increases, the initial viral load, and the initial number of responding immune cells increase. This mechanism decreases the time it takes for immune cells to control and remove infectious viral load. This mechanism also underpins the dose-independent magnitudes for measles viral load, and the loss of immune cells. Together, these findings suggest that the outcome of measles depends on how the immune system responds to incoming MV right from the beginning. This work encourages prevention, vaccination, and early diagnosis of measles.

Caption: Measles infection dose responses: insights from mathematical modeling. Top: Model-data fits for acute viremia in response to changes in MV infection doses. using model parameterizations and assumptions in Table in the paper. 104, 103, 102, 10 and 1 TCID50 correspond to red diamonds, blue stars, orange triangles, magenta dots, and green squares respectively. The solid lines represent the trajectories generated by the proposed model parameterization. The shapes represent data. The dark grey dotted dashed line represents the limit of detection < 0.3. Bottom: Cartoon illustrating that the healthy body adjusts the response of the immune system to remove measles infectious particles sooner when the measles infection dose increases; however, the peak viral load remains constant

Modelling the Impact of NETosis During the Initial Stage of Systemic Lupus Erythematosus

by Vladimira Suvandjieva, Ivanka Tsacheva, Marlene Santos, Georgios Kararigas Peter Rashkov

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NETosis, or formation of Neutrophil Extracellular Traps (NETs) in response to a pathogenic stimulus, is a suspected contributing factor of autoimmunogenicity in Systemic Lupus Erythematosus. Our ODE-based mathematical model studies the interaction between macrophages, neutrophils and two types of antigen with origin in apoptotic waste and NETs during the initial stage of the disease. Analytical and numerical calculations help classify the bifurcations between equilibria, in particular those with presence of autoantigen. The model predicts that even in parameter regimes with efficient clearance of NETs by immune cells, autoantigen can persist stably in tissue, causing chronic inflammation and loss of immune tolerance in the long run.

Sketch of the interactions between cells and antigens studied by our model.

Morphological Stability for in silico Models of Avascular Tumors

by Erik Blom & Stefan Engblom

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We develop a simple, stochastic model of an avascular tumor that displays known behavior at the considered scales. In parallel we develop, analyze, and simulate a surrogate PDE model to explain the growth pattern and shape of the stochastic model. 

We investigate the emergent tumor morphology of the stochastic model (Fig. a) through the PDE model and through comparisons of respective model’s numerical experiments. The stochastic model displays the three characteristic avascular tumor regions -- the proliferative rim, quiescent annulus, and necrotic center -- and sigmoidal volumetric growth pattern in line with the PDE model under radial symmetry (Fig. b). The analysis predicts morphological instability under low oxygen and cell-cell adhesion conditions, as well as an ever-present creeping effect where the tumor as a whole migrates towards the oxygen source -- an effect observed during simulations of both models (Fig. c). The analysis further displays a capacity to predict non-trivial morphological patterns of the model tumor (Fig. d). 

The Unification of Evolutionary Dynamics through the Bayesian Decay Factor in a Game on a Graph

by Arnaud Z. Dragicevic

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The study unifies evolutionary dynamics on graphs by employing a decaying Bayesian update in the context of strategic uncertainty. It demonstrates that the replication of strategies leading to shifts between competition and cooperation in well-mixed and Bayesian-structured populations is equivalent under certain conditions. Specifically, this equivalence holds when the rate of transition between competitive and cooperative behaviors matches the relative strength of selection pressures. Our findings help pinpoint scenarios where cooperation is favored, independent of payoff levels, expanding the application of Price's equation beyond its original intent.

Caption: The basins of attraction of the Price-wise unstructured population replicator dynamics

Convex Representation of Metabolic Networks with Michaelis-Menten Kinetics

by Josh Taylor, Alain Rapaport & Denis Dochain

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Polyhedral models of metabolic networks are computationally tractable and provide insight into cellular functions. For example, flux balance analysis is a linear program in which reaction fluxes are optimized over polyhedral mass-balance constraints. In this paper, we augment the standard polyhedral model of a metabolic network with a new, second-order cone representation of the Michaelis-Menten kinetics. This enables us to explicitly model metabolite concentrations without losing tractability. We formulate conic flux balance analysis, a second-order cone program in which reaction fluxes are maximized while metabolite concentrations are minimized. While not as tractable as linear programming, second-order cone programs with hundreds of thousands of variables can be solved in seconds to minutes using modern solvers like Gurobi and MOSEK. In addition to predicting both fluxes and concentrations, we can use conic duality to compute sensitivities to kinetic parameters, i.e., maximum reaction rates and Michaelis constants. We also incorporate the second-order cone representation of the Michaelis-Menten kinetics into dynamic flux balance analysis and minimal cut set analysis. These tools provide new, tractable ways to analyze reaction fluxes and metabolite concentrations in metabolic networks. The Python code for each tool is available at https://urldefense.com/v3/__https://github.com/JAT38/conic-metabolic__;!!NVzLfOphnbDXSw!CB8YzXwI0ErdeBFcgljtFA36uhpf2ATRf6MEgYTiLhceaAzDS6gF7M5m047C62AYZH8xjVWlPanu7H7qQcsSzjBGP_RJ4rc$

Discretised Flux Balance Analysis for Reaction-Diffusion Simulation of Single-Cell Metabolism

by Yin Hoon Chew and Fabian Spill

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Metabolism comprises thousands of biochemical reactions. It is commonly modelled using Flux Balance Analysis (FBA), a method based on linear programming, because this method requires very few parameters. However, conventional FBA implicitly assumes that all enzymatic reactions are not diffusion-limited though that may not always be the case.

