| 1. |
- Bengmark, Samuel, 1965, et al.
(författare)
-
Combining engineering and teacher education – ideas and experiences from Chalmers University of Technology
- 2021
-
Ingår i: Bidrag från 8:e Utvecklingskonferensen för Sveriges ingenjörsutbildningar. - 9789178672714
-
Konferensbidrag (refereegranskat)abstract
- In response to the lack of STEM teachers in Sweden, Chalmers University of Technology offers a double degree program in engineering and education. This article investigates which ideas behind the program’s design have been of particular value in implementing the program and their added value. The five main ideas are: involve skilled schoolteachers called master teachers in the education, having many entrances to the program but only one exit, using interviews as part of the admission process, using a competency model to ensure coherence in the education, focusing on concrete work skills at the beginning of the education and later move on to theory and further development. We invite other universities to consider double degree programs in engineering and education and be inspired by these five ideas in their implementation.
|
|
| 2. |
- Borgqvist, Johannes, 1990, et al.
(författare)
-
Cell polarisation in a bulk-surface model can be driven by both classic and non-classic Turing instability
- 2021
-
Ingår i: Npj Systems Biology and Applications. - : Springer Science and Business Media LLC. - 2056-7189. ; 7:1
-
Tidskriftsartikel (refereegranskat)abstract
- The GTPase Cdc42 is the master regulator of eukaryotic cell polarisation. During this process, the active form of Cdc42 is accumulated at a particular site on the cell membrane called the pole. It is believed that the accumulation of the active Cdc42 resulting in a pole is driven by a combination of activation-inactivation reactions and diffusion. It has been proposed using mathematical modelling that this is the result of diffusion-driven instability, originally proposed by Alan Turing. In this study, we developed, analysed and validated a 3D bulk-surface model of the dynamics of Cdc42. We show that the model can undergo both classic and non-classic Turing instability by deriving necessary conditions for which this occurs and conclude that the non-classic case can be viewed as a limit case of the classic case of diffusion-driven instability. Using three-dimensional Spatio-temporal simulation we predicted pole size and time to polarisation, suggesting that cell polarisation is mainly driven by the reaction strength parameter and that the size of the pole is determined by the relative diffusion.
|
|
| 3. |
- Borgqvist, Johannes, 1990, et al.
(författare)
-
Turing pattern formation on the sphere is robust to the removal of a hole
- 2024
-
Ingår i: JOURNAL OF MATHEMATICAL BIOLOGY. - : Springer Science+Business Media B.V.. - 0303-6812 .- 1432-1416. ; 88:2
-
Tidskriftsartikel (refereegranskat)abstract
- The formation of buds on the cell membrane of budding yeast cells is thought to be driven by reactions and diffusion involving the protein Cdc42. These processes can be described by a coupled system of partial differential equations known as the Schnakenberg system. The Schnakenberg system is known to exhibit diffusion-driven pattern formation, thus providing a mechanism for bud formation. However, it is not known how the accumulation of bud scars on the cell membrane affect the ability of the Schnakenberg system to form patterns. We have approached this problem by modelling a bud scar on the cell membrane with a hole on the sphere. We have studied how the spectrum of the Laplace-Beltrami operator, which determines the resulting pattern, is affected by the size of the hole, and by numerically solving the Schnakenberg system on a sphere with a hole using the finite element method. Both theoretical predictions and numerical solutions show that pattern formation is robust to the introduction of a bud scar of considerable size, which lends credence to the hypothesis that bud formation is driven by diffusion-driven instability.
|
|
| 4. |
- Gerlee, Philip, 1980, et al.
