This grant funds an ambitious plan by an international collaboration of six laboratories to achieve a milestone that science, and humanity more generally, has imagined for quite some time: building some version of life from scratch. That claim must be qualified by cautions that the effort may not succeed and by clarification that the proposed entity is probably better viewed as a minimal form of life rather than as a (much more complex) natural cell. To be clear about the version of life herein proposed, the goal of this project is to design and build a protocell from nonliving chemicals that is capable of indefinite cycles of genetic replication, growth, and division, and which—over generations—exhibits environmentally driven Darwinian evolution.The project team is led by Jack Szostak, Professor of Chemistry at the University of Chicago and includes researchers Irene Chen (UC Santa Barbara. U.S.), Sheref Mansy (University of Alberta, Canada), Arvind Murugan (University of Chicago, U.S.), John Sutherland (Medical Research Council, United Kingdom), and Anna Wang (University of New South Wales, Australia).Project activities will consist of laboratory experiments guided by theory and computation, organized along three primary research thrusts.First, the team will conduct research to achieve indefinite cycles of RNA replication by achieving high-fidelity copying of the entire RNA-based genome. The two major components of this first thrust are optimizing the chemistry for copying a given gene sequence from a template and ensuring that the entire genome is copied. The second research thrust focuses on achieving indefinite cycles of cell growth and division. Here the primary challenge is understanding and controlling membrane growth and division, and the team will experiment with several different approaches to using fatty-acid vesicles as the primary protocell container. In the third research thrust, the research team will address issues associated with making the genetic and compartmentalization systems mutually compatible. After these major goals are achieved, the team will then observe this primitive living system over several generations as it increases in complexity, adapts and evolves, and as its genome grows to encode more information about the world.

Understanding how cells cooperate to achieve new capabilities is important since cooperation underlies the transition from single-cell life to multicellularity. This grant supports a series of experiments by Karine Gibbs, a Professor of Biology at the University of California, Berkeley, aimed at improving our understanding of the mechanisms that facilitate collective migration (swarming behavior) of the model bacterial organism Proteus mirabilis (P. mirabilis). Understanding the swarming behavior of P. mirabilis may eventually reveal fundamental principles for forming and possibly manipulating biological collectives.Professor Gibbs' work focuses on studying the role kin-recognition plays in P. mirabilis swarming behavior. Kin-recognition is a form of intercellular communication that relies on direct cell-to-cell contact. A filament carrying a toxin at its tip extends through the membrane of a P. mirabilis bacterium and punctures a neighbor's membrane, thereby delivering the toxin to the neighbor. If this neighbor-cell is genetically the same as the 'attacking' cell (i.e. it's a relative, kin) then the neighbor cell encodes the toxin as well as the antidote and the cell lives. If, however, the neighbor is not kin, then it does not encode the toxin nor the antidote and the neighbor dies.Prior studies by Professor Gibbs suggest that kin-recognition plays a role in P. mirabilis swarming and here she will use this model organism to study how collective migration is modified by two loosely-related mechanisms: intentional mutations affecting kin-recognition, and random mutations acted on by fitness-guided selection. Gibbs will leverage her prior work that correlates growth conditions with bacterial colonies exhibiting different levels of collectivity. Specifically, by varying bacterial growth conditions Gibbs is able to create two types of bacterial communities corresponding to fast/cooperative collective migration on the one hand and slow/independent migration on the other. Professor Gibbs will use her recipes for creating fast and slow swarming communities to pursue a research project with three aims.Under Aim 1, she will develop quantitative, physical metrics that can be used to distinguish fast-swarming colonies from slow-swarming colonies. Experiments involving microscopy and subsequent image-analysis will establish quantitative descriptors of cell morphology, physiology, and motility for both single-cells and for the cell-groups observed to facilitate fast swarming. Quantitative descriptors will be developed both for single-cells and for cell-groups in both fast and slow colonies. Potential descriptors include cell area, curvature, instantaneous speed, location and trajectory, adjacency to other cells, and the number of interactions over time, among others.Under Aim 2, Gibbs will use the quantitative descriptors developed in Aim 1 to assess whether and how mutations affecting kin-recognition influence collective migration. Experiments will be performed for two 'environments': growth conditions that enable fast/cooperative swarming and growth conditions that inhibit fast/cooperative swarming (thereby favoring slow/independent migration). The experimental approach involves comparing 'regular', unmutated P. mirabilis to P. mirabilis strains with mutations that disrupt the toxin secretion system used in kin-selection. This will allow Gibbs to test the hypothesis that kin-recognition provides a fitness advantage in environments where efficient swarming is possible but not in environments where independent behaviors dominate (efficient swarming not possible).Under Aim 3, Professor Gibbs will perform experiments intended to identify genes that promote collective fitness; specifically, the ability to participate in efficient swarming. Random mutations arise at some frequency and if you start with a mutant P. mirabilis that can grow but not swarm, the bacterial population will increase (growth) but will largely be constrained to its initial location (no swarming migration). As the number of bacteria increases, eventually a mutant cell will arise that has acquired a mutation that restores the ability to swarm. This mutant will swarm away from the static population, identifying itself by spatial separation and thereby allowing experimenters to capture and genetically sequence this mutant. The researchers will then determine the genome-location of the swarm-enabling mutation as well as the alterations to the descriptors of collective vs. independent behavior.