To enable the exploration of diffusion effects on cellular metabolism, we present a spatial method that implements FBA on a grid-based system. The method discretises a living cell into a two-dimensional grid; creates variables that represent the rates of reactions within grid elements and diffusions between grid elements; and solves the system as a single linear programming problem.

Simulations using the method suggest that factors such as cell shape, diffusion regime and spatial distribution of enzyme can influence the variability and robustness of metabolism at both single-cell and population levels. We propose the use of this method to explore how spatiotemporal organisation of compartments and molecules in cells affect cellular behaviour.

Yin Hoon Chew is a Research Fellow and Fabian Spill is a Professor of Applied Mathematics at the University of Birmingham, UK. Yin Hoon designed and implemented the method, with feedback from Fabian.

The method can simulate living cells with different shapes and heterogeneous enzyme distribution. Simulations suggest that cell shape (perimeter-to-area ratio) does not affect cellular behaviour such as biomass growth when diffusion is fast, but there is a strong effect at low diffusion.

Mathematical modelling of parasite dynamics: A stochastic simulation-based approach and parameter estimation via modified sequential-type approximate Bayesian computation

by Clement Twumasi, Joanne Cable and Andrey Pepelyshev. 

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In an era marked by global health challenges and re-emerging infections, the need for sophisticated and robust mathematical models to better understand infectious diseases has never been more pressing. Our impactful study focused on a biological system known as the gyrodactylid-fish system. While existing modelling studies have fallen short in capturing vital information to reflect the biological realism of this system, our research introduced a novel individual-based stochastic simulation model to realistically simulate the spread of three different strains of Gyrodactylus across three different host populations, enhancing our knowledge of this system given observed experimental data. This study contributed mathematically and biologically to the gyrodactylid-fish system, offering insights that may apply to modelling other biological systems. Expanding on the existing studies, we have added to our understanding of this system and provided answers to open biological questions for the first time through model-based Bayesian analysis. The study also led to robust extensions of likelihood-free Bayesian estimation methods, commonly known as approximate Bayesian computation (ABC), to aid in calibrating complex models with mathematically intractable likelihood. After conducting additional posterior predictive checks, we found our proposed ABC methodologies' efficiency highly compelling and can readily be adapted to fit other highdimensional multi-parameter models.

Second-Order Effects of Chemotherapy Pharmacodynamics and Pharmacokinetics on Tumor Regression and Cachexia

by Daniel R. Bergman, Kerri-Ann Norton, Harsh Vardhan Jain & Trachette Jackson

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This paper presents a novel computational framework that constrains high-dimensional ABM parameter space with multidimensional real-world data.  We accomplish this by extending and validating a first-of-its-kind method that leverages explicitly formulated surrogate models to bridge the computational divide between ABMs and experimental data.  We show that Surrogate Modeling for Reconstructing Parameter Spaces (SMoRe ParS) can constrain high-dimensional ABM parameter spaces using unidimensional (single time-course) data.  We then demonstrate that it can constrain parameter spaces of more complex ABMs using multidimensional data (multiple time courses at different biological scales).  To validate our method, we compared the SMoRe ParS-inferred ABM parameter space with ABM parameters inferred by an often computationally expensive direct comparison with experimental data.  A strength of SMoRe ParS is that it allows for exploring ABM parameter space even at points that are not directly sampled and where ABM output was never generated.   Computationally efficient methods to connect ABMs with multidimensional data are timely and important as ABMs are a natural platform for capturing heterogeneity and predicting emergent behavior in multiscale systems.  SMoRe ParS is a robust and scalable computational framework that can explore the uncertainty within multidimensional parameter spaces associated with ABMs representing complex biological phenomena.

Caption: The schematic diagram for using SMoRe ParS to infer ABM input parameters from experimental data via a surrogate model. The solid arrows connecting the Experimental Data and Agent-based Model boxes to the Surrogate Model box represent the direction of information flow in the first few steps of SMoRe ParS. Green (control), yellow (0.75μM oxaliplatin), and red (7.55μM oxaliplatin) colors in the Experimental data box refer to the dosing regimens that generated the experimental data.

Brief description of the roles of the authors (e.g. student, group-leader etc):

Daniel R. Bergman, postdoc

Kerri-Ann Norton,  computational modeling collaborator, and developer of SMoRe Pars

Harsh Vardhan Jain, co-senior author and co-developer of SMoRe Pars

Trachette Jackson, co-senior author and co-developer of SMoRe Pars