Bio

Dr. Michaëlle N. Mayalu is an Assistant Professor of Mechanical Engineering. She received her Ph.D., M.S., and B.S., degrees in Mechanical Engineering at the Massachusetts Institute of Technology. She was a postdoctoral scholar at the California Institute of Technology in the Computing and Mathematical Sciences Department. She was a 2017 California Alliance Postdoctoral Fellowship Program recipient and a 2019 Burroughs Wellcome Fund Postdoctoral Enrichment Program award recipient. She is also a 2023 Hypothesis Fund Grantee.

Dr. Michaëlle N. Mayalu's area of expertise is in mathematical modeling and control theory of synthetic biological and biomedical systems. She is interested in the development of control theoretic tools for understanding, controlling, and predicting biological function at the molecular, cellular, and organismal levels to optimize therapeutic intervention.

She is the director of the Mayalu Lab whose research objective is to investigate how to optimize biomedical therapeutic designs using theoretical and computational approaches coupled with experiments. Initial project concepts include: i) theoretical and experimental design of bacterial "microrobots" for preemptive and targeted therapeutic intervention, ii) system-level multi-scale modeling of gut associated skin disorders for virtual evaluation and optimization of therapy, iii) theoretical and experimental design of "microrobotic" swarms of engineered bacteria with sophisticated centralized and decentralized control schemes to explore possible mechanisms of pattern formation. The experimental projects in the Mayalu Lab utilize established techniques borrowed from the field of synthetic biology to develop synthetic genetic circuits in E. coli to make bacterial "microrobots". Ultimately the Mayalu Lab aims to develop accurate and efficient modeling frameworks that incorporate computation, dynamical systems, and control theory that will become more widespread and impactful in the design of electro-mechanical and biological therapeutic machines.

Academic Appointments

Honors & Awards

  • Hypothesis Fund Grantee, Hypothesis Fund (2023-2024)
  • Postdoctoral Enrichment Program Transition to Faculty Award, Burroughs Wellcome Fund (2022 - 2023)
  • Gabilan Faculty Fellow, Stanford University (2021 - 2024)
  • Terman Faculty Fellow, Stanford University (2022)
  • Postdoctoral Enrichment Program Award, Burroughs Wellcome Fund (2019 - 2021)
  • California Alliance Postdoctoral Fellowship Award, The California Alliance (2017 - 2019)

Professional Education

  • Postdoctoral Scholar, Caltech, Computing and Mathematical Sciences (2022)
  • PhD, Massachusetts Institute of Technology, Mechanical Engineering (2017)
  • MS, Massachusetts Institute of Technology, Mechanical Engineering (2012)
  • BS, Massachusetts institute of technology, Mechanical Engineering (2010)

Contact

  • Academic
    mmayalu@stanford.edu
    University - Faculty Department: Mechanical Engineering Position: Asst Professor

Additional Info

Links

2024-25 Courses

Stanford Advisees

Graduate and Fellowship Programs

All Publications

  • Biomolecular Control Circuit With Inherent Bi-Stability Is Applicable for Automatic Detection of Gut Infection IEEE CONTROL SYSTEMS LETTERS Huang, Y., Mayalu, M. N. 2023; 7: 2251-2256

    View details for DOI 10.1109/LCSYS.2023.3285766

    View details for Web of Science ID 001024195800004

  • Synthetic mammalian signaling circuits for robust cell population control. Cell Ma, Y., Budde, M. W., Mayalu, M. N., Zhu, J., Lu, A. C., Murray, R. M., Elowitz, M. B. 2022

    Abstract

    In multicellular organisms, cells actively sense and control their own population density. Synthetic mammalian quorum-sensing circuits could provide insight into principles of population control and extend cell therapies. However, a key challenge is reducing their inherent sensitivity to "cheater" mutations that evade control. Here, we repurposed the plant hormone auxin to enable orthogonal mammalian cell-cell communication and quorum sensing. We designed a paradoxical population control circuit, termed "Paradaux," in which auxin stimulates and inhibits net cell growth at different concentrations. This circuit limited population size over extended timescales of up to 42days of continuous culture. By contrast, when operating in a non-paradoxical regime, population control became more susceptible to mutational escape. These results establish auxin as a versatile "private" communication system and demonstrate that paradoxical circuit architectures can provide robust population control.

    View details for DOI 10.1016/j.cell.2022.01.026

    View details for PubMedID 35235768

  • Systems Level Model of Dietary Effects on Cognition via the Microbiome-Gut-Brain Axis European Control Conference (ECC) Mayalu, M. N., Sarma, A., Xiao, F., Doyle, J. ., Murray, R. . 2021: 312-318

    View details for DOI 10.23919/ECC54610.2021.9655216

  • Multi-cell ECM compaction is predictable via superposition of nonlinear cell dynamics linearized in augmented state space. PLoS computational biology Mayalu, M. N., Kim, M. C., Asada, H. H. 2019; 15 (9): e1006798

    Abstract

    Cells interacting through an extracellular matrix (ECM) exhibit emergent behaviors resulting from collective intercellular interaction. In wound healing and tissue development, characteristic compaction of ECM gel is induced by multiple cells that generate tensions in the ECM fibers and coordinate their actions with other cells. Computational prediction of collective cell-ECM interaction based on first principles is highly complex especially as the number of cells increase. Here, we introduce a computationally-efficient method for predicting nonlinear behaviors of multiple cells interacting mechanically through a 3-D ECM fiber network. The key enabling technique is superposition of single cell computational models to predict multicellular behaviors. While cell-ECM interactions are highly nonlinear, they can be linearized accurately with a unique method, termed Dual-Faceted Linearization. This method recasts the original nonlinear dynamics in an augmented space where the system behaves more linearly. The independent state variables are augmented by combining auxiliary variables that inform nonlinear elements involved in the system. This computational method involves a) expressing the original nonlinear state equations with two sets of linear dynamic equations b) reducing the order of the augmented linear system via principal component analysis and c) superposing individual single cell-ECM dynamics to predict collective behaviors of multiple cells. The method is computationally efficient compared to original nonlinear dynamic simulation and accurate compared to traditional Taylor expansion linearization. Furthermore, we reproduce reported experimental results of multi-cell induced ECM compaction.

    View details for DOI 10.1371/journal.pcbi.1006798

    View details for PubMedID 31539369

    View details for PubMedCentralID PMC6774565

  • Model of Paradoxical Signaling Regulated T-Cell Population Control for Design of Synthetic Circuits 18th European Control Conference (ECC) Mayalu, M. N., Mehta, H., Murray, R. . 2019: 2152-2158

    View details for DOI 10.23919/ECC.2019.8795764

  • Modeling of Collective Cell Behaviors Interacting with Extracellular Matrix Using Dual Faceted Linearization ASME Dynamic Systems and Control Conference Mayalu, M. N., Kim, ., Asada, H. 2018

    View details for DOI 10.1115/DSCC2018-9164

  • Latent space superposition of multiple solutions to predict emergent behaviors of nonlinear cellular systems American Control Conference (ACC) Mayalu, M. N., Asada, H., Kim, M. 2017: 2146-2151

    View details for DOI 10.23919/ACC.2017.7963270

https://orcid.org/0000-0002-9678-0157