A central challenge in computational modeling of biological systems is the determination of the model parameters. Typically, only a fraction of the parameters (such as kinetic rate constants) are experimentally measured, while the rest are often fitted. The fitting process is usually based on experimental time course measurements of observables, which are used to assign parameter values that minimize some measure of the error between these measurements and the corresponding model prediction. The measurements, which can come from immunoblotting assays, fluorescent markers, etc., tend to be very noisy and taken at a limited number of time points. In this work we present a new approach to the problem of parameter selection of biological models. We show how one can use a dynamic recursive estimator, known as extended Kalman filter, to arrive at estimates of the model parameters. The proposed method follows. First, we use a variation of the Kalman filter that is particularly well suited to biological applications to obtain a first guess for the unknown parameters. Secondly, we employ an a posteriori identifiability test to check the reliability of the estimates. Finally, we solve an optimization problem to refine the first guess in case it should not be accurate enough. The final estimates are guaranteed to be statistically consistent with the measurements. Furthermore, we show how the same tools can be used to discriminate among alternate models of the same biological process. We demonstrate these ideas by applying our methods to two examples, namely a model of the heat shock response in E. coli, and a model of a synthetic gene regulation system. The methods presented are quite general and may be applied to a wide class of biological systems where noisy measurements are used for parameter estimation or model selection.

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Parameter Estimation and Model Selection in Computational Biology

Brain function is thought to emerge from the interactions among neuronal populations. Apart from traditional efforts to reproduce brain dynamics from the micro- to macroscopic scales, comp...

/pdf/the-virtual-brain-integrates-computational-modeling-and-4com6h84b1.pdf

The Virtual Brain Integrates Computational Modeling and Multimodal Neuroimaging

We present a powerful experimental-computational technology for inferring network models that predict the response of cells to perturbations, and that may be useful in the design of combinatorial therapy against cancer The experiments are systematic series of perturbations of cancer cell lines by targeted drugs, singly or in combination The response to perturbation is quantified in terms of relative changes in the measured levels of proteins, phospho-proteins and cellular phenotypes such as viability Computational network models are derived de novo, ie, without prior knowledge of signaling pathways, and are based on simple non-linear differential equations The prohibitively large solution space of all possible network models is explored efficiently using a probabilistic algorithm, Belief Propagation (BP), which is three orders of magnitude faster than standard Monte Carlo methods Explicit executable models are derived for a set of perturbation experiments in SKMEL-133 melanoma cell lines, which are resistant to the therapeutically important inhibitor of RAF kinase The resulting network models reproduce and extend known pathway biology They empower potential discoveries of new molecular interactions and predict efficacious novel drug perturbations, such as the inhibition of PLK1, which is verified experimentally This technology is suitable for application to larger systems in diverse areas of molecular biology

/pdf/perturbation-biology-inferring-signaling-networks-in-1ffwi5haer.pdf

Perturbation Biology: Inferring Signaling Networks in Cellular Systems

Gaussian filters for parameter and state estimation

Supported by the Korean Health 21 Research & Development
Project, the Ministry of Health, Welfare, and Family Affairs,
Republic of Korea (grant A030001); the NIH (grants P50-AR-054144,
NO1-AR-02251, and UL1-RR-024148 from the National Institute of
Arthritis and Musculoskeletal and Skin Diseases), and the US Department
of the Army, Medical Research Acquisition Activity (grants
PR-064803 and PR-064851).

https://onlinelibrary.wiley.com/doi/pdf/10.1002/art.24982

HLA-DPB1 and DPB2 are genetic loci for systemic sclerosis: a genome-wide association study in Koreans with replication in North Americans.

