A Probabilistic Modeling Approach to CRISPR-Cas9

CRISPR-Cas, a particular type of microbial immune response system, has in recent years been modified to make precise changes to an organisms DNA. In the early 2000s scientists discovered through the study of Streptococcus pyogenes, that a unique CRISPR locus (Cas9) exhibited specific RNA-guided cleavage near short trinucleotide motifs (PAMs). Further research on Cas9 eventually led researchers to create methods that actively edit genomes through Cas9-dependent cleavage and to manipulate transcription of genes through engineered nuclease-deficient Cas9 (dCas9). These techniques have enabled new avenues for analyzing existing gene functions or engineering new ones, manipulating gene expression, gene therapy, and much more.While great strides have been made over the last decade, CRISPR is still prone to inaccuracies which often generate sub-optimal editing efficiency or off-target effects. The primary interest of this thesis is the investigation of targeting efficiency concerning changes in the guide RNA (gRNA) composition. While many different factors affect the ability with which a given gRNA can target a DNA sequence, we have focused our research primarily on the formation of the R-loop: the hybrid structure formed when the Cas9/dCas9:gRNA complex binds to a host DNA site.In our investigation, we have attempted to account for several experimental findings reported in the literature as influential for binding efficiency. These include position dependence, base pair composition dependence, and the effects of runs of consecutive mismatches. Using a Gambler’s Ruin Markov model to mimic the process of R-loop formation, we fit our model to experimental data and show that the match/mismatch configuration between the gRNA and the DNA target allows for accurate predictions of R-loop formation in bacteria

Target-based Surrogates for Stochastic Optimization

We consider minimizing functions for which it is expensive to compute the (possibly stochastic) gradient. Such functions are prevalent in reinforcement learning, imitation learning and adversarial training. Our target optimization framework uses the (expensive) gradient computation to construct surrogate functions in a \emph{target space} (e.g. the logits output by a linear model for classification) that can be minimized efficiently. This allows for multiple parameter updates to the model, amortizing the cost of gradient computation. In the full-batch setting, we prove that our surrogate is a global upper-bound on the loss, and can be (locally) minimized using a black-box optimization algorithm. We prove that the resulting majorization-minimization algorithm ensures convergence to a stationary point of the loss. Next, we instantiate our framework in the stochastic setting and propose the $SSO$ algorithm, which can be viewed as projected stochastic gradient descent in the target space. This connection enables us to prove theoretical guarantees for $SSO$ when minimizing convex functions. Our framework allows the use of standard stochastic optimization algorithms to construct surrogates which can be minimized by any deterministic optimization method. To evaluate our framework, we consider a suite of supervised learning and imitation learning problems. Our experiments indicate the benefits of target optimization and the effectiveness of $SSO$

A Diffusion-Model of Joint Interactive Navigation

Simulation of autonomous vehicle systems requires that simulated traffic participants exhibit diverse and realistic behaviors. The use of prerecorded real-world traffic scenarios in simulation ensures realism but the rarity of safety critical events makes large scale collection of driving scenarios expensive. In this paper, we present DJINN - a diffusion based method of generating traffic scenarios. Our approach jointly diffuses the trajectories of all agents, conditioned on a flexible set of state observations from the past, present, or future. On popular trajectory forecasting datasets, we report state of the art performance on joint trajectory metrics. In addition, we demonstrate how DJINN flexibly enables direct test-time sampling from a variety of valuable conditional distributions including goal-based sampling, behavior-class sampling, and scenario editing.Comment: 10 pages, 4 figure

Video Killed the HD-Map: Predicting Driving Behavior Directly From Drone Images

The development of algorithms that learn behavioral driving models using human demonstrations has led to increasingly realistic simulations. In general, such models learn to jointly predict trajectories for all controlled agents by exploiting road context information such as drivable lanes obtained from manually annotated high-definition (HD) maps. Recent studies show that these models can greatly benefit from increasing the amount of human data available for training. However, the manual annotation of HD maps which is necessary for every new location puts a bottleneck on efficiently scaling up human traffic datasets. We propose a drone birdview image-based map (DBM) representation that requires minimal annotation and provides rich road context information. We evaluate multi-agent trajectory prediction using the DBM by incorporating it into a differentiable driving simulator as an image-texture-based differentiable rendering module. Our results demonstrate competitive multi-agent trajectory prediction performance when using our DBM representation as compared to models trained with rasterized HD maps

Critic Sequential Monte Carlo

We introduce CriticSMC, a new algorithm for planning as inference built from a novel composition of sequential Monte Carlo with learned soft-Q function heuristic factors. This algorithm is structured so as to allow using large numbers of putative particles leading to efficient utilization of computational resource and effective discovery of high reward trajectories even in environments with difficult reward surfaces such as those arising from hard constraints. Relative to prior art our approach is notably still compatible with model-free reinforcement learning in the sense that the implicit policy we produce can be used at test time in the absence of a world model. Our experiments on self-driving car collision avoidance in simulation demonstrate improvements against baselines in terms of infraction minimization relative to computational effort while maintaining diversity and realism of found trajectories.Comment: 20 pages, 3 figure