The thermodynamics of computational copying in biochemical systems

Living cells use readout molecules to record the state of receptor proteins, similar to measurements or copies in typical computational devices. But is this analogy rigorous? Can cells be optimally efficient, and if not, why? We show that, as in computation, a canonical biochemical readout network generates correlations; extracting no work from these correlations sets a lower bound on dissipation. For general input, the biochemical network cannot reach this bound, even with arbitrarily slow reactions or weak thermodynamic driving. It faces an accuracy-dissipation trade-off that is qualitatively distinct from and worse than implied by the bound, and more complex steady-state copy processes cannot perform better. Nonetheless, the cost remains close to the thermodynamic bound unless accuracy is extremely high. Additionally, we show that biomolecular reactions could be used in thermodynamically optimal devices under exogenous manipulation of chemical fuels, suggesting an experimental system for testing computational thermodynamics.Comment: Accepted versio

For T Cell Receptors, Some Breakups Might Not Last Forever

Does the affinity or half-life of peptide-MHC-T cell receptor (TCR) interactions determine T cell activation? In this issue of Immunity, Aleksic et al. (2010) propose a role for the on rate through multiple rebindings to the same TCR

Stochastic and spatiotemporal effects in T-cell signaling

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, 2011.Cataloged from PDF version of thesis.Includes bibliographical references.T lymphocytes are key orchestrators of the adaptive immune response in higher organisms. This thesis seeks to apply different techniques from engineering and the physical sciences to understand how T cells balance the risks of autoimmunity and infection. (1) What features of proteins do T cells search for that correlate with pathogenicity, distinguishing self from foreign? Two contrasting theories have emerged that attempt to describe T cell ligand potency, one based on the half-life (tv12) of the interaction between T cell receptors (TCR) and peptide-MHC complexes (pMHC), the second on the equilibrium affinity (KD). We study an extensive set of TCR-pMHC interactions in CD4+ T cells which have differential KD and kinetics of binding. The data indicate that ligands with short t1/2 can be highly stimulatory if they have fast on-rates. Simple models suggest these fast-kinetic ligands are stimulatory because the pMHC bind and rebind the same TCR several times. Accounting for rebinding, ligand potency is KD-based when ligands have fast on-rates and t1/2-based when they have slow on-rates, unifying previous theories. (2) How do T cells make optimal responses with the imperfect information they receive through their receptors? Recent experiments suggest that T cells sometimes make stochastic decisions. Biological systems without sensors and genetic diversity, such as some bacteria, make stochastic decisions to diversify responses in uncertain environments, thereby optimizing performance (e.g. growth). T cells, however, can draw on considerable environmental and genetic diversity to diversify their responses. Using T cell biology as a guide, we identify a new role for noise in such systems: it helps systems achieve complex goals with simple signaling machinery. With decision-theoretic techniques, we suggest necessary conditions for noise to be useful in this way. (3) How can biological systems, like T cells, maintain desired responses in the presence of molecular noise, suppressing it or exploiting it as needed? We develop a semianalytical technique to determine how small changes in the rate constants of different reactions or in the concentrations of different species affect the rate at which biological systems escape stable cellular states. A single deterministic simulation yields the sensitivities with respect to all reactions and species in the system. This helps to predict those species or interactions that are most critical for regulating molecular noise, suggesting those most promising as drug targets or most vulnerable to mutation. These projects and others discussed in this thesis recruit techniques from random walks, statistical inference, and large deviation theory to understand problems ranging in scale from individual molecular interactions to the population of T cells acting in concert.by Christopher C. Govern.Ph.D

Dysregulated RasGRP1 Responds to Cytokine Receptor Input in T Cell Leukemogenesis

