Whole cell models (WCMs) use theory and simulation to capture an ever-increasing set of processes and functions exhibited by natural cells. These computer-based, or in silico, cells provide a platform for eventually understanding how a holistic agent emerges from many distinct yet coupled processes. The WCMs developed to date are primarily chemical-kinetics models that accurately represent chemical reaction rates but don't explicitly account for physical processes and how they vary in space and time.  This grant provides ongoing support to Roseanna Zia, a Professor of Mechanical Engineering at the University of Missouri, for efforts to develop and expand a whole cell model that explicitly tracks biomolecules and their interactions as they diffuse through a crowded cell interior. Zia’s efforts focus on understanding ribogenesis, the process by which cells build the molecular complexes (ribosomes) responsible for protein synthesis. Zia will develop complex machine learning tools that can simulate in a computationally tractable way the complex process of ribosome formation, and then validate these tools, both against experimental data and against existing simulations that require more computational resources. Zia will leverage these computational tools to expand her model to better understand and simulate important elements of ribogenesis, such as the compaction of rRNA strands into folded, functional configurations and how this folding is affected by intra-cellular conditions like pH, cellular crowding, and protein abundance. These improvements, if successful, will allow Zia’s augmented WCM to simulate about half of ribosome synthesis, and would represent substantial progress towards the ultimate goal of modeling in silico the construction of a full ribosome.

In the search for exoplanet biosignatures, researchers have obtained an unprecedented volume of atmospheric spectra in recent years; primarily due to the James Webb Space Telescope (JWST; online since 2022), with more data expected in the coming years and decades as several planned, ground-based, large telescopes come online. The relevant signal for quantifying a possible biosignature is the atmosphere’s spectrum: the amount of light transmitted through the atmosphere, at different wavelengths, when the planet passes in between its star and Earth. By determining which wavelengths are transmitted through the atmosphere, one can, in principle, determine which molecules are present in the atmosphere and then infer if those molecules imply the presence of life on the planet. There are challenges, however, to analyzing atmospheric spectra for the presence of molecules that signal life. First, current analysis models are too slow (computationally inefficient) and therefore only able to analyze a spectrum for the presence of one or two molecules at a time; this compared to a list of about 14,000 candidate biosignature molecules. Second, the library of known/tabulated molecular spectra is small, containing data for only a few hundred of the potential 14,000 biosignature molecules. Funds from this grant support a team led by Cecilia Garraffo, Director of the AstroAI Center at the Harvard/Smithsonian Center for Astrophysics to address both of these issues. Garraffo and her team will use advanced statistical techniques to iteratively improve a widely used analysis model, called POSEIDON, so that it can progressively analyze atmospheric spectra for many molecules at a time, rising eventually to an estimated 2000. In parallel, the team will use advanced machine learning techniques to develop, train, and validate an AI tool to predict how a molecule’s characteristics determine what sort of atmospheric spectra its presence would produce, adding an estimated 2000 molecules to the library that astronomers could use spectral analysis to search for. The effort, if successful, would lead to a significant improvement in our capacity to search atmospheric spectra for signs of extrasolar life.

The progression from matter to life on Earth must have involved something akin to Darwinian evolution: molecules became increasingly more complex and functional and eventually organized themselves into self-replicating systems. To investigate the principles of chemical evolution necessary for this complexification and organization, this grant supports efforts by Wilhelm Huck, a Professor of Physical Organic Chemistry (Radboud University, NL), to develop an experimental platform capable of ‘prebiotic evolution.’ Huck intends to develop a machine-learning based and robotic ‘chemical evolution machine’ that leverages changes in the (experimental) environment to evolve chemical networks towards functions important to life. The project focuses on a prominent prebiotic chemical reaction (formose) and aims to evolve networks that achieve three goals regarded as important to the rise of life on Earth: enhanced yield of ribose (a key building block of RNA); self-catalysis (the emergence of molecules (catalysts) within a network that enhance the chemical reactivity of the network); and self-compartmentalization (the emergence of compartments that encapsulate the chemical mixture from which the compartments emerge). Huck and his team will leverage a machine-learning guided robotic system to evolve chemical networks towards targeted properties. The experimental apparatus will expose chemical systems to various conditions (reactant and catalyst choices and concentrations, variations in temperature and pH) chosen by the learning algorithms. Networks will be selected based on how closely they approximate a targeted property (ribose yield, self-catalysis, self-compartmentalization). This process will be repeated again and again, and it’s expected that the learning algorithms will become increasingly more effective at identifying conditions that lead to the targeted property. A successful project will uncover the mechanisms by which environment change nudges a formose chemical network towards functionality, while also establishing a workflow that can be used to discover how other chemical networks did or could achieve functions important to living systems.

Organisms must continuously transform molecules found in their environment in order to extract the energy and building blocks necessary for life. While the second law of thermodynamics establishes that energy loss (dissipation) is an unavoidable feature of any energy / matter conversion process, we don’t yet know how it constrains a living system like a cell. Basic questions remain unanswered: What fraction of environmental ‘input’ energy is available to drive living processes and what fraction is lost to dissipation? How does this partitioning of energy vary with the physiological state of a cell (e.g. maintenance vs growth)? How does it vary with environmental conditions? This grant provides support to a collaboration between Pablo Sartori, a Principal Investigator at the Gulbenkian Institute for Molecular Medicine (Lisbon, Portugal), and Shashi Thutupalli, a Professor at the National Center for Biological Sciences (Bangalore, India), to address these questions by developing a thermodynamic framework to describe the energy and matter transformations that drive a simple living machine (a cell); transformations that are implemented by a complex network of chemical reactions collectively known as cellular metabolism. Experiments and theory that focus on a single-cell organism (a yeast) will measure and model its uptake of energy / matter (food), as well as the fraction of this energy that’s exported as (heat or chemical entropy) disorder to the environment. Measurements will be made for a range of environmental conditions and for two physiological states of a unicellular fungus (S. cerevisiae): maintenance and growth.The team will conduct a series of experiments that can separately measure how dissipation is distributed between 1) heat exchanged between cells and their surroundings, 2) chemical (entropy) exchanges between cells and their surroundings (nutrients in, waste out), and 3) biomass growth (nutrients transformed into new cellular components). Experimentally disambiguating these three contributions to dissipation will allow the team to develop and test far-from-equilibrium thermodynamic models of the underlying physical and chemical processes. To this end, Sartori and Thutupalli will develop both macroscopic (phenomenological) thermodynamic models and microscopic models of metabolic networks. ?They then intend to demonstrate how the macroscale models arise from systematically coarse-graining (averaging) the microscopic metabolic models. A successful project will result in a validated, predictive thermodynamic model that links cellular metabolism and energy dissipation across a range of environmental and cell-physiological conditions.

All known forms of life rely on DNA as the carrier of genetic information and on proteins as the primary functional agents. The present-day molecular systems that implement DNA replication and protein synthesis are highly-evolved, and it’s reasoned that life began by leveraging much simpler systems. In the late 1960s scientists began to hypothesize that a molecule that could serve both as a carrier of information and as a catalyst that facilitates replication was critical to the emergence of life. That molecule was -and is- thought to be a version of RNA called a polymerase ribozyme, and the aforementioned hypothesis has come to be known as the RNA World Hypothesis. This polymerase ribozyme would possess the unique ability to copy RNA sequences, including its own, and thereby sustain a self-replicating, evolving system. While biology has revealed naturally occurring ribozymes (RNA molecules with the enzyme-like ability to catalyze chemical reactions), none are known to possess the ability to replicate RNA. Funds from this grant support work by Gerald Joyce, President and CEO of the Salk Institute for Biological Studies, to use directed evolution -a method that mimics natural selection- to develop a ribozyme that catalyzes its own replication and undergoes Darwinian evolution.  Directed evolution is a laboratory technique that uses cycles of mutation, selection, and amplification to evolve molecules with targeted functions. Joyce and his research team have made impressive strides using directed evolution to incrementally enhance the catalytic ability, speed, accuracy, and generality of polymerase ribozymes. Their current ribozymes can synthesize ancestral (smaller) versions of themselves and drive exponential amplification (increase the number of selected molecules), but they are not yet capable of full self-replication. A crucial requirement for evolution is, of course, the ability to accurately propagate genetically encoded information across generations. If the replication process introduces too many errors, information important to the successful propagation of an organism is lost. Joyce seeks to increase the fidelity of polymerase ribozymes to facilitate the faithful replication of longer and more information-rich RNA sequences; ultimately achieving the synthesis of an entire polymerase ribozyme itself. If successful, the  project will realize an artificial lifeform that serves as a platform for studying emergent complexity, adaptive evolution under environmental pressure, and the origins of more sophisticated genetic and metabolic networks from simpler molecular systems.

The vast majority of planets are too distant to visit, so remote observation and subsequent analysis are essential to the search for extrasolar life. Telescopes such as the James Webb Space Telescope are providing an important new opportunity to directly observe exoplanet atmospheres for signs of life, but we currently lack a quantitative framework for understanding what observations of a planet’s atmosphere provide compelling evidence for life on the underlying planet. Developing a framework that allows one to infer whether or not a planet is inhabited is a two-step process: understand atmospheres in the absence of life (abiotic baselines), and understand how life modifies an atmosphere (atmospheric biosignatures).  This grant renews support for a team of modelers and experimentalists -the AEThER collaboration (Atmospheric Empirical, Theoretical, and Experimental Research)- to tackle the former question.AEThER seeks to develop a framework to quantify the abiotic atmospheric baseline for rocky planets commonly found in our galaxy. Developing such a framework will provide a flexible tool for quantifying how different conditions driving the formation and evolution of a planet lead to different abiotic atmospheric baselines.  Funded activities under this grant include a series of experiments to broaden our understanding of how readily so-called “volatile” elements and compounds—which include nitrogen gas, oxygen gas, hydrogen gas, water, ammonia, and carbon and sulfur dioxide—dissolve into magmas and liquid metals at the high temperatures and pressures common during planetary formation and evolution. The solubility of these molecules plays a key role in determining the viscosity and possible solidification of a planet’s mantle, with significant implications for heat transfer throughout the planet and atmosphere, as well as gas release back to the atmosphere, and thus habitability. In addition and informed by this experimental work, AEThER will continue to develop their theoretical models, including modeling the impacts of atmospheric hazes (suspended small particles) on planetary evolution, which, under different conditions, can either raise or lower planetary temperatures appreciably. When completed, the funded grant work will represent a notable advance in our understanding of planetary processes, and serve as an important complement to research aimed at identifying atmospheric biosignatures. 

Identifying signs of life on another planet would rank among the top scientific discoveries in history. Two of the most daunting challenges to achieving such a discovery are developing instrumentation to study exoplanet atmospheres and developing a quantitative framework to assess whether life creates distinctive atmospheric biosignatures. Here, David Catling will address the latter challenge, aiming to develop a quantitative framework to identify atmospheric signatures of life on distant planets. While billions have been invested in telescopes capable of studying exoplanet atmospheres, we lack robust methods to interpret this data for signs of life.   The project focuses on assessing how living organisms impact a planet’s atmosphere. As such, the research team makes assumptions about life. Firstly, they assume Earth-familiar microbial life because Earth contains the only known examples of life, and because it’s thought that if life exists elsewhere in the universe, it’s more likely to be microbial rather than plant- or animal-based. Secondly, they assume redox-based metabolism would be universal to any life form because reduction / oxidation (redox) reactions are the only class of chemical reactions that release enough energy to satisfy the high energy demands of organisms. Rather than looking for individual gases that might indicate living systems, Catling proposes examining chemical disequilibrium—multiple gases coexisting that should normally react and eliminate each other—as a more reliable biosignature.   The research team will build an integrated model simulating planetary evolution from lifeless to hosting various biospheres. They'll quantify two potential biosignatures: free energy dissipation (which should increase dramatically with biological activity) and the information content of atmospheric disequilibrium. The final step involves determining which measurable gas abundances and fluxes most reliably indicate biological activity.

This grant will support Professor Zhiyue Lu to develop a theoretical framework to understand biochemical networks that operate far from equilibrium and outside steady states. Living systems exist in far-from-equilibrium states; this characteristic is one of the most striking distinctions between living and non-living systems.   Unlike non-living matter, living organisms demonstrate remarkable sensitivity and complex responses to environmental changes. While most scientific understanding focuses on near-equilibrium or steady-state systems, Lu aims to explore how biochemical networks respond to time-varying environmental conditions.   The research focuses on three key properties of living systems: sensitivity and robustness to environmental changes, ability to manipulate energy to power various processes, and capacity to extract energy from fluctuating environments. Through mathematical modeling and simulation, Lu's team will investigate how network structure influences these properties under different temporal patterns of environmental change. Specifically, they'll study how networks process complex information patterns, combine weak energy sources to power energy-intensive processes (or distribute energy from one source to multiple processes), and extract energy from environments that fluctuate at different timescales.