About

Cole Mathis is an assistant professor in the School of Complex Adaptive Systems and in the Biodesign Center for Biocomputation, Security and Society at Arizona State University. He is a physicist and astrobiologist whose research focuses on the origin and nature of life on Earth as well as the possibility of life beyond our planet. His work aims to connect theoretical concepts with experiments and empirical validation, with the ultimate goal of helping create de novo life forms in the laboratory. Cole Mathis holds a PhD from Arizona State University and is affiliated with the Biodesign Center for Biocomputation, Security and Society as well as the Global Futures Scientists and Scholars group.

Research topics

• Computer Science
• Data Mining
• Stereochemistry
• Mathematics
• Organic chemistry
• Combinatorics
• Biology
• Astrobiology
• Bioinformatics
• Inorganic chemistry
• Theoretical computer science
• Crystallography
• Chemistry
• Algorithm
• Physics

Selected publications

• Better origin(s) of life research through collaboration across disciplines

  Cell Reports Physical Science · 2026-04-01

  articleOpen access

  Understanding the origins of life has inspired scientists from disciplines including evolutionary biology, organic chemistry, theoretical physics, and beyond. In this issue of <i>Cell Reports Physical Science</i>, authors of a two-part review by the Origins of Life Early-Career Network and advisory board member Sudha Rajamani reflect on the importance of interdisciplinary collaboration in origins research.

  PublisherDOI

• Elemental Stoichiometry as an Ecological Biosignature with Applications to Life Detection

  ArXiv.org · 2026-05-19

  articleOpen access

  The vast chemical space of possible small molecules, estimated at 10^60 compounds for molecules composed of just C, N, O, and S, is only sparsely occupied by biology. We propose that where life selects molecules within this space constitutes a detectable ecological signature: a fingerprint not of specific compounds, but of the statistical structure of elemental composition across molecules sam-pled from ecological systems. Here we introduce a framework combining Van Krevelen diagrams and element scaling laws to characterize the elemental composition of regions of chemical space occupied by biological systems and contrast them with other chemical systems. Applying this framework to 11,834 microbial metagenomic samples, we show that microbial metabolisms occupy a region of chemical space, which is enriched in heteroatoms such as P, S, N, and O relative to C, shifted toward higher O:C and H:C ratios. We observe sublinear element scaling with system size, yielding insights into how elemental constraints dictate how biological systems occupy chemical space. These patterns are distinct from a sample of 18,000 compounds from the comprehensive Reaxys synthetic chemical database. Critically, datasets from molecules detected in planetary science mission data occupy statistically distinct regions from both terrestrial biological and Reaxys distributions, demonstrating that with standardized methods for data collection, the approach could be developed to discriminate biotic from abiotic chemical signatures in small molecule data from planetary science missions. Our work shows how a combination of Van Krevelen fingerprinting and elemental scaling laws can provide a new class of ecological biosignatures for life detection leveraging mass spectrometric data from planetary missions, which could generalize beyond Earth's specific biochemistry.

  PublisherOA PDF

• What it takes to solve the origins of life: An integrated review. Part 2: Theoretical methods and emerging trends

  Cell Reports Physical Science · 2026-04-01

  articleOpen access

  <h2>Summary</h2> The origin(s) of life (OoL), which has puzzled scientists for centuries, remains a major scientific challenge in the 21st century. Understanding the processes relevant to the OoL demands theoretical frameworks that can connect processes across scales, from microscopic dynamics to emergent levels of organization. While experimental studies generate a wealth of data, theoretical and computational approaches provide the structure necessary to interpret and generalize these findings. In Part 1, we examined the most widely used experimental techniques in the field. Here, we focus on the mathematical, physical, and computational techniques used to model phenomena relevant to life's origin(s). We discuss methods ranging from quantum chemistry and molecular dynamics to chemical reaction networks, autocatalysis, and evolutionary modeling, as well as information-theoretic and phylogenetic approaches that link chemical and biological organization. We further highlight emerging trends such as synthetic biology, omics-based methods, and laboratory automation as novel points of contact for theory-experiment integration. Ultimately, we aim to provide an educational tool that can facilitate more post-disciplinary collaborations in OoL research by helping scientists understand what they can do about the problem of life's origins, rather than telling them how to think about it.