Research over the past few decades has revealed that many cellular processes require levels of accuracy and sensitivity that cannot be reached near equilibrium; living systems must exist far from thermodynamic equilibrium (TDE) in order to achieve the levels of sensitivity that allow them to survive.This grant supports research by Jordon Horowitz, Assistant Professor of Biophysics and Complex Systems at the University of Michigan, to improve our understand of living systems by studying the biochemical networks active within cells through the lens of thermodynamics. Horowitz will study broad classes of biochemical models in an effort to establish quantitative trade-offs (inequalities) between the maximum achievable sensitivity of any biochemical process and the thermodynamics driving that process. If successful, this line of research will improve our understanding of the factors constraining biological function while also revealing how closely living systems operate from the maximum achievable biochemical sensitivity. 'Sensitivity' here is being used as a flexible term intended to capture a range of bio-performance metrics such as the ability to discriminate between binding to chemical A vs chemical B, or the ability to determine whether there are few or many food molecules in the local environment.Horowitz’s research is divided into three sequential aims. In Aim 1, he will perform numerical analyses of comparatively simple models to gain insight into how network structure and thermodynamics constrain performance in simple biochemical networks. These models are too simple to represent actual cellular processes yet simple enough that numerical analysis of the available phase space is practical. Simulations will be used to study networks with varying topological structure and thermodynamic driving force in order to determine the maximum possible sensitivity, along with the model parameters that achieve that sensitivity. These numerical findings will be captured in a 'library of kinetic networks' classified by sensitivity, network topology, and thermodynamics. In Aim 2, Horowitz will attempt to use graphical methods and the Matrix Tree Theorem to mathematically (analytically) prove that these discovered limitations are in fact rigorous bounds. If successful, the result will be a set of mathematical inequalities that quantify fundamental limitations on sensitivity imposed by network structure and thermodynamic drive.In Aim 3, Horowitz will attempt to expand and apply these findings to more complicated models that have been developed to capture actual cellular process, including generalized ‘butterfly’ networks, the ‘ladder’ model of adaptation, and a generalized bacterial-flagellar-motor model.

Whole Cell Models (WCMs) provide a useful platform for understanding how a holistic organism emerges from many distinct yet coupled processes. WCMs developed to date, however, are primarily biochemical/kinetic models that don't explicitly account for physical and spatial cell processes. This grant supports a project by Roseanna Zia, Associate Professor of Chemical Engineering at Stanford University, to fill this gap through developing a more biophysically-focused whole cell model. Such a model would differ from a kinetic model by, for instance, explicitly tracking important biomolecules as they execute Brownian motion in a crowded cellular environment; one where molecular motion is influenced by hydrodynamic forces within a viscous cellular fluid and where interactions between important molecules are explicitly accounted for via measured and/or computed atomic-scale bio-molecular structure.Specifically, Professor Zia will build a physically- and biochemically-resolved model of the JCVI-syn3A minimal cell (henceforth, the "minimal cell"). The minimal cell is a synthetic version of a bacterium created at the J. Craig Venter Institute. Starting with a bacterium having a small genome (M. genitalium, 525 genes), Venter researchers repeatedly grew the bacterium, each time removing one gene to determine if that gene is essential to life. If the bacterium can—absent a given gene—grow, replicate, and divide to make offspring, then the gene was not essential to life. The minimal cell (493 genes) is the cell remaining once all non-essential genes have been deleted from the original genome.While kinetic WCMs seek to unify the relevant collective biological knowledge by assembling many different models and associated datasets, the approach proposed by Professor Zia is closer to a first-principles approach to modeling. It's more geared to simulating basic physical and chemical interactions between bio-molecules using a limited set of input data. By accounting for physical and chemical interactions between bio-molecules, a physical model could predict many of the chemical reaction rates that would instead be inputs to a kinetic model. One significant benefit of a physical model is that it's better positioned to discover cellular phenomena. For instance, while gene functions are 'hard-wired' into kinetic models, physical models should be able to discover the function(s) of various genes by accounting for the proteins encoded by the genes and then studying what those proteins do in the in silico cell.Professor Zia will pursue a multi-scale modeling approach that strikes a compromise between computationally expensive modeling that is accurate on an atomic-scale but can only simulate nanoseconds of cell life, and systems-level modeling that sacrifices atomic-scale accuracy but can simulate cell processes over minutes at a time.The model will consist of three basic elements: a confining container (cell membrane); individual representation of the physical shape, size, and relative abundance of biomolecules; and accurate, computationally efficient representation of biochemical and physical interactions between biomolecules. Zia will pursue three proposal aims to develop the model. Under Aim 1 she will specify what's in the cell and where it's located; specify the interactions and transport properties of bio-components; and benchmark the model against experimental data. Under Aim 2, she will make a list of proteins and other molecules whose atomic-scale details (physical structure and surface charge) are explicitly taken into account in the model. Under Aim 3, Zia will use the model to address several open questions in cellular biology that explore various mechanisms by which physical processes influence biological function.