Enhanced signaling by the small guanosine triphosphatase Ras is common in T cell acute lymphoblastic leukemia/lymphoma (T-ALL), but the underlying mechanisms are unclear. We identified the guanine nucleotide exchange factor RasGRP1 (Rasgrp1 in mice) as a Ras activator that contributes to leukemogenesis. We found increased RasGRP1 expression in many pediatric T-ALL patients, which is not observed in rare early T cell precursor T-ALL patients with KRAS and NRAS mutations, such as K-Ras[superscript G12D]. Leukemia screens in wild-type mice, but not in mice expressing the mutant K-Ras[superscript G12D] that encodes a constitutively active Ras, yielded frequent retroviral insertions that led to increased Rasgrp1 expression. Rasgrp1 and oncogenic K-Ras[superscript G12D] promoted T-ALL through distinct mechanisms. In K-Ras[superscript G12D] T-ALLs, enhanced Ras activation had to be uncoupled from cell cycle arrest to promote cell proliferation. In mouse T-ALL cells with increased Rasgrp1 expression, we found that Rasgrp1 contributed to a previously uncharacterized cytokine receptor–activated Ras pathway that stimulated the proliferation of T-ALL cells in vivo, which was accompanied by dynamic patterns of activation of effector kinases downstream of Ras in individual T-ALLs. Reduction of Rasgrp1 abundance reduced cytokine-stimulated Ras signaling and decreased the proliferation of T-ALL in vivo. The position of RasGRP1 downstream of cytokine receptors as well as the different clinical outcomes that we observed as a function of RasGRP1 abundance make RasGRP1 an attractive future stratification marker for T-ALL.National Institutes of Health (U.S.). Pioneer AwardNational Cancer Institute (U.S.). Physical Sciences-Oncology Center (U54CA143874)National Institutes of Health (U.S.). (P01 AI091580

Signaling Cascades Modulate the Speed of Signal Propagation through Space

Cells are not mixed bags of signaling molecules. As a consequence, signals must travel from their origin to distal locations. Much is understood about the purely diffusive propagation of signals through space. Many signals, however, propagate via signaling cascades. Here, we show that, depending on their kinetics, cascades speed up or slow down the propagation of signals through space, relative to pure diffusion.We modeled simple cascades operating under different limits of Michaelis-Menten kinetics using deterministic reaction-diffusion equations. Cascades operating far from enzyme saturation speed up signal propagation; the second mobile species moves more quickly than the first through space, on average. The enhanced speed is due to more efficient serial activation of a downstream signaling module (by the signaling molecule immediately upstream in the cascade) at points distal from the signaling origin, compared to locations closer to the source. Conversely, cascades operating under saturated kinetics, which exhibit zero-order ultrasensitivity, can slow down signals, ultimately localizing them to regions around the origin.Signal speed modulation may be a fundamental function of cascades, affecting the ability of signals to penetrate within a cell, to cross-react with other signals, and to activate distant targets. In particular, enhanced speeds provide a way to increase signal penetration into a cell without needing to flood the cell with large numbers of active signaling molecules; conversely, diminished speeds in zero-order ultrasensitive cascades facilitate strong, but localized, signaling

Stochastic Responses May Allow Genetically Diverse Cell Populations to Optimize Performance with Simpler Signaling Networks

Two theories have emerged for the role that stochasticity plays in biological responses: first, that it degrades biological responses, so the performance of biological signaling machinery could be improved by increasing molecular copy numbers of key proteins; second, that it enhances biological performance, by enabling diversification of population-level responses. Using T cell biology as an example, we demonstrate that these roles for stochastic responses are not sufficient to understand experimental observations of stochastic response in complex biological systems that utilize environmental and genetic diversity to make cooperative responses. We propose a new role for stochastic responses in biology: they enable populations to make complex responses with simpler biochemical signaling machinery than would be required in the absence of stochasticity. Thus, the evolution of stochastic responses may be linked to the evolvability of different signaling machineries.National Institutes of Health (U.S.). Pioneer Awar

Identifying Dynamical Bottlenecks of Stochastic Transitions in Biochemical Networks

In biochemical networks, identifying key proteins and protein-protein reactions that regulate fluctuation-driven transitions leading to pathological cellular function is an important challenge. Using large deviation theory, we develop a semianalytical method to determine how changes in protein expression and rate parameters of protein-protein reactions influence the rate of such transitions. Our formulas agree well with computationally costly direct simulations and are consistent with experiments. Our approach reveals qualitative features of key reactions that regulate stochastic transitions.National Cancer Institute (U.S.). Physical Sciences-Oncology CenterNational Institutes of Health (U.S.) (P01 AI09158001