It is system dynamics that determines the function of cells, tissues and organisms To develop mathematical models and estimate their parameters are an essential issue for studying dynamic behaviors of biological systems which include metabolic networks, genetic regulatory networks and signal transduction pathways, under perturbation of external stimuli In general, biological dynamic systems are partially observed Therefore, a natural way to model dynamic biological systems is to employ nonlinear state-space equations Although statistical methods for parameter estimation of linear models in biological dynamic systems have been developed intensively in the recent years, the estimation of both states and parameters of nonlinear dynamic systems remains a challenging task In this report, we apply extended Kalman Filter (EKF) to the estimation of both states and parameters of nonlinear state-space models To evaluate the performance of the EKF for parameter estimation, we apply the EKF to a simulation dataset and two real datasets: JAK-STAT signal transduction pathway and Ras/Raf/MEK/ERK signaling transduction pathways datasets The preliminary results show that EKF can accurately estimate the parameters and predict states in nonlinear state-space equations for modeling dynamic biochemical networks

http://digitalcommons.library.tmc.edu/cgi/viewcontent.cgi?article=1006&context=uthsph_docs

Extended kalman filter for estimation of parameters in nonlinear state-space models of biochemical networks.

Co-occurrent KRAS and TP53 mutations define a majority of patients with pancreatic ductal adenocarcinoma (PDAC) and define its pro-metastatic proclivity. Here, we demonstrate that KRAS-TP53 co-alteration is associated with worse survival compared with either KRAS-alone or TP53-alone altered PDAC in 245 patients with metastatic disease treated at a tertiary referral cancer center, and validate this observation in two independent molecularly annotated datasets. Compared with non-TP53 mutated KRAS-altered tumors, KRAS-TP53 co-alteration engenders disproportionately innate immune-enriched and CD8+ T-cell-excluded immune signatures. Leveraging in silico, in vitro, and in vivo models of human and murine PDAC, we discover a novel intersection between KRAS-TP53 co-altered transcriptomes, TP63-defined squamous trans-differentiation, and myeloid-cell migration into the tumor microenvironment. Comparison of single-cell transcriptomes between KRAS-TP53 co-altered and KRAS-altered/TP53WT tumors revealed cancer cell-autonomous transcriptional programs that orchestrate innate immune trafficking and function. Moreover, we uncover granulocyte-derived inflammasome activation and TNF signaling as putative paracrine mediators of innate immunoregulatory transcriptional programs in KRAS-TP53 co-altered PDAC. Immune subtyping of KRAS-TP53 co-altered PDAC reveals conflation of intratumor heterogeneity with progenitor-like stemness properties. Coalescing these distinct molecular characteristics into a KRAS-TP53 co-altered “immunoregulatory program” predicts chemoresistance in metastatic PDAC patients enrolled in the COMPASS trial, as well as worse overall survival. 

Distinct Mechanisms of Innate and Adaptive Immune Regulation Underlie Poor Oncologic Outcomes Associated with KRAS-TP53 Co-Alteration in Pancreatic Cancer

Dysregulation of Sonic hedgehog (SHH) signaling drives the growth of distinct cancer subtypes, including medulloblastoma (MB). Such cancers have been treated in the clinic with a number of clinically relevant SHH inhibitors, the majority of which target the upstream SHH regulator, Smoothened (SMO). Despite considerable efficacy, many of these patients develop resistance to these drugs, primarily due to mutations in SMO. Therefore, it is essential to identify druggable, signaling components downstream of SMO to target in SMO inhibitor resistant cancers. We utilized an integrated functional genomics approach to identify epigenetic regulators of SHH- signaling and identified a novel complex of Ubiquitin-like with PHD and RING finger domains 1 (UHRF1), DNA methyltransferase 1 (DNMT1), and GLI proteins. We show that this complex is distinct from previously described UHRF1/DNMT1 complexes, suggesting that it works in concert to regulate GLI activity in SHH driven tumors. Importantly, we show that UHRF1/DNMT1/GLI complex stability is targeted by a repurposed FDA-approved therapy, with a subsequent reduction in the growth of SHH-dependent MB ex vivo and in vivo. Implications: This work describes a novel, druggable UHRF1/DNMT1/GLI complex that regulates SHH-dependent tumor growth, and highlights an FDA-approved drug capable of disrupting this complex to attenuate tumor growth.

Xiaodian Sun

Papers

Extended kalman filter for estimation of parameters in nonlinear state-space models of biochemical networks.

Distinct Mechanisms of Innate and Adaptive Immune Regulation Underlie Poor Oncologic Outcomes Associated with KRAS-TP53 Co-Alteration in Pancreatic Cancer

A druggable UHRF1/DNMT1/GLI complex regulates Sonic hedgehog dependent tumor growth.