(författare)
-
Autocrine signaling can explain the emergence of Allee effects in cancer cell populations
- 2022
-
Ingår i: Plos Computational Biology. - : Public Library of Science (PLoS). - 1553-734X .- 1553-7358. ; 18:3
-
Tidskriftsartikel (refereegranskat)abstract
- In many human cancers, the rate of cell growth depends crucially on the size of the tumour cell population. Low, zero, or negative growth at low population densities is known as the Allee effect; this effect has been studied extensively in ecology, but so far lacks a good explanation in the cancer setting. Here, we formulate and analyze an individual-based model of cancer, in which cell division rates are increased by the local concentration of an autocrine growth factor produced by the cancer cells themselves. We show, analytically and by simulation, that autocrine signaling suffices to cause both strong and weak Allee effects. Whether low cell densities lead to negative (strong effect) or reduced (weak effect) growth rate depends directly on the ratio of cell death to proliferation, and indirectly on cellular dispersal. Our model is consistent with experimental observations from three patient-derived brain tumor cell lines grown at different densities. We propose that further studying and quantifying population-wide feedback, impacting cell growth, will be central for advancing our understanding of cancer dynamics and treatment, potentially exploiting Allee effects for therapy.
|
|
| 5. |
- Gerlee, Philip, 1980, et al.
(författare)
-
Computational models predicting the early development of the COVID-19 pandemic in Sweden: systematic review, data synthesis, and secondary validation of accuracy
- 2022
-
Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 12:1
-
Tidskriftsartikel (refereegranskat)abstract
- Computational models for predicting the early course of the COVID-19 pandemic played a central role in policy-making at regional and national levels. We performed a systematic review, data synthesis, and secondary validation of studies that reported on prediction models addressing the early stages of the COVID-19 pandemic in Sweden. A literature search in January 2021 based on the search triangle model identified 1672 peer-reviewed articles, preprints and reports. After applying inclusion criteria 52 studies remained out of which 12 passed a Risk of Bias Opinion Tool. When comparing model predictions with actual outcomes only 4 studies exhibited an acceptable forecast (mean absolute percentage error, MAPE < 20%). Models that predicted disease incidence could not be assessed due to the lack of reliable data during 2020. Drawing conclusions about the accuracy of the models with acceptable methodological quality was challenging because some models were published before the time period for the prediction, while other models were published during the prediction period or even afterwards. We conclude that the forecasting models involving Sweden developed during the early stages of the COVID-19 pandemic in 2020 had limited accuracy. The knowledge attained in this study can be used to improve the preparedness for coming pandemics.
|
|
| 6. |
- Gerlee, Philip, 1980
(författare)
-
Derivation of a Generalised Replicator Equation in the Limit of Weak Selection
- 2023
-
Ingår i: Springer Proceedings in Mathematics and Statistics. - 2194-1009 .- 2194-1017. ; 429, s. 249-260
-
Konferensbidrag (refereegranskat)abstract
- The replicator equation is often applied for describing the change in frequency of competing subpopulations, but has only been formally derived under strong limitations on the dynamics of the total population size. We show that a generalised replicator equation can be derived from a wide class of population dynamical models that allow for a factorisation into density-dependent and frequency-dependent birth and death rates. The method relies on weak selection (i.e. small fitness differences) and the generalised replicator equation is obtained as the zeroth order approximation in a perturbation expansion. We apply our theoretical results to a specific model with linear frequency-dependence and logistic dynamics for the population size, and show that the error introduced by considering a zeroth order composite solution scales as the fitness difference.
|
|
| 7. |
- Gerlee, Philip, 1980, et al.
(författare)
-
Predicting regional COVID-19 hospital admissions in Sweden using mobility data.
