This grant supports Ben Machta at Yale University who will use tools from information theory and statistical physics to explore how bacteria process signals from their environment, and how they use this information to drive behavior. Machta will use bacteria to study one aspect of information processing: how noise (spurious signal accompanying the information-carrying signal) limits bacterial behavior. Specifically, Machta  will investigate how bacteria navigate their local chemical environment through chemotaxis (movement along a concentration gradient of a substance) and how they communicate with one another through quorum sensing (chemical signaling that reflects the density of nearby bacteria). Grant funds will allow Machta to determine the theoretical limit on the rate at which E. coli acquire behaviorally-relevant information (the concentration of so-called attractant molecules) and to measure this information-acquisition rate, to provide the first direct measurement of whether any organism’s biochemical sensory system approaches the performance limits imposed by the laws of physics. Additionally, Machta and colleagues will study how E. coli amplify signals without introducing noise via experiments that will test whether equilibrium or non-equilibrium models do a better job of describing chemotactic signal amplification. Finally, the researchers will use V. cholerae bacteria as a model organism to study the fidelity of information transmission as multiple signals propagate through the quorum sensing signal processing pathway. Collectively, these experiments will provide an important demonstration of how the tools of  information theory and statistical physics can be used to gain mechanistic insight into the information processing that drives behavior in simple living systems. 

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.