Tag Archives: population

Are we individuals?

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It has been estimated that there are 150 species of bacteria in our gut with a megagenome correspondingly larger than the human genome; and that 90% of the cells in our bodies are bacterial [1].  This challenges a simple understanding of individual identity because on one level we are a collection of organisms, mainly bacteria, rather than a single entity.  The complexity is almost incomprehensible with 30 trillion cells in the human body each with about a billion protein molecules [2].  Each cell is apparently autonomous, making decisions about its role in the system based on information acquired through communicating and signalling with its neighbours, the rest of the system and the environment.  Its autonomy would appear to imbue it with a sense of individual identity which is shaped by its relationships within the network of cells [3].  This also holds for human beings within society although you could argue the network is simpler because the global population is only about 8 billion; however the quantity of information being communicated is probably greater than between cells, so perhaps that makes the network more complex.  Networks are horizontal hierarchies with no one or thing in overall control and they can adapt to cope with changes in the environment.  By contrast, vertical hierarchies depend on top-down obedience and tend to eliminate dissent, yet without dissent there is little or no innovation or adaptation.  Hence, vertical hierarchies can appear to be robust but are actually brittle [4].  In a network we can build connections and share knowledge leading to the development of a collective intelligence that enables us to solve otherwise intractable problems.  Our ability to acquire knowledge not just from own our experiences but also from the experience of others, and hence to progressively grow collective intelligence, is one of the secrets of our success as a species [5].  It also underpins the competitive advantage of many successful organisations; however, it needs a horizontal, stable structure with high levels of trust and mutual dependence, in which our sense of individual identity is shaped by our relationships.

References:

  1. Gilbert SF, Sapp J, Tauber AI, A symbiotic view of life: we have never been individuals, Quarterly Review of Biology, 87(4):325-341, 2012.
  2. Ball P, How Life Works, Picador, 2023.
  3. Wheatley M, Leadership and the New Science: Discovering Order in a Chaotic World, 2nd Edition, Berrett-Koehler Publishers Inc, San Francisco, 1999.
  4. McWilliams D, Money – A Story of Humanity, Simon & Schuster, London, 2024.
  5. Henrich J, The secret of our success: how culture is driving human evolution, domesticating our species, and making us smarter, Princeton, NJ: Princeton University Press, 2015.

Ice caps losing water and gravitational attraction

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Map of the world showing population density is greater in the regions furthest from the polesI have written previously about sea level rises [see ‘Merseyside Totemy‘ on August 17th, 2022 and ‘Climate change and tides in Liverpool‘ on May 11th, 2016] and the fact that a 1 metre rise in sea level would displace 145 million people [see ‘New Year resolution‘ on December 31st, 2014].  Sea levels globally have risen 102.5 mm since 1993 primarily due to the water added as a result of the melting of glaciers and icecaps and due to the expansion of the seawater as its temperature rises – both of these causes are a result of global warming resulting from human activity.  I think that this is probably well-known to most readers of this blog. However, I had not appreciated that the polar ice caps are sufficiently massive that their gravitational attraction pulls the water in the oceans towards them, so that as they melt the oceans move towards a more even distribution of water raising sea levels further away from the icecaps.  This is problematic because the population density is higher in the regions further away from the polar ice caps, as shown in the image.  Worldwide about 1 billion people, or about an eighth of the global population, live less than 10 metres above current high tide lines.  If we fail to limit global warming to 1.5 degrees Centigrade and it peaks at 5 degrees Centigrade then the average sea level rise is predicted to be as high as 7 m according to the IPCC.

Image: Population Density, v4.11, 2020 by SEDACMaps CC-BY-2.0 Creative Commons Attribution 2.0 Generic license.