- 2021
-
Ingår i: Scientific reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 11:1
-
Tidskriftsartikel (refereegranskat)abstract
- The transmission of COVID-19 is dependent on social mixing, the basic rate of which varies with sociodemographic, cultural, and geographic factors. Alterations in social mixing and subsequent changes in transmission dynamics eventually affect hospital admissions. We employ these observations to model and predict regional hospital admissions in Sweden during the COVID-19 pandemic. We use an SEIR-model for each region in Sweden in which the social mixing is assumed to depend on mobility data from public transport utilisation and locations for mobile phone usage. The results show that the model could capture the timing of the first and beginning of the second wave of the pandemic 3weeks in advance without any additional assumptions about seasonality. Further, we show that for two major regions of Sweden, models with public transport data outperform models using mobile phone usage. We conclude that a model based on routinely collected mobility data makes it possible to predict future hospital admissions for COVID-19 3weeks in advance.
|
|
| 8. |
- Gerlee, Philip, 1980
(författare)
-
Weak Selection and the Separation of Eco-evo Time Scales using Perturbation Analysis
- 2022
-
Ingår i: Bulletin of Mathematical Biology. - : Springer Science and Business Media LLC. - 0092-8240 .- 1522-9602. ; 84:5
-
Tidskriftsartikel (refereegranskat)abstract
- We show that under the assumption of weak frequency-dependent selection a wide class of population dynamical models can be analysed using perturbation theory. The inner solution corresponds to the ecological dynamics, where to zeroth order, the genotype frequencies remain constant. The outer solution provides the evolutionary dynamics and corresponds, to zeroth order, to a generalisation of the replicator equation. We apply this method to a model of public goods dynamics and construct, using matched asymptotic expansions, a composite solution valid for all times. We also analyse a Lotka-Volterra model of predator competition and show that to zeroth order the fraction of wild-type predators follows a replicator equation with a constant selection coefficient given by the predator death rate. For both models, we investigate how the error between approximate solutions and the solution to the full model depend on the order of the approximation and show using numerical comparison, for k = 1 and 2, that the error scales according to epsilon(k+1), where s is the strength of selection and k is the order of the approximation.
|
|
| 9. |
- Hamis, S., et al.
(författare)
-
Spatial cumulant models enable spatially informed treatment strategies and analysis of local interactions in cancer systems
- 2023
-
Ingår i: Journal of Mathematical Biology. - : Springer Nature. - 0303-6812 .- 1432-1416. ; 86:5
-
Tidskriftsartikel (refereegranskat)abstract
- Theoretical and applied cancer studies that use individual-based models (IBMs) have been limited by the lack of a mathematical formulation that enables rigorous analysis of these models. However, spatial cumulant models (SCMs), which have arisen from theoretical ecology, describe population dynamics generated by a specific family of IBMs, namely spatio-temporal point processes (STPPs). SCMs are spatially resolved population models formulated by a system of differential equations that approximate the dynamics of two STPP-generated summary statistics: first-order spatial cumulants (densities), and second-order spatial cumulants (spatial covariances). We exemplify how SCMs can be used in mathematical oncology by modelling theoretical cancer cell populations comprising interacting growth factor-producing and non-producing cells. To formulate model equations, we use computational tools that enable the generation of STPPs, SCMs and mean-field population models (MFPMs) from user-defined model descriptions (Cornell et al. Nat Commun 10:4716, 2019). To calculate and compare STPP, SCM and MFPM-generated summary statistics, we develop an application-agnostic computational pipeline. Our results demonstrate that SCMs can capture STPP-generated population density dynamics, even when MFPMs fail to do so. From both MFPM and SCM equations, we derive treatment-induced death rates required to achieve non-growing cell populations. When testing these treatment strategies in STPP-generated cell populations, our results demonstrate that SCM-informed strategies outperform MFPM-informed strategies in terms of inhibiting population growths. We thus demonstrate that SCMs provide a new framework in which to study cell-cell interactions, and can be used to describe and perturb STPP-generated cell population dynamics. We, therefore, argue that SCMs can be used to increase IBMs' applicability in cancer research.
|
|
| 10. |
- Lindwall, Gustav, 1992, et al.
