44 research outputs found
Universally Sloppy Parameter Sensitivities in Systems Biology
Quantitative computational models play an increasingly important role in
modern biology. Such models typically involve many free parameters, and
assigning their values is often a substantial obstacle to model development.
Directly measuring \emph{in vivo} biochemical parameters is difficult, and
collectively fitting them to other data often yields large parameter
uncertainties. Nevertheless, in earlier work we showed in a
growth-factor-signaling model that collective fitting could yield
well-constrained predictions, even when it left individual parameters very
poorly constrained. We also showed that the model had a `sloppy' spectrum of
parameter sensitivities, with eigenvalues roughly evenly distributed over many
decades. Here we use a collection of models from the literature to test whether
such sloppy spectra are common in systems biology. Strikingly, we find that
every model we examine has a sloppy spectrum of sensitivities. We also test
several consequences of this sloppiness for building predictive models. In
particular, sloppiness suggests that collective fits to even large amounts of
ideal time-series data will often leave many parameters poorly constrained.
Tests over our model collection are consistent with this suggestion. This
difficulty with collective fits may seem to argue for direct parameter
measurements, but sloppiness also implies that such measurements must be
formidably precise and complete to usefully constrain many model predictions.
We confirm this implication in our signaling model. Our results suggest that
sloppy sensitivity spectra are universal in systems biology models. The
prevalence of sloppiness highlights the power of collective fits and suggests
that modelers should focus on predictions rather than on parameters.Comment: Submitted to PLoS Computational Biology. Supplementary Information
available in "Other Formats" bundle. Discussion slightly revised to add
historical contex
The sloppy model universality class and the Vandermonde matrix
In a variety of contexts, physicists study complex, nonlinear models with
many unknown or tunable parameters to explain experimental data. We explain why
such systems so often are sloppy; the system behavior depends only on a few
`stiff' combinations of the parameters and is unchanged as other `sloppy'
parameter combinations vary by orders of magnitude. We contrast examples of
sloppy models (from systems biology, variational quantum Monte Carlo, and
common data fitting) with systems which are not sloppy (multidimensional linear
regression, random matrix ensembles). We observe that the eigenvalue spectra
for the sensitivity of sloppy models have a striking, characteristic form, with
a density of logarithms of eigenvalues which is roughly constant over a large
range. We suggest that the common features of sloppy models indicate that they
may belong to a common universality class. In particular, we motivate focusing
on a Vandermonde ensemble of multiparameter nonlinear models and show in one
limit that they exhibit the universal features of sloppy models.Comment: New content adde
Noncanonical splicing junctions between exons and transposable elements represent a source of immunogenic recurrent neo-antigens in patients with lung cancer
Although most characterized tumor antigens are encoded by canonical transcripts (such as differentiation or tumor-testis antigens) or mutations (both driver and passenger mutations), recent results have shown that noncanonical transcripts including long noncoding RNAs and transposable elements (TEs) can also encode tumor-specific neo-antigens. Here, we investigate the presentation and immunogenicity of tumor antigens derived from noncanonical mRNA splicing events between coding exons and TEs. Comparing human non-small cell lung cancer (NSCLC) and diverse healthy tissues, we identified a subset of splicing junctions that is both tumor specific and shared across patients. We used HLA-I peptidomics to identify peptides encoded by tumor-specific junctions in primary NSCLC samples and lung tumor cell lines. Recurrent junction-encoded peptides were immunogenic in vitro, and CD8+ T cells specific for junction-encoded epitopes were present in tumors and tumor-draining lymph nodes from patients with NSCLC. We conclude that noncanonical splicing junctions between exons and TEs represent a source of recurrent, immunogenic tumor-specific antigens in patients with NSCLC
Epigenetically controlled tumor antigens derived from splice junctions between exons and transposable elements
Oncogenesis often implicates epigenetic alterations, including derepression of transposable elements (TEs) and defects in alternative splicing. Here, we explore the possibility that noncanonical splice junctions between exons and TEs represent a source of tumor-specific antigens. We show that mouse normal tissues and tumor cell lines express wide but distinct ranges of mRNA junctions between exons and TEs, some of which are tumor specific. Immunopeptidome analyses in tumor cell lines identified peptides derived from exon-TE splicing junctions associated to MHC-I molecules. Exon-TE junction-derived peptides were immunogenic in tumor-bearing mice. Both prophylactic and therapeutic vaccinations with junction-derived peptides delayed tumor growth in vivo. Inactivation of the TE-silencing histone 3-lysine 9 methyltransferase Setdb1 caused overexpression of new immunogenic junctions in tumor cells. Our results identify exon-TE splicing junctions as epigenetically controlled, immunogenic, and protective tumor antigens in mice, opening possibilities for tumor targeting and vaccination in patients with cancer
Imaging and multi-omics datasets converge to define different neural progenitor origins for ATRT-SHH subgroups
Atypical teratoid rhabdoid tumors (ATRT) are divided into MYC, TYR and SHH subgroups, suggesting diverse lineages of origin. Here, we investigate the imaging of human ATRT at diagnosis and the precise anatomic origin of brain tumors in the Rosa26-Cre::Smarcb1 model. This cross-species analysis points to an extra-cerebral origin for MYC tumors. Additionally, we clearly distinguish SHH ATRT emerging from the cerebellar anterior lobe (CAL) from those emerging from the basal ganglia (BG) and intra-ventricular (IV) regions. Molecular characteristics point to the midbrain-hindbrain boundary as the origin of CAL SHH ATRT, and to the ganglionic eminence as the origin of BG/IV SHH ATRT. Single-cell RNA sequencing on SHH ATRT supports these hypotheses. Trajectory analyses suggest that SMARCB1 loss induces a de-differentiation process mediated by repressors of the neuronal program such as REST, ID and the NOTCH pathway
We argue that any va...
ABSTRACT: Successful predictions are among the most compelling validations of any model. Extracting falsifiable predictions from nonlinear multiparameter models is complicated by the fact that such models are commonly sloppy, possessing sensitivities to different parameter combinations that range over many decades. Here we discuss how sloppiness affects the sorts of data that best constrain model predictions, makes linear uncertainty approximations dangerous, and introduces computational difficulties in Monte-Carlo uncertainty analysis. We also present a useful test problem and suggest refinements to the standards by which models are communicated