Funds from this grant support a project by Michael Murrell and Enrique De La Cruz, Professors of Biomedical Engineering & Physics, and Biophysics & Biochemistry respectively at Yale, to explore the role that thermodynamics plays in driving cell division. Murrell and De La Cruz hypothesize that the behavior of a cell’s actomyosin cytoskeleton, a ring-shaped network of filaments, motor proteins and connectors that contract to pinch a cell in two, is shaped by thermodynamic principles, and that contraction of the network can be explained by reference to the fact that contracting and dividing would move the skeletal network into a more energetically favorable state. Murrell and De La Cruz will leverage a ‘reconstituted’ actomyosin cytoskeletal network comprised of purified and synthesized cell components which will allow them to study the system’s properties and behaviors outside the complicating environment of a cell. The team will develop new measurement techniques to measure and quantify key thermodynamic parameters of this system: how much entropy is produced, the energy input to the system, the energy output as mechanical work, and the energy lost as heat and validate these measurements to ensure they yield consistent findings (i.e. no missing energy). The team will then apply these techniques to various configurations of the system, measuring how efficiency varies with system structure, composition, and dynamics. They will then insert the artificial network into a cell-sized lipid membranes to measure how these thermodynamic properties vary during the various stages of an actual process of membrane division. This will allow them to test whether ring formation and contraction in a cell-like geometry is energetically favorable compared to a non-contracting steady state.  The proposed experiments will quantify the thermodynamics of the actomyosin cytoskeletal system at the heart of cell division, and in doing so make an important contribution to an emerging body of knowledge about cellular thermodynamics.

Funds from this grant support research by Patrick Oakes and Jordan Beach, both Professors in the Department of Cell and Molecular Physiology at Loyola University Chicago, to better understand how a cell’s energy supply is linked to the mechanical forces it generates—an important gap in our understanding of how cells regulate energy resources to coordinate basic functions such as movement, division, and changes in shape. Oakes, Beach, and their team will use Sloan funding to measure how cellular ATP levels (the primary form of usable energy in cells) relate to force generation by the actomyosin cytoskeleton, an intracellular protein network that drives contraction and is a key player in cell division and motion. The work will be done using live cells, with experiments that both increase and decrease ATP  availability to see how the actomyosin skeleton responds, as well as with experiments that stimulate cytoskeletal activity to see how cellular ATP levels and other major energy-consuming processes respond. The project will also examine these relationships at finer spatial scales inside cells. Using imaging-based metabolic sensors and force-measurement methods, the research team will map and quantify where ATP is higher or lower inside the cell and compare those patterns with where contractile forces are generated. They will also field a series of experiments where ATP levels are manipulated in localized regions while observing the behavior of the corresponding section of the cytoskeletal network. If successful, the project will produce quantitative measurements describing how cellular energy availability and mechanical force generation influence one another at both whole-cell and subcellular scales, along with datasets and analysis that can help clarify how cells regulate mechanical behavior.

This grant supports experiments to assess whether “xenobiotic nucleic acids” (XNAs)—DNA/RNA-like polymers not found in nature—could serve as alternative carriers of genetic information, with implications for understanding possible early-life chemistries and for building simplified synthetic cells. A team led by Lijun Zhou at the University of Pennsylvania will use Sloan funding to characterize how efficiently and how accurately a specific class of XNA polymers (NP-DNA and NP-RNA) can be copied from a template without the use of enzymes. The work will measure copying speed and error rates across a diverse range of XNA sequences and varying environmental conditions (such as pH, temperature, and ion concentrations). The team will also investigate how the addition of reactivity-enhancing biomolecules affects copying speed and fidelity and whether and how genetic information could be transferred between the two types of polymers. In addition, the project team will run laboratory evolution experiments to determine whether these XNAs can undergo Darwinian evolution to expand their functionality, focusing on evolving XNA sequences that can catalyze useful reactions, such as joining short XNA strands together.

This grant provides support for the Build-A-Cell research coordination network, a collaborative, international community of more than 100 scientists drawn from about 100 labs across the globe whose work focuses on building synthetic cells. The Build-A-Cell network will use Sloan grant funding to run two in-person workshops each year and to support ongoing working groups that collaborate between workshops. The workshops are designed as hands-on working meetings that bring participants together to exchange methods, compare results, identify shared technical challenges, and coordinate community-led projects. The working groups will pursue targeted objectives across areas such as modeling, integration of cell components, education and outreach, biosafety and security, international engagement and policy, and biomanufacturing. Sloan funds will be used to support the logistics needed to sustain these activities, including travel support for workshop participation and partial support for a coordinator who will organize meetings and help keep working groups moving.

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.