(författare)
-
Bayesian inference on the Allee effect in cancer cell line populations using time-lapse microscopy images
- 2023
-
Ingår i: Journal of Theoretical Biology. - 0022-5193 .- 1095-8541. ; 574
-
Tidskriftsartikel (refereegranskat)abstract
- The Allee effect describes the phenomenon that the per capita reproduction rate increases along with the population density at low densities. Allee effects have been observed at all scales, including in microscopic environments where individual cells are taken into account. This is great interest to cancer research, as understanding critical tumour density thresholds can inform treatment plans for patients. In this paper, we introduce a simple model for cell division in the case where the cancer cell population is modelled as an interacting particle system. The rate of the cell division is dependent on the local cell density, introducing an Allee effect. We perform parameter inference of the key model parameters through Markov Chain Monte Carlo, and apply our procedure to two image sequences from a cervical cancer cell line. The inference method is verified on in silico data to accurately identify the key parameters, and results on the in vitro data strongly suggest an Allee effect
|
|
| 11. |
- Lindwall, Gustav, 1992, et al.
(författare)
-
Bayesian inference on the Allee effect in cancer cell populations using time-lapse microscopy images
- 2023
-
Ingår i: Journal of Theoretical Biology. - 1095-8541 .- 0022-5193. ; 574
-
Tidskriftsartikel (refereegranskat)abstract
- The Allee effect describes the phenomenon that the per capita reproduction rate increases along with the population density at low densities. Allee effects have been observed at all scales, including in microscopic environments where individual cells are taken into account. This is great interest to cancer research, as understanding critical tumour density thresholds can inform treatment plans for patients. In this paper, we introduce a simple model for cell division in the case where the cancer cell population is modelled as an interacting particle system. The rate of the cell division is dependent on the local cell density, introducing an Allee effect. We perform parameter inference of the key model parameters through Markov Chain Monte Carlo, and apply our procedure to two image sequences from a cervical cancer cell line. The inference method is verified on in silico data to accurately identify the key parameters, and results on the in vitro data strongly suggest an Allee effect.
|
|
| 12. |
- Lindwall, Gustav, 1992, et al.
(författare)
-
Fast and precise inference on diffusivity in interacting particle systems
- 2023
-
Ingår i: Journal of Mathematical Biology. - : Springer Science and Business Media LLC. - 1432-1416 .- 0303-6812. ; 86:5
-
Tidskriftsartikel (refereegranskat)abstract
- Particle systems made up of interacting agents is a popular model used in a vast array of applications, not the least in biology where the agents can represent everything from single cells to animals in a herd. Usually, the particles are assumed to undergo some type of random movements, and a popular way to model this is by using Brownian motion. The magnitude of random motion is often quantified using mean squared displacement, which provides a simple estimate of the diffusion coefficient. However, this method often fails when data is sparse or interactions between agents frequent. In order to address this, we derive a conjugate relationship in the diffusion term for large interacting particle systems undergoing isotropic diffusion, giving us an efficient inference method. The method accurately accounts for emerging effects such as anomalous diffusion stemming from mechanical interactions. We apply our method to an agent-based model with a large number of interacting particles, and the results are contrasted with a naive mean square displacement-based approach. We find a significant improvement in performance when using the higher-order method over the naive approach. This method can be applied to any system where agents undergo Brownian motion and will lead to improved estimates of diffusion coefficients compared to existing methods.
|
|
| 13. |
- Liu, Y., et al.
(författare)
-
Ladderpath Approach: How Tinkering and Reuse Increase Complexity and Information
- 2022
-
Ingår i: Entropy. - : MDPI AG. - 1099-4300. ; 24:8
-
Tidskriftsartikel (refereegranskat)abstract
- The notion of information and complexity are important concepts in many scientific fields such as molecular biology, evolutionary theory and exobiology. Many measures of these quantities are either difficult to compute, rely on the statistical notion of information, or can only be applied to strings. Based on assembly theory, we propose the notion of a ladderpath, which describes how an object can be decomposed into hierarchical structures using repetitive elements. From the ladderpath, two measures naturally emerge: the ladderpath-index and the order-index, which represent two axes of complexity. We show how the ladderpath approach can be applied to both strings and spatial patterns and argue that all systems that undergo evolution can be described as ladderpaths. Further, we discuss possible applications to human language and the origin of life. The ladderpath approach provides an alternative characterization of the information that is contained in a single object (or a system) and could aid in our understanding of evolving systems and the origin of life in particular.
|
|
| 14. |
- Malik, Adam, 1991, et al.
