**I am currently a 1st-year PhD student in the Mathematical, Computational and Systems Biology (MCSB) program at the University of California, Irvine.**

My central research interest is in understanding the extent to which natural systems have complex structure and motion, and what the role of different mechanisms and conditions are in producing it. This has led me to become interested in biology, as living systems are the most complex we know of. My dream is to have a *general* and *elegant* way to understand how nature takes advantage of different properties and mechanisms to produce the complex behavior of living things. Putting together a massive web of mechanisms is one very monumental task, but how do we then make sense of it and see the forest through the trees? How do we determine what physical properties are necessary/sufficient for certain coordinated motions of matter?

More concretely, I am considering studying biological motion at the cellular or sub-cellular level. I am interested in how the properties of different atoms are exploited to create molecules with particular mechanical or chemical properties to create tiny machines (e.g., intracellular transport motors) that perform work or make decisions (e.g., ubiquitin markers). I’d like to approach these systems with mathematical physical modeling aided by computational approaches, if possible.

Some relevant fields that I have been interested in over time include: biomechanics, organic chemistry, thermodynamics and statistical mechanics, information theory, philosophy of biology, mereology, differential equations, network science/graph theory, group theory, numerical analysis.

I majored in Computer Science at Pennsylvania State University, and there I also minored in both Physics and Philosophy. In the physics department, I worked with Dr. Dezhe Jin on the voice identification problem in parakeets using ML methods. In the philosophy department, I investigated the concept of “soul” and definitions of life with Dr. Mark Sentesy. I also worked with Dr. Michael Hallquist and the DEPENd lab to develop software for pre-processing human-subject psychology study data, was a grader for an introductory computational theory course, and had a summer internship with a Lockheed Martin research group. Eventually, I took an online course from the Santa Fe Institute on complexity science, and found a question I wanted to pursue more rigorously.

I then enrolled in Binghamton University’s Systems Science program. Here, I worked with Dr. Hiroki Sayama’s research lab to produce my MS thesis on measuring the complexity of a collection of Lennard-Jones-like particles in a collective motion model and mapping this behavior to the interaction force parameters (published as [1]).

I also worked extensively with Dr. Luis Rocha’s group, the Complex Adaptive Systems and Computational Intelligence (CASCI) lab. With them, I studied symmetry in Boolean functions and its role in Boolean network models of biological regulatory networks. I also contributed to a paper examining how local measures of canalization in random and empirically-derived Boolean network models correlate with the global dynamical stability of the network (see [2]).

For more information, please take a look at my CV, or send me an email at amarcus2@uci.edu.

**Marcus, A. M.**, & Sayama, H. (2023). Effect of Physically Realistic Potential Energy Form on Spatial Pattern Complexity in a Collective Motion Model.*Complexity*,*2023*. open access link- Costa, F. X., Rozum, J. C.,
**Marcus, A. M.**, & Rocha, L. M. (2023). Effective Connectivity and Bias Entropy Improve Prediction of Dynamical Regime in Automata Networks.*Entropy*,*25*(2), 374. open access link - Cao, N.,
**Marcus, A.**, Altarawneh, L., & Kwon, S. (2023). Priority-based replenishment policy for robotic dispensing in central fill pharmacy systems: a simulation-based study.*Health Care Management Science*, 1-19. link