Source: Thomas Halliday, Otherlands: A World in the Making, London: Allen Lane, 2022

Modelling from the cell through the individual to the host population

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During the lock-down in the UK due to the coronavirus pandemic, I have been reading about viruses and the modelling of them.  It is a multi-disciplinary and multi-scale problem; so, something that engineers should be well-equipped to tackle.  It is a multi-scale because we need to understand the spread of the virus in the human population so that we can control it, we need to understand the process of infection in individuals so that we can protect them, and we need to understand the mechanisms of virus-cell interaction so that we can stop the replication of the virus.  At each size scale, models capable of representing the real-world processes will help us explore different approaches to arresting the progress of the virus and will need to be calibrated and validated against measurements.  This can be represented in the sort of model-test pyramid shown in the top graphic that has been used in the aerospace industry [1-2] for many years [see ‘Hierarchical modelling in engineering and biology’ on March 14th, 2018] and which we have recently introduced in the nuclear fission [3] and fusion [4] industries [see ‘Thought leadership in fusion engineering’ on October 9th, 2019].  At the top of the pyramid, the spread of the virus in the population is being modelled by epidemiologists, such as Professor Neil Ferguson [5], using statistical models based on infection data.  However, I am more interested in the bottom of the pyramid because the particles of the coronavirus are about the same size as the nanoparticles that I have been studying for some years [see ‘Slow moving nanoparticles’ on December 13th, 2017] and their motion appears to be dominated by diffusion processes [see ‘Salt increases nanoparticle diffusion’ on April 22nd, 2020] [6-7].  The first step towards virus infection of a cell is diffusion of the virus towards the cell which is believed to be a relatively slow process and hence a good model of diffusion would assist in designing drugs that could arrest or decelerate infection of cells [8].  Many types of virus on entering the cell make their way to the nucleus where they replicate causing the cell to die, afterwhich the virus progeny are dispersed to repeat the process.  You can see part of this sequence for coronavirus (SARS-COV-2) in this sequence of images. The trafficking across the cytoplasm of the cell to the nucleus can occur in a number of ways including the formation of a capsule or endosome that moves across the cell towards the nuclear membrane where the virus particles leave the endosome and travel through microtubules into the nucleus.  Holcman & Schuss [9] provide a good graphic illustrating these transport mechanisms.  In 2019, Briane et al [10] reviewed models of diffusion of intracellular particles inside living eukaryotic cells, i.e. cells with a nuclear enclosed by a membrane as in all animals.  Intracellular diffusion is believed to be driven by Brownian motion and by motor-proteins including dynein, kinesin and myosin that enable motion through microtubules.  They observed that the density of the structure of cytoplasm, or cytoskeleton, can hinder the free displacement of a particle leading to subdiffusion; while, cytoskeleton elasticity and thermal bending can accelerate it leading to superdiffusion.  These molecular and cellular interactions are happening at disparate spatial and temporal scales [11] which is one of the difficulties encountered in creating predictive simulations of virus-cell interactions.  In other words, the bottom layers of the model-test pyramid appear to be constructed from many more strata when you start to look more closely.  And, you need to add a time dimension to it.  Prior to the coronavirus pandemic, more modelling efforts were perhaps focussed on understanding the process of infection by Human Immunodeficiency Virus (HIV), including by a multi-national group of scientists from Chile, France, Morocco, Russia and Spain [12-14].  However, the current coronavirus pandemic is galvanising researchers who are starting to think about novel ways of building multiscale models that encourage multidisciplinary collaboration by dispersed groups, [e.g. 15].

References

[1] Harris GL, Computer models, laboratory simulators, and test ranges: meeting the challenge of estimating tactical force effectiveness in the 1980’s, US Army Command and General Staff College, May 1979.

[2] Trevisani DA & Sisti AF, Air Force hierarchy of models: a look inside the great pyramid, Proc. SPIE 4026, Enabling Technology for Simulation Science IV, 23 June 2000.

[3] Patterson EA, Taylor RJ & Bankhead M, A framework for an integrated nuclear digital environment, Progress in Nuclear Energy, 87:97-103, 2016.

[4] Patterson EA, Purdie S, Taylor RJ & Waldon C, An integrated digital framework for the design, build and operation of fusion power plants, Royal Society Open Science, 6(10):181847, 2019.