(författare)
-
The Impact of Elastic Deformations of the Extracellular Matrix on Cell Migration
- 2020
-
Ingår i: Bulletin of Mathematical Biology. - : Springer Science and Business Media LLC. - 0092-8240 .- 1522-9602. ; 82:4
-
Tidskriftsartikel (refereegranskat)abstract
- The mechanical properties of the extracellular matrix, in particular its stiffness, are known to impact cell migration. In this paper, we develop a mathematical model of a single cell migrating on an elastic matrix, which accounts for the deformation of the matrix induced by forces exerted by the cell, and investigate how the stiffness impacts the direction and speed of migration. We model a cell in 1D as a nucleus connected to a number of adhesion sites through elastic springs. The cell migrates by randomly updating the position of its adhesion sites. We start by investigating the case where the cell springs are constant, and then go on to assuming that they depend on the matrix stiffness, on matrices of both uniform stiffness as well as those with a stiffness gradient. We find that the assumption that cell springs depend on the substrate stiffness is necessary and sufficient for an efficient durotactic response. We compare simulations to recent experimental observations of human cancer cells exhibiting durotaxis, which show good qualitative agreement. © 2020, The Author(s).
|
|
| 15. |
- Rosén, Emil, et al.
(författare)
-
Inference of glioblastoma migration and proliferation rates using single time-point images
- 2023
-
Ingår i: Communications Biology. - : Springer Nature. - 2399-3642. ; 6:1
-
Tidskriftsartikel (refereegranskat)abstract
- Cancer cell migration is a driving mechanism of invasion in solid malignant tumors. Anti-migratory treatments provide an alternative approach for managing disease progression. However, we currently lack scalable screening methods for identifying novel anti-migratory drugs. To this end, we develop a method that can estimate cell motility from single end-point images in vitro by estimating differences in the spatial distribution of cells and inferring proliferation and diffusion parameters using agent-based modeling and approximate Bayesian computation. To test the power of our method, we use it to investigate drug responses in a collection of 41 patient-derived glioblastoma cell cultures, identifying migration-associated pathways and drugs with potent anti-migratory effects. We validate our method and result in both in silico and in vitro using time-lapse imaging. Our proposed method applies to standard drug screen experiments, with no change needed, and emerges as a scalable approach to screen for anti-migratory drugs. The spatial positioning of cultured glioblastoma cells is used to estimate cell motility and drug effects from single end-point images in vitro.
|
|
| 16. |
- Spreco, Armin, et al.
(författare)
-
Nowcasting (Short-Term Forecasting) of COVID-19 Hospitalizations Using Syndromic Healthcare Data, Sweden, 2020
- 2022
-
Ingår i: Emerging Infectious Diseases. - : Centers for Disease Control and Prevention (CDC). - 1080-6040 .- 1080-6059. ; 28:3, s. 564-571
-
Tidskriftsartikel (refereegranskat)abstract
- We report on local nowcasting (short-term forecasting) of coronavirus disease (COVID-19) hospitalizations based on syndromic (symptom) data recorded in regular healthcare routines in Östergötland County (population ≈465,000), Sweden, early in the pandemic, when broad laboratory testing was unavailable. Daily nowcasts were supplied to the local healthcare management based on analyses of the time lag between telenursing calls with the chief complaints (cough by adult or fever by adult) and COVID-19 hospitalization. The complaint cough by adult showed satisfactory performance (Pearson correlation coefficient r>0.80; mean absolute percentage error <20%) in nowcasting the incidence of daily COVID-19 hospitalizations 14 days in advance until the incidence decreased to <1.5/100,000 population, whereas the corresponding performance for fever by adult was unsatisfactory. Our results support local nowcasting of hospitalizations on the basis of symptom data recorded in routine healthcare during the initial stage of a pandemic. © 2022 Centers for Disease Control and Prevention (CDC). All rights reserved.
|
|
| 17. |
- Thorén, Henrik, et al.
