Multicellular Phenotypic Studies of Single Gene Variants in Myxococcus xanthus

There are several systematic methods designed to link genes to cellular processes. These methods are derived from different hypotheses and are largely complementary to each other. This dissertation presents a systematic study of functional genetics and related phenotypes using quantitative methods. The first part of this dissertation will report the successful identification and characterization of 28 genes in the multicellular bacterium Myxococcus xanthus using three different methods: sequence homology, transcription activation and protemoics. The results from this research extended the list of M. xanthus genes involved in multicellularity, and expanded our knowledge regarding the possible molecular pathways underlying physiological and morphological changes. Although the cellular function of some of the genes in the genome of an organism can be deduced from effects of mutation on phenotype, the disruption or deletion of most genes produces little or no discernible phenotypic impact. The reason for this may be redundancy or complementation, or it may be due to the limitations inherent in available assays. The second part of this dissertation will focus on a population genetics approach to the characterization of phenotype for a collection of mutant strains containing insertion mutations in each of the ~200 ABC transporter component genes in M. xanthus. More than 50% of those mutant strains exhibit at least one phenotypic characteristic that is different from the wild type, and an average of 6% of mutant strains have a gain-of-function phenotype. We also demonstrated that the morphological features used to measure phenotype are not entirely independent variables. These results indicate that a rigorous and quantitative phenotypic characterization will provide significantly more data to understand the phenotypic space of M. xanthus, and that a more rigorous definition of phenotype may help us establish a more accurate connection between genotype and phenotype

Kairos: Practical Intrusion Detection and Investigation using Whole-system Provenance

Provenance graphs are structured audit logs that describe the history of a system's execution. Recent studies have explored a variety of techniques to analyze provenance graphs for automated host intrusion detection, focusing particularly on advanced persistent threats. Sifting through their design documents, we identify four common dimensions that drive the development of provenance-based intrusion detection systems (PIDSes): scope (can PIDSes detect modern attacks that infiltrate across application boundaries?), attack agnosticity (can PIDSes detect novel attacks without a priori knowledge of attack characteristics?), timeliness (can PIDSes efficiently monitor host systems as they run?), and attack reconstruction (can PIDSes distill attack activity from large provenance graphs so that sysadmins can easily understand and quickly respond to system intrusion?). We present KAIROS, the first PIDS that simultaneously satisfies the desiderata in all four dimensions, whereas existing approaches sacrifice at least one and struggle to achieve comparable detection performance. Kairos leverages a novel graph neural network-based encoder-decoder architecture that learns the temporal evolution of a provenance graph's structural changes to quantify the degree of anomalousness for each system event. Then, based on this fine-grained information, Kairos reconstructs attack footprints, generating compact summary graphs that accurately describe malicious activity over a stream of system audit logs. Using state-of-the-art benchmark datasets, we demonstrate that Kairos outperforms previous approaches.Comment: 23 pages, 16 figures, to appear in the 45th IEEE Symposium on Security and Privacy (S&P'24

Elastically-Constrained Meta-Learner for Federated Learning

Federated learning is an approach to collaboratively training machine learning models for multiple parties that prohibit data sharing. One of the challenges in federated learning is non-IID data between clients, as a single model can not fit the data distribution for all clients. Meta-learning, such as Per-FedAvg, is introduced to cope with the challenge. Meta-learning learns shared initial parameters for all clients. Each client employs gradient descent to adapt the initialization to local data distributions quickly to realize model personalization. However, due to non-convex loss function and randomness of sampling update, meta-learning approaches have unstable goals in local adaptation for the same client. This fluctuation in different adaptation directions hinders the convergence in meta-learning. To overcome this challenge, we use the historical local adapted model to restrict the direction of the inner loop and propose an elastic-constrained method. As a result, the current round inner loop keeps historical goals and adapts to better solutions. Experiments show our method boosts meta-learning convergence and improves personalization without additional calculation and communication. Our method achieved SOTA on all metrics in three public datasets.Comment: FL-IJCAI'2

Bow-tie signaling in c-di-GMP: Machine learning in a simple biochemical network

Bacteria of many species rely on a simple molecule, the intracellular secondary messenger c-di-GMP (Bis-(3'-5')-cyclic dimeric guanosine monophosphate), to make a vital choice: whether to stay in one place and form a biofilm, or to leave it in search of better conditions. The c-di-GMP network has a bow-tie shaped architecture that integrates many signals from the outside world—the input stimuli—into intracellular c-di-GMP levels that then regulate genes for biofilm formation or for swarming motility—the output phenotypes. How does the ‘uninformed’ process of evolution produce a network with the right input/output association and enable bacteria to make the right choice? Inspired by new data from 28 clinical isolates of Pseudomonas aeruginosa and strains evolved in laboratory experiments we propose a mathematical model where the c-di-GMP network is analogous to a machine learning classifier. The analogy immediately suggests a mechanism for learning through evolution: adaptation though incremental changes in c-di-GMP network proteins acquires knowledge from past experiences and enables bacteria to use it to direct future behaviors. Our model clarifies the elusive function of the ubiquitous c-di-GMP network, a key regulator of bacterial social traits associated with virulence. More broadly, the link between evolution and machine learning can help explain how natural selection across fluctuating environments produces networks that enable living organisms to make sophisticated decisions

Elasticity and lattice dynamics of enstatite at high pressure

The behavior of synthetic-powdered ^(57)Fe-enriched enstatite (Mg_(0.980)Fe_(0.020(5)))(Mg_(0.760)Fe_(0.240))Si_2O_6 has been explored by X-ray diffraction (XRD) and nuclear resonant inelastic X-ray scattering (NRIXS). The Pbca-structured enstatite sample was compressed in fine pressure increments for our independent XRD measurements. One structural transition between 10.1 and 12.2 GPa has been identified from the XRD data. The XRD reflections observed for the high-pressure phase are best matched with space group P2_1/c. We combine density functional theory with Mössbauer spectroscopy and NRIXS to understand the local site symmetry of the Fe atoms in our sample. A third-order Birch-Murnaghan (BM3) equation of state fitting gives K_(T0)=103±5 GPa and K'_(T0)=13±2 for the Pbca phase. At 12 GPa, a BM3 fitting gives K_(T12)=220±10 GPa with K'_(T12)=8±4 for the P2_1/c phase. NRIXS measurements were performed with in situ XRD up to 17 GPa. The partial phonon density of states (DOS) was derived from the raw NRIXS data, and from the low-energy region of the DOS, the Debye sound velocity was determined. We use the equation of state determined from XRD and Debye sound velocity to compute the isotropic compressional (V_P) and shear (V_S) wave velocities of enstatite at different pressures. Our results help constrain the high-pressure properties of Pbca-structured enstatite in the Earth's upper mantle. We find that candidate upper mantle phase assemblages containing Pbca-structured enstatite are associated with shear velocity gradients that are higher than the average Earth model PREM but lower than regional studies down to about 250 km depth