  PublisherDOI

• assembly-theory: Open, Reproducible Calculation of Assembly Indices

  The Journal of Open Source Software · 2026-01-06

  articleOpen accessSenior author

  We present assembly-theory, an open-source, high-performance library for computing assembly indices of covalently bonded molecular structures.This is a key complexity measure of assembly theory, a recent theoretical framework quantifying evolutionary selection across chemical, biological, and engineered systems.assembly-theory is designed for researchers and practitioners alike, providing (i) extensible, high-performance Rust implementations of assembly index calculation algorithms, (ii) comprehensive tests and benchmarks against which current and future algorithmic improvements can be evaluated, and (iii) Python bindings to support integration with existing computational pipelines.

  PublisherOA PDFDOI

• Elemental Stoichiometry as an Ecological Biosignature with Applications to Life Detection

  arXiv (Cornell University) · 2026-05-19

  preprintOpen access

  The vast chemical space of possible small molecules, estimated at 10^60 compounds for molecules composed of just C, N, O, and S, is only sparsely occupied by biology. We propose that where life selects molecules within this space constitutes a detectable ecological signature: a fingerprint not of specific compounds, but of the statistical structure of elemental composition across molecules sam-pled from ecological systems. Here we introduce a framework combining Van Krevelen diagrams and element scaling laws to characterize the elemental composition of regions of chemical space occupied by biological systems and contrast them with other chemical systems. Applying this framework to 11,834 microbial metagenomic samples, we show that microbial metabolisms occupy a region of chemical space, which is enriched in heteroatoms such as P, S, N, and O relative to C, shifted toward higher O:C and H:C ratios. We observe sublinear element scaling with system size, yielding insights into how elemental constraints dictate how biological systems occupy chemical space. These patterns are distinct from a sample of 18,000 compounds from the comprehensive Reaxys synthetic chemical database. Critically, datasets from molecules detected in planetary science mission data occupy statistically distinct regions from both terrestrial biological and Reaxys distributions, demonstrating that with standardized methods for data collection, the approach could be developed to discriminate biotic from abiotic chemical signatures in small molecule data from planetary science missions. Our work shows how a combination of Van Krevelen fingerprinting and elemental scaling laws can provide a new class of ecological biosignatures for life detection leveraging mass spectrometric data from planetary missions, which could generalize beyond Earth's specific biochemistry.

  PublisherDOI

• assembly-theory: Open, Reproducible Calculation of Assembly Indices

  Zenodo (CERN European Organization for Nuclear Research) · 2026-02-05

  otherOpen accessSenior author

  This minor release implements timeout functionality, references our new JOSS publication in the documentation, and fixes some GitHub Actions infrastructure. Changelog :rotating_light: Breaking Changes As part of implementing timeout functionality across all interfaces (by @jdaymude in https://github.com/DaymudeLab/assembly-theory/pull/133), there are two breaking changes: Adds a new --timeout flag for the CLI, timeout parameter for assembly::index_search in the Rust crate, and timeout parameter for assembly_theory.index_search in the Python package. Assembly index search runs normally if timeout == None and otherwise stops after timeout milliseconds, returning the best upper bound on the assembly index found so far instead. The assembly::index_search function and its Python counterpart now return an Option<usize> for the number of states searched instead of a straight usize. It is either the number of states searched or None if search times out. :memo: JOSS Publication Update READMEs and documentation to reflect new publications by @jdaymude in https://github.com/DaymudeLab/assembly-theory/pull/136 :robot: GitHub Actions Update deprecated macOS 13 GitHub Actions runner by @jdaymude in https://github.com/DaymudeLab/assembly-theory/pull/134 build: only run actions jobs on semver tags. by @AgentElement in https://github.com/DaymudeLab/assembly-theory/pull/138 Full Changelog: https://github.com/DaymudeLab/assembly-theory/compare/v0.6.0...v0.6.1

  PublisherDOI

• What it takes to solve the origin of life: An integrated review. Part 1–Experimental methods and data repositories