(författare)
-
Model uncertainty, the COVID-19 pandemic, and the science-policy interface
- 2024
-
Ingår i: Royal Society Open Science. - 2054-5703. ; 11:2
-
Tidskriftsartikel (refereegranskat)abstract
- The COVID-19 pandemic illustrated many of the challenges with using science to guide planning and policymaking. One such challenge has to do with how to manage, represent and communicate uncertainties in epidemiological models. This is considerably complicated, we argue, by the fact that the models themselves are often instrumental in structuring the involved uncertainties. In this paper we explore how models 'domesticate' uncertainties and what this implies for science-for-policy. We analyse three examples of uncertainty domestication in models of COVID-19 and argue that we need to pay more attention to how uncertainties are domesticated in models used for policy support, and the many ways in which uncertainties are domesticated within particular models can fail to fit with the needs and demands of policymakers and planners.
|
|
| 18. |
- Vitos, N., et al.
(författare)
-
Model-based inference of metastatic seeding rates in de novo metastatic breast cancer reveals the impact of secondary seeding and molecular subtype
- 2022
-
Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 12:1
-
Tidskriftsartikel (refereegranskat)abstract
- We present a stochastic network model of metastasis spread for de novo metastatic breast cancer, composed of tumor to metastasis (primary seeding) and metastasis to metastasis spread (secondary seeding), parameterized using the SEER (Surveillance, Epidemiology, and End Results) database. The model provides a quantification of tumor cell dissemination rates between the tumor and metastasis sites. These rates were used to estimate the probability of developing a metastasis for untreated patients. The model was validated using tenfold cross-validation. We also investigated the effect of HER2 (Human Epidermal Growth Factor Receptor 2) status, estrogen receptor (ER) status and progesterone receptor (PR) status on the probability of metastatic spread. We found that dissemination rate through secondary seeding is up to 300 times higher than through primary seeding. Hormone receptor positivity promotes seeding to the bone and reduces seeding to the lungs and primary seeding to the liver, while HER2 expression increases dissemination to the bone, lungs and primary seeding to the liver. Secondary seeding from the lungs to the liver seems to be hormone receptor-independent, while that from the lungs to the brain appears HER2-independent.
|
|
| 19. |
- Wacker, Andreas, et al.
(författare)
-
Estimating the SARS-CoV-2 infected population fraction and the infection-to-fatality ratio: a data-driven case study based on Swedish time series data.
- 2021
-
Ingår i: Scientific reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 11:1
-
Tidskriftsartikel (refereegranskat)abstract
- We demonstrate that finite impulse response (FIR) models can be applied to analyze the time evolution of an epidemic with its impact on deaths and healthcare strain. Using time series data for COVID-19-related cases, ICU admissions and deaths from Sweden, the FIR model gives a consistent epidemiological trajectory for a simple delta filter function. This results in a consistent scaling between the time series if appropriate time delays are applied and allows the reconstruction of cases for times before July 2020, when RT-PCR testing was not widely available. Combined with randomized RT-PCR study results, we utilize this approach to estimate the total number of infections in Sweden, and the corresponding infection-to-fatality ratio (IFR), infection-to-case ratio (ICR), and infection-to-ICU admission ratio (IIAR). Our values for IFR, ICR and IIAR are essentially constant over large parts of 2020 in contrast with claims of healthcare adaptation or mutated virus variants importantly affecting these ratios. We observe a diminished IFR in late summer 2020 as well as a strong decline during 2021, following the launch of a nation-wide vaccination program. The total number of infections during 2020 is estimated to 1.3 million, indicating that Sweden was far from herd immunity.
|
|
