At the Intersection of Computer Predictive Modelling and Cancer Researches

We constructed a 3D assembling model of the branched actin network. We revealed how intracellular proteins regulate the elastic properties of the network and then affect cell migration, providing greater insight into the fundamental physical mechanisms of published experimental observations.
Published in Cancer
Like

Share this post

Choose a social network to share with, or copy the URL to share elsewhere

This is a representation of how your post may appear on social media. The actual post will vary between social networks

At the beginning of PhD study, I told myself that I should spend my PhD time on a valuable research topic, which can benefit human beings. After discussion with my supervisor Dr. Hanxing Zhu, we decided to study cell migrations with the backgrounds of computational mechanics and simulations. I spent about three months reading literatures to learn the lamellipodia-based cell migrations and the latest researches. Meanwhile, I wrote a 40-page review draft from over 150 research papers, which built a strong foundation for this research.

The highly dynamic lamellipodial branched actin network supports cell migration through heterogeneous mechanical extracellular microenvironments. Thus, its elastic properties play essential roles in determining cell migration 1. However, we found that it is unclear about how various intracellular proteins regulate the elastic properties of the network and then affect cell migration. The major challenge to investigate this question is that the highly dynamic and stochastic remodeling behaviors hinder one from performing an adequate number of biological experiments to study the quantitative relationships between the macroscopic elastic properties of the network and the microscopic structures regulated by various intracellular proteins.

To address the above question, we developed a 5000-line computer code tool to construct the realistic stochastic self-assembling multi-scale model of the in vivo lamellipodial branched actin network. Our computer codes take into account of five types of key proteins, i.e., filamentous actin, Arp2/3 complex, capping protein, filamin-A and α-actinin, and their mechano-chemical assembling interactions, such as filament polymerizing, Arp2/3 complex branching, capping protein inhibiting polymerization and actin-crosslinking proteins binding and unbinding. The spatiotemporal remodeling architectures of the lamellipodial branched actin network during driving cell migration are obtained over more than 4000 different stochastic models. Then, with 24000 finite element simulations, the role of each type of intracellular proteins in adapting the elastic properties of the network and then affecting cell migration is deciphered quantitatively. More importantly, we reveal a resistance-adaptive intracellular physical mechanism of the elastic properties of the lamellipodial branched actin network for cell migration. Our simulations predict many experimental observations 1-12. The revealed quantitative results have important clinical values for cancer cell metastasis. For example, our results suggest that creating intracellular inhibitors for Arp2/3 complex might be more effective for reducing cancer cell invasion and metastasis. Besides, the revealed physical mechanisms also provide insights into the understanding of endocytosis, phagocytosis, vesicle trafficking, intracellular pathogen transport and dendritic spine formation, where branched actin networks are generated.

Finally, we highlight that constructing predictive multiscale models at the intersection of biology, computer science, physics and chemistry is an effective way to study the highly dynamic, stochastic and complex physiological activities and pathological mechanism 13. It not only can get massive amount of data, but also can capture the quantitative relationships between various factors/behaviors to analyze the underlying biophysical mechanism, which is extremely important for drug developments. As one of the reviewers of the paper points out, biological insights can be gained from this engineering-based approach.

The article is published in Communications Biology https://www.nature.com/articles/s42003-020-01335-z

References

  1. Bieling P, et al. Force Feedback Controls Motor Activity and Mechanical Properties of Self-Assembling Branched Actin Networks. Cell 164, 115-127 (2016).
  2. Wu C, et al. Arp2/3 is critical for lamellipodia and response to extracellular matrix cues but is dispensable for chemotaxis. Cell 148, 973-987 (2012).
  3. Paul CD, Mistriotis P, Konstantopoulos K. Cancer cell motility: lessons from migration in confined spaces. Nat Rev Cancer 17, 131 (2017).
  4. Boujemaa-Paterski R, et al. Network heterogeneity regulates steering in actin-based motility. Nat Commun 8, 655 (2017).
  5. Mueller J, et al. Load adaptation of lamellipodial actin networks. Cell 171, 188-200. e116 (2017).
  6. Pollard TD, Borisy GG. Cellular motility driven by assembly and disassembly of actin filaments. Cell 112, 453-465 (2003).
  7. Dang I, et al. Inhibitory signalling to the Arp2/3 complex steers cell migration. Nature 503, 281 (2013).
  8. Maritzen T, Zech T, Schmidt MR, Krause E, Machesky LM, Haucke V. Gadkin negatively regulates cell spreading and motility via sequestration of the actin-nucleating ARP2/3 complex. Proc Natl Acad Sci USA 109, 10382-10387 (2012).
  9. Pujol T, du Roure O, Fermigier M, Heuvingh J. Impact of branching on the elasticity of actin networks. Proc Natl Acad Sci USA 109, 10364-10369 (2012).
  10. Bear JE, et al. Antagonism between Ena/VASP proteins and actin filament capping regulates fibroblast motility. Cell 109, 509-521 (2002).
  11. van der Gucht J, Paluch E, Plastino J, Sykes C. Stress release drives symmetry breaking for actin-based movement. Proc Natl Acad Sci USA 102, 7847-7852 (2005).
  12. Flanagan LA, Chou J, Falet H, Neujahr R, Hartwig JH, Stossel TP. Filamin A, the Arp2/3 complex, and the morphology and function of cortical actin filaments in human melanoma cells. J Cell Biol 155, 511-518 (2001).
  13. Singla J, McClary KM, White KL, Alber F, Sali A, Stevens RC. Opportunities and Challenges in Building a Spatiotemporal Multi-scale Model of the Human Pancreatic β Cell. Cell 173, 11-19 (2018).

Please sign in or register for FREE

If you are a registered user on Research Communities by Springer Nature, please sign in

Follow the Topic

Cancer Biology
Life Sciences > Biological Sciences > Cancer Biology

Related Collections

With collections, you can get published faster and increase your visibility.

Neurological disorders as a window into cognitive function

This cross-journal Collection shines a spotlight on research exploring neural mechanisms underlying cognitive functions in people affected by neurological conditions.

Publishing Model: Open Access

Deadline: Jan 31, 2025

Artificial intelligence in genomics

Communications Biology, Nature Communications and Scientific Reports welcome submissions that showcase how artificial intelligence can be used to improve our understanding of the genetic basis for complex traits or diseases.

Publishing Model: Open Access

Deadline: Jan 12, 2025