  Cell Reports Physical Science · 2026-04-01

  articleOpen access

  <h2>Summary</h2> The origin(s) of life (OoL), which has puzzled scientists for centuries, remains a major scientific challenge in the 21st century. Research on OoL spans many disciplines, including chemistry, physics, biology, planetary sciences, computer science, and mathematics. The sheer number of different scientific perspectives relevant to the problem has resulted in the coexistence of diverse tools, techniques, data, and software in OoL studies. This has made communication between the disciplines relevant to the OoL extremely difficult because the interpretation of data, analyses, or standards of evidence varies dramatically. Here, we hope to bridge this wide field of study by providing common ground via the consolidation of techniques rather than positing a unifying view on how life emerges. In part 1 of this review, we cover common experimental techniques that have been used significantly in OoL studies in recent years, while in part 2, we review theoretical, computational, and integrative methods. Here, we discuss the use of spectroscopy, spectrometry, chromatography, microscopy, and sequencing methods for characterizing diverse materials. We further discuss the role of data repositories in facilitating the analysis and dissemination of experimental data. This review provides a baseline expectation and understanding of the analytical aspects of origins' research. Ultimately, we aim to provide an educational tool that can facilitate more post-disciplinary collaborations in OoL research by helping scientists understand what they can do about the problem of life's origins, rather than telling them how to think about it.

  PublisherDOI

• Historical Contingencies Steer the Topology of Randomly Assembled Graphs

  ArXiv.org · 2025-07-01

  preprintOpen access1st authorCorresponding

  Graphs are used to represent and analyze data in domains as diverse as physics, biology, chemistry, planetary science, and the social sciences. Across domains, random graph models relate generative processes to expected graph properties, and allow for sampling from distinct ensembles. Here we introduce a new random graph model, inspired by assembly theory, and characterize the graphs it generates. We show that graphs generated using our method represent a diverse ensemble, characterized by a broad range of summary statistics, unexpected even in graphs with identical degree sequences. Finally we demonstrate that the distinct properties of these graphs are enabled by historical contingencies during the generative process. These results lay the foundation for further development of novel sampling methods based on assembly theory with applications to drug discovery and materials science.

  PublisherOA PDFDOI

• Spatial Patterning and Selection: How the Environment Shapes Molecular Complexity

  ArXiv.org · 2025-09-04

  preprintOpen accessSenior author

  Assembly theory predicts that a distinguishing signature of life is its ability to produce complex molecules in abundance, opening new possibilities for life detection. Experimental validation of this approach has so far relied on abiotic controls like meteoritic material, or simple, well-mixed chemical systems. However, decades of research in self-organization have shown that spatial patterning can foster dynamical self-organization. This raises the possibility that systems with nontrivial spatial patterns might promote abiotic formation of molecules with higher than expected assembly indices, potentially leading to false positives in life detection approaches based on assembly theory. To explore this, we used a model of artificial chemistry to investigate how spatial organization can influence the development of molecular assembly indices. Our findings reveal that transport factors, such as diffusion, significantly affect the distribution of chemical species within a system. Additionally, system topology critically impacts the distribution of assembly indices: while it does not enable arbitrary complexity, ordered lattices can shift the threshold for abiotic chemistry upward. Moreover, we demonstrate that diffusion can impede the formation and detection of these high assembly index molecules, bearing important implications for life detection experiments and astrobiological missions.

  PublisherOA PDFDOI

• Prebiotic Functional Programs: Endogenous Selection in an Artificial Chemistry

  ALIFE · 2025-10-01

  articleOpen access

  Artificial chemistry simulations produce many intriguing emergent behaviors, but they are often difficult to steer or control. This paper proposes a method for steering the dynamics of a classic artificial chemistry model, known as AlChemy (Algorithmic Chemistry), which is based on untyped lambda calculus. Our approach leverages features that are endogenous to AlChemy without constructing an explicit external fitness function or building learning into the dynamics. We demonstrate the approach by synthesizing non-trivial lambda functions, such as Church addition and succession, from simple primitives. The results provide insight into the possibility of endogenous selection in diverse systems such as autocatalytic chemical networks and software systems.

  PublisherOA PDFDOI

Frequent coauthors

• Leroy Cronin

  25 shared

• Sara Imari Walker

  24 shared

• Harrison B. Smith

  Blue Marble Space

  14 shared

• Graham Keenan

  University of Glasgow

  7 shared

• Haralampos N. Miras

  University of Glasgow

  7 shared

• Emma Carrick

  6 shared

• Piotr S. Gromski

  University of Glasgow

  6 shared

• Marcel Swart

  Institució Catalana de Recerca i Estudis Avançats

  5 shared

Education

• Ph.D.

  Arizona State University