Agency, defined here as purposeful or goal-oriented behavior, is often framed as a distinctive—perhaps even defining—feature of life. Beings have an agenda—whether it be finding food, reproducing, or avoiding environmental danger—and it's difficult to understand how a living creature could consist of inanimate matter passively following physical laws and yet also exhibit behaviors that seem—at least from the outside—to be purposeful. Improving our understanding of how agency emerges in a matter system will advance our understanding of how matter transitions to life. Gaining a scientific foothold on agency, however, is not so simple. One needs a living system that resides in the 'Goldilocks Zone' of complexity: complex enough to exhibit agency and yet simple enough that there's reasonable hope of achieving a mechanistic understanding of the processes underlying behavior. Philip Kurian, a theoretical physicist and founding director of the Quantum Biology Laboratory at Howard University, and Michael Levin, Distinguished Professor of Biology and director of the Tufts Center for Regenerative and Developmental Biology, propose Physarum polycephalum as such a system. Physarum polycephalum, henceforth Physarum, is a multinucleate slime mold with the remarkable ability to reassemble into a single organism after being broken into several fragments. Physarum can also "mass-sense", detect and grow towards the heaviest mass in its local environment. This grant supports research by Kurian and Levin to try to gain a scientific foothold on agency through the study of reassembly and mass-sensing in Physarum. Physarum is a good choice for this project because it is a simple cellular organism with no brain to decode; its behaviors are simple motions whose direct mechanistic causes can plausibly be determined. The project has two related goals: obtaining a corpus of novel data that informs the development of behavior-explaining, predictive models of mass sensing and reassembly in Physarum, and testing a hypothesis about how Physarum coordinates these behaviors—Kurian and Levin speculate they are controlled via superradiance in Physarum's cytoskeletal network. The overall plan is to develop a multi-scale (molecular- to cell-scale) model while also performing experiments to characterize Physarum during its search and mass-sensing activities. Dr. Kurian proposes to refine his existing cytoskeletal-superradiance model to better capture Physarum dynamics; in part by adding additional cytoskeletal components (actin & actomyosin) and in part via iteration of experiment and theory-simulation to pin down various model parameters and to benchmark model predictions against observations. The new model will also account for ultraweak photon emission, weak light associated with the metabolic production of reactive oxygen species. Microscopy imaging will then be used to create spatially- and temporally-resolved 'maps' that characterize Physarum during its reassembly and mass-sensing behaviors. They hypothesize that problem-relevant information is stored in cytoskeletal structure and read out by Physarum using a combination of bioelectric, optical, and calcium-signaling transduction mechanisms. These 'maps' will then be used as a way for researchers to access some of the information thought to be informing Physarum's decision-making processes. They will also use a novel Ultraweak Photon Emission (UPE) detector in order to track UPE as an indicator of metabolic activity. For more information visit: https://www.quantumbiolab.com/

One challenge associated with building a life-like synthetic cell lies in how to power synthetic cell processes. A common mechanism used by natural cells to energize cell activities is the exploitation of an electrochemical gradient across a membrane. This grant funds a project led by Neal Devaraj at the University of California, San Diego to build an artificial system that couples the formation and maintenance of a pH gradient with anabolic chemistry to establish a primitive metabolism in a synthetic cell. Devaraj and his team will use photoacids encapsulated in lipid vesicles as the primitive protocells of this project. The photoacids release hydrogen ions (protons) when exposed to light, leading to an excess of protons within the vesicle. Reagent molecules will diffuse into the vesicle from the surrounding “environment” and become trapped as they acquire an electrical charge by bonding with hydrogen ions inside the proton-rich vesicle. These charged molecules will accumulate within the vesicle due to so-called ion-trapping, a phenomenon whereby a membrane that is permeable to neutral molecules becomes impermeable to charged molecules. The concentrating of reagent molecules within the vesicle will then stimulate an anabolic reaction: synthesis of phospholipids. The synthesized phospholipid molecules are building blocks for cell membranes and developing ways to synthesize these building blocks could prove useful for future efforts to build artificial cells that must be able to grow and divide. Devaraj expects that the synthesized lipids will—driven by hydrophobic interactions—associate into and thereby modify the pre-existing vesicle membrane. Microscopy will be used to characterize changes in membrane morphology, changes that could include membrane growth and division.