[5] Verity R, Okell LC, Dorigatti I, Winskill P, Whittaker C, Imai N, Cuomo-Dannenburg G, Thompson H, Walker PGT, Fu H, Dighe A, Griffin JT, Baguelin M, Bhatia S, Boonyasiri A, Cori A, Cucunubá Z, FitzJohn R, Gaythorpe K, Green W, Hamlet A, Hinsley W, Laydon D, Nedjati-Gilani G, Riley S, van Elsland S, Volz E, Wang H, Wang Y, Xi X, Donnelly CA, Ghani AC, Ferguson NM, Estimates of the severity of coronavirus disease 2019: a model-based analysis., Lancet Infectious Diseases, 2020.

[6] Coglitore D, Edwardson SP, Macko P, Patterson EA, Whelan MP, Transition from fractional to classical Stokes-Einstein behaviour in simple fluids, Royal Society Open Science, 4:170507, 2017.

[7] Giorgi F, Coglitore D, Curran JM, Gilliland D, Macko P, Whelan M, Worth A & Patterson EA, The influence of inter-particle forces on diffusion at the nanoscale, Scientific Reports, 9:12689, 2019.

[8] Gilbert P-A, Kamen A, Bernier A & Garner A, A simple macroscopic model for the diffusion and adsorption kinetics of r-Adenovirus, Biotechnology & Bioengineering, 98(1):239-251,2007.

[9] Holcman D & Schuss Z, Modeling the early steps of viral infection in cells, Chapter 9 in Stochastic Narrow Escape in Molecular and Cellular Biology, New York: Springer Science+Business Media, 2015.

[10] Braine V, Vimond M & Kervrann C, An overview of diffusion models for intracellular dynamics analysis, Briefings in Bioinformatics, Oxford University Press, pp.1-15, 2019.

[11] Holcman D & Schuss Z, Time scale of diffusion in molecular and cellular biology, J. Physics A: Mathematical and Theoretical, 47:173001, 2014.

[12] Bocharov G, Chereshnev V, Gainov I, Bazhun S, Bachmetyev B, Argilaguet J, Martinez J & Meyerhans A, Human immunodeficiency virus infection: from biological observations to mechanistic mathematical modelling, Math. Model. Nat. Phenom., 7(5):78-104, 2012.

[13] Bocharov G, Meyerhans A, Bessonov N, Trofimchuk S & Volpert V, Spatiotemporal dynamics of virus infection spreading in tissues, PLOS One, 11(12):e)168576, 2016.

[14] Bouchnita A, Bocharov G, Meyerhans A & Volpert V, Towards a multiscale model of acute HIV infection, Computation, 5(6):5010006, 2017.

[15] Sego TJ, Aponte-Serrano JO, Ferrari-Gianlupi J, Heaps S, Quardokus EM & Glazier JA, A modular framework for multiscale spatial modeling of viral infection and immune respons in epithelial tissue, bioRxiv. 2020.

Citizens of the world

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Last week in Liverpool, we hosted a series of symposia for participants in a dual PhD programme involving the University of Liverpool and National Tsing Hua University, in Taiwan, that has been operating for nearly a decade.  On the first day, we brought together about dozen staff from each university, who had not met before, and asked them to present overviews of their research and explore possible collaborations using as a theme: UN Sustainable Development Goal No.11: Sustainable Cities and Communities.  The expertise of the group included biology, computer science, chemistry, economics, engineering, materials science and physics; so, we had wide-ranging discussions.  On the second and third day, we connected a classroom on each campus using a video conferencing system and the two dozen PhD students in the dual programme presented updates on their research from whichever campus they are currently resident.  Each student has a supervisor in each university and divides their time between the two universities exploiting the expertise and facilities in the two institutions.

The range of topics covered in the student presentations was probably even wider than on the first day; extending from deep neural networks, through nuclear reactor technology, battery design and three-dimensional cell culturing to policy impacts on households.  One student spoke about the beauty of mathematical equations she is working on that describe the propagation of waves in lattice structures; while, another told us about his investigation of the causes of declining fertility rates across the world.  Data from the UN DESA Population Division show that live births per woman in the Americas & Europe have already fallen below the 2.1 required to sustain the population, while it is projected to fall below this level in south-east Asia within the next five years and in the world by 2060.  This made me think that perhaps the Gaia principle, proposed by James Lovelock, is operating and that human population is self-regulating as it interacts with constraints imposed by the Earth though perhaps not in a fashion originally envisaged.