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Ramu Anandakrishnan

Publications and source records attributed to Ramu Anandakrishnan.

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Identifying Multi-Hit Cancer Drivers Without Massive Parallelization: A CP, MIP, and Column Generation Framework

Cancer is often driven by specific combinations of an estimated two to nine gene mutations, known as multi-hit combinations. Identifying these multi-hit combinations of gene mutations that drive cancer is critical for understanding carcinogenesis and designing targeted therapies. We formalize this challenge as the Multi-Hit Cancer Driver Set Cover Problem (MHCDSCP), optimizing the selection of gene combinations to maximize tumor coverage while strictly minimizing normal sample misclassification. While existing approaches rely on exhaustive enumeration and massive parallelization, we introduce fast heuristics based on constraint programming and mixed integer programming formulations. Evaluated on real-world cancer genomics data, our framework matches state-of-the-art supercomputing methods using a single commodity CPU in under a minute. We also propose a price-and-branch heuristic which, by solving the root node to optimality, provides the first provably optimal solutions for over half of the benchmark instances, thereby verifying the near-optimality of our fast heuristics. These findings demonstrate that on real-world problem instances, the MHCDSCP is far less computationally demanding than previously believed, providing an accessible baseline that enables the exploration of previously intractable multi-hit modeling assumptions.

math.OC

Looking for (Genomic) Needles in a Haystack: Sparsity-Driven Search for Identifying Correlated Genetic Mutations in Cancer

Cancer typically arises not from a single genetic mutation (i.e., hit) but from multi-hit combinations that accumulate within cells. However, enumerating multi-hit combinations becomes exponentially more expensive computationally as the number of candidate hit gene combinations grow, i.e. on the order of 20,000 choose h, where 20,000 is the number of genes in the human genome and h is the number of hits. To address this challenge, we present an algorithmic framework, called Pruned Depth-First Search (P-DFS) that leverages the high sparsity in tumor mutation data to prune large portions of the search space. Specifically, P-DFS (the main contribution of this paper) - a pruning technique that exploits sparsity to drastically reduce the otherwise exponential h-hit search space for candidate combinations used by Weighted Set Cover - which is grounded in a depth-first search backtracking technique, prunes infeasible gene subsets early, while a weighted set cover formulation systematically scores and selects the most discriminative combinations. By intertwining these ideas with optimized bitwise operations and a scalable distributed algorithm on high-performance computing clusters, our algorithm can achieve approximately 90 - 98% reduction in visited combinations for 4-hits, and roughly a 183x speedup over the exhaustive set cover approach(which is algorithmically NP-complete) measured on 147,456 ranks. In doing so, our method can feasibly handle four-hit and even higher-order gene hits, achieving both speed and resource efficiency.

cs.DC

Biophysical comparison of ATP-driven proton pumping mechanisms suggests a kinetic advantage for the rotary process depending on coupling ratio

ATP-driven proton pumps, which are critical to the operation of a cell, maintain cytosolic and organellar pH levels within a narrow functional range. These pumps employ two very different mechanisms: an elaborate rotary mechanism used by V-ATPase H+ pumps, and a simpler alternating access mechanism used by P-ATPase H+ pumps. Why are two different mechanisms used to perform the same function? Systematic analysis, without parameter fitting, of kinetic models of the rotary, alternating access and other possible mechanisms suggest that, when the ratio of protons transported per ATP hydrolyzed exceeds one, the one-at-a-time proton transport by the rotary mechanism is faster than other possible mechanisms across a wide range of driving conditions. When the ratio is one, there is no intrinsic difference in the free energy landscape between mechanisms, and therefore all mechanisms can exhibit the same kinetic performance. To our knowledge all known rotary pumps have an H+:ATP ratio greater than one, and all known alternating access ATP-driven proton pumps have a ratio of one. Our analysis suggests a possible explanation for this apparent relationship between coupling ratio and mechanism. When the conditions under which the pump must operate permit a coupling ratio greater than one, the rotary mechanism may have been selected for its kinetic advantage. On the other hand, when conditions require a coupling ratio of one or less, the alternating access mechanism may have been selected for other possible advantages resulting from its structural and functional simplicity.

q-bio.SC

Building Water Models, A Different Approach

Simplified, classical models of water are an integral part of atomistic molecular simulations, especially in biology and chemistry where hydration effects are critical. Yet, despite several decades of effort, these models are still far from perfect. Presented here is an alternative approach to constructing point charge water models - currently, the most commonly used type. In contrast to the conventional approach, we do not impose any geometry constraints on the model other than symmetry. Instead, we optimize the distribution of point charges to best describe the "electrostatics" of the water molecule, which is key to many unusual properties of liquid water. The search for the optimal charge distribution is performed in 2D parameter space of key lowest multipole moments of the model, to find best fit to a small set of bulk water properties at room temperature. A virtually exhaustive search is enabled via analytical equations that relate the charge distribution to the multipole moments. The resulting "optimal" 3-charge, 4-point rigid water model (OPC) reproduces a comprehensive set of bulk water properties significantly more accurately than commonly used rigid models: average error relative to experiment is 0.76%. Close agreement with experiment holds over a wide range of temperatures, well outside the ambient conditions at which the fit to experiment was performed. The improvements in the proposed water model extend beyond bulk properties: compared to the common rigid models, predicted hydration free energies of small molecules in OPC water are uniformly closer to experiment, root-mean-square error < 1kcal/mol.

physics.chem-ph

Optimal Physical Multipoles

Point multipole expansions are widely used to gain physical insight into complex distributions of charges and to reduce the cost of computing interactions between such distributions. However, practical applications that typically retain only a few leading terms may suffer from unacceptable loss of accuracy in the near-field. We propose an alternative approach for approximating electrostatic charge distributions, Optimal Physical Multipoles (OPMs), which optimally represent the original charge distribution with a set of point charges. By construction, approximation of electrostatic potential based on OPMs retains many of the useful properties of the corresponding point multipole expansion, including the same asymptotic behavior of the approximate potential for a given multipole order. At the same time, OPMs can be significantly more accurate in the near field: up to 5 times more accurate for some of the charge distributions tested here which are relevant to biomolecular modeling. Unlike point multipoles, for point charge distributions the OPM always converges to the original point charge distribution at finite order. Furthermore, OPMs may be more computationally efficient and easier to implement into existing molecular simulations software packages than approximation schemes based on point multipoles. In addition to providing a general framework for computing OPMs to any order, closed-form expressions for the lowest order OPMs (monopole and dipole) are derived. Thus, for some practical applications Optimal Physical Multipoles may represent a preferable alternative to point multipoles.